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Detection/market_regime_detection.ipynb @@ -0,0 +1,5711 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "code", + "source": [ + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "# Load the dataset\n", + "data = pd.read_csv('market_regime_detection.csv', parse_dates=['Date'], index_col='Date')\n", + "\n", + "# Summary statistics\n", + "print(data.describe())\n", + "\n", + "# Plot the price\n", + "plt.figure(figsize=(12, 6))\n", + "plt.plot(data['Price'], label='Price')\n", + "plt.title('Price over Time')\n", + "plt.xlabel('Date')\n", + "plt.ylabel('Price')\n", + "plt.legend()\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 657 + }, + "id": "PSa_ytj6PaQE", + "outputId": "7a026d95-9627-4fe4-f8cd-651ae8533d7a" + }, + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " Price Volume Volatility\n", + "count 2000.000000 2000.000000 2000.000000\n", + "mean 1.372255 551.366000 0.029518\n", + "std 0.453612 259.417514 0.011530\n", + "min 0.816194 100.000000 0.010007\n", + "25% 0.950933 322.750000 0.019689\n", + "50% 1.228658 551.500000 0.029311\n", + "75% 1.777283 774.000000 0.039570\n", + "max 2.381876 999.000000 0.049962\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Plot the volume\n", + "plt.figure(figsize=(12, 6))\n", + "plt.plot(data['Volume'], label='Volume')\n", + "plt.title('Volume over Time')\n", + "plt.xlabel('Date')\n", + "plt.ylabel('Volume')\n", + "plt.legend()\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 495 + }, + "id": "VntUH0MPPb3M", + "outputId": "d273b2ed-dc50-403e-8c14-de6422156314" + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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4/YEHHsCjjz6KwYMHY++998att96KAQMGYPbs2ejYsSMAYODAgVizZg2GDRuGhoYGXHnllbj22msxZMgQAEBVVRVOP/109O/fH0899RRmzpyJq666Ct27d8e1114b6/NaLBaLxWKxWCwWi0lsa2jCIbd9Fnu+s+8YgM7t2dTRsrIyXH755XjxxRfx97//vXhE3FtvvYWmpiYcfPDBOOWUU3D77bfjpz/9KcaNG4ff/OY32HHHHXHFFVdIyXn77bfjscceQ+fOnXHxxRfj4osvRocOHTBkyBBUV1fjggsuwKBBg/CXv/wFAHDvvffilVdewVNPPYX9998fo0ePxmWXXYadd94ZJ510kpQsYSSq2J955pk488wzib85joNHHnkEt9xyC8477zwAwEsvvYSePXvivffewyWXXII5c+Zg6NChmDRpEo4++mgAwKBBg3DWWWfhwQcfRO/evfHqq6+ivr4ezz//PNq3b49DDz0U06dPx0MPPWQVe4vFYrFYLBaLxWJJAVdddRX+9a9/YdSoUTj55JMBNLvhX3TRRXjmmWdw6qmn4tZbbwUAHHDAAZg9ezb+9a9/SSv2d911F44//ngAzVsCbr75ZixatAj77LMPAODHP/4xvvzyS/zlL39BXV0d7rnnHnzxxRfo168fAGCfffbBV199haeffjq7in0YS5YsQXl5Ofr371/8rlu3bjjmmGMwfvx4XHLJJRg/fjy6d+9eVOoBoH///igpKcGECRNwwQUXYPz48TjxxBPRvn374jUDBgzA/fffj02bNmGHHXYI5F1XV4e6urri31VVVZqe0mKxWCy6yecdzFhZiYN37YqO7UqTFsdisVgyx4bqOmyoqccBPbskLYpFgE7tSjH7jgGJ5MvDQQcdhOOOOw7PP/88Tj75ZCxcuBBjxozBHXfcgRtuuKFoDC5w/PHH45FHHkFTUxNKS8XHf/fe/Z49e6Jz585Fpb7w3cSJEwEACxcuxNatW3Haaad50qivr8eRRx4pLAMLxgbPKy8vB9BcUG569uxZ/K28vBy77LKL5/eysjL06NHDcw0pDXcefu69915069at+F+fPn3kH8hiPGs2b8OExRuSFsNisSjm+bFLcMET43Dty1OSFsViabNMWroRP316POas0WMsqW/M48t5Faipa5RK59OZa3Dps1+joqpWkWRtg6Pu+gKnPzwai9ZVR1+cUt6YtBw//+8EbKltSFoU5eRyOXRuXxb7fwV3eh4Ke+m3bNmCF154Afvuu6+QFbykpFkNdu/zb2ggv9t27doVP+dyOc/fhe/y+TwAFGO9ffzxx5g+fXrxv9mzZ2vfZ2+sYp8kN998MzZv3lz8b8WKFUmLZImBfveOwE+f+RpTlm1KWhSLhZv7h87Fz575Gg1N+aRFMY4Xxi4FAIyevy5ZQVLOio1bcdZ/xuD96auSFsWiiKHfrsGZ/xmDhRVbtOf1k6fGY8KSjbjihYla0r9/6Fxc+cIk/N8rcgt4v351KsYt2oA7PpqtSLK2xZSl2Z1D/eV/MzFmwXo8M3px0qKkilWbtmLp+hquQHlhXHzxxSgpKcGQIUPw0ksv4aqrrkIul8PBBx+MsWPHeq4dO3YsDjjgAKK1fueddwYArFmzpvidO5CeKIcccgg6dOiA5cuXY7/99vP8p9tYbKxi36tXLwDA2rVrPd+vXbu2+FuvXr1QUVHh+b2xsREbN270XENKw52Hnw4dOqBr166e/9oCn8xcg8lLNyYtRuLYMrCkkSdHLsL4xRswfM7a6IsN4pEv5uOHD47Expr6pEWxRPD3977F7DVV+OPr05MWJZPMLa/C8feNwFuT4zMm/N8rUzFnTRWue2N6bHmu21IXfZEAQyYsBwCMWbCe+97N2xpwyr9H4t+fzyt+t2lr8n1SPs+uCPFcqxMHZsihk83b2C32m2rq8cMHR+KhYfM1SmQ2G2rqUVXbgNqGJiXpbb/99vjpT3+Km2++GWvWrCnun//Tn/6E4cOH484778T8+fMxePBgPPbYY7jxxhuJ6XTq1AnHHnss7rvvPsyZMwejRo3CLbfcIi1fly5dcOONN+L666/H4MGDsWjRIkydOhWDBg3C4MGDpdMPw1jFfu+990avXr0wfPjw4ndVVVWYMGFCMRBBv379UFlZiSlTWldnR4wYgXw+j2OOOaZ4zejRoz2uFcOGDcOBBx5I3F/fVllYsQW/eXUqfvzU+KRFiQ3HcfDfr5YEFPnsD0mWLFPXmC6L/SNfLMCS9TV4ZvRiOI6jbODnRVe+ST2PDDSZZV2cLeHc8MYMrKrchj+//U3seW+ti6+elgi43upm8LilWLyuBoNGLCx+p8i4KMzEJRtx+O2f4c1J0Qs9E5dsRN9/fs50rW6SLrc4yHM85HNfLcaS9TV4dPiC6IszjsqqcfXVV2PTpk0YMGAAevfuDQD47ne/izfffBOvv/46DjvsMNx222244447QgPnPf/882hsbMRRRx2F6667DnfddZcS+e68807ceuutuPfee3HwwQfjjDPOwMcff4y9995bSfo0ElXsq6uri/sOgOaAedOnT8fy5cuRy+WKBfzBBx9g5syZuPzyy9G7d2+cf/75AFAsqGuuuQYTJ07E2LFj8bvf/Q6XXHJJ8SVfeumlaN++Pa6++mrMmjULb7zxBv7zn//ghhtuSOipzWTFpm1JiwAAqKptQH1MisnHM9fgzo9mBxYz2sKgZImmoSnPtSrPw6sTlimbgDmOg8e/XBh9oeHkHQfXvDQZB906FGs17m0leQZMWLwBB906FA8Mnas0r/GLmtN98LN50RcbwstfL8NBtw7FBzNWB34zTx0zi4ottbj749lYsr5G6P7axgQXgWJ8ubr0ehlLcSPB2p30XOD/XpmCmvom3PS/6IWea1+ejOq6RqZrddMWplA8zhHb6tO12J4W+vXrB8dx8PHHH3u+v+iiizBr1izU19dj2bJlAWv90qVLcd111xX/PvjggzFu3Dhs3boV06ZNw2mnnQbHcYoR908++WQ4joPu3bsX77niiitQWVnpSff222/3uPHncjn88Y9/xNy5c1FfX4+KigoMHToUJ554oorHp5KoYj958mQceeSRxQiBN9xwA4488kjcdtttAICbbroJv//973Httdfie9/7HqqrqzF06NDiGfYA8Oqrr+Kggw7CqaeeirPOOgs/+MEP8MwzzxR/79atGz7//HMsWbIERx11FP70pz/htttus0fd+TGgJ67cWo++t3+OEx/4Mpb8Zq22px1Y6Jz+8Ggc8c/PlQdQ2lhTj7+/+y1u+t83Sqy5YxduwL9SpDjSyAH4Yk7z1qp3purbw/3dO4dhyjKvl86dHzfvpX1i5CKptL9dtRl/efub4sLEPz+cBQB4LEULL7e+9y0A4A+vTQv8Zoqh9dUJy/CwgW6tvx8yDc+OWYLzHx8bfTGBJBXJOF+tSLCsJIjTpXzZhho0SsRHMckLwl+PTW2vMvDsFbdxb9hoaMqjMW/LSpZEj7srrILQyOVyuOOOO3DHHXdQr+nRoweGDBkSmk/fvn0xZswYYTkt8TC5JeBKeUyRaLdat9JUM2j4Amypa8TfzjpYS/oFq9voBevx46N2V5bu1vrWesfjzkdj9eZkvG2+XbUZj41YiD+fcSD23Xn7RGQQ5fmvluKoPXsoT/ecQV8BAFZVbsMrvzxGefqWZv7+bvPiw4+O2BX77WLO0VrTllcC4Nt/60ZFfyBKnIphiaascoqXJ+J6HUO/XYP/e2UqTj5wZ7x45feF0tBVpiL4F0RMba9Ac0DQ0pIcenfvxHVfE4fJ3iqr0TTlneJpGX13756sMCnH2D32lrZHaWm8I9PW+vjdHhdWbMHPnvnaHqsnSV1jE/49bD6eGb0YU5ZtysxJBo7jYPyiDVzBpRqbvBOMuCaj5z0+FkNnlWuLcC3Dl/MqcOmzX2PFxq3E39sx9DWO42DcwvVCQf0WxBBhPAlUK04iuI0BSfThYchaeJNU7OM0+KqqR1tqG/CL5yfizZZgg2kN2vb8V0sBACPnyZzakXzbjKI6xjgOLGyrb8IJD3yJ4+4bwaWoA3yu+PWN6ayXcVIf0zakxqY8ttQ2KIvObyJWsbdIM2TCcpz32FfSkW5LY3Ylo00KdU4Ofjl4MsYv3oCfPvO1tjySYG1VLc597KvYAvdU17ZavS96chwuenKctrOR4xwAhs+pwM+e/RrH3z+C+Z4mnzUgrsltYSK0YqMZ8TncXPnCJIxbtAF/fnsG8feyUu/QR3rFH36zBpc+NwGn/HukBgnTw6rKbRj67ZrmiNsG6A7uCXWpSWZKBmobmvDRN6uxibJYlKRhL40W++fGLMGo+etwk6Zgg3H1/CUKZuKsZTpmwTrMK9e78EgbMk1rreurW+esvO7yPItw1mJvDgsrqrFkfU2mT+Gxir1Fmr+9OxMzVm7Gvz+fJ6UElcU8Saupj98Vf22VnmN+kua+T+fim5WbYwvcs6U2+O5mrKiMJW+dfDmveY85TwBJv6UhtQvRrubPomPcP3QuTn94FKpDttRsqG4evP39UrvS6KFv2OzmYwMrt/K7Va+tqsPx943AXM0T6Dg4/r4R+L9XpuLdaauMmJi7J8lpU+wfGDoPvxsyDZc+NyFpURJF1SKC/5QG5R4lMfWltPLgeRqWMl1YUY2f/3ciBjwymiNlftJoDeUVmed6k/fYp/FdyVDf8i50BUaWQdW7sIq9RRmvT1qBS5+dIFw5S1yTNNHzWBua8swuVXEe71PAoPg2SiEp2johKXO6jvCtVzAoT1q6EYPHLdUyiJKiOZOYuGQjXhqvR4YkeHLkIsxfW43XJy6nXkN70g5l3qFPR7tcVWmeJ4MMYxfxnw2uA3f/HvdisCwfftN80gDNu4jXHVglcQa0U5VVwPMmpa74KhY6WJJYtK5aOh8ZdFWxOkE3brc8vNtgeNpqQ1Py9XJhRTWeHLkI21o8Vdu1awcA2LqVvF3NEj/19c2GiNLSUql0Eg2eZzEHVQPi+MUbUNeYR8d2/BXTPUlrchyUcK6+NzTl0e/e4ejasR2G/+mkyInK1gayMpoRvSdW4lYWSQsJuvan/v3db9G7Wyf88KBdhNP4ScuRinv06Iz9e7YGmuMReVNNPbp3bheo16wW+4ufbpZhrx23w4kH7MyeseGIKEP+Pfa2zbNhwsKkeyFLtfv4yk1b8fL4Zbji+L2waze+YFoqSDZ4Xnx5qVpEYImVIUNcCwUqisOAplnEXWruuYGOGB33fjoHT49ajPd+ezy+06e78vRp8LRVEyz2/R8aBaB5+8Gt5xyC0tJSdO/eHRUVzV6CnTt31rq45zQ2K611tbUoyQdVz7r6xuI1tbX6AmgX8siX5rXmw0s+n8e6devQuXNnlJXJqeZWsbcoh9Y3OI6D+z6di0N364Zzj+gd+N29+t7Y5IB3bWDZhhqsr67H+mq2vTNJWOyzStwTUpLFXsXiwqrKbdhp+/boUOatfDe+NQNTbj1NOv0l62s8ij0rYxeux8DnJuDCI3fDQz/9juc3v8U+qhSWbagBYJ5irzMwm79M/JY+CxsmBM9zB4tU7Yp/+X8nYvH6GoxesB6f/vEEpWmzkOT6UpyLNrTXtqW2AXWNeey0fQemdMpUbE4PIa5hTUU9ZlHK4nrF7nJzf9ZRx54etRgAcN+nc/D6tf247nWXGe+rTqsr/oQlrYGbe/XqBQBF5V4nFZtaPNi2dED7smC7rW/Mo6IlTlf7bfoWVQtydGxXgoZKtn4mLkpKSrDHHntIL7BYxd4SG1/Oq8DTo5s7YZJi7w6e17yPUtwdxXGCg8jrE5dj+opK3H3B4SgtySk5Q5yX5KfFeoh7QlpdF9wfJevFOn1FJc5/fCwO6tUFQ687US4xCn4RWfvv/wxfAAB4Z9qqgGIftNhb83MULHvsLWayaWvrwq3qhYbFLUdc6grEGUWSbTfORRuap8Xht38OAPjm9tPRtWO7yHTKVFrsCWUf19ugBQ7mmd+zXBvXdgt3PY5r0V8kPp27NHjbHp/F3pwxua6htaByuRx23XVX7LLLLmho0Lfn3HEc/PKdZo+BJwcehb17BY88nL92C27/YAoA4NnLj8bLXy/DFcfthT133E6pLL98ZyQA4Ht79cB9Fx2kNG1Z2rdvjxIFi5VWsbcohzZB2BBhSXevWsvuNSTd/dd3ZgIATjpgZ5x5+K5S6bOyeWsDZq3ejGP32dETQyBrxD0frdbgiv/etFUAQAx4puvxWEUOm3Sw7rGX5c3JK/DpzDV47NLvYrsO4kPH7R/Mav733ENViUaFVm7tGRSC7LZWdjZUe4N9muCKf+q/RxU/p3VPNY0Et9jH7Iof/vuiimocuccOkenwxFiormvE74dMxdl9e+PHR+3OdI/OhZaNNfWYV74Fx+7Tw6NwP/T5PNxw+oHc6cV5qkEUDuWzThGbJN8V7918e+zNsdjXEQLzlpaWSu/rDiOfd7BqS7MhLVfWHh07dgxcU1JWV7zmx89Oxtb6JoxYsAkT/tZfqSyFPA6oB1GOLGDNFhYA8ShmUavFbsVep7ISZzTMC54Yi0ufm1A8Zzer6FqVHzJhOc5/fGxAwSDVD9kqk+T+1gK0JhI2iQgedxeO6FPe9PY3+HLeOjw3ZolgCs0LXS+OW4oXxy1F5Vb6Qp/q+Z//1VpXfDZ++OBIz98G6Q7STF2+SflJGrJdSLLn2McZPE/VHvvoYysLPD1qEb6ctw43vkU+ApNUuXW+jdMeGoWfPfs1Pp65xrOo8uiIhULpsaxxyFioeXAn7a7TOr1CRNqO+5U7nLo3z3yj0SCLPc+JO0lROIpa5ylScfZ3cWNnN5bY4GlG0hZ7A5Q0oNW1c+iscgDp7kzCylR1cRfy+tu7MzF9RWXRFZ3lHlHCJga66hNrdQhrDoFFjghRZR+lqlZ8YazBtQjhb+MqmsaHM1bj21WbI6+zrvhsVLk8Y3It/zMJf13eWt+Is/4zBvd8Mif0vuq6Rlz4xDic9/hYrRPd1ZXb8PrE5czbvkRPg1GBCXvseeEJnhd5bGXMc4YNLedofz5rraKo+AxeSG5FVuPjeoPn6cvHjUjbcfdnvN4/PHMCsyz28W9BNWM2HsQUPUE1dnZjAaB2UKel5f8+bEIVl3sxCR2NvXN7fW5OcTB/7RZ8985h+O9XZGutSpfYp0YtwtF3fYElLYsiQOsKbjE/Qnby1jK5+/2wKAysMoctOjT5rAFR7+IfH8xKbEDzBlJSq0lMXLIRv39tGs4Z9FXktX5X/IyO75nH/9remboKs9dU4ZmWWC40qlxeWyqOs6RxxiOj8dd3ZuKRL6IXJoFk62GcSzaqFohKXftR83lH+eJEPJ6MYcHz2B+I99llXdfDoI0vprnie4+747vXPSYvWLsFz4xeRF3AM0uxT1YWenDteOUYMbcCFz05LpPKvVXsLQDUNipaWu5V6SdHLsIBt3yKsQtbz0Z2KyR+ZYVbBsHfdNG5fbrDWdzy7rfYtLUBd340m/i7SOAaGvd9OhcbaupxlysvlvmArBtrqEcCZ1qPDl+AA275FBMWb4i+mIEwDxaRRbDyKvFjXlTNzVTP8eaVBwOe0UrGuuKLITIxn726Cg8MnUs8yUIWf5tlnUCL9BSTlm7EQ5/P45qkFzweRs9fx3R9ssfdxRk8L/x31lJwB8+rb8qjtkFiIIrZFb+YByHQrwhhSdTUNaK+Me/JR9YrkhWPK75OxV5yDsIfPK/182kPj8Y9n8zFE1+St1EYFTwvAcXeVOV56vJKrNlszpF3qrCzG0tsuDv1+4fOBQDc3BLQzk+jpKYo04/o6IO2a7HY+8e1x79ciFcnLFOfoWKirMA6gljxLs7ISiBt8c87uOeTOfh05ho8NGw+AODW97/15SHmfh42B/MrAyzPIWMxk5mchdUT0WRZXps/X78rfop3yBjPWY+OwRMjF+H+T+cqT9v9VrfVNzHvyfSerc3GT54aj0dHLMQrX+vrr5Oc/sbZBlR567R3teP/e2WKXGKEjnPGikrMWh29tcfPt6s242/vzsS6LWz1UedxdzV1jTj0H5/huPuGe/p9nYtI1OPuWvKv2FKLbfVqXcJFlEdvLAC+e0nlN40Ss6PRIIt9U97Bg5/NKwYLNoWkxuAsjv3pNiNaMoW7n4xjNTlqcrFmc/N5l7t2kz9TszMhiviyDTX412fzAAADj9lTOg+dRCmCul8XS+crW2fC99hH3//57PKAC7D/vtmrqzDg0F5c6TZfp9Zin9hg5nHFT0gGANZgHz8zGWIf8OJuFv3uGx69j1oB7i1CYayu3MaddlxWVBLuPv7JkYuwpbYBN52h5zioqLbP2jW4LfYj57F5RfBy9qNfYel9Z3PdU9gOtKZyG1648vuR16vwlqCtDcxa3ezJtN53KpFsXVu3pQ61DU3o06Nz4Df3QqrfYl9RVYvv3zMc27Uvxaw7zpCSwY3I87jl5DVO8CyM1BtksQeAx1o8C84/crdY8mNafE+oiEyLG6MCq9hblEPrIHlW6WX32MtakOsam9Dv3hEAgAV3nykUbMutjHVuF9xjv4VwZFtq0dAphwfrC/7GumL/zw9noTSXwy3nHOK7n08+PyTrTGAP8LRVQtaZ0Kj4gT32elFlbQsbULnOb275V+a5Se8+iyv5MuRycu9et5s5SamfsmwT+vTohF26sB1rVJKLXqRkVcKuf2N68TNrsSW6xz5XkMEpetT97Pt7EBU3WaLKkLUYTDrijcTsNcHtQX4cqOlraGXh+dq9pzzCiFzb0IQpyzbhe3v1QPuy4Pzne3d/AQCYdutp2GG79p7fPBZ7332Tlm4CANQotthLH3fHa7HnMML7T65paxjqiQ8gm+O8tVtYAMTT8CL31Sm02DtOc/Tunzw1DoPHLaXmQ7wX3kni1jqxAci9l6losaeUwcVPjdeyBzUudEzaeVNkEWFDdR1eGLsUz321BFt80d11GMtIiw1vTVnJnU7YpMW/CMbmii+OzL06uhmTrQFZQ+bd67FG09OcvHQjLnpyHL5/9/DQFNwTu7ISuSmRW5rlG7cG84q8P0GLfVGxb/1OVwRtUc/zBWu3YPwiNXFL4oDVA7tUo3ZB0esjFeHrXp+Ogc9NwL2fhp8wsXh9deA7d8peV3xvHf9qwXqoQiQqPu1YPqb8iMYFbhEsCZNBvd4q9hb9rNtSh/4PjcKzHOdfu5WV6rpGnPHIaDzY4rbOynNjlmDS0k34xwezuO4DfB20YMuvcSnqnQgWezcTl27EaxOWi2VkALqtcSzuUoUq4zgO1XrvjoDtv4LXQyCAxuBLYdkHz7HXvS9C/NawdiU6t807Dt6eshJL1wcVKVayuGqvA5ly0qHYh7WLHz81njs9Fm8akTIoKs0R15ngiu+LAqIlL1FL+2kPj8bPnv0aS1u2Q5iuSLGOi0zWdgVpuKWJqmuFI3pf9BlG/EQaSkKC51323wlMx5OyIGKxpy1AMN1LuD6uhbn6xjzenLQCqwS2+ySBqnKp2FKLNyYtVxufIYNjv3XFtyjnxbFL8csT9ilOkh4bsQALK4KrumG4lZW3J6/A3PItmFu+BTcOOJA5jW31aizgopNZ9xFtLBaKJK01smixxQkEs8nnHVzwxFjssF17vEjY2+iey/itJLLPQHzFigombIKY5NGQvLjruCpl+s1JK7B0A0WppxSNvzhVKAimRv41BR3FI5okTRYmxT5sC0nIfWuraiODqSXZlAvOCrHUY8m2v3h9NfbaaTvjx0yWhRrHcSDpKAIAIWm4zmp3vVvWRQeRV0ULnkdi2vJNOGy3bgK5eBHxdhcpjwKkhYS4hoAnRi7EI18sQOf2pZitME6B6fz4yfFYvnErvlm5GXdfcLiSNLO4x95a7C0BZAf2ez+di/+53I0bGGcr7kG60bVvmF0a9wBG3w8aGeHdUaNkuxWuwqewLmQ7QoC9tGDCpN0BsHBdNWas3EwNpOR21/NXj9DgeZyyyN7nJ3SPvYArPstYtnkbORCZqoFQ1XBKVepdxDHfuuKFSTHkkixSrvg6tusoSNJdn1kU+7BLwsRhPcs+KQrl4O5OFARrJ6Jqb3yca2m0/jAMVvdwFeXB5NXmUn5ZvUOi4mqQUslTFeZc4J2p2msv4jXIswChIj9VFI7PLBiP3p++Cq9NXI6q2gbu+fvU5Zvw0LD5qNd4DJ6qoipsb/pizlo1CSKb3npWsbdowR00RmRy4B50SIFbyKhzIvTvCxOB162yc/twd/1EiYyPoNkV35c/0Q0ucOxb+Iq6fyIkay0jDRBR5cKaZVgygT32jGmG8dE3q3HEPz8vHtvnRuq4uxDhTFs555VmFONZ5WmGNskv31wbeaSTyB7YuNHlig8ADQYdeUWidbuAe/FTlyu+3P0ifYXMo7wzdSWO+OfnGDScb3GGdTFLiWJPScJzdr1LHmbFPuJ3lphFJFkKbFUUW0hFXCYeTOnOHMfBH1+fjpvfmYm+t3/OfezjhU+Mw6PDF+Cl8Uv1CKiBsPa/tb4R/3j/W3y9OD2xOFRjFXtLANU6Wtig5dn/5crXrawIRaSHo8wkKDq5ca/ospRp5/bptdjHFXguXIbo68OuCb1f8PlUlUvYpCWgMDGUQ9TE+OZ3ZgIAHiVMZOMInqdbyWepWu5L3Eqr4ziYs6ZKW2CxtDFl2UYce+9wXPLM16HX6QmwqTZNFgWLdTzwLyKar9i37LGPxRNfrn0X3juPqDLP9df/NfeH/yYsdIbBbhXnFomQBmWPveuze6yQ3f9fIHLxOsIqXi0YnNiPbP/C25dEGQ50EpbNZ7PErNmL1vFtl02SsHc1aMRCDB6/LHI8KmCWOUENVrG3BFDRN7nHgrCBgdYRugfEDswWe58rvmCTdRRNF0mDetikMCrAnsno3uvIMvFh0W89iy1+mWUt9gqGCFoaYZMW1q0unnwiRK0LcctTZcQLk1pVfVJVK298a0bx85uTV+DM/4zB1S9OVpR6eiC9+iETVgAAJi/bFHqvqa74bso0WuwbDTvL2g/psXRNepX1IXFpUoLysi4gqlj0oononnO4DSbMFu4obz3Cd+4FhIAnne/a58cuwbINNWyyhCB0jr3rFt7bkwx0qYOkj45UNeYvWcdXl3R5JSWJVewtAPTuQRVpNzSLPU9nKuPu54nkKpiGW1aWTivpjlWGuLebkcoz7ziR7yqs+sjusSe64kfGc2AruDC5g1Hx5VG93666rhFDvy1XG82WA5H66X6d701fXfw8eNwyAMBXC9Ud1ZQGcpBTyHQc5Sza77AEz1uzmRxxmnUBz19W5lvsm/+N5+hbubEu7m07wnMABf07KyzzHfredzoqXfFpo9PvhkxjkiUMkTJ0j8/8HoLh6cWFqvbKsg2JxEffrMYFT4wNjdCvuk8Ja/+8i2TpnXXTSa/vr0UbzR2cuuoedkYr7Se3suJW7Osb8+jEsBfdCUk7+niWyOSZyFKQbBmFWRTZ42ccz28Ocrlc6OAtYjX5/WvTUFPXiP/+4mhiGalSZsJkowXP27ytAT99ejzO6bsrfnfK/moEgdik+jevTsXo+evwg/12Kn6X5H57f9ZcLr0qBdFM+eZajFmwDqPmr8PmbQ146arvK7dQsE5mTXXFd6dRVtpaNv3uHYGl950duJ61+PyPa7piX1C2VZxcUehvabCm+/mscvTu3klJ1PQkYDVE0OJP8BQ/9bg712e31whrdRRZhPGOveTPbsqrarnSHzmvAmuratHR5eUof449773JjQQ6shZdbCssyvzj/W/x3C++R7xGx2k4NLjfox4xEsUq9pYASlzxXcNJSchKIK2DanANQO7geXWNTWyKveNEKgdhHYyKjtO9Wl/4mGKjfJFfvTwZT//8aM93uge5T78tx+mH9MIPD9qlJb/gNf6BvbBANXPlZlzxwkT85cyDcMTu3al58D5CbUMTPpzRbMldVblN67sNmyAGj25r/uKFsUuKx0T6FXsZUUWesxDF123lPvae4Rj+p5MkJJFnU0093p6yMvIYsjhoaMoLxRMJ44z/jEbl1tZo3ovW1WC/XbZXmgfrgBH3OfYihC1CFxBpO7mc+cdS5gAsXV+DD2asdn3H/7QPD5uP1yctx7u/OR69u3ciXsOiRMxZU4VrX24OBOZfYHnl62XYcfv2XO/f2LHXCS56RS2MkNAWPC/SFT+YjldhJo3L9OujWLeljngCiew59rwzX56FSpUtvynvaJlviVrsC4icGiFK+Nzd7H42DqwrvgWAtzEod5sRsHC4Bx33ZEuFi3BczZ53Imv6mbwFSMFZdPellVsbcOWLk0I7bdovf3h9GjbU1OOmt78JDWgY9rpI+brfby6XE5oEsxYbjzWicGWYhfA/wxckHvxtW0MT/uM6AkzHxDtqkP/D69Nw9ydzsL46WcX+8S8XYv+/f4opEfvUeXEr9QDbHvJwgvcz12GGC+esqcJDn89DjaJI2bywHXcXtlBNt0yZbrHP5XI4+cGRxJMwePjP8AVYW1WHweOWUq9hqYaLQ/bKDp9bgXMfGys8Zr45eQXX9boXBVRUDXrwvNbv3WOW1nPs3S7unu/lqdxaT/y+ybev/6lRi/DVgvDtUt5z7PnkILria54HNTblcfKDX2LGys2teTLcR9tW5CZqbPjflJV4Z+pK6u+hsYcVl0tYekkeQ2gKVrG3aIc1Kj4N9yDhDupV29CEOz6cjXGLgp23A/E99jJuum68gdqyTVxutoVsSLkFLAMt/za6/OFD99FzPoPbQkCra6rKRXb/v5+Xxi/Dc2OWCMmiLaCWpnSbIVuNxkRM/Igpaajr//psHgDglve+VZ62G/ajQ8lI7bFnKLcz/zMGj45YWCwPEnvt2FlcCAJusVQedxd0xfd+8f70VWwJxQTpuWTed9dO7ULyioq0zpaHaFO86e1vEl/McxM8qjV4zQczVuOhYfOp/Q/LqxIJnhfpXRGh4LIsSvPUsw5lZI9Nd/8yYm4F7vt0Li777wTmdHnHatL1MiPD6PnrcOdHs0ONV0s31GDFxmgl3c+VBA8HP2GetVW1DfjTWzNww5szsLW+ddHVvWDA+uy6t9mlaUuFLqxibwHgHWjVHyFE/40paqzrGrdi/9yYxXh+7BJc+myw8272whbvQJS44pOi4ssniw3VdVgdEqhEBwJjuzRRUe1J15PPkg9Pkylt0neu8bcklyO+XFVjBs9gxZrnF3OaPS821dRj5aat1OuWbajBH1+f1vqFQvNV2GRCBUs3bMV9n87VmodKdE8y3HvIVcEqc9Q5925mrd4cfREkgudReiwmxV4sy8DzF45QMwUVNaO2odULaJcuHeh5EfvK1ndSU9eIpQoipYexqYZs+SVBUkYWr6tW4lniwKGOPe5y+sNr0/Do8AWYuGRj5LW0791KNrPrekTFIAeRI0PLkqcd0xYn3XKEBXIrUNvQhAUVrUe8ycb0keXy5yfiv18twStfL4PjOJhbXqXMy2du+ZbIa8K2IdW6gt42NDY/+OezytHv3hHF71UbH8Lwi1rX2IT5a7fAcehtqS1h99hbAKh3xXc3PJZ9i4DffYts7Xa7Dy/fSFdGgJDxiOEBVSxueFaqWfJkzPKou74AAMz4x+noFmIVCaO+MY/r35iOfvvuiMuO3VMoDTdxdaY87vJRCwP+n/ld8bxut6T6Fh0Vv/nfNZu34W/vzMSS9eTJLM/+QdYrpy2vxKrKbTj+vubBecot/bHj9t7JeC4H/HLwZM8ESKVqqFmvBwA8NWqR/kwUYfqkhFzH2eB5tFC3Ts9nteXlV+wXravGvjt7YxKIBB/MIWixNw0Vp7JUVLVawcMs9lF5Xf78RKb8ZJrLtgb2rUh+cb9ZWYlzHxuLnbbvgMm39BcXogV/1Qh7rI2UBQmW9+e22LNu7xKqFQls7XQbUljK4vzHx3oUXt6+V9dxdys3bcOQicvx93e/xakH7YL/XuEOSKdvwORdZPePq0mGELn02QmYsmwTHrv0SP4FGj0iJYq12Fu0Ixsd162wud2UQjtvivWWhU019bj46fHE/AHg42/W4MdPjou0mus5t7k1zRURCxthvDdtFT6euUad66+G3jFMMSf/Fp1O6MKAoCwAffGKtQrc/M5MfDlvHZZuIL/TUKuof1LYci2Ly9v05ZXFz/MIq/odyko8Sr1qaBZSEwJd6T7f9st5FbjwibFYtE7caqSSTTX1+GJ2MH5GgVwu2iMmDJPOsafd56+Pp/57VMAqK1QtcrkU7LEP/662oQmfzypHNcVK/diIBbjgibFMebEu9kchU6NqG8Tfx7CWdqLKnZ9HqaQVHTUqvjt4nmtbGnvwvIhtE6Qtc67P3sX0eDo4FsXeb8Xm7UuI70zB4zlw8N+WbXLD51ZEX6+oX+Vtk4EtEZLbHLkCYfr+LsSmeX3iCuMXx+PAKvYW7bBaAqhuWq7Pblf8sAHHgXhU/Je/Xoa1VfQB+7dDpmLysk247f1Zoel7z7Ev5Ck3oXGPxTIWlqpatRFM3Z2px21bAt5JQHCPffB+0T32pLTcikoulyO+W9YnKN8cftyPLutAVBVqT4jSrlLfVWElFMGEof/KFyZh6vJKzxnO2uUKyeC8x8fily9NDr2d1KeyyqxqwiWSDGvQTVJ99FtIw8aVMNHSqNi7+cf7s3Dty1Pw21enEn9/8PP52MDq3q5o25KMUlPLY7EXziUaxxE7qs0PS1fqroKsC21Rhlyifut2VvR9H4feJeIJpsIVPy0BkEnwHsji3xJhypNbvd4q9hYCSlzxXZ9ZOtm7PpqN8x5vXe33yOD67LXYR8igWWeIUo55J7IsV3sjsXMlrxX3vOT96auVnF5AzqfFYk9U2r1/kyccDvEz7frwtLzpEt2Uo14qYxXR5ebmlpmURYd2wUBFBcVGRf1LSrE3Cfde3yQD+URtbRr6bTlGtRxbKEJeQ5fAvqigLk/R7SOmu+KTFiYL39U35vFGSyR51joQVpXj2IITBY8rvmr87Zx2JBxPoDGa0cDdx7rzYW2PIsYI9/is2oLKkpxI7BZZQ0La4S2zDn7FXtAbUjXc8+5svUYAVrG3tODVo9XWdJYO47mvllB/o+23Dz92SN0qO09pDJ+zFi+NXwrAuzquLohaa0ImKUX+OqNCNB53++brHUS99bD79eyx0z9q+Muey6XNVVyk+/yDt+de9myoeKwEBtXnpDB5klFV20hUhlgXI1S54nvGA9a8fW2VdhdLeqzV1P+8jTpWNhRCeqzCd8+OWaw0L1Vjl5wrPs8ee7V9k3/o4Fl4aqRcTJtmuUVvdC0usbbHqCePXLt2yJ91IrLVQybGjkrCkiVuhZLIy+0pwltmHX2L/lGBjQvoHuaztuAigg2eZ9GObEOmtVP/QOZRUkLy1dnsrx7c7Mr63T120BIV3z2o87pOyRBlOfC/I119d1inzdKhh7k88kZ19Qfik9l/rBK+hblWoUn3hbni53I56Qd0T/BjVeslxNb5TnVXFx3px+2KrzpvXi8JViVv3ZbW7VxpCJ5Httg3/zu1ZQ+ruryC3wmVjkSR+hX7ON+O98z14LgUJsvvhkzD2YfvGnhfLLXSvceeOXhe5NGEjuff5s+eK7zXx1DSJQJzI95+IIlz7FXjnU/yjcABV3xDnp37uDtjNhGow1rsLQGUuOK7+giR1XnaKq+78/UPOAHl0vU7KTCYTiq21HoVP0W9ntcV3xwLp45OnZRkIR+aa/zrE5cTvne7BdLT516x9xngRCarSQ8qURZ70mwxR/9JIH9z6rCfuCTT6bYaC4wiq3o0EQugCeVq+h570ry+2N8KpUi/S53FXvy9bqtPbo+9vz7y1k/SIhGtL3Uvyrut9OzB88J/L6RCC1LrHXOpvjJMsrAiUr/yxbmFgwVrt0S2VxVxEZKmSUCxL7zDgCt++E1KCY2vZUBfnzRWsbcEUN0sZCPg0uQJd8X33jXgkdGu3zjzp1wf9VS8Ac9YOiQZ1ymdBAPXKYCovIdZ3L1bOqL22PP8RqLJs3CjJk0ViEaXZb2tUO1EJk/+uUOcXie8JDE9sHOSZtiLge1K1r6Y5SpRpbTRdIt9nHkZMHZt80XFD5NI9Zvzj5eB4+5a/qYVE2khgMkVX+Ac+8g3FZGM6j6NZTFHrI02p/vW5JU47eHRuDYikOiGmvpALCHdLVwojk8IDS7rRKhiT/jJ74pvgkKdyyV77J4pGDytsiRFU5ODq1+chMdGLFCSnrwrPrmlJh08L6r/IJ2ZLisTbX9d0hh5jn1Lqbu/Db8/JGPCb35lgbRdIcnXpaL+hyYhkL5/8pDU4pSMpU+nl4XudqTHsybeSi7yDH7vGplJqGiVNd1iL3OqB3dehO9E3olMfebZYy8bH+GrBes9x+MGYj5QHoTv+WgW+1aaXCsIrOUdpSSTxlmaF5Lj0J5J7TggY7H/b4tx4Mt50UEi//7uTM/fJii3bkbMXYsfPzmO+ru7PkQdH+2HxxU/zjGC+x2Y9cqUYBV7S4DPZpdj+NwKPPj5fCXpCbniM3wOC8rnCOZLY+zC9cWzMlnRMUknLRbEQaQ7HsXioBrpPfaS99Oud+AITfhbLTPidTVQ9hz3uvNlHRAL94hI7H/OrxdvFEglHmJzxXfIn6PvM2NGkqQYrHmHWSc94wtDeoV6QSp/qndXTl3wQF2oXmMLe1xVUfFlSpRnoUXmuNExC9bhsv9OwHH3jSh+508uGNwxPD9S2bKUqdcVv5BWeF7k2DGkuk/2YGOr9mrbBs0TbGNNPc76zxg8RwgGKeJa/9aUldz3sKCqNK56cTImh8xb3RZ73vYfdMWXk1rVMxtq+4oVq9hbAvDsPaPhnsDLDuJhkyXflZ57orJlPUpmQ3UdBj43ARf5Vj6jXfFdkhGewS8/S3/kDbpjTg+mQxLSQNG6x57wG+VaN37rQdj9UbI0hW3YL+bBVjIq36VuV/wCIgtnfgv9xKXmKvZJwFoP5qypwjH3DMebk1ZoligaFVW3tqEJL45dwp8343UqF1lLcjnMLa/CsfcOxxuTgjE90grP0WqyKNtjH9MQKOMp9/XiDYHv/EokrX7yueKTL/Z4rPlc8R//ciGOv28EyjfXkjMCQ1T8iKJRba1lWnyjLFg/NmIhZq+pwl0fzwmmS0nry7kV+GL2Wl4xhQkbA1RvYfEHcaRnHPyqQ5k/Kj799jinqibEU0kaq9hbAHgbnuqGIXKmqBeyPP6BTFd7dkc39uQXcR95Bdg94PDL4umI+W8n8vrE5fhsVrlUGjQ3eLk0o/NxQxtYWK0HssfdEF1ZExhjeMo+J6HZ0+YYW+sbqfeUhfQFcXrly7wXne+UNenr35iOii11uOl/33Cmr074qtoGPPLFfCxeXy2d1sNfzMftH87mvo85eB5H1PEocjngxrdmYG1VHf7yv5mB34j3IFnPBhbIwfP0CB2n2z8Nnvx4i8FdlqQFE098FjhKjlrzR4Kva2wKeCU0+gwD//psHlZvrsVDw+ZR82Id17yeL3TrfRztwD03dJdtXSPdaEUq07rGJlz54iT88qXJ2Ly1ITLfJJq4TJ/eyLo1g/BT8Bx7Vo8/psuEcb/vh4fNx+Zt4e/N8G5ZCKvYWwKo7nhFVhmpR6e4PoetF4S5Rsdl6fYO3orSZF1hjcB971/fmYlfvTxFQqr43J94zqEnXSp67BXR+u/2yICY63YhWSlXfOE7/UdE8g3MJInfm7YKh9z2GdHVEQhf5IvTYmgSHosaY6OWcQ1WxR0fzsYjXyzA/LXyij3Plgzv2OAEviMR6oof7XjjIZfLoaGRfKXpynsYcS6sqcor6VNFaERFGHe386Y8YeEp0goexN1/1jfmceQdw3D8fSM8adHmD2G7Eli34dEW0JNoE25XfOa+suUy9/O6763cVh+dhJnVkUqjhKGorNRnWAu5Ns5icY8F/xm+AH97Z2bI1dnEKvaWACoaobvJS7viU74PWOx9f0S5+7FOLmj5y0bFD+TfcvmG6jpUbCG7xvlX3OMienDnm5iIUgzUQ1S0KZNtTyAfzw/e+zmFZtkzm8gKPkemOQEPksI9pPZ13RvTAYDo6giwH6ljQtTsJEjTxJAUcySpBYeocmOf3Edfl4O5CqUMpIU1qcXjkN+U7bFX+BrCkgpsm4vIN2ru4V4UzjsO99jjkBRxV5bLN9Zga30TKrbUeeq+SPuM6ooLsrvbBG2x0omp5Xgt9mw5korG3SZMDVwsQ6NrRYfXFT9AmME/xoHN/75Hzw8PgpimMZcVq9hbWtCnMMrup6Pvsaenu3pzLRatq5HKVxbSOfZR54Y35R0cddcX+P7dw4lRe1UFz1OpNzXlHWxicFPjhWxx57ifUMChwfNCrBaku/zWD1KZGr/fK/JkCb4jcKJQGdBSBpm3ovON6p47KlWECIn9+a0Z2tIu/kb4HPVYwaj4IlI1Y0gVVo7flRsAlm/cil8OnhQaOHbGikr8cvAkvrwM2bbECq9s7gVMUn3xnydPOwmRVtX49tiTFXtmDy3Sgk/EPbxeMDywpOcuC/ajLoPXub/jPa7y68Ub8MvBk7Fy01ZldZt8moR4eu7FitC5CkMerHMdFZ55YX2w/3VXh2wNzCplSQtgyT4iR1p5J2/kwci/6u/uV85/fCx3nlRZPG5l7L3o3RSrZRhuZX7dljr06dHZ87sOV3xZvpijJ7AMUTHPF6wDDPczphl2fRgsA1mkS6WG2WxxEYn7Prbrwlzxo1BlqcsS3hgerJPQVn769HgM+tmR2KVrR6Vy8chQ4J1pq/DQT78TnwxO4d/wclMdPI83OdICmXkLBEGBfvPq1Mg9qucJjLXqXPE58pT83XNtxMXuOQ/p0rxPoeIdB0hXs8jv2R7oUb7p+Ud660WkR91WqRH3woqMxd59K8spCu4kLnnmawDNsUh4iHN9y7vHXi6tsNtp83nhvDi8A0xeMNSFtdhbWpAL6haasqao+EFXfP5JsU7qGsMHgmBUfK9LHmk/ssiKuwrCVllJpyhoc8UXSNd9Tz5kMuMeEH769Hjqb8W0fO+CbNkwe1RxtyFWSQt3iLjLs7riG0ECoopY7Ccs2Yg7BRYRZUlqwkRSIKJECd02w2ldzOXUjCGmTThJzTlKqQ8j7PmUbbVRWIgqX4dn7CY8q3ePfdAVP+qxyBZ78rXuSxsFDAOR59hHKMT+w2N4jokUxS1ymCeeVwaSxb4VUVf81ZXbPH//cvAkroWcWas347SHRmGYhsj8tO0TAYiBNcP/TgpeOUyfo4lgFXtLC3SXnLrGJrw8fimWbeBwbXd1BGEDA3U1lWGyJTo1SNLSHSWze+wgXetW7FkHLN34g6iogmhx5+iEifvwGYNoTVgSHczLryyQz/uNTEYamRVqT1B8Vle6lptEdHRjXPENHctFPTgqt0YHdgLUKi9KJ0SiL6RosQ+/jPWMahYxzKjB6olzzY3oii9QnwxtxpFl6d/3zjuWR7ni0+pxE6c7OUBz/+ZJR+1bYsnb44rPKCt5gaL1y0YGiz1LB/LFnAosqGAPOHrlC5OwoKIa17w0WauXD/vRvOTvWedWKsbecFd8U3uF+LCu+JYA/mbx+JeL8OjwBQCApfedzZSG23pJ2rtXYMXGbaFnqAJ0Vy6/RVublViz61CBbfV5T+GTJj/+Y3JMoF1p8AXrKrPCfIjnXbsv5YmqH4UnKr5DmRhEpKHjDYqmSbovbB6h2mJviM5PpSnvMLljypCm+EwmzJ+c4r/hwgQn93zCeya9uZySLTSm1fc4F90Me3QAamWKDJ7H6SXih1j9GB5A6KSeyHSdgEzu9pGnfO/JQnGFEN1jn887mFu+xfVdKw0CiyIF/M/HE8SwgnLcsgpUKtwmjAcA/xhqitwqsYq9JYC/on+9eINUelHBMp4ZTT4aK4okrH86+4DfDpmKv591cPFvYtAdRXvsSTiOI6SstdNksSfBpXxHrMDzpE36xa8sEJWLBAYNLot9RDBH4j0t7Zn1rbvLPKx6Pes6Is/Eif85g77CgrVbsNP2HbTlIWptSP0pAhzyE4NcRRRb2ESaZRHSs6As4IqvOuiVDlTXoLCtTmmLis+LuzoT99h795ME8o6qkzzB8zz30U7VCckuKtVinAuKW3fAZTskDVV4FxbYEs/ngQ9mrPal0/q5Ke/gpfFLVYgXsUdcSRbc8B53y7O4ydbHsqdnLfbhWMXeAsC3cudqhI9/uRATGdySlcvjCb5CviYseF4c8MylC88Ttfp69yete2WjFHvVOA7HEYCuRYAygkuGindBngC0WAc4Jvfe/X70esUrs3ePPc1ir79SUnNgeJneRTe+gZ1VmWQt1y218UWvFXkvc9ZUAQDKq4IeRsqCIDImI5qfymCNOvtb1qRZZWC+jpKzu9/IQdEmewPwLrrpXRzybDNTFBU/zmO0vPlGXRFelv5YOXRLNjkd0jSAd4+95xryrS3phkfFL3xuaKSkLeIl4GJ15TZ06ViGLh3bCdzNEzzPwaJ1Phd5161rNm/Dbe/PCk2Dud9S2IGoagKyJ4KylrP/sidGLkS7khIcvdcOlOsd3P3xHBy8a1cp+doSVrG3AKCvsP7rs3mxyxKG13IiFjyvAPM0hkFhZGHMgvDzNP2QBlR/NF2ViKama489CT6DffDisL2MrHvECjT5rB8ilojC7ypLsCgJQ2GJWOxJ94bLI55HkvAcy6NuchWd0Lb6psBRnqySfrtqM7p2aoeugpNkEyAtQvNa7Hnfl/tyVcHzTHCy8CrbevPyLCKoSlPmXt/NKh/fY7EnJBy2wEz7znM/QQtz91e0ILEix+WyBs+75NmvA9/5ZeFlzeZtOO6+ESgtyWHRPWcF0qbK5PrM7oofTNtdXiyBJFUvNLrZvkOZkqPiPHK4P0t2aqH3U37bUF2HB4Y26xivXXMs8ZoxC9bjua+WMMvBOy9O0ZSEGRs8zwLAN1lStH+wsSmPkfMquI/78ENT2EUnIrxPt3TD1tZ7hS1lwJuTV3Lf4ycsqu2W2gaMnFfBeCwLyeJNf7awsibvsVcAQZ6wPfZB18/gNbzu9mEEIxkTypQxraQGlyh7fdh7F3GnNSUuhKmwlM7A576OvojC/70yFac9NEr4fjdJWUvdjJ6/Dk15R2CPfSss+0w9p5XkzHh2FQQ8ERSStiLiETdq7hHVNzJ6wTPdX8CzSEvxePRvAWCBNShswaPJn09AFo4Hnrx0EwA5T8U4o+IriS1ESaNbJ72Lsdyu+Bz3k36ZsmwT5q11xzMg37+JMTBsAeuKby32FgKq2sXToxcLW/xZJlsBi72m9vy3d2cSv9du4SB0dGHBb37x/ERMXV6J35+yH/50+oEC+YlRpimcMkkeXqu6P6WweQ138LwIq0vz9/EPMsUsNVXQgnsoqyKQxDnGUSjf06koHZY6OHV5ZeA7nle9tkpNMKakXqU732fHLMEeO26HC4/cLfQe2aj47u9FFGATrPMkPM+lfTzTkGZClTAqXz5vH4dpH7r3d4LF3vUCPYFdXdfQlFyZcSpSIXSof0R8q4ZYo+Izpi1S3J3blwrnz4KstwHPdqdF66px0ZPjPN+rWljkD55nyKREIdZin1FYJzIFmM+z5OCdqXwWaho0ZcytT8bVOEVdl4SkI1mcacFv0Drhf3uKWLlzPY/rWuIePE3vI0zx8Vd5dQsDZFjG+ahmWGh3al3xORAKntfyb8Zd8XlQVd/TVD6myDr02zWRdT6qb1hVuQ0vj1+K2sYm4v2eZ83OFvuAJ4JO/GWoJE01ySjHGzwvPJ5AsxGbd1E5PE/aOEezpIcRFXuB7D3nzpMpG6W4ZWK19pMuc39VLxEVP5guf1q5HMV7QlEr4B3DgtsW2K+dvboqcA3PwiJtESCn6MSStGMt9hlkQ3UdTn94NM7puyv+ed5hTPdQ3bUEiUNRcQ84OoPK6UKkmBt9AdtUkgY36UKZ8az7s+7341bsA6745OvqKMqCKgL5im4X4XTFYz2VQkQcUy2cNFS1nDTNSZLqL/xl1K60JHIyFzU+nPHI6NDgjV6Xdf73pNrNXRVei72+/btK0iO5SifUYKKKyv2z2B57B7UN9HGDHBXffb83reJnkK8Jg3iiQ8Tb9ebpfVaitwGbKEIwn89O3J7Y+pnFYq+iOsZZpUW86eh1X9wzQiVpGkN1YS32GWTwuKXYUFOPweOXMd9jmlXNKw9ZoBLPCjWPK5GDT2eu8eydZ5bLMxHivp0vL8J3YRZ7k1AhGXnCw349qXy8EyqH+hsLfq8Y2mSn7+2fc6UbJ26FQ2aiR2JDdV2zmynMq7PqlQ5F6aRgcc002pWWMFjsQzx1EH0ig/vuJI5Z1YW7vul+qmhlULMA/vw0trVIK7f7M8EV/9UJy3HQrUOxqnIb+f6WGzbV1BcXrdz1khYkj9b/Fr5duWkr7vpoNlZuap0bEQ69oT4LCRabi85Xz+qKT7bYt37JsseelSiRkhgneaLivzh2Cd6dtirwPfUehjes6pFZ37fqfE3CKvYZRKSeyh5JohPawFRSQh7IWPj1q1NlxeLCcYITp6j5IemRPHvsFb8o0a0FuuoLMap90WRPWF2P+Lv5Nrq0snvs6Rb7kJV+DYVXSFL3RD2q/h511xf405szpANFpQFVSoLo3DEJVVPnhCg8be+P7UtLGCbKcvJ4LPY5fUph3GsGUUfQhVFRVSscGFfEg0F5fYupMyJavH3zLb8o9306NzTNvAPMX7sFR945DJc9NyGQD80jgNYXF76//PmJeO6rJbj8+Yku+aNc8cMt3UnMLd3tkzkqfsSegvqwcbyYBlNWSssh7kXl1Zu34fYPZ2Pmqs2e7/OOg3s/mYP/iW4FVTWGptB7VzXWFd8SRFFPocy1jyKO93iX6MjIakQRz4P3TmLwPI2u+CrRNekP3yMfbo1ovj8sbT5ZZI/QUkVgQcMhf0++N7w+kSZ1xeB5DM37nWmrcM+Fhwdk42H+2i3YafsO6LFde/6bJeDpvpRNrlJkPlAqqcRzty8r4RaGNzvHNafP5fS19Rzi7df9CxasVG6tx/fvGc6Vl44yk0lTZznzWLmbF4X5F5XfnNSsQI1fvCE0ffdfUVsAFrcco7nYdZwma1R8v3xkWeTrAe/97FHxCd+5Pocu0HMS9b5Jc2cd23lIiztR1NSRt4hs2tqAp0cvBgBcdNTu3nzYJiPkr3n7ar7LM4m12GcQkY5TtVUtJ3kkkFce8iDhd8U3GZEFAaLFPuS4O1lE09OljEQNtH4CAbIIF6uccPgDEZlSBXkkWbC2uvU+xgLg3WPvhtcrYmFFNU5/eDS+e+cw7rxkSULHFrbYJ+AenthCli/fdqW5aDfvgKrj7kejJtne60tyOf5nZ3w9cb9Hz9Yyjvvmlm+JvIY7GFfkO+S/JymirdyuzwLPkM8D23x78N11h2alF+lfouoFj1t5Mn0qo8U+Yo89i8WelTCJHFC8IBDcskFjfXUdxi1czycTY+IsQTbfnaYmcDaNsG4yRWvj2rCKfQYRCX5HO/fUBGjy+F3xTZPbj4opm1uxV31eZ9gEwz/hjBwEFIhGVOxbviQlTysPt6wyQRY/mbkGZ/1nDBavq25Ji55PGN7zhvXBUt/+8cEsrekDcv3JxCUbxW8Oo0WoDG2XTgAzOtx2ml3xHSepyN76M/VsbYuxMZjQ7lQZHkh4ouITn9U73+KVxIGDupDgerQUPRZaxlyj4wWEK8Q0t3ytuLJh3mOfj1gAZNknziadUkh5nvyvkbi0ZYuGTDokWBYfr39jhlDaKhDx6swaVrG3AJBfQSahyvpAk8azpywfz9q9uFVbZPAOYqTFXq0YofBEtY+acPDym1enYvaaKvz57W8ABOMdmDJkyNRRnutY27fIUZqFlEsTHKGScMUXxaQ99ix7HEfNX4fR89eJ5ev7myV4nizuCaHOI5W8+6TDr/3XZ3PR/6FR1H3ui9dV44QHRmDIhOXUNLx77DkEFcA4V3yNlcYbFT/iuDsH3IOH44B6NGMgfc/3/PMHkXrhTprlGFqdsO65JimEnq8YkmGOwB+5MER2xWed5VbXhQcDJcnhl+neT+fgtIdGYYuvfykVOBeTpd6pqhc8RyKHyZNmrGKfQWTrqQrrhOx+IJoHgfuzNwqsVHZGQhokPMok5U2zlDyvmzsvKpZZSCkU948TfqRa7Bmu4aG6JYJ2YMIQUx0MeB34nklUDN53xnyOfcjkIToPA0x7DJjqEqwT2hMfffcXnqjafmrqGvGL5yfi8ucnYlt9i3Ii8Z7bl0Ufd6d6TNT1tj3ePBHP9PiXi7CwopqquP/t3ZlYsXEb/vbuTGoangULPlG5kY2KT3ZPZkdlXxKVVJT3g19f5K1PecdpbTsEmbzKvPe+KHn8iLji06z0Uc8ZdsRfaxp8pcUcPC/iO7VtPsyqnIxl2V83nh61GAsqqvHK18s983kBvZ4JVY8cbvxpG+O0VewziPQee8PqPlWBdXUwTXlH2WopK7yLF7z9IVl5dV/AmWBkfsm++Ka8g81bw6MshynmQYW35R/X1ypOFSjUO/9+fZk4CjxzzjMeGc2Upi4K9cQv8pRlZLd5mWUHXe7BBSlUpW5an5kkG2vq8fCwBdTft7oUEpaJvB9/P9W8x57vHppFk8Qlz3wtvXDDWs88x08y3kPrE1UG+/KzeZtYNPwCJrQ7mTcamW/EA8oeW5t3CHvs3cGEKdqNO5Aca66RixSEhHi3mzmOg0HDF+CgW4fia1cwQNHu350nqys+Obq/3Hui56UsKWVy0VLxW+xFxmQdHsH0vEIWTbTmbA5Wsc8gQgqG5P06Yem3klBKw8pJmziuhJP0UqBZBMK+i+KCJ8biiDtaz3wPO4eexc0+zOKvgibf5IzZtZHwHY9cCyqqQ38vlA3v+EuSYd7aYJCsolLsy+CiJ8cL5RGGLuuAaszqMeMhrM8NDW6keK9qWQnDHnv/3xwZrKrcFrBAauvfPQFh2TKRWfwSfYx/vM8fmyPqcb6Yszb8fu4cw4nLFZ+Yt08Okaj4tQ3ehZucp+6406dYzBmV1qio+DyLas3PSr7u38PmAxCrW+H5s18XmENoqiOR60KUNq1enmhDx9b6JuN0grD2FVZGxO0Whj2bCuxxd1lEyGLP0VMzIGtoo4tDWYl22MWWOrJO8FYdq6oyz0EcrIVTU8M3KzdHXsPbaTffo2fVPZCPwD3Pj10ScKlUlS/vo3K7yTOn616MMsNinzWSKCYVLUl2/27hb92TM3+9jeVoVcYsaItf3F0dx8sor6qNvCZsoZWU1R9fn86cf2ua7A+psu+PKiq3YhatGPPXJsdxAt4u7mxocU1EykDkHHvv7wx5RJQXb3puWF3xVRhKeBYRQn5NxlBFyXNrPdt+fen8FfSpOYR7aLQVzzqr2LchHMfB+MUbcHCvrtjBdya0Q/ksg7K5JlVha/3cxGFFUWXp1nGuqJvovWv60vbDvmOQ9o0aeBT7qK0MKifnDiW/KJ5pOfdVKaKLT6zXtVzIqnTLlLIuhVX1AJ/0NpYkCHvkOIsjz7KqG1Ayoy1VnusjrllYUS114kYBkepOG4e4pTG8Dk9dtinwnZwrvr4F/ihPI3/9432OvBOxjYUyT6Ad9xqWPdkIEC4wLQgk7T5/cEpZaNvvQu+BE3hWxfYuV7piqaluoSzPVyNpeAjLk+l7zqfWbdBIA9YVP4PQ6ukHM1bj0mcn4LSHR4XepHvPjo50WCOfAuqPiSOhwqXLgYPfvzYNN77VenQITyAagQzZL02oMyy64kco7W68Fgv1MrXmw5a4molLyKp0MR91acqkLzI5KpSRSAReHnSf3BEffM8xZMJynPf4WKyvrhPOManFDH+2TU50y5NdxAtbNGxoyqP/Q6MwICT2BWs187pTs/YnbGmTiOMV3v7BLPxy8GSpcXdueRV++szXCqXS++yRi56SCqPjIOCKT0meuojFvHWM/VEi84fDtyigAtZ+Kk9YYDH5GOgCMmJ550YUi31dI3WhiDkfxWUnOm6b+g5VYxX7DEJroJ/NKgcArK+uD97jarlx792OcgunDUbBaxgVE6K1mn1VNy6WbtiKD2esxttTVhZX5925x3mOPXdaCmQjK+/0dFncZcPul9mTHveAkQ/J2xRrAClhXtGSdMXnyTltE4a/vTsTM1ZU4qGWfa0iCD+y4rLK56O9tfJ54K3JK7CwojlmhKxVx/0nS5A6Vu8uT/A8RhmpbSThSlnof18ctxRfzFmLma6tVrzebrNWVSmVLU6I8w3P7/zO+HnHQR3jcXfupEXmDJHvKiJJ7i1eirt81vxvfe9bPDFyEfVelmRY36Mpw4XXm4N8jd9ir8swFkeZxGHUMwHrip9BZFfUVNT9HHj24Eb9Hi0Qq7sVQN5zdcQ/PydcqQ6SdM9GuGFvrGm1phUejzZgq5BH9L3r6ivDAuSRsmRxxXeofwhM9hGdny7yjoPSRE4vb22PYtFx+QpJ39E6il9WwvMF0cmwVHyHkGdm9l4RqMP+d5dnUIw+mLEaH8xYDQBYet/ZAnnS/xatolH3sQfPo93PJ49uZLYqdO3Ujvg9TzuO87g7d17kaOuuz+AfO/KOE3oEIz3ejPszYxuNiBEQBc39XxReBVumq9emxArPtfQ1alrSsjGAAN/7kE5NRg6279KOtdhnEFpFZZ1EmRYlkkWavMOzxz54YVUtW4AQ1jxIl/kHyPemrw5NY1NN6zEjjS3n1Hj1erIwwm5KIb9xW7MBvDh2CT6cEf6MBRqb2I5mCqubeVoSApMZfthbjYrpZdiEQ/gRNVlWZCZZJRETZFMwrc9kxYQy3VrfiDr/fmGuhdroy6evqKT+xvLugu1NvtyixgjWHEokVr/inHDL6NVdOpJtUDLVN+wIxOh7w3/3BLIjLjB7NXud+4JpCn/YIrebaFf88IULnt8A9V5aMvXa71kReT3z/JB//NbhmOPd2klOqKa+0TfvFMnH93eELLowYbyLA2uxtwDwd2Dx5k3ssNwdjm91u/X71r/49thzCKcIR2DwrtzaumXizckrsXlbA7q6Jjgy74lc5OwJRk0IF6+rwe0fzgYAbN+hDD88aJfQ9OoZFXuq8g6SK36QsEdk3webI6cVY8MJnTi1/MtrDWV2I2y5TOce+wLuRaqmvIOyUjOj5Cc9X0iiVFSsH9U1NuGIO4ZL5dtssQ/HH6uB3zvHe6/HYq+w8KMUQuI9tKOxEl5sYu1r35m6EkvW14SmpaN+6yydEpfJ7OEvgttdvC7Q/JI0GzJClENKXiLPHKVoR82n8pS5HA31rvjib5rfFV+NDGs2B0+dcBz1dZZl3u+32KdZOSbV1TQ/Dw2r2GcQWVf8mauijx2LhKN3jnTFZ0iD67g7mY5e+E5+Nm1ttdjf+VGzkvyjI3q3yqJYGJXJubcRXPnipID765L1NXjws3n49cn74rDduqGOEAiI9HxhwfNo5UGzWKhEx6AblV/Ub7qjybJ7ALnz4MvErY81OY6yAat18cMiSui7ZHzNyzZuJf/AMX405aOPh5JVFmT7DVL+JJGiXLhJSG1X0dxpeaKdU1rbDW/OIH7vhiUwqiw8dcR97T/e/xZ777Qdrjh+79bfXc9Kkl3WPZ2soNDSd8jfe5RWzrGC51rucSUqPT7Luftq/n5AclWEIVk/s1ZXMcXtUE2hXJ8bs9izsFBd5/VmFbLYE/IJu8bNoorgop9wl0fIZPT8dVi0rgbH7tMDXTqSt/ykDavYZxCRlXr3PVMIx8okCUtHzuOKr+JYoigCbn4IHqUShb9DBbxWfJnJJrflNCwtDiW7wNUvTsLi9TX45Ns1WHLv2USLPSmJsFfnj7NA3NsYkT4PCRrsqRM3GZgXxlquZLfY88tXSNvtZhzmraEDnvYax6vfUF2HxRGWzThR8czUqsGxaMDS95f6XqbfFTqKMFdS0WNPSdmKWOxpFlWd7t2suPtruej9FGVAQmZV9w4evwwAvIo9R7y5Zg8QPmEix3539XYr/HniJaFEbe2L2mrgX0Ag5ev5TrUrPqUsuO9V2MuHzWNohrVcjiz/ui1B6z4rpAWQuz6e47lma32TVBn6mbx0E9qRPO8o6T725UK5DD1ZBDMpeJZ+dt2JOLCXVewthkJteCH9ZZLeKFFR8T3fU+ZjPMq6jF7PeySYDCxBd0isqtwGx3G499qr3GMYlVRBQSmkQ7LYh6XME/Hes2VDl8Ue7HVDxbwldI99QfHmdcVnLJqiKz5rupTPLLiVFp4AmSrgaw/6ZTvxgS+Vnyksg+gjqy4qljYt7YrPsGgYBnObF9ljb2ZQfADed+MWk7cPpLV9qbPoFS/H5fMO3pqyAq9PWoENhJOHfJl75OCVxInYfsIbOyGsrkR5hERJz+KK756/qQ+YqsYAwtKeknDr7v8Q/ZjNKGjeHG6Cc2sBw6Er7bs/mRNypRyRC2ohoid4AI9yrGJvARCPxYmad5SSyGhRYT+vNP6nFdoeQfiOdb/a0G/Lcebhu3Lmp65ceJ+3vimosJDeZ9iiTMCqFmkaaOWrBeuxbgvfmd7+VWxmCwhydEEYMS3iNSu8bc89yVPZbouLE4oG8zheR5hSn8SkJDQAlNZ8vTQxHHcnG5DLuzjlU/IFn1ZVVHz6HvtotL4nx/cMEq9AR38n052QirymvhF/+d9MtrwpFm1WeKzkbrzzB8b6RcyfXWiWK+tdrueeBSAF3jCeKihxpO2n35Zz5Rt+nXkDOO/CfhoJPe44Rjl0Y6PiZxChVUPGe655aTLTdTyN5PmxS0J/p3aCrq9fnbAMkxm3EKhyxdc9mY7aRxc2jIxfvIE/Q2ELHMGzgDOxWtIee8J1xY6Z8CPLRJi2MHLZfydE3luA9trjHPAcynPIyMF7Bi+rR8g/W1zdAP4JutuFmidApgq4XPFTOtmREVvFM6uwcDXlo+tuwGJP+UyDJTCnEnyLhSzQ+6PkK6VXrxcfMGltP6lHJOVbU8fuTRNYFOZ8jqiu0Jt+tFU2DBFXfFDydyg3uPeUR+YX+qv89d579VQw8XqrWB6H+DEUkWGYbZFRf2MOy8Fa7C1GI9I8WO8ZNnutQOr80Pb00Br/65NWMKetam86l6uuSF4RCrNqPSe809Pn1g9wRMUPSZe2YCP6zqLwujtyDEtKXPHl0/DD7YrP+BzuIw+5lQ138DyD3RSStsDIKE2iiD4xS1nxpO04LBZ7jgQJ+OM7qNxzSoO1Tqk6HkxHHfa44nvE5JOZus1KQCYV95IgxcSh5u1fjOWUJmoOQ1u4EulCI4PZRf3OmSfr0besyLRPfW3bDLxzI7pUsSjdPFkIdnnheWRHs7eKvQWAng7MVDdXAwwZTJAt1q2f12yuRUNTHu1Kg443Gp02gveJ3eaBuMee6LEQMvgwWK516oZxWsjCz7Fv/o3b7ZBTBpHmzVv+boVV5R77oteBgm0RbRYFxSZk/fHVg3emrULH9qWh9/gXJnnbqn9iyxvVXOQECdbyve39b3HMPj2w+w6d2W5wZ6GzP4QveJ5EWtRFPYkH+HDGauzQuR1uPP1A7oVrEjU8ir3rM0/g3+L9hL1f7jQ84wPNSMLqEUIoG289JRkgKJ8ZMnVb73kVyq8Xb8CnM9fg2H12JKbBX85817OKKzpXUN1eWetDY5O7DKOF2K17J2o+SRJWn7Jksbeu+BmE1ojirLcqGwnteURXEaUs9u6JCk8wQlWb7F3f3fnRbFz05Dj+dCniqI36ypdWA+MqfYgnfkjwPNdnn5VdhjisdjJ5809K2G6QKTfmfcOEvOKOis9FwhOXJCYlKmIeqOpzhkxYHvp7mCs+C0m09bBs3K7pNfVN+OXg4BY5EybT7jqyaWtEQLnQdMTuq2/M489vzcAHLq+hAltqG/H4l4swZsF67nRpe+xZCdtKxQKXK75HsSWPf2EyRAfPC8c/tvNeH8gvJIFLnvkag8cv80RSl7LY63LF15KqHGHltNUV34VF9pKAZhl9F897Eh3uwvLIkF5vFfssInTcnQmzABciAxArSbn08nYcpPfo/+6bleSjUXgi1RbvMasKcCnvQPC9EstPwTPSlCjZPbE8hLvNCabJnHfzvyJWLhkLSNxR8XkwVzJ9KAlmqGi9Mwr/cXee9BgSDC7Uun9T9/ZZFT5/2c8t3xJMi2nLg9qxNZC+S0f7v1emCqcj6or/+qTleGvKSvzhtWnUazbW8C84SO+x93zmn7FFtz2+8uK1ZPLUFbc3Hu9z8mwxcsu5xHUsqEO5hgVuCz9zumaMGCxR8QGg0bWqzttf1jY04dqXpwjJR01f9L4wxT5DJnvrip9BVBmHTUF1Hyil1zPf61cy+cuYZKFklV13HZCxYJVvDp67SnROICRceH6WiPlErwRNA2qzZyRb2m5XQ1FYTgfQPU6pP5YoHJXB81TXA0PmabGi4nXEVWz+tsD7vmILnufJI2QRk+EBkq6TjuOEKAt8wrF4Y5FYz3DSSaFuyJYXlyu+bwGHvz5GeHRQxmfWU3XcRCnXtHH2gaFzMbd8C47YvbvrB7Y8ZXEv8puiRLtJctujJz13PWG+jq/vGTJhORavq6FfzJA/iSnLNqJz+zIcvGvX4nfR8SBCFrA48zcZq9hnEJHGb1rfxyKOcOdo2sNSIFuc1ciuuwzCkj/uvuES6dITplt13KvSwllzyaKbsD2+ou6DvI8TR8A2t0hajqlUFgckHX1KXLDGwhBagBS4x++Kz0uYQsQiDusim8eSy7B45+f5r5ZgyMTlGPLLY4yokaraLD0wanj6usqA9D5Zt5MBJIWKT9KosYfWxtyLo6w5Rp8NTk7piZGLAAAbOD0iRN+ZO1qKyAIGURZNFcjEOLBhz/rO1FWuC1nSar2oqrZBQioyG6rrcdGT4wEAS+87m/m+sHLPkMHeKvaWZlT3Myon/YHjUiRJwhXfcQRc8Ukr4az3iqQd0rOHyc6yZcCNTPEX99gT0mCxqqlwPSW6JoI/AJIMOpRc1oWKOBc03FnF3W55+rCk1wpFJyVJy61lsYaAW7Fvrr/uMSVahkDfEoPc4RZZ8q93fNR8tOTDXyxgy0PzY6hqsqL1hGvfruTxljwysi7g8OTvTZ88Z6L6T/AqPL6FifoQL7QtLsVOzQIoS3t1Xy0+5vPKy9ovxFGfmdLzpE1P3H0kNYsINI8RVWzeJrZYED7HzY5mbxX7DEKru2F7SEy2YtNEE5VYSrFkzFVGKQ+7Xt1EiZAfR9qRlhLeAZRwA/n5QxYMGM46Vmux9/2tLmnuvFl/C02TN++4XfEN7qMs8SGiHLjHvqY8/yKc3xgrq5jRYE0rapGr+QhATqWE62q29GgycLueU/RGHgWXRqFuyL7HRo7BxZsXf32MPO6ONmcSULiiFJ5/fjgb93wyx5uP67P/xJvIfBVURK8rvng6/AsBetLVhYjRjEV2U+N8cS9gpRSjg+c1NTXh1ltvxd57741OnTph3333xZ133unbn+Tgtttuw6677opOnTqhf//+WLDAu1q9ceNGDBw4EF27dkX37t1x9dVXo7q6Ou7HiQ2TlXRWWPf+iBCHguDPQayjY/2S/zKlrsOSiwTUZDkXRwKKNmmxQNOrd5x4B+u8rw/0yBKTDO5xUHmfU5hsu55G8fHGSkm6xzXN2hDaThmvU0mpq3h4FLDWe+KpfLQI5n6iHqGsNJd4nQTULaTSYgpE6ohx9sk8ir3PiswrJnlhnmKld33v3cLFliuLwtPQRE/Lc3ydxvdBM1y589R9BCxzugbO0Zk99jj32DMvGDBeZ4nGaMX+/vvvx5NPPonHHnsMc+bMwf33348HHngAgwYNKl7zwAMP4NFHH8VTTz2FCRMmYLvttsOAAQNQW9saoGvgwIGYNWsWhg0bho8++gijR4/Gtddem8QjGYvqfmZueZWWfUQqOkQZl172VW6278LzYrNii0B2xVeUONR4J5AGkMLEhMeaT5vkyD6uisUb4by1tC3G61r+dU+SdHnJe70tzB36TZyopQGRoGgiRV3it9hz3u/W6x2/DDHU/aA84Zm2K2Wb2umutararCrLP4m/vzsT70xdKW2xE7XY5wW8K6It9uRxTuR9sI7NNOoavKcFyPaVuqzo5Lz0tBDRVFXPMbx1g/Eehus8aSkyRqkgrP5bi31MjBs3Dueddx7OPvts7LXXXvjxj3+M008/HRMnTgTQ3OgeeeQR3HLLLTjvvPPQt29fvPTSS1i9ejXee+89AMCcOXMwdOhQPPfcczjmmGPwgx/8AIMGDcLrr7+O1auDZ5tmARPmmJ9+W46FFWq8IljcynhIxGIvkCWP8sp2t+tX4qKBuk5PxYBI3sdIv95v1ZFweAiFZh1NymKvSg7mmtVyobsc4lBsdeyxVzWWJ97npnVSElO5lbj22PsVMJZ35+9bdI0hrMlG5d+utIS7bJU/kqMyeJ6wCJFsqW3EDW/OkH5+nv5Jdl1INHge9bOADKz51zY2Eb+n3iuYJ60LlBmbeIccVe03NrRZ1vmfb83mbdz38BLuip/WQTSI0Yr9cccdh+HDh2P+/PkAgBkzZuCrr77CmWeeCQBYsmQJysvL0b9//+I93bp1wzHHHIPx45sjJo4fPx7du3fH0UcfXbymf//+KCkpwYQJE4j51tXVoaqqyvNfmhBy+zbMEcY78ClepZRILuxW1crNiLkVhDzUpC2r9DblHfxy8GT867O5lBV9echeBQ71N5bj7lQOqCL7FXn405szqL+FTziaf1ThIRKGexzUdca8rLWJmi5DUhka52OH3ftDjQUxCvc59nmhPfZ0zZJnHzcPoRb7iCzbleaY2ktSp6PwZsty4omKfGTgUuw9W6n463RNXROqfcfr0edM3rwKjFu0nvi9H9lu0O2mn8QsU8B4TLmb5Wq91mmd9Vll8Fxv8EI2/vnhbMYrxQmTJUvDvdGK/V//+ldccsklOOigg9CuXTsceeSRuO666zBw4EAAQHl5OQCgZ8+envt69uxZ/K28vBy77LKL5/eysjL06NGjeI2fe++9F926dSv+16dPH9WPphVauwuNbG6WXu+BJNvIeRX427szhdJTpSBwRdFVkiPHhDniOqLSzCHH2IUb8MWctXj8y0Ucd4UJRPiKIGSYCyp1wKEMMurPMleb3v+mrqSuYnMHxlKpFBcWDtyu+Jq2ILvlTuI0C1ZM7j9NJqat65662pj3uj7f9v63kfe7LcaOo28ZnHVBO2oMKysxY2qnaqylphM1zsWoSooubjYHOuS7p3D6AT1N8md3OY5duIEvU0r6xN8Ff9OCjCFHkyHFFIu9yIkBLJelcWtalhbyzej9Kbz55pt49dVXMWTIEEydOhWDBw/Ggw8+iMGDB2vN9+abb8bmzZuL/61YsUJrfqoRaVImN0OSbFe8MEk4PVUKgup9oCwo27NIKFWezri+KSIgjgIxeZMIvlbCwoAGBbfwWcc7p9XVsCqs2xpAcsWPI7iYjsmQqsGcVYHI5x2jFigSsaCxWJLDJBMQ2p2lv/y/mBP0jPITeGeavXWi0o1qC+3LSowY05Wd4kI9xz6COC32IQHk/HiD2KlHZB2k1rcXXgdMbd91DfloWTK0vtzdl5gSPE8U1X2NdzuGOs0+znLzLP5LZGxaAFoZjFbs//znPxet9ocffjh+/vOf4/rrr8e9994LAOjVqxcAYO3atZ771q5dW/ytV69eqKjwDtyNjY3YuHFj8Ro/HTp0QNeuXT3/ZR3TFtgc2pKzAuTOURddlRfPU0d+PGXw5uQVgclvVBfIq4SRFxr4rmdy11TwHugTiPjg3WOvo/55XPEVK6uFpN2pqoyKn8TWI8dxcNajY3DaQ6O0lVfaiOstuN93Yz7PnW9sizGM2UTJU1bCViOEJvaMOHCo/dQbk1dwKZIyE/a4ED7uzlFj4aTtmXd/Dhs3Hv5iPnte7GLFho7YN7z3pm2Pvcg0m6WfcC/E6X5Ud/qj56+LuJYujLXYx8TWrVtR4nMpKy0tRb7FOrT33nujV69eGD58ePH3qqoqTJgwAf369QMA9OvXD5WVlZgyZUrxmhEjRiCfz+OYY46J4Snih+qKH1JxTdtj78YzSDnAiLlrqdcypafJFT+OvlrVwMHqir9uSx1uevub8LRISnnoHWyQniEsXf/lUXvsZd+Xf1CMc6zWkVfhPU5ZtjHiuiAiR4gx4UrWlMkQCRbRauqbMLd8Cxavr4klUFAaoL3TMOuJ7NGhIs4lwcCcfBZX1jkja7pR9a1daYkRi/VhY+0TI9m3cdGj4keMc8w58EGaS/H0T34lXLWc/j38pM++OzBhcXi/L5q/N5fouqu8LGTu1dSI4tqCFAXroo/nHsMs9i+MW6oknQzp9ShLWoAwfvSjH+Huu+/GHnvsgUMPPRTTpk3DQw89hKuuugpAc0Ca6667DnfddRf2339/7L333rj11lvRu3dvnH/++QCAgw8+GGeccQauueYaPPXUU2hoaMDvfvc7XHLJJejdu3eCT6cPZWemG4JftqtenCyVnkygL9Y7/e/AgaMk6qZeV/zgdf4APbrg3fPPUww0S4Ys/rR0LI7RnjNsgUJUjkI6Fz05nunCnC8gmW505KHK/Y5XMtX9bZoi+rIoGjqPdWrM56WC5/mVE9HJf9Q7Cz/HPvib24rfrjRnxGJ9mJfNnDXsQYlpHgqRSqKmiQ0pWS6Lvetz3nFQqlitqKptHbdNqAdJImWxV5SX/+2KiDR/7Rat75J3Kx7rNbrr350R8SbchEqSniE0EqMV+0GDBuHWW2/Fb37zG1RUVKB379741a9+hdtuu614zU033YSamhpce+21qKysxA9+8AMMHToUHTt2LF7z6quv4ne/+x1OPfVUlJSU4KKLLsKjjz6axCPFQ8b6cdUdQ1JbXJW42ym6jiwKu3zvTF1Z/Lxsw1bG9PmQdSknXari3ZP6fxZLhEp44zuwiMZbt9zloMti7277KiPvq9+rGJ2gifOG5M9p5st/a32jJ8o2cy7ufZh5h3tM8SuoKkqNeOQo4yIkSdFtcAkpZLFX3ibUHctJ616SGMppbYZn4TFgUVfQObjrtFvZkW3ipAWoJLckUtNg2GOvLC9JRAw0eQd4ZvRipXJ46iHrPZzppoUs7bE3WrHv0qULHnnkETzyyCPUa3K5HO644w7ccccd1Gt69OiBIUOGaJDQTNxN6vNZ5Tj90OZYAuFR8fkGpTgtRKpX/2Qsf+H7vsNuFM7Sl78ii33EpDKKL+e17mW65b1gVGkV70n2WckLAwoHnIB7bnywTAy4AwVxPoDOPfYF3DLFHXQuiVMvREnrlIS3zv3i+YnS+YgsEPnHDJEJMS9hYpKagluxLzPEFT+8n2IXUNRTTcuWJUqaLIubjuPgqVGLcf/Qua3fNfvzqRLPCEKWcwSNDhKySFnsNTUiwWTfn75amxgqj7szoOvhJkVOb5EYvcfeIoa74V378pSQK9OB6k4ijr26pCxULIaocpcSteqywlvEUfvhpWSJyEdJHjFvsg+1hAmmyXsGbxwWezdx77EX3e5BQ6b569pLnIQLv8zWmElLN4nl6frc2ORwZ+yv37xyk4qZaAllzIXUFtyeDO1K+YPn6YB3cYKGqCu+jm6JliTrwqNbqQfQEp/Fe6/KZslmYY2+ZlXlNgx4eDRem7hcWiYd0IpMqgoYZLHXgX9blCqlXWUsI5WEyZIhvd4q9llEpB2Z1PiAYIejEplOVXj/snCO8aWj0sKtQs5NWxuk0iUH9VNXmZJsMrzH3bG8x7EL1zPlTSpD5VHeW0ZZt9gqAw4Vko1TrxXtdv76v28w4JHRaoVpIW6XycamPF4av9SVP/k6nVslROpqk281QsX4FLlYE6oUkxR7bwPhPYJSdU1wEOWKL2+xT2L/OE0WlnpFW1D3fy3SLeneS373x7Mxb+0W3PzOTPHFYwWvi/udS833eLNiXBznF0U7So8CNvEBI0hTnJoojHbFt8QHl8LkxDwhdkmnosPQFZE0HvdM1oEj/DpyxHmFUnO+KF0DqP9aJe+eYmmLcyzz1jU1OY9duIHr+hJXOWhzxXd9VrnHngW+Pi5atiteaHUj53mU1yetiLzGtCkJrX0Ombgcz321pPW6mFqNx2Kf58+1SeVZiyG4y62mvom67Y3Uj9U3tn754rilWFtVp0VGHkIXIHnSEexf4gxoytI/PTSMcKQc4bZcLqdMO1KlbG6rZz+ecMaKSmF5dAbO5L6Xdw2B8XtTLPZuyVglYhFdxisrKUwbQ2WwFvsMItJnGNPPkDDIYs/KH16b5vl7bVWtEuuYKv1Juwum3uSZKDyjWxaVi0Tu+9dtqYt1D3hYTrqOHyym1ZKYW9/gtQ7K5GsiLLKJupFnienLKz1/x3Y8vCcfh7suhcXrE1VEoixE5z8+Fr8bMo34W5TFfpqvnGnoXljRHjwvIg0te+wpZdbEENTxsS8XEtPzy1lioJbBY9FcuYl8nKeckh1+N008E/fYJxXA2U/QFZ/lHgbPFCNmgCTocmXIYG8t9lmEVnXDOmbTGmJBnjcmLcfTiiOBSln+BG9VFfSE+T0lvLhTSXCjV0FhUGERlbxvX608BX4VcyyLUAtWbIs2MVjsPS9RvaugqrE86d4zLZOSpMrJU4sEhHC3N5Gxkv0cey8fz1yDx0nyROyxN4GoPbs80sbtrROGjMWelp6/TjVH6Fbj9aZKWeNNM25oUc1lDCrantPAAmQ+x57lGvMeL5IsRcW3FvsMItKRcQWK4k5dnL/8b6Y3bwWZm7JaKoKqDpPoiq+wXIguhyHo3OurI17Dze/MxFOjFqlJTIB8yDMlUb3jcMU3ebLA77JpxsNom7fSvvcVVFx7/P3bpHjLPzQ4pMJHIBXHPZ/MCXwXddydWN7q30V4LBD2/Kh77EPSmLp8E2rqGqm/q0a0D8w7BA8SpcHzWI0BERZxFbIwLTLIp+G5nu9yqXtZZTNlDioiBsszmho8L5Ts6PVWsc8iQo1VuRTmIrWC6/rsX+GLowxZRY+6jBzEJ1u1gOiSrmBv+owVlXht4vJYIsHTYDo33WXGVTm4klzx49iGYHLtVNl2NtbU48t5FVxlmkOzVXnkvAqsr05+bzUN/xPFNemTXdyT3b4lE5jpmdGLsamm3iePlDhFdJd/mGcRlyu+wANf+MQ4vDVlJfd9UVAt9sJxAIKo1DFUvWMVXkE6z5TX4oqvqYGYcs57wBVfVbqK0lFNWLGnxeuNBavYtyFC660hHU2Bez6Zi9qGYLAWFVKqUkKSUISVnWNPVHqVJC2ErpVx2XxMRZUlTARS3VG9yFFYNNPhbQGwtl19I33Ys5z1nzG48oVJGMJ5rNQ701bhihcmof9DoySl00fQuyQmi70vH+499mEKqohAnJT4Zp3mBN+i48CJCJ7HY7GnpRE/MlHxSZAUKv/7Zk1HlLBbSZLobLeqq3asFnvGO0yx2IvA8oze4HnJP+xXC9ZjekhARyBTBnur2GcSze0ortXGd6au0pKuTKea9Hxq9eZapuui3lGaBxZWyHvss/HgvOfYqz3xIPiVPou9vIdFHKisVuVVzW3881nlXPd9MXstAH3xLbhgVMRiiLnYnK9ngYi/JrkXrpLoQvwSi1iwSW3U/Y2WQHOKgueJnmOvA1qWUn2g71aVwfNUuL830yrU4HFLhWWRXZTndsVPIKZSQsly4wiMr6zV3BSvBAC47L8TcP7jY0OvydJxd1axzyAiyos5TbCVym310RcJIHp0jp+AK75BhRglCtEVX6P8Mmc2k64TVfJMekcymLZAoWtbgi6LfQFVg7kJsqVhXuLvB+Kqx14LEj+yLuW0V1PfmC/ujQ/rI/3Z8za3m9/5Bt+9cxg2xLxNI9yziD0dWtnwjAOq2gdNFpk+UKsFXFE67vJ7/Mvk4svQ0NH96eqfTFF6Ax5UCuUy5BE98HqmpBWr2FsASKyiamy9pMA3ao6MM7DHiR2S9SY97nWipDKoCwGH8hnQ/1yk5FUtloWhst2GJbVuC7/yw9t2WK7msdrlcjmzFHtGWWjvQb07rrfd844jOhauGpryOOrOYTj+vhEMHlbRCyJhSbw2cQU2b2vAa5zbO2QJ9yxiL1OjouJTvpfpn/zVS8QVnyaZKUokIOkWH/EcNfXBrZvN98nkqed6U16JiMcOa30qtgdDnjUKo8ZPSaxin0Go7S6k4vIMsu4r7/10LvN9vHw6k88VlRWZAdhkd2A3IpFltVrslaXTnJI698J0wnuMlMpBu3jkoOvSOKLix8X37v4CI+dVcN2jo67xTu6NOq6HprD7/k7GYs+fp2dB0J+2YC2ta8xjS10jKrbUobYhH1qH/OUkHoHd+7fu00jC3u/66npmDwJawP9EXPEpecos/gTKSWlUfLnrCt2QuqNBObwsYs4veK+e69uCccnx/asSHUq4UeOnJFaxzyCkjmza8k2ePet1jd7VTdF+5hnFZ8y7Wby+Rku6uvrUtCj9QPwDS3R+8ciTlQFV195k0eLRZVHT7YpPQ/dRhiwKFfc+W3VGPn1IupQLZ+tzceHNNjR4noJncCJGD38e5P3yLItyYTKoJyy/hRXVOOquL5jSoXkEqZSZ/Vg48tdNEp2yv5zELPa0xNUkoyQqvpRRpfAvp3eUlMVeT16mxDjynBLE2C+yP6MhD+kiTCRrsbcYDanyXvDEOM/fN7w5w3uPToEUokJOKYt9SgoqUo0mWewF0mHFhIBTfjlS8iqJeLcUeJ9E1XGO1GuI3h5qS5M0yJq8cJb0HnvHcVJhbwhEp4/pnXqDRIG78ct6pMhOGv1jlqh12LwFXbl0TBlXALVeSyL1xcAYcUKsqtyGGRERzHmQeTZd5WLyWBYFq+xFT/y0TJozhFXsMwhLM/r4mzXeezjaXtrbqSmrpUlCVOJ1umVG1Er+4HlimLiKLEL4MVJ6IdcdXXl5LQqqCZs/J72Cz2OxN61W09q7/x0mYbEXqUdxHHfHEzyPZB1m257kX1jRhwN17zeOesLqiksrZ6WKvbKU1CmRqlyVWerpeY+PDcQ6EY8DJXaf7L1JpCsDr8Ku6jpTSHq8V4lV7C0A0r2CyIvcHns672o6nk8E3mBMgF6FzZS9kGkbbGjoWoThS7f1Wl0T76Rc8Xnz0xM8j8din47jevxlGlXfpq+oxLvTVsrn6/kc5fgexL3VRKwehr+bqDT9rugNTcEbRs1fFymFqij1rOi22CexpEWTRWWARZVtmXXBJ+o6Na747NeurtwmnQYgO7flu3d9dR3KGY4kNsWKHeyP+e+hXscRD4kX0aoYVhfsHnuL0Qg1JDP6mWgUyKmjo/l68Qb89Z2Z6hNmYBslGmwozEqvmsJSXeZMA1DEfaYMriKEziElFjRYLiOlFYcnhMlvK+ngeQ5Et9gnW6pRpymc//hYXP/GDExZtlEqH9m23kRQpFWlXUwn7LeAxT549UPD5kfm4W+nuputqrIxyxWfjEqLvcg59ikezphI4ohbkXsvenJc5DWmeI2KeMSxiq6zPupYxBZpc6ZiFftMorfXSHoyKIvUHmTKvYvWVQunKcvBtw3FJzN9Wysi7qFMk4LfKHOlDE+IebCQlCONdXc9IXJ0YM8w5Tde2BZMghdpO+/X/ZmSh9h+1Ja0Qu7lSVfH03Pl7zhGuRLSqoO/7tAmuP6vF62TC6TqrUf8/Zru49aiUne3rw9mrMaTI8WCO8aqUDiOsvxoSrMpnmCA6j5QocVeUToq+peNNfVGHV0YhYikqyieBp5001MEAbiPuzOI8OB5Bg2gkpQlLYBFPSLtybwmSEaFYqbjWZVGsRXg+jeme/5ubMrj1ve+xQ/23wkDDu0VuJ7UObMG1BOhkE6cVnJSXqaslLPy+qTlePzL4CSe5TmEFF6ONx6Hm7w/aq+p6IiezO2KzyVBMzrdD7+YvRYj53uPDfTXW9bJn6yUnroKAcVec1T8qHTc5fSH16ZJ5BFvI1I1uacl88bkFUrS54EePE9dHiqth6peuYq+4o6PZgvfm88DiwUMKHKGHOFbQzFF6Q30iwzjP68RxownjSY7ar1V7DOJSENKi1uyisFTVafqnneXJqzY+7P/bNZaAMDLXy/D0vvODlxfQ3Dfj3Jdl6FQ5lQLHasbWPE6Ubc8uqXbREhKPRDeXskLNOqeNg5XfFJrMtnbQs9iIU/+DnI5fgc8nWX6y5cmR+fPmL2sNYXF8yP0fk+/wX9/lPjRMVG4s6SkE/Tvaf2kvi7ottgnAnUMUyejykVZk/tNHv701nR8MrMcFx+9O9d9Uq74msrOlPm2zm27jqYjeXWRIYO9dcXPImITFz3XqkaFAiEzR6BZKZPuFHhX0zdvawh8p1MhLKQd50o1KSeT5ocy5P1L7S7i3lMom6dsHkJ7yx3xe9UQXWC8Fvs0EAjWxNi/jJ6/Dg0Sq7r+BT3e4tJdvFHWMnV71ZUkw0SzZ4QqufULzhwVnPK9ShlVetLIipULfEiGT2aWAwDenMwXTNP9+LzlquyVBvo9A2FV2JnbiYNlG2owXeHRhQWEg+eFiG5d8S1Go3ERLnFUTBSSDnSlA97sqwiKPQnVA5tu10xGMVIP4bSrIqSBV2WdJ29xUF+yD30+D4+OWNiar/Ic1KEl8m8Mrvi6oJeH9we6B4/3hw9mrEafHp3UyCPwrpJcuAJ0Wuz1oj8qfvzQREk6eB4NFqkcJ1phS6J/UR2JX8fpJST8x0mbSsB/h+GBebwrT/rXSF6RmBCtF1nxXonCWuwziNAe+5TUdxWDpyorgrtzKUm4JfH2cyTFnnXfvQiFiRlrUC0aonvAW79LSUWPQFuwOh7PHcH7WHEr9bryCIPTtqM8f15XfKM0ewr+d8hTj9+YJH7snTfYpMMfE8H9WaGHDGsaqpRG/ykEuuNkhC1AcqUTQ9tnPsee0taT9gbLyNCmHLlgsuH3duvUjvj9b4dMjUhXWCSlCHn3Ml5n0mJcAarXXwrGTh6sYm8BkJ6VLBWDp44nTd5ir8AVn3CdMlf8wr+KCp8tnfCFCgPHHWbCRNe9p5B0RZKusiIudFHS8j6Ojuv5g+cJlIOm10YTXcRCVKBUYrbib/dxN/3INxMhkDrLt5JkmHAcdXLHsceedayjiWKiIgOAqZGxPHtaXZV1vpbtO6Tb6dnryMRoXGF22deH6qCv6azZdKxin0GEXPENHZP8qNljL7OCS/6+NOFDMMNy96/KNjTlycHziBZuScFaaA2eR06QP3ienBxpJzR4HuN34nkHv4tDYUja3T0MLYuFHKOzabWavT2zSy4zmZNd0Atvb/Kl3xBh2la2IOqTVXe9USe3OdDqgsqxRWl/zXpdxIVpVX5kyjKqTFLvASjSFzIvgJlXNnSLfVprNxmr2GeQ1Hc2Iah4NL87ogqSttiHjbr1vqBTW+uCSj1A7rBVW9hjDZ6ncaEiadxVmPZIbkWokTHwmGj5xGKxj/Hl8TbnpBcdHCcd7oT+d8jTFcusncp4CpDuV825g76K2GOfPot9c36KPL5iaPvMrvg0i73CKOBqY6LEm58qlInkejju4HkRUojKaKKHrHLjisZH9M9pZUnB0MmFVewt3CQ5CKiYKOhxxdeQqATtSlsFqmv0doLUMiQpwsqi4hcs9sxZh6cnKJf32c0bXFnhPVf7Fy9MYkqXpUQKZe89Y15tWSbhdRAnLNlz7bF3nFRMTkTPsQfkrCru+rli01aBBIgfW9IWk8nN6s21ob+3xiiRyyz+c+xjzS6UKKVOdqxTa7HnT4t2h+w7L7S7NCwckhBxNy9eH2mx55fHJFTWM9Hr4oS+nS9mQTRjFfsMkvbOJoxKxmjuYejYg5y0K48/9zKXH29dg1exp04ASN8pdqWMc2KZ4WYQQfDJZzAeOcPyfkxzxTdhTNZRr3msSw7MmpzI7lcmIROg1P16/vnhbGxraBRPzJ82wzUs7yaszMYuXI9NNfXyW5F8hi6d3bHKNqEkQKGyRWry9yoVe5X9adsdB5uReS1R78FEd3MePFuUwFhWjM8cR1wMXuhzCIMGTwVYxT6DCK3CpaSDenLkIuk0dDxracKz6qpa+kS1tsHrek97fqLrupRUwbRV9fUmu4zHjdIJtAEyJJlHa16c12tIn99ib/7kJGjtZi85me1O/jFx/ZZ6qft5+WzW2ug8QrJ48PP5OP2R0dL9cdz9n7rgq+b02zRZVHoHJzFMZXePvXhhRvVP4uOl4I2KERGD9R4TFXsqaa3cFKxin0F0dxomDbIiyPQ3tLJN+rg7P+535HfFp1vsg7+oUqaigucNmx098eUlao+9KYOrCGGi62//QRlUj+E6F5ma02pOLUxX5NEjE99jrz57Lfj7E55yk1LsJQvIff+t733r+y2e0l+3pU5aMQ9rpzqeQ3WMFhOg7mRTKqSIcYbve16S9koURc5DU1/apsFaf1mfudFAxZ4mUTprNh3D1BFLUpjXBPWhY2HCtEHP3fnWNZKD5TGlo0AWdzqkQWHFxq34cMZqtnQ4RlLStZm02Ef8zZUWw81kV/w4LPbk73U0Pe7geRr6FO7j7gTKQddrY02Xb4+9oDAItom7P5nDd38M3YZCL9iQ+30LKxpHfgfqPb5MgCaK0j32vB5DITfEUa9Mxv1o3F5NkeUiVnCmFLf/vTMdd8vsiq82wJ0KaLIbNn2XJt2HMFqIZLmTVoGUxZ7yfdKu+H7ccgYs9hwr+6rqUuEkAlLHuiYicBQJYRc4wfuMw3HgOA42b2tQ2t7ZTowgeXaok4E9V4Pgdt2PvoHLFR/pmJz46wlfVHw1wfOE7g95X3HWy3s4FyT8xL6wqSg/k7wE846DweOXBr5X6XrMm5L0gg9DjinoXoh495HzFVRkVHxzqqUQ7udjfRTW6xqazCscusU+rbWbjFXsM4jYHns915qIDpfDxI+78+O22AeC55Gfn+dbUWTnPlz1lOH+pryDVZu2ScmUFH96awbemboq8H2hfotUyZ89+7WQLKoVBp3bQprTav5XVavV0SWWcGj2zWVjWB9EIHCOOtcee5l85WDZIy8LS1m8OG6pVB7+/jeubTvS6Rg051i2oQZbCDFtVMoo0tdRx3VlvvhqkokbuT32UWm3PVirk4l77OP0+ksSq9hnEJF+3KQVcd1I7bmi3GzacXeh75NqsddniQ3bYy80iRGUy3NEG4Dr3pjOvA3AJByAqNQXfhNlbvmW6Lwd779A0lHxc1A9xdKu8DCkzzvZMGlywlp8PAtCUounGt+nSUpnFGHlrfoxHEdd2ahIRpVVbkM1OfCi2uPu9F7fptDgoVn8XfCdm9Jn+GMOsci1oaaOKW0T99jT3qhBQ6cS7B77DGJiczKJJh0We8M0e/cjXvfGNAwavqD1N9o9jN/JyBOnKyh5L7j37zQq9UmT2B57g3s2HY/Po8jmnXScYy/jii8TxyQNsTXikDD2qPgmafaKoJWhynmF0j32DGk5Tsi8oOABlooeJojMW8m6xV5E/se/ZDuZysQ99jRMi5Eli1XsLQDkXZzTRNKT8DhwP+Laqjr8e9j81t84NHvV8zL5vYCFf6MTIrpzp772NhNWjknoMKq3t+iM9wC01iPagG5G8DyO/FNSrYOKfTpc8ZNMXSVxnmOvEpP6bdqxdkrPnhdzu6R8rUYww6Y4zLjLkndxIut77Ms3t25DVP0ojSbusad6/WULq9hnEfPaU2ZIS9GGR8ml7MXTuK856ri7uHBPbNM+KNPQ/ViFeuLOJxZXfP1ZeOCZBPJb2KKviSMqftwE99iTr6tvyuO8x77yfJfkcXdxEE8ASjnLLl9e2dxjT7PMK40BovB6k8qOB2XxT9zu5rzB86Is9qKu+IbMJP3Wd5VSmeiKT5UoBWMnD3aPfQYRUZ7Ma4IWGcLeZ30j2eTA4rouLI9Dz0MkHbaL5fJKK7rP1dZtTQco20I8+wFdfxgwKPNPxKPvyOVyzO/SgSPkKhv3BNP/OLTnW7yuJvCdnMVe33Mqq/sxvIq459qqY7SYAO3kEKXBwjQsFIpS9G7Sl4VWpFzxFaSdhgVXAMorkZGKvbXYWyxkdCsOJpP2R59XvgUn/Wsk8/WqJsSFOqNigvbi2CX4ZOYaoXtNmiDKYFobVB4VnxRkUaXmU9w3GvozR3LJu+KnYQLpL6W49tgb1lwSI85z7JvTNysdFdAU+AT1ejghb5IlLXMVVPlMvWvAvHusokz2bEkELjOpQmvCxD329HPsUzB4cmAV+wwi0mfYiY8cpilaNHEedu21Z7pHlcW+5d9lG7dKpbNoXTVu/3A2ahuiBw2S6O5JmSnucLrQNVSRrelqy5JkFTOsiUnB6orP+sypKRqfoDJ77H910j7s2cZg0ZRPR/9bjNOItmLjVmX9gkltP45jvHQc7Rl+kbLsjEPquDvJ39OEA7X1zsg99pTvM6bXW1f8LGKakpkt0l22YZF7Ner1yDsOXvl6GW5571updFiPWqFRsUXuflNIck9l67aK1oxUz3VJ6Zncr+mQrCTHka4TXMhZX51cXae9Kv8km6fe+PfY/6LfXnh61GJe0do0/oUUmf3HUbw4bilKDTstRgU6TtXxw22xD7mBNdAsrX8tfB13VHxlC2YSCanYY581pZGVVJ1jH68Y2rEW+wwi1pzY7zKvuVpYCT8WR5+V1HGA+z6dqyYxjjwt8aDaFZ+UnnePfetnIwZlDXtiedwDHTiB64++6wtmOR51HYcZL+wF5y8O3uMAdaGyj9RN3HNtVZN7k7ryeCz2IveEK+ZhsCjtcSuoOhZy+YPnhV/P5AxBWqTmkiIeVBd3g4mKPaXkrSu+xXjEBgX1crQl0lJ8YZMSssVenStlXWOTknSYr83wUTWyFhqpvAnpqx7DdU+GCmnRxnMzjrtjD54nW/4PhWzRUUnguDuObZj+yZcJc7HundslLQIXJnu9hGKQ3LTgeSrJ+jYxFtQF7tXois+YtAFdFRMqa10j7VxIA0nL+2HFKvYWCwcGzS+ECBssdUY7zzsOGih7rniySHv5x4H2MorBTT7KYm8avLLd8dGsyGv4guc5QoquPuWYXCD+98pjSS+VUOx11Z0+O3RWt/ipJJVwwsrb6PaVtAAuYnHF1+ABJErUIqj+nNWlwn2OfZQrPtPpJlxZZgYT99jTXlfW3pFV7DOISHPisoQa2F6TJi1lEj6RJihT+kTRTlreiQhJPhopb9WuzqTJs3sS5ZmsaRqUdSqOk5ZuirymJJdjfs8OxPbAJt1GeKxy4xdv8PzN87zpcMXX/zIMDFTNRNL11E08Fnve60Oi4rP54jMQ8x57A7a4qLDYkw0mBlXoFlR7iTQY2NnQnzBbmr1V7LOIgZ1GVvAqFOnrDEJduDUOQKom1tZFMZokmn8srvgK84gOiqQuL1FyOXY5TJCXBb+YMv0Ll0eDcC5R6aak4FtI63GfJpVzPBZ7dXmk9JUbcdqEij32aUJlXTHRYk8Nnpe+qXwoVrHPIEIW+8x1UXrwBvBKX5mFumIyfidCEkWlan+ciYQGQWx5cl2Dlc4gi6F5qM1CKUnL5sCsyQk1Kr5/j72UYs8RXFCnq7Iqy6KaZMLzcML/NpU4jH8rN21jui6OrcNxu+KHtaRCXxx/8Lzk04m8l8UZglBuJrY75cHzDNxjTw2eF7McurGKfQZJYo9tW4HWMaRlYYR7Iq3osZRZ7Lm2jKTjnaSNQqm6i1e5Kz5hTvDo8AV4etSiwPdyxzCR7+UOnqeprvH0K2mYnAQs9hJp8Sj2bXrQcsF73KkpxCHb+9NXM13XFMMqA78rfshiGmsaERfG3b+o6lNlUokMwMtylKDJDcuPSou9iVHxrcXe0pZIVeeTIO4xPY2u+GFzEnIk8uQHVx3pZJlEvCNiCJ4HAPe2HJkom5/qhThtrt4cs3KR7ij2qhIInieRlgHB8xxHYR8Zw8vwF1laFqRNWqSNx2LP97zhx9jKSpMMJngLpmHLlipUP0u6ouKnby4fhlXsM0haButMkZIiD3fFD/6myjiRxMQs2osuJS+NE+0OO4QM1O+xD5uomvfeTBDJpIVG1uKQc8Vnv9aA12PJCHHEKVCZg+w4V7g7GVd8FSUhscdeQcq5nFpPpbRgpMWe8r1BQ6cSrGJvAcDX0WRVIWIh7c/Oe9yRCavmFrMgvUrVk92oOcGIuRVK8qEN6PyPo76Cc207QUpd8SXqDc9Chs7FIHV77JPtJE3uo1XIpmry3hRHVHyBHXNyLufmYYInjIp6Z3K7ctNch9QJa2bwPLvH3pJS/HV33ML13PdYyKS9nELPsSd9Z8A+N+GEUv6uwuA8tVCPDK6MVM91wxYKVlVuw7UvTyn+LeSCziAv1w5uja7eItcl7dVAK7tA8DwJjyAei72BxiMjSMt4lvSih5s4ouLzEibSS+OXSaf/2axyDJ+jZjGVGSd5xTpyj71g4gZWIeWYGTyPjEnebiqwin0G8Xcalz43IRlBJDmwZ5ekRQggG6AmacIUpk++WYMhE5Z7vlP1XKrO/lU5wcvq4Kp7EkyOih+fxX5jTb3SvEiQ3CfjxoGDLXUN7De45iZJ121a9irrJk/wvG0NTcrydaO0nJOucAaTRH0+Zu8e6Lt7t8D3cZxjrxKm/jLkkbbWNeJXL0/Bms216oRiIKqUWZu/3HF3UWlHkxadUfUY3mCgxb6t9LFWsbcA4Ov8Cu1ft1XIxA7RJMuBCGGK/dBZ5fjbuzOxurL12J84JlR8ke45rk35uwqD5dl0BYSJxxWfnl7n9mXK8lFVQjpq2mMjFuL7dw9nzN/xvG9Ta37As0BCUp7xob7RPOuRH1PfmQkkUTYluRw6tisNfB+HKz43jtx8zGn5H4mkFDRVUfqTPuoy6UVWHlTK2hjHGZWcUI+7M1DXkMEq9hZhjBzgNMN6PrOpsPS1NXWNrr8Mc8XnyTMl70Q1xYU3XaVOSFa5K35Igv5BWGRMZrK08KSnoairahujL6KQtCs+DZXnqJsSydjQoiYSiHEQ+qs5mFSfTZz36FzELuXZ86IQ+lJDM6zu01KxBww4xjStGGmxp5A1xV6d6cOSbgTa4Gez1qqXw3DS01WRYbGslpW2rvcZNJ+yuAh7L3G9MrcMqutJaAgBg7xIrn1pMs7uu6teYRhwHO/khFX3iLt9hyuWfJgwGVPqiW/7WipJFE0uB4CwEG7iHntZTBhPAvkaYLFns8iHX5TLmbUwFRdNJlrsKa/BlEViVViLfQbR3YUU0v9iThtU7FPeQTMp9iXqXXrVRY6OP8+0Uaij+lzx9e+xD7OK+fNK8jV/Pnst/vj6dCMiALvfdtIWIVp9CLw7KYu9GSRd1jKkZjxLQEzawpGRFntHXxElVUcchPcPseyxF77TlYZ51YWI6jpkwpjoh6rYmzKYKMIq9hlEpCMWaYL799xe4K50QxvT0zK5Y5mTtDPYYs9bt8PPQ5eVpm1CPsdebWGGvTcV8+pC8qoG9KT3E/ot9m2hbvMEz0sDSY8h01dsTjT/MBKx2CNHXD2K4xx7XmQlilKikyDSEs66tKfdYs8vgmllrYP6NEXFj1UK/VjF3iKM7s7JzCMoWh/aROmiYJmUeBQERVOq/361WEk6XBZ7hCuBJk7QWAl1VY9LBldG6o+7C/vNvPfWaIAVzz3RNbGMyIjLacLw4DhOqibpYcrSnDVVMUrCh0meBWZa7M2TSRYHEfMPxvYv0xeyzH+irjChn2JB9cKimRZ7skxm6hriWMXeAoBvYChcm7ZjX1SQ/uB5DAOVhr3TX8R9Bm4LYfXaBGVMB7rrYhwW+7D0VAZgo5HL8Q32SU9iHHgnkEkrH7TclQbPM2QyZtp2Ja48489SiKUbtsaeZy4HYgEZaIhsRsoyrcaz7QCFXpzRFnvGdKRkYLkm/CLHSc/8UOUCUdJebCSsxd5iiUB3EBkTG1tK+mcqvPP9ND+v4zihz5u08iNDOcOZwrr0HvIee7V56LbYR1kneLNIepHIPyGL+8xpVvzlnt4WqB5bFunARG8YUyRSuT1GWVwemQUPRdekAdXVus7AI0apz2iisiGBVewziEgDFWnTKdaLhDFwTGemWdFlcS1rvcY0Fz++c+zDJ2FpVuxfHLeU+lvhqeJ8dXHusTesSgIAGg0w47nnJqc/PDoxOcIIWuwNfJmcZOEZLOyYOG7IVsHwrV3JPG+UpZt1EUFKegVtO5dLPn5GEtQ1JD8mBqG44scshW6sYp9BRDoRXoWp+R7NFnsDWxs12nPMcojC+8pMm7PyihMmv4kTNCVofmmF5N39jOqyDI2KH7D6SuzTVjSkJ22xByDYYeqRm7plKZbc00kSCwSm9e8mQdvqYaLFHpBXHlm3z4ShcnuMqr3rMu2KyWIfcRHpdxMXAx2o7Y+3NTQpTE0vpmzrUoVV7C3CmDrA6STNT+w4bAqYZ499ip/YccLrqIl7wFSizxU/iOoIuOGu+AoyUFytTVgkSsPUROVxd6aQgUewEKC1p6TjaZCQVupDbudpoyUKOyHHUTP7kEmD18OReo15VUY7Jir21OPu4hVDO1axzyBZccU3cREtzYsZUa7pxHtMe1xOgcKeN83vMowknmprvdpBPPy4O/1PyNv3JO2K7z/uzlTiqps7bd8hlnxUVsVkuqNs9oE60R1bSAhDRFLZB0Va7FUlFHYrw71R16ShXwbUexHUmqjYU75PyztixSr2Fm5+/coUXPTkOCOsVKZgomsVCZZX5r4kJY9FIc3+BuLEFRXfnY/qQTw8Kr46qy9tQE9b8DxA3bYCrfj32GvK5qGLj9CUcpB095EWGrS+IYunAanaY6+yD4pWmFn32Ot1xY9Mw0nPFiSVfVmtgXvs6Rb7FIydHJQlLYDFDHgU00lLNwEAOrUr1SWOuZjaIzPQfOYyp8Vekyyi8MjjOIATMrbYCbkowYLbpthiHzZ3NvG9Ja3Yi05eky5LXQuiWbPAWOKn+bS7YP000WLvQLIth8wNknLFB8LnK8zH3Wm22FvI1BlpsSe/0KyNF9Zin0Hi6ot071E2cRXt1QnLid+npf9n2jPmusY0TwRecXjOQ88Kuv0USOWm2hU/fAuFfPqqS8iEfbeik5M423hcOcU1djjKQ07FS1b7QBVQg+cZaLHX+R65ko7zuDvm4HkSMijYP59rXiFSJpMuVIuUpj32WcMq9hYAYo1atyu+iatoH89ck7QIwjTvsee/Rze6FNGomAJZ7eObjwlyMH1FZWx5xumKr3KPvaouJulAjI5jVgAg2huKK3ieiWNHFG1l0pl2zLTYy8kUdvfEJRupv7Ur9TY0lc1OVSlLlQ3T1sXwiwysLlRUzsWS9mLjwUbFtxhPXBaYNDVcS0uUeBM1ew54B55wl27DHk4hL41fho++0bMI5fj+BTRY7Dm2UJjwFk3oC8Ut9mrlCM0r8Hf6XfFVlV/bjAgSH7xVgnZ9wnEyqSQxnHXv3B4nHbBz8W+lwfMinof5HHu9ej1bgL00tO0UiCgL7RGzpdZbxd5SQKBRt/XgeaPmr8NNb89o/iMlRcHmiu/6nJYHI9ActKZtWuzjtNYD6t3uQt+bAs1e9aJO4lHxkQ6rQ1zKR2yu+CnrRIILKxYaaQqeJ1sPVdVjtRb7cKGYz7GXkYHpuLtwSHKmeV6VZmjvMwVDJxdWsc8gIl2GSEfToHlfaRra2puTV2Llpq1Ji8GEA4ffFd+w8YdXHtPkj4PmQEr6HjwOT4cwPVnlvNqtDMsEfkraYp9W75MsuOIrcxlO5ytsc5jpim8GKhcXm7eUqUkniXvdaRhYZQI4xf9re1jF3mI+MTXOpoT3lUaRywH3XHC49nzy+fSswPJOSkwzTvCI37yQEWb5VSCQgehW8mKJu5DAOfalEpp9mr2XdEhOjbDty01XVWV1023rZLUPJMH/qOQ6tLCiWlYULcgd6yZ2dw5epUhlVPwoedjbuEy5MFzTlhpRyqG9qqyNF1axtwAQG+C1T2YlG1tJLoddu3dUJAydbQ1NqZggFYKqcd2TkgULEo4TtTCR3mczAo3FxxP0UKSOFpJ3byHwK/Y83U/SUfFFc49b6rjWgtO5x95iEmma65uiXKo9xz58ucGU4+5YXPEDY5YZr8uDKXVIJ9Tj7mKWQzf2HPsMElfzTNr9NIoc4mmwAx4ZjR23ax9DTvKwvDLPHnvDXjGvOGH7IU17trQQR7mF1VNVFvtpyzdhY0198e+ykhIAYppn0lHxZUhyQqcteJ6WVIPo3vZiUYdI8Ly0vNqk9tjnct5yNXExRKZo6hqjY8dElV1a6hDQhhcXTay4EliLfQYRmWiIdD66rVSyTS3OgFIbXAqCyfAqRWnu6Jsn3eG/Z5E0TSRohNZT30+iVqKHhs33/C21xz7pc+xT+s6zsMdeFUksELSlRYmsP6kJr1Jlu4uaqzAHz5MomFcnLGfIgF8GA15VABNlUg2tKqRwuAjFKvYWYXTtdbXogTWIi8eKFsM7/sNr05iv5Rmk//DatNBo7bb6iuEU/9UZoI/+m7/fEXLFJ+RRVio+HJruvRRGnJLHN8GNb6qm6hniiV0RQyYZIY2LQ6I4DoQroNt4otYVP6q+Mh53p0SasPTVLEBY9ENV7DP2jqxibwEgNjnWPZlV0dgy1l6lEFWAdLO+Wp+3w4vjlmhL21REAyGZRFj8jsBpd4IP6y8lmeB5Sbvip8UlPCBhyi32aShzN2mTVyW8j55DLjUT/qReq1+RL1GoUUQ9E7vFXl4WGZqP3TWftETvl4H2eClp5szYPfYWYdIQCTotA7NJGL3HnlOesEWD9Ku/ZHS/sziUgzBvoG9Wbfb8LdrGAxZ7GcU+aVd8CeJs44FFmZTvsQcUBs+L4T0EgnjpzzLVmDb+0ZBdzJW517PHXqXFvvh/0fkmSVQdWblpG/c9Fj3Qz7E3pTapwSr2GUSkzxDbY6/XSpWtppY8Iu/YNAsP7/QlTH7DHs3iIuzVPDp8gfdaoXod/M595M2EJRsxYclG9vT4RVCK4zipqM9xHXeXtYmaKkyuI53alYZunZKFd+ywVYgflWXmOE5oO2a22Avcw4PBTYqLrBo6WMhaU7eu+BlEaKIrkE8qLPaZa7L6cSifTYC3yrXJ4Hlx5aMxo7DTDAJyCOYR3GMv0Vck7e4pKELcC3dxZRdrVPwU7bKPa2FFhN126JS0CB7SpNjLR8UXSyCX85aTygW1KIl453bDZq/F/6asFBeIgmnGDxmyrtxTXfFT1NZZsIp9G8dxHGyorhO6tyENin3GGqwMcXly6IR3EDVM/FjIwkQjjsCc/jxk9tinlZr6Ji1WUtrri8sVvMR2/ES+XVWF8s21SYuRCtJkFCAFAxVJQxalJRaxN503Kv41L01GTb2Gvi7Gu3SSgWlDNLTgeSlq6ywYr9ivWrUKl112GXbccUd06tQJhx9+OCZPnlz83XEc3Hbbbdh1113RqVMn9O/fHwsWeF01N27ciIEDB6Jr167o3r07rr76alRXV8f9KLHBs+p28zszcdRdX+CLOWu589FtsbfulGphVfia3Xod3P3xbLw/fZVmqfjgrXFhCmIWFGAaOh8t6XPsVUDqI0sl+pukLR2igY8mLtmII/75uXqBGNHVBmMdOlK0xx4A/u+VKfFkZBgZ7u6NQakrvoHKryXdUOtUxlQNoxX7TZs24fjjj0e7du3w6aefYvbs2fj3v/+NHXbYoXjNAw88gEcffRRPPfUUJkyYgO222w4DBgxAbW3rqvTAgQMxa9YsDBs2DB999BFGjx6Na6+9NolHMo7XJ60AIDboaVfsDUmjLTJy3jo8O2YJZqzcHH1xjPDW0xQ4lSiH9ZGvOn5vrXLIwPPaRJVD/11t0WKvC9oEKhg8L+UofIC4ymL6ikpXnql/A/pIUXcgu0AmenvO9f+AWk+Z5sVKumCmeOWoivGSNG0iKn7b0OvNDp53//33o0+fPnjhhReK3+29d+tk1HEcPPLII7jllltw3nnnAQBeeukl9OzZE++99x4uueQSzJkzB0OHDsWkSZNw9NFHAwAGDRqEs846Cw8++CB69+4d70PFQNYbJxdZa7ESsFYLB8A6we0ZuuF2xW+LwfMYn+vsvr3w/NglAsk7PNmIkUCIcJk99knXJcewQw7p5RFX8Dw96ZJQtsPenNeXKvbdeTssWlejPN00TR1i6JGZUFlmyT8NGyb1u5ZwaG/KlEUiVRhtsf/ggw9w9NFH4yc/+Ql22WUXHHnkkXj22WeLvy9ZsgTl5eXo379/8btu3brhmGOOwfjx4wEA48ePR/fu3YtKPQD0798fJSUlmDBhAjHfuro6VFVVef5LE3aCYJHG0DqkNnieoQ8ZE6JbXZI4lkt5+k7w/cu54lvc0LbAxGWxT+OeyTjiSmSRPXp0TlqE1OPAkQqgpwNV579HWf7lM9CXtCUesuatZ7Riv3jxYjz55JPYf//98dlnn+HXv/41/vCHP2Dw4MEAgPLycgBAz549Pff17Nmz+Ft5eTl22WUXz+9lZWXo0aNH8Ro/9957L7p161b8r0+fPqofzcKAigEjjRM8AOjZtYPyNFnHNpPnl9zH3YVcb/JzysD6WCa3DJ53I/oa/XmkeXA3zY2SNXieLuIywKgIWlZMK4H3d8u738afqWJ0xeLJ5XKp0dmSavv+slcbFT/8oXiyMk2vN6mvdmOoWMqguuKnd+gnIqTYjxkzBpdddhn69euHVauag2u9/PLL+Oqrr5QKl8/n8d3vfhf33HMPjjzySFx77bW45ppr8NRTTynNx8/NN9+MzZs3F/9bsWKF1vwsFj/3Xni4+kS5lCUzu3jeAdHUAVQnjsP29mQs9lOWbcKUZZuE7mfKI5ajv7yUlYivc2c5EKMIVMU+YLLXU25pdK2Mq891F83i9epd2EXR/cZ422gcNUhdNXWkmpLMve5HUHuOfUS+zOfYZ69v7tSuNGkRUgmtLqR5UZ8E90zmf//7HwYMGIBOnTph2rRpqKtr3ou7efNm3HPPPUqF23XXXXHIIYd4vjv44IOxfPlyAECvXr0AAGvXeiO6r127tvhbr169UFFR4fm9sbERGzduLF7jp0OHDujatavnP0v8yFrbHcdJ7Upcsp4GcpMEnfDKFRbg0dRnlIX1sWTGsoueHCd+MwOxuPv7MpHQ6xNHlduqKlgn09pc8WPdY6/mKbLaH7Ei+vgpHeIByG3/cWNK3VGpHznF/yPDM0fSWTxCwfMCsUY4F500VPq2sDhNe8Q0LgSHwT2Vueuuu/DUU0/h2WefRbt27YrfH3/88Zg6dapS4Y4//njMmzfP8938+fOx5557AmgOpNerVy8MHz68+HtVVRUmTJiAfv36AQD69euHyspKTJnSesTLiBEjkM/nccwxxyiV12Ie2WqucrBOQN+astIoJcENf/C8kN8kZUk7ootHsVjTebxLFIkjZbFXI0JmYD7HXlfwPD3JBmg+GjSmzBSRtUksu+VWT7oylBhiKXQgsbDiegSlBomIhsV+jr1epTVLHgFZV+5t8DwK8+bNw4knnhj4vlu3bqisrFQhU5Hrr78eX3/9Ne655x4sXLgQQ4YMwTPPPIPf/va3AJpdSa+77jrcdddd+OCDDzBz5kxcfvnl6N27N84//3wAzRb+M844A9dccw0mTpyIsWPH4ne/+x0uueSSTEbEBzLUOCXbmq69d7GQoOhPj1ps7GSVV6zwPfaGPqQkrI9lcvOI483480i9O55B9Zk9eJ4emU2u2zTien2mFo24XJr22GtJ1Ysyiz3MWFxUe459eP/Ak5VpFntZdNRNE+qPdigvqzTF3nokuB+nV69eWLhwYeD7r776Cvvss48SoQp873vfw7vvvovXXnsNhx12GO6880488sgjGDhwYPGam266Cb///e9x7bXX4nvf+x6qq6sxdOhQdOzYsXjNq6++ioMOOginnnoqzjrrLPzgBz/AM888o1RWi3k0u+KbOo0JR0vHnYE99ryRow3SdWJD97tLwk1eR/r+LMokFPuk65lpi1RxSfPXMw+i/BJfv6/qWU3tc7MCbxPJ5XLo2E7vjF/VYqJs81cVEV/1OfbhebPlpTuwqIrgeSJ106KOrFnsuc+xv+aaa/DHP/4Rzz//PHK5HFavXo3x48fjxhtvxK233qpcwHPOOQfnnHMO9fdcLoc77rgDd9xxB/WaHj16YMiQIcplM5WsTA9UNDXV7bVbp3bYvK1BbaIEdHTcPPUiaT1hly4dULGlLvA9d/A8wd/aAhkby7jxv39T3GJFMak+00Jb+BUI2X7m2hP2wX2fzg18H9erVFnmvEd5ipK1dq/zefbfpQvGLtygLf0svAuP+73C54laxOez2JvUO7YyZMJyvDF5BZ69/KikRcGazbUYt0hfXTcBqit+ysd+P9yK/V//+lfk83mceuqp2Lp1K0488UR06NABN954I37/+9/rkNFiEUKHcty9c0yKvfYcwkl6GKS9Ot7Jb+jkIOmH1ATrnkLRVepY3OQTyCTN59gnnX8Amis+22XM0CZkcVq0VHlLxOV1kdYjYOMmB+C4fXfEi+OWastDmcVe4hz65vvVoLJmRVraOaLia7XYSyT+t3dnAgAeHjaf6z5dLfimt7/RlLIZ2OB5FHK5HP7+979j48aN+Pbbb/H1119j3bp1uPPOO3XIZxEgaWurKTiOo7wD5HUFF8VGPaXB9wxhCwGmruLLYuoee545bBLH3UlFxTegbRkgQhFWUeoam7TKkSbien31TXm8/PWyjIwH7HD3KTngtEN66hGmBVUKRXLn2HvHEaWu+FF5K8tJDrGo+F5q6jj7QVMePmXQ+rzSjJUnt8W+QPv27QNH0VnMICsKi4oxQrXyEl+Ao4R7moQnfbTn5xWrupbuXdHG5rUBhOuYYLmVluSQb2K7Wfe7cXx5/Pio3VHbkF4l07S6TF0A9X29aJ2ec9Tj7D2V7bGP8R3e+t632Hen7eLLMKXkcjmc/53eeG/6ai3pG2MpVLQPXe059uEzWR6vHNP6Rz+Gi5cZrCs+hdraWgwaNAhffvklKioqkM/nPb+rPvLOYhFFyz71FO+D5Npjrz57JfB6TIQpDqY+Y1yIjmWiC4fN7ZFRsRfKgQ/3c/Q/uCc+mblGIq3kMWlB1zctyCxqx4N439+yjVtjzU8XrO+AO0BZy/KQzreiSp9wHDk5Ze71HndnHs1lo+8tClnsJcUxsZzTQFtxxedW7K+++mp8/vnn+PGPf4zvf//7NjqjgbB2GrNWb9YriCSyVutmt5t01s/Eo+InrCPQuhVzVBdzYXWxNdoVP4YK6M8izcfdmaTUA/FtWaIRa91W9KhxBc8rkN7aHg+FOqTzvajcY58E/jlarFHxFaYlg4qy5x3vrN4lBq2UVR07aQrciv1HH32ETz75BMcff7wOeSwxcvajXyUtQigmuuK3FZKemNNQKZahj6gERtVeLG1RV3we10mxLNjT97me+veKiqSXNCbIEEUKREyMuN+fHRvDKRSPzrHQlD32MgupuqLiRwW9Yy26NPQ5vDLatisGrZ5LxdcxEO7H2W233dClSxcdslgsxhNbwCENHfeYBeuYr01aSaA9vspJlmlWTlEeuvgIz9+sTxW3gZrLypDAq0mzO17S7dVPU9zmZx9xxShxWv6nApmtIJZohN9SCiz2gBl9gMp2F22xN2OhWCx4ngEvy1IkzWM/CW7F/t///jf+8pe/YNmyZTrksSggK12GEou9fBIe4ipbHRPTG96cwXytsXXIWMGSY7funTx/s0fFj/e4O9P0en8ecsfdJVsxHZjVNGgLcG0tEjsPpGPVdM437asIp1D2++6yvbY8TNn+09x/8FeIXA6eSZbS4HkIl4k5L0dvv6MkZf4DGywKyZpiz+2Kf/TRR6O2thb77LMPOnfujHbt2nl+37hxozLhLBbTSPNxdzwkPQGnKZ1KLfZtfGIbdxXjmcTqrn/+c59zyJ47XpIwBsXXRpz9p86qyh5ukp+21v2JBs/79Un7or4xj89mlWPJerWnOKiqp6aMZarPsVeWlrqkgmknUPh2j70YtFdlygKbKrgV+5/97GdYtWoV7rnnHvTs2dNWMAMxpZM3AdX1M8We+JlAZfFnpZn46zir5UV0lVp0IsOTXywWe18mMn1F0n2uP2ZA0pgao0M1uh+zJJdrM2VpKp3al+KvZx6E9dV1yhV7lUG7pKLiC97sl17tOfbhQrH211F79WURStrx/2nbOAuH9u6KWaurhO+nlXPW1FhuxX7cuHEYP348jjjiiOiLLZaESa0rftZ6GkUoHaAzOmF2GP2yzY6Kr08OGlmLjJsk2WxZZHTW1RKOIyJ5yWj3FwJv5HHf3wolKaAseJ5kHVkucfSh+wnUnmMfXke5stJQ13M5dW2I35ukbdKlYxnO6bsrPvpGLB5JW+nzuJ0PDzroIGzbtk2HLBZlZKP2xhUAyUSS1jFM7QCt9UodonVM9A3wWew1u+I73jxyuZxUMMHkq6W6IG4qoAXPi83jKU5XfI1pa91jb1B9AcSfVVcZBRR7DfmUqDruzpBXGWe7Y46K7+ip64XxTCx4nhxJzw+TRGYxjFbuprQfVXAr9vfddx/+9Kc/YeTIkdiwYQOqqqo8/1mSJyuV1MTj7tqKK37Sk744zrHPSDMJlBXrc8XtFcKl2Mfwcvx5yEyys1KXVJGVMSgK3c/ZloLnmSaPHx2GBpVbe5Pa6+0eR1SOKY4TPgvhCsaqw2Lfmrr6xDlyb2u05UUNVrhd8c844wwAwKmnnur53nEc5HI5NDU1qZHMYlGA6sE4rsEz6c7L1EmWyvI39RllYY6Krzl9Pya74jcHd07vjIF1+0Vc0NppXAuGcS5a6RwTdEZrNqi6xAL/a/KWvY5XoSpoF++j7bhde2yoqVeStxsTg+fp6hoL9UGFnFmai6jcoqAaalBXUwUWhFux//LLL3XIYVFItqqoWbSVsk36OakWe5WRcjPSmQeLSnPwPKG71LmdqmDcog2BQFgyE3cT6lLyErSS8DH2mUHrMUwG1Fk3SS9m+4nDFV/VAhRv//PEwO/ip898rSRvbXvsI/M145QVkZQdx8H66jpXGnLxH0yiJJdDk6byzkkuv9PK2ayeUB5uxf6kk07SIYfFohwHOlzxYwufF1M+ZAyb8xWxrvjqEG4bgpWDz3VS79shRbc2eK4UiWl1OelYGHG+y7TusW9ryAcoU/8yyhKy2KtCZ1T8fET0PPY99pqUTMnAlpc9N0E8b+E79VOay6HJuBGpGbrFPl45dMOt2I8ePTr09xNPPFFYGIvFjexqto7GGl9U/JgyopD4HnvK0GXPsQ8S2GOv2RVfdDXetOPu3CTd3lRggtdAAVo7NUjEVKCzWk5bUakxdfOQHdO0uOIn1PGo3KriTipOV3yevLS44hfSFkx8bvmW4ucs9Yu6q7SObVZJz3dVw63Yn3zyyYHv3AVt99gnj0kTvKzRVoLnmYqt2tE07ymMLijRATKfF7qNaxKbxHuWmS8kbaE2rc9P2hU/1qj4Gp9V5/aVd6au0pZ2nOgqIX8d0vEqVNVTU5q/0uB5UKOQO9AUPK+wx15AyixHxVcVN4KE7HNTY78Y0n5UwR0Vf9OmTZ7/KioqMHToUHzve9/D559/rkNGSxtFRfeQVlf8pM+xT7qji+Pxs9OXixWWaBmLKrE8+cWtKOdycm0u6faia/IqCj14XrZwHAeL1lVrS5/k5XJO31215ZdGWOsUvyt+LvRvFahTghLao60zDoFK7zwNPU+hPigJnieYt4no9kIx98nNgdti361bt8B3p512Gtq3b48bbrgBU6ZMUSKYRZysTZ5kUB4VX2lqdJLuvEyzAOogq8/I+lyidUxU6dYaCCxhkrbYmwbtHPu4OtC4Jr6rN9fiX5/N05Y+Se9LetE3rZhoJVXZJybVBXmC5ylsd5GLlYxlp+vEEJmo+LLvyuQuwKQguX6oe+zjFUM73BZ7Gj179sS8efoGOEvbw8jOK66JacLP/uDn8xPN38RXbyqi59iLTiqpSlsEPNapJAZauWi7yeI4ycvghj6BMklK8yEp8QbPm42G22Lvt0arE6WIKiVIPjCgGP50lEbFV3kCjrqkihjqnJA42vsnmdNraN9nqPwBAYv9N9984/nbcRysWbMG9913H77zne+okssiQdYqqQzKXfHVJkfFZFcri/mw9AGibUO0f+GxNMbtTSHb3pLuczdva8B/v1qSrBAukvZgSHphVBWkx8jIoymDtapxHynm/1tDpVKlBCXZ2tzlolTZVfRU+vbYt7jiJ1D6JvcBOvfYy0KvB9lSmrgV++985zvI5XKBidexxx6L559/XplgFnGy6mIsQnr32MeSjbHE4W6alWbiLyn2qPiCFnthV3yh2+IjxcHzTIPqiW+Dj3KR5e0rscNtsddf9ir3I5vgDaPaYq9qvqVlj72MKz7DN+F5m9sv6JZNZhGeVg9EAwKbCrdiv2SJ1ypQUlKCnXfeGR07dlQmlMUCmDk5S37otKjChImQDnQvPsWxxz52PTknOWHIZlUShta2bDHxYffYm4OOYlf1LmW3GYiSy3l7TZULUU+MXISbzjiQnjeApy47Cqsqt+HOj2bTE3IcPRZ7hWllafxI6ghHFrJUzmFwK/Z77rmnDjksCmkjdZcJ5cHzbOHGQhxDQ1bepfDkUDQqvuDqtslR8WVJm7y6odWR2LzJzJ1bckFq2xl5tNiRDp6noeSVueKb0v8oLqL/fLEg9PczDuuFmrrGcMUeeufESUTFNxmjXfGp32fpDTAq9o8++ihzgn/4wx+EhbFY3JhomYirAzDw0TNHtrryVnQ/l2jwPNOrtEybM2VebQq0hQ5bTHwQ66TpDSl22GoVr/Ibxzn2qpQg/n3kCh8m5/6otpDqGuV9pJvLRn3PUwh8KHSOvaQ8Js8PS5SFZCcj9ext5Bx7JsX+4YcfZkosl8tZxd6iDNH2e+GRu2H5xq24+gd7a9hjrzY9Gm0+eF4c59hnrDMv4ugNnidsneZxxRfLQZgcJKPiZ7YyiUFV7G3/yQXJtTkrz+bH9Ocy/bi7JPBLH6exNumiU+uKn53xI411Ojul3wyTYu/fV28xnKzVUk5226ETHvrpdwAAC9ZuUZp2bFHx09c3WhIi7qoiqtjzyJkX9ApIinRJq5+UvT5jIe+xj1+OONDtDcebun+hQYcHoaokk9pjD3jLKYm6GZWnw7jQzZ9vrpg+L9LbQgzuA3Tusc/l9BxLm6F1FQCS59g7jpOplSaLufyinyGxHdrIOfZJE8se+4yqY8a64htcp3O5nHXFzxAm1zVZDN7CmgjMx92ZeI69quB50BMgjhfTvS5UUnhSFcX+5bx1nHmbW84m9720NpK1uaCQYv/SSy/h8MMPR6dOndCpUyf07dsXL7/8smrZLIJkq4o2U8Ixm3Ffqf4c+5j22BvccWeFCYs3Ji2CEvx1nHWxVbSGxbHHPm19WNYmBhZzyerYYPxzaXHFV59mnMQRh0AWXYsercfdJXCOvYHlXEB38Dwtz56x4Zs7Kv5DDz2EW2+9Fb/73e9w/PHHAwC++uor/N///R/Wr1+P66+/XrmQFj6y4kUh/BSelp/OqPgmd9xxEEfgxFHz+VbJ04LuKiq+xd7g4+4gp1hY13OzaOPdp4UAvyu+/28NUfGVhcXnu1zlk+R0WlLC8i3+G52nnoXXQvA8ATI8XujcYy/bBtvKMazciv2gQYPw5JNP4vLLLy9+d+655+LQQw/F7bffbhV7A8haJQXMWc2PbY99HHnkrAtxFiC1DZ0W5KYY9tjHTQ42Kr4lHeiOOp02WJuebFR8k4PnOTDDa0h1EamYo+jbY68+Tea8k8s6Eu0We4mnp7riZ2wA51bs16xZg+OOOy7w/XHHHYc1a9YoEcoiRkVVLeavrcbW+qakRVGCaPPV6oofUwcQx6BRmsuh0dAOzeSBy3RYX6moV4Rw8DyO7NI20KZN3qxj4lGp6sjys+mD22Lvq0N69tirSYc/foCajHM+NUu1tbZdaQnqFR15p5rCuxMLnid73J25fYDJUfFppb59R25V2Gi41373228/vPnmm4Hv33jjDey///5KhLKIMXrBelz23wlJi2EUqruY+Kbv+jtHZW6AlkQJ7LHXXEvzgvMsnpX2JFzbZVqD8BGAFi1kpWcj1SqD581mI9lEdSgssmmaVhdUy9OhlK6iFJRbtqj46vvn1vHM9v1udE8rdXjWXd//APFEDYR5meLbb7/FYYcdhjvuuAMXX3wxRo8eXdxjP3bsWAwfPpyo8FvioyzDihpPY9Y52GVpj73OY0lkMVi0Nk99k7Bmbyw5SV98O7WzxIXBzSgRWJU23gXPwB57Ha74knO2HFrd8HnmJqoeJZfzlovqKWj7shKgTj4dnf1zMvFgzMVkgxGtD9hx+w4xS6IXZot93759ccwxx2D9+vUYMWIEdtppJ7z33nt47733sNNOO2HixIm44IILdMpqiUD33pa04D1XNZ1lEofUWV4Iasswu+LrFUMqPxP2i/KQt9HzjCKl3T4A4IjduxU/p/gxYoO15X0ys1wqXRNd8QsWf1MchlTPt9qFWOxZcaB3j71I0tLyGNwx6DYYpblvjwtmi/2oUaPwwgsv4MYbb0Q+n8dFF12Ehx9+GCeeeKJO+SwcWEUtSFpLJI4FCZNXVk0JlphGDJnjBeCp0rUN8vsqefDvFeXF1DK3pI+ovt/kPaxZorHJ16oNdMVvvp+/91H5KDrH6nZlqtJW30Mn2Q5N7gFMnle2lYGaeTnshBNOwPPPP481a9Zg0KBBWLp0KX74wx/igAMOwP3334/ycr7VUIt6smyx53kyO+9hI8v1pS1Bqu8sFoG424npizU2Kn52ML2uhRHVLdvxLR4afcFE9FjsJVN1WY1N6IJU182w8mHNSndgU9v3e9E/rbRb5qLg9nPZbrvtcOWVV2LUqFGYN28efvKTn+Dxxx/HHnvsgXPPPVeHjBZGykrtiO8nrZOgOMQ22fJjsGhEvrtH96RFaMXQ0SvLx3RV1zUmLYIlI5jcL+tE9LF1KVYNPou9jteiSK835lQOmQW1doT5q6rH0uqKL5C4Ia+LCd7AcjoNRrLtxZR2ohupqdZ+++2Hv/3tb7jlllvQpUsXfPzxx6rksghQmuWZMwdZmBbFEjzPVhclHNGnO+69sG/SYhQxdX+6yVbUXM5s+SycpPhVRin2WVX8TZtz1zZ4jw3W0T+wvst//bgvcU4gWhdUPotbBJmqec0J+wS+YxnLWPLUUbVk9tjL5x1fH3DticH3Eobu/sl61kUjPLUfPXo0rrjiCvTq1Qt//vOfceGFF2Ls2LEqZbNw0i7DrtX+xnzYbl2Zrk3rZD0OuU2Oip8mfnLU7tiuQ2li+YvWlbS2DYsl07iaZRuZhxqJO4ghoCkqPmOaNEXOo1wmEZ3dJ5fMFJT0iGFHq/K8Dy0We4gHLpQ+x17qbs68ODMzeeGxrfSnXIr96tWrcc899+CAAw7AySefjIULF+LRRx/F6tWr8eyzz+LYY4/VJaeFgba0Z/rO8w5jus7gPiaUOOQ2OsiJRRjHMXMAM7kt5mC2fBY+0vwu2+oee5Oeq6wkh0uP2dPznY7hUvoc+8IHzg5fafA8RYYUXQvUzeOhhnPsi4sq8Y+2JrUVP7r1EKkgtyZOjDTAHBX/zDPPxBdffIGddtoJl19+Oa666ioceOCBOmWzcNKW9tiHBlUxudczCJNPUUjbO0xSXn/Wuo73kcX0d2q2dBYe0vwuoybG1tNGP/323bH5DHUXOvov2TSLx90ZspQr8zg6hwc9FvvkMLkPMNtib0Y70Q2zYt+uXTu8/fbbOOecc1BampzbqYVOlvfY+wdAg/sOJViLfbpIY0lmvQ1ZLGnEPWm3TTSaNE/VWePc0OqBuw9PxHLs+n9AbqGCdGdegUbuwNEUPE98k72sPCaP3bqnlSY/uykwK/YffPCBTjksCjDZAqsa1lXBtHYCcVg3Td5jb65kZJIsyoDF3jHl8CMvRndPufT2FZYgpnuHhOFV1oIY3Y4yjJ499pKu+AWLvSHdvdTTEMoi7LkKC2BswfM0uOIX0842vFXU5C3BprQT3WTXxNsGMblBxUmK53RF4ngEW1/UYNpYYawrftICRJBmZdCSHaKUPVtN9UPqC3S4P4f1Oacf0pPh/uZ/k+zvVUXFJ6FCIXccTeVjQNmbiG5PUJO3IZiCVewzBOkc0KzgfzJ2i306yyQWV3yDy8Zg0YzDP9CJTDTiWOMxuS3ayUK2MOltXnPC3lzXRwfPM+npkieus6njjorfjsFP3z2G8xSDqmfxpyMzpyDdabLSLBPfwODHCsA7NrrrwG7dO6kWRwp7jr0ldWR5j72fsPEjC3sUYznuzlrs1WDgYGGeROltixaz+NWJ+2DMTT9MWgxmzj9yN67rIy32MsJYmCCVsY5yD33XDJZwE9zB3aKpDp6XZ3iwqCx1lU2iwfMMXtzLu17aP350SIKSBDFxXqSDtqMJtgEyvcde4cpwGmjrwfPS9npNcYU0mbTIaTGbkw/cBX16dA69Js11LXLSnuJnSwukVxC3xZ4lu1ZXfL4BSJXhwF8mcsHzOO/luFxP8DzxtKWD58ndzpcXIbMw7+AuHVtDt3Xr1C4WeVgx0AajBebgeRbzaUsW2NABUeOer7iIZY99SsvGEg3LRC/+tmFuhctpDp6Xy7WdSYVu0tan87uyqk3PogYdxoQwRZglP/f9JnQvMiVEflwVe+wdrScGmFDucdPcB5GfvH1ZCcb99RTkcsCazbXaZenToxNOO7gXnh+7RHteacEq9hki0xZ7H4webHYSFEKZwVs37Htjh1RSLC6McWO6QqazztGnQRYdpLn/sMHzkieuIg571ywGisKUj7dvUVmHVBlSSLc2MQxkLF4COhZVi3vsBRKX3esdZx9AzCpi/t27ZW99uQbFXjTeFtB2zrE3d2Zv4SbLFnv/RM3kPUZKiMUVX38ebQEThwoWmdxtKo5nyHiLDSXrW4fiJG0lyfvqbb+cPMSo+BracFh8PCZXfBSUSzXy8BKYl0m0TlLxbtraEJI3G7oPfxVJe/jcCqk8kx5Oksqe1Aa5FHsTJ2sasENIhjDZAquasKacDVf8th08L63vLQlIZWVi9FeT32kO+l3xLWpgUbDSXN4e92pCMza4206EuLq6uIPn8dVzh6vPV2qxdwcrlgqep69i6xgPReQ9qFcX5XLohrzIxXa9jnfqT7PZG47t/Zo3K9JD29EE2wBlbWjTdNYt9m39uLs04TjpViTiwnT3aJ3Smf7saSJtbY3bYu+64eBdg4qArUv6IZWwjnoXNo9hyc60MTwJaSLzdPQodK41FSY6ty/FlcfvpSjvZN87a/5xSMnTBAy0d2jBKvYZwmQLrCyB6Kth12biuDs2Ljt2D+E8TI7JYK5k6YBlAIt7TmjYHNSD7oVCk589bWS9KN3d8qG9u+F235FRti7phxgVX0M+oUOw2/MwIvfEXPFz3rIybaGhgNao+IyavcqSSXqPvUlTR7461zY0e6vYZwiTFTXVhLsCuf/QLooW4vBIyPJCUJwkH/E8+B5Zgg7FjaFzviI65TN1wptGWIrSpPLmta75h68TDtiZ+rslvoBYOsbkUFd8hjfdqlzyoc3iK+WKr+d6x/X/KuE97i6Xy6k7ZlBJKhL5MxZ+HN1w8vMv87CKfYZoS4pa0q5IumF9OrlgNQaXocmy+TBxUMkbKJTpbVZrVHyzHz1lRBemSWOhjCs+QHhaW5mk+EW/PRmu4ttXLArrsb30+13xGBTII4IqO4po/8tSTlos9pyBC9Paanm9VzwxrhQ/dY4gjw2eF8Qq9hnCaEVNEtEnM12ZoBHHq2xLHh5ZhlRXWBT72N++wdVNd3tL6tF/eODO0RdlkDR3bSUu4eNyCW9L7N8zOoAZsdxjPsfeozBTLuO1Gkelx4s/GRlPGV19cPM59urhPmowB3WNN+m5vkGd0I2nH8h8rVXsLRaDYe3Xku7/RGEOTiLxfCUGz37NlSyIiRHoG010xU9agAiy6IpvkuU6Tkxa5OaVJOp6gx7NCLIbFT/6/sIlYaprSQ649sR9OCUTQ/U59uHX51ryTKhBtOR7zUuT2S5XmbXCtCLzIkXFD7te0SkJLOkfuUd39D+kJ/O99hx7iyWlmDSpM5lSW07K2L5jWWJ5k95i3kTF3uD6pl2yxOae5pa5OObVbZW4lb1cLkc43knsnR695w5ScrUlSCUcuys+w3v2uOJzNAtlj+Krn3LH3SmQh4ADMyy1WeqLTXmWHTq357rehHoQB1axt6QCnn5E1Z6vRIlBcJOteYaMG8x07dgOL175Pey3y/ZJiwIAaGJxxY+5kFP2SpWS1LNncfEubZMz7j32EbMy0VdqUkBBlehwQydvgVBffmFec0yvS9AVXxdSrvgae0kt59jzXp/LkCe+ZGwIVXnzZmVIM9GOVewtqYTdFT+dkxnm55PIw2TFPo2cfOAu+MF+O8WeL6mON+X50mhfKj4UZGVbjM6+IqltL6Wlhhe6AFmfnAUt9Aj9mz1dwRvbICQlU4/FXk6xL9wf1ibSOgdShaPpHHveLj2trvgy+ZtW9UxZANONVewtmcO0zkSEOB7BZGueuZIFMXGsYHHFd5dx+zL9Q4HJFsOsBs8zuY2Lkr7JGd87iFIYRF9pBquCEDzHyHnvU094VHwGOVv+5Q0Ql4XgeTzX6znHnk/gLC2wMB93p6HVkFJk9ciwe+wtFoPwdxDsHUs6YT8nVPwJTQ6eZ2GH9BYb83wm+w4Sij3z6r1wDvGgUz4bPE8dJgarVIl3j33wd9E+3+SFNRl0TNbJ5a48m4hz7OXud3NO310ZJeLH4xptYBVzWv6nGm5XfKhT7pNeJAjLPU7JTKxvJmAVe0vm8OyxT2nDj0Nsk+f8SQ9cPJioZ/DGzpNzxU/Pu6KT09pXJBy4OVMYGBcyFOlz7FVZVzNYF0QQ9njQYX1k3KtM62NZI+f33b077r7gMN+36jH2eGEtFnu914empS4psfwT2n6Xy0mmmbKxQxSr2FtSSeiKYQZmMPYc+3SSRNUj5dnE4orvuk/GFZ/5kdt0dUvm4Y2daEuQdXdKb3AodXu9TbfYmzRuG7HHnqPtOgyh3/vs0Jk5PVaaA8KFe5jogjUvXXvsecnl1PXGyTcVttgQ+uXky8CEehAHVrG3pAKTVtmzgsmu+OZKlg5YFHs3ceyxN70t6nXF15h4CMlPADWQstkZ7ysIWOz929AMb0eiiG6x0OIxpXALRBiqgufx5qWrX5BJVufCjpY99pxPm6VWm+S4IlNPsr6Nq4BV7C2pJMnjNuIgjsmb6RYcizhxKvZZiIrf7OKnT8DEXPGTydYiQdRWMvHgebY2AOJtQkfphZ9jH03hlYZ5sRSu0abMG77HHtDk5ZOoK77Be+zdC0ia5eQt07ah1lvF3pJ1DB1oVCEzWGS8aBIhiQGXlCfvOfb+PfZH9OkulT/5urZLUhMxUyfaMqRtcsarUEd5Uom+UoMdtACYtfBAkkSHeO5n3q59KfU3WtbF4+4Y3M1lzv+mphnIIzrlv591MFNaqnAcXRZ73usVxnFJuKmYtpjP+nrbiMHeKvaWdBAYQML2+Lg/mzNX0IKMwmBy2Zgsmx8TxwqW4+7c+C32Zx3Wi/1mxndluoeITvFMV6rSRNYnZyU+hc5fL9O8x/70Q3oqT5O3Oph0XKA7zXsuPDz0dBJSveeRKY73H2cNS9xqzZl9eVWtHkESgHX+rSdvSxRWsbdkDwMmMLLE8QgmWUgs4hCD53FqP+3L/NYijvxZr2vD1Y2lrd153qHq883gNChtwfP499hHpSf2Tk2oCZd8v0/SIjBBaq+6z+Tu2K4UPzxwl+LfLIp44QqW7l7X+/caUhhkplyianzwez4A5iy+KzvhQk0y4vmzWuxV5yuZvin1QDdWsbekkqQ6lixhctnUNvCdw24KpiivTU2cir3PFZ/HumPKM8ui8zFYyqhXt06J5Gs6fkU3bcfd8RJ9jr1Yukks5E6/7TSvDAaMOiwyJOGKnwNQUuL+LTr/wv1NjoNvVm4m59HyNO4tHrrOUzfBM4n0bDqCpiVZl5Pu10Ozp/x46kG7kH9QkDnr67XB8ywWk+BRNDy3GTDSmIrBRbO1vjFpEVINr8U+zAVUFSa3RceB1tkS63nTSeRrOv5FprRNzmTOu1apPCRRF7p3bu/5W4u3RcLVQVWcm+bj0Mj76nO5cFf8t6es5MpLJSzB83T0f+zH3enx8Uk0Mnzi2xDY8vfWDQ0eL5xJpmvkEMcq9pZUkoH5qhKyMHFPOyYqGkYed2d0XdX7Dk3Y35xW/EVnXmtTS9QEWHSCbII1NS2QilhHG/YHtOONMF+QaeKSjQx5kRcNZAi4RlOEjtPYQkxdR/A8gcdIWiFXReipVN63zXQPV94yZZj1waMFq9hb0klYx5JjusxoYtljn9rSMYvDdutW/JxEiRL32HMq9l06lnn+5nLFz0g90uqKrzFtU3NWRaB+pWxyxts+Sn2u+AGX7JjkyCyC1mOqNVpGFNfNJSX+bRg56rUieeta2GFZMOC9RiUO9HiKtGlXfKFFDfXYPo2MVewtqYCn+Xrc2TLe7lVNKkwjLXP3Z35+FI7dZ8ekxQjA6op/y9kH47DduuK3P9zP8z1X8DzGaw2ubnAcve0hqW0IJrdxZrLwDBxEvTOTorqbQFzBFGlKhIwl3+t6n/Mo3znfdaQunS8Wiv55EYvFXjsSe7DTQtJtOTQqPsXrRIXHSy5HPjKQtQ9IW+BVUaxibzGK3XdoDSDF7u6TPeJ4vmyXoH5KcsDph3IcC6cJcrAgtnt/ecI++Oj3J6Bbp3beNHnyZ72uDVc4pj2mGsqHluQJ+++kPjNN+C2NaZuc8b5X//MGLLeCctjtIM2wlAIxKj7NYi+1uu797MmXSVD2a3RY7HM5rwi0PFTtwxdB2zn2IlbrjDTBtD77JzPLkxYhFqxibzEK93Evop2xruBDWcOEjpZKgnP3kw/cWVjxMbpME8b0tqhTvqSePAv10f9esmZ986Nrj30W6kJcEF3xNeeTQ/jcRdYVX1f/5m6OtDzi7PtJOWWty0h6LA3Lnb7VQn3etk8jYxV7SypgOfqF5b6sIReRN+OFI8h/LjkS7Uqju0ZTJgsq3qK/HpVwmHdMjnbPiu53aVoZmSYPD9/dY4ekRdBK2D7r5u/E0k3zOzcB+h57CVd8V6IluZzn3Qc8VaRd8V2fNY39LNZ4quKnTAbfQiAcPcfdJdiekm7KIs9u+5/4sIq9JXNkofuIJXheFgpKA7Zc2qYrvk75WNZJSPmfffiuUvnSLWjpwV12P/t+H+ywXXv6xRkg4IqP8L9F000CE7wtmBQM4iVUzZ7I9f0PwBXH7cWcjd+tXZX7euESPVt92LaJ6D72LAmy8RRihFrsXT+627u6hRvyZ0srVrG3pALWAQRoW41dZpBsQ8XEjWjZpHXSElAAuaLnKb1MGw/+5Ajqb46jVz5RC1mn9qXo2bWDeL4x72fVgbtNdeuUPqWef4+9y2KvIsGwtAxCVD4t+6cJ0vBGxe/SsQy3nH1weD4+K7r33TO44nMUmtcThP2+KBwG7Y2p/+MUihqoj+DpYMB6UrYQqD86Y3yYsGBoElaxt2SaNE1g3aRUbGUk2U+nrezjDLome60Iv/3hvkrS+fFRu+OO8w5VkhYvwu7TsvlqSjdO0r6nknexL2rbmbjF3uzCi6vPLyvJ4YsbTsKdnH0BrfzkrOle1/sS14ycpKCyykRC1/t3y1XKUEZJVENzguepefikDQihhjWal5jv6126iC1Y+0+SsASxir0llYR1bG2psUs9qeETPVW0kcdUCpfBnvFikcnIz4/dE+f07c19H42wya3OesLy7Dr6LbpVKz2NgjdQuEk88OO+3PdEWVbtHns5Skty2G+X7fHzfntRr+EJVBd+9Fd4mXt+znmv9/0klD41L1XkgLxLaabFZmF6FnUieXBc/6+SJFtT0i2Zvd61lrv/Dtsd6SNViv19992HXC6H6667rvhdbW0tfvvb32LHHXfE9ttvj4suughr16713Ld8+XKcffbZ6Ny5M3bZZRf8+c9/RmNjY8zSW3hRscqbViU/jkmYySWjI9iNakgiJlGmKup40CpoRu1w4MQyAXAcx8io+LmcvLXpHz86RJk8SRDH+du6uPjoPtxlHbUXXrSeml52wq74nNeXMQQbIF0hZKHl+D3nf7O+DEWj4hfu0xVjwX38JN2rQcOiJce1eiz2hjcojYRa7F0/usvdXzdE+rEcyO3AhGOHTSI1iv2kSZPw9NNPo29f7wr49ddfjw8//BBvvfUWRo0ahdWrV+PCCy8s/t7U1ISzzz4b9fX1GDduHAYPHowXX3wRt912W9yPYFFIaJfQdvtbLtrKuMT7mG15wC7AZ7FXn6YukpKhRHCkVbHYcOXxewfTNeBdsJImWVXgt9qy7LVmSldCpixRKqjh0sqdenY7U6Le9MPiKxAXkrkeRc8CmVd5i8qZnrkqmQJR8TXtsRcRV5lXQsKNOcltCN5Em/856YCd8d5vj1effkpJhWJfXV2NgQMH4tlnn8UOO+xQ/H7z5s3473//i4ceeginnHIKjjrqKLzwwgsYN24cvv76awDA559/jtmzZ+OVV17Bd77zHZx55pm488478fjjj6O+vj6pR7JQcBi74LA+wrMKntLZDLPYEs9nilVWN1lX1FU8XsBNjnDNf39xNNO9qomjnjrNGWmDLXgU4StJmej3p6dNXHlc68JEGvss/uB5EekJymH6Hvu4KCtlsNgTreP81uioIvfusc953j3L6+LbY+/9OypiPws5eL3reOMQxEUKHABTRej2E9dnd7FHxQ5hz5vMd/p0T7yemUIqFPvf/va3OPvss9G/f3/P91OmTEFDQ4Pn+4MOOgh77LEHxo8fDwAYP348Dj/8cPTs2bN4zYABA1BVVYVZs2YR86urq0NVVZXnP0v8qDgqw3/fiD+dhN+fsh926NxOVCxLyhBx6xceIDIysJCef6ftycFumPfYCxROnBMyna9OfF+0ZL4prpBXHr8XJv7tVPTbd8fid6ZM3Lp0KNOWtnufci6XUzchNqTsVMPbv5cyuM8Q242AZZ5nj30u573eH2uBlBSP84Ff6f7Hjw7BsOtPZE+AQj7E3boALXaA5xpFfVUwFYfZYMSVj5DVWlHeapIRz19AgKhjPIXkUJBGFjFesX/99dcxdepU3HvvvYHfysvL0b59e3Tv3t3zfc+ePVFeXl68xq3UF34v/Ebi3nvvRbdu3Yr/9enTR8GTWFQiGrCmrKQEfzr9QJx+SDb25MgMhiZP9GxUfHaUDJAM+9/0WY/13EdMK6G3m5TXiEzE7qQpK8lhl64djQyedxshbgEN3joXdb34HntTSi9ZmPbYs+v1AIDe3ToS0mDIx/c5rK6T1y/Y36l3ESGHXC6HngS5ecjlvF6WLP2NOpd79mv1LBAn156Mbsu0xZ3AliIDJgTqkzMCoxX7FStW4I9//CNeffVVdOwo1wHxcPPNN2Pz5s3F/1asWBFb3ha1GN0BhhCH2OksGUsckK1DtAFbH6rnYzTLjePo7Ssklt+0LHLpCqSlksL7yHm/TEQWPzrrSqQ7thnzYSF0KFi8SbLssScq9iFu5v/52ZGcUgTTzOVy3GfNM13TUmFI/beKKuE57o4hKj4NXQq/tj32CbanpJsya//nib+gQNsMei/5DRICaYqLYyxGK/ZTpkxBRUUFvvvd76KsrAxlZWUYNWoUHn30UZSVlaFnz56or69HZWWl5761a9eiV69mi2yvXr0CUfILfxeu8dOhQwd07drV858lWQL9SEhrDHeNUyGNOWTteQokuSdOpkwTsQgryDLQvEiTQEnrr6iYKks0qXrFNgEXu483zebvze84RCU8Zu8eSuUgwSMb/x778MmqaLmkYTEnDlgs9qRSpgaG8+2N58FvyfYu6njt+bKu+J58xW4jwu+Kr7siBtPXccpOqUDnrGy7QcJtOXSOzfhL0s9QIK3GvzCMVuxPPfVUzJw5E9OnTy/+d/TRR2PgwIHFz+3atcPw4cOL98ybNw/Lly9Hv379AAD9+vXDzJkzUVFRUbxm2LBh6Nq1Kw45hN2dzhI/Kjx10jqZyWJns0ePzkmLwEH2yl8WeqAmtrISqdLNlnT++6jpUb93tL5x0cBlsjKluRspWuxD3JP9PPPzo/AcJcijSlRYn2hEPa/o2MBTB/faUU9fbUJ9pJ21HkW4Usa+EEq7K4fwd0s+XpX9WUjPLTvPaM6fwRXfk2f0NWx5s6FrLbeUIQhjVlERX0BF8Ly2+wbC0RcBRgFdunTBYYcd5vluu+22w4477lj8/uqrr8YNN9yAHj16oGvXrvj973+Pfv364dhjjwUAnH766TjkkEPw85//HA888ADKy8txyy234Le//S06dCAHg7IkB+vCKrOVMJfDXecfhlve+1bofos63vxVPxx77/DoCy1c6LCAkOa+slG1TVisolpuFC8g+EnqyWllrlMxVUWr6OzuyXGdZ8zT5njfvaed5XKBBOLwfPnB/jth6YblgjnR0eKKz5mm8B77EI8lFUYIf6BEZa74LdfwRtxnJZ93pRshQxzElReb54cukh1LWecC7q1vcRRXLpfj7hCSn5Wox2jFnoWHH34YJSUluOiii1BXV4cBAwbgiSeeKP5eWlqKjz76CL/+9a/Rr18/bLfddvjFL36BO+64I0GpLbzwKAX+S92W4sJvWTn+RKZTilvPSkqv433VWQ1IGAbLajrdFVW9PL4cdGegHbYJONnqp6OvSqsrvily66zzUYsu4kqkGWVHIy7xmPbYM34HNCs5EiOGKx3/OfZB12V/X8Cz2EoMiMp8Nx2WiPNMUfEVvX9/Mo7jaOlDhVzxzW6CzIQ9B90jIxf6t4q8Lc2kTrEfOXKk5++OHTvi8ccfx+OPP069Z88998Qnn3yiWTJLGJ3alWJbQ1Pkdbr361qSg+ed6TieJqvoGOjIUfHJGbFmLyam2noQNsHTarEXdsWXdZVNL4Ui47VixgHX++SUuSRCCXJnPfiq7+MXz09kE4NL5PgLOq7F9jJRd5UQF3LRwHTebRfeUicFgRPKpAUdFtNczrvHnnqd+qzpeREy0zGfYFkg0kXS/aBI9qpc8U1MwzRS4JBnaUuoGNxFj8JLI1KB3jJWFqpoi8XCUhekLfb/3955x9lV1P3/c/Zu77vZbMmWbHqy6dm0TW+EAIEEAtJCSIgIJHQpoigIKlhQ1Ae7AiqooCiKyiM1Pmj4qflJxzyi+EOFgI0ktGSze39/bO7uKXPOmXrK7vf9ekHu3jtnZs6cmTnznW8ZDeaqqvQGTDAmhRlp82nVKvmqxxTzjYCcsOQQduKpigeT9fAGz/P/u0nguLKkWDvoJrKo+IHrCsFK9OfpzMN/U4d9l1ybB4zEuXvRMbfap9Qgd4XQNIL903+j2dtyJjaN8iV87HWNwNhHckDHsfdhR1R8z7wmWTR3eD71/NIKCfZEYgnWrvG/ZIeioJZIhsBzGCy3yDbFHyx358W0spCn7Uy0rt+iJQ1PMsl1ZD3OiY0V7LSCd1KQGViWhfl6i+SchECyQeMsSb7RbIsln7SWzzPmGfOuNEF+8Oy+wN9opuZvLlN8e5wMwyObqbE3YYpPGnsmUTZLAqa0REKCPTGkyE0EcU+MQ5E07YymqX8YEQh9/L3Z5fPVQOb5Z7N67883dp7p4HkaNHo6y02FtU5OY2/XYiak2m4hacvCdlQU6/FsLMx3abY03bOQKX5C2tkEsgJZkJ6QreEXyzPPshx93d3H2FHx+WGlVXb1scC1K8rVn3T1c9ffps6xl3bp0EDca6lgH3ufzWTPRpWkfl3zrQ/GuY4EeyKxBE4eBvJMI3FP8CKItP1gCW7Iw/TWam15lRfpES5Yj8r/jGLOPGWFW42DNsgU3yTSx93xrpxF89Weo35Y8ldSNiTctbA8HtK23wSrbBcYmMKc5EaHWKA1MxjJV3BM85hQMy2WfDYELEtlblPLR/b56/Q5d86pHBZChgT4/u8ZjWLkHHuJDaKETF/KBN2G/R4dbhpulyINbeF+1jJ5DpZnYocEeyIVqJjXp0n4FSVNk1KKqiqNzPNYOn64YpkDhVaXFgSmnTeqljNPvu9MM1g2eOIap/6L30irIUW/H3DM9WDBfB9pqqjdFL+HEZlMflNbQLA3ZrYdP7qj4vc9elkrAOcmjcPH3uWrrLoxyvJ9Vn3MFiyuZ8oVFV+tKr5ks2ZC8cZ73F28BLrC2p5ka21JQDrJsiWvG0qQYE8MOngnHSJaRDRGcQp0UQs9OosLa2Nu7bpArXhTytxnFtnIRmzc2mATxfu7TySfXN2dwk5MlXHBdFXxSyuYd4FNo9zd0+u1DtCgHR7KyBxTBgS5teh5JnmW5fSxd6VVNcW3J9b5euWJiu+oRiw7xME/37p5Dh68dIlQluRjz8beLBXFBfjV+1bgtx9YxUiXjAlpyoiquKugHRLsiUTB+47QHRkzrajca+QCrGB5F64Yq6XcpGt9Vatnb1a/AF7CeQqYQfMu1JLwHvczxTd9vKK8Kb5auf7m4Ql4GCHkaugMFpeMerMiPOtq0nCNveX4i5dktJxeLEt8/uTxjWaPj4A1hwYLJ8tVLs/1YsHzBj7rNE23z6lBmx/9n33y0TUnubPJInx+Ly3MYGy92LtTTmNv5h6jJtjH3vl3c3UJhlcUeb7X8U70bHoKtu/G+W34r9NmSdUjyZBgTyjBOzZVF84qCzq3H9tgQqVVo14ki764L109gdt0PCnEJXjcu30hLlw5DucsHa0lP9m7uPzICQF5xiPcAkDnyJrA33UH6XMjrdHTEdyKmW/yyfk0J0WzY4elRde1iWL3AT/Um9XiRyp6XQKbXBsZiWPKAKAon71czrP84yuEEeRjr8tSY+D6gQx0yfW8IUBMWN3wbCKYxC/mQhTEvcEZrFjzmQfdf0vcgu5n+5H1U9EocGRoWiDBnlAirulFZMfQ8Zv+qsRKmu5HZlKuKA72GTeB/DItHiyrLwDfpUeMR1F+Rluenu84rgvz8RdF1wL0++d2heZnckHId9ydN41lGbI4iaiD12joD3YFa1IETrfS17L0RWu295VDPcE+9rLB08IwNQOaCGImiqxv9KSmSub3FuSP+HJbX/j52APqpvhOjX2uDIEMfLArbfy18ezPjjTqVTmcj1dlH9btcm39kfVTuMuR6UemNzUiQ2L97Y1LIrsZxmH+McQhwZ4ghig88+p5y8YIveyCSKLmLWrG1ZfHXQVOBIQAW9Ig/9W4tNZ9ZfflIeoPqovYNkB9v0/+WGT62MdUFzdC55wrlHOot5e7nDDEgufJlREHonsFXMHzGEkyeZYj/sFAWr/j7sTKybOCz7FnZ8CfhBUVX8c8wNP+9lJMrwNY2YdVMXfNxvkjucvJxHjcXdxrqaDSeV32jNxBiuYtk5BgTygRla+mkBkhAiaQwTbwDbf/tOYqrJnSqCWvwdb0Mvxo+0LH3zoenzuysg5EfEbdAaB889RYF1mCXIJMCrvymxpmCk6D4JZ7HkkMnud+MBb09R/7PR7qZQWPlNR0GUorVAfND1AmN9ngecHIah/tny3XXG4XxBX6PkvQ1bjByXOEKM9zF78/n7nN9XcWZqye4oyKH3dA/qBn5Vc399c69kXSsEEdByTYE0okcVglZvE3SNDVnGJR8XNv4uhVrCr9J+xa031T14tO1mxXt9+h7qfvt8Az3ctkF7bKfrWC3+tGRZDLdSVnpPBkTO6eWkiYpvJwqCdYYy+StdCYNtTMuk3xRXPLs/jmKH8/Ybalhg5rJAvBz5Ztis9fcJ5rowDQ85wdZ5X7bSQiPI1JwmI8SW0QyZjiS5TDzCd2jb3EBr7rh7itDgYzJNgTqSROs96hRN+iRb8mKulEVVctxYiab4plafsuPPNM0BslwR3AtO+vvLWCWpvFHWBKBaYpfkLq7Y2Kr69i9rz6gue5f5dDRA6JW3AQQSQwL09EfEDQQtBiCyk8ebitnUT7umg9c+iNis9TOPOjM4mud5cnNkHWjMZeMgijDuIenoEae5+JJtCSVlPZ6Zm1zEKCPaFEEgUgEW1K2lG5Nb5rNS5YU/IcTFbT83KzLIysLdWXv8HKx2WKr5O4gnepPBczsfOS8DSCYZniJwUV1zARmMfdBQRYC6yHUJ2jR+Yxi16ifPa43xyoIbu+DQL7b+53hVq5TI29wPUs+vofhym+4xrFQgXKysHrYy+ClMZe083HPSfKCNfu5tLRFgl8NSQCEuyJVBLn0VlJIor70WY+JpFTAoIoCyHTVutnNuOSVeM1lR+9dYU9adBiR6avZrN6+3hcpvjSZ/Yqlhu3AK+08Wg5/00SLKHLt56C9bfn0x0SFV8s3wQ2ZMTw+kWLPEoL8icieKwxfDT2WY7I7r5lHP7X3Dn23rLcOANg8ml0w+Adb1mAo/HEH2CcPvZxj+RAU3zOuC56TpIgWJBgTyjBO8h43yO86QJ3DMlUhwteUz9d60GRiTzXDeaPHqan8IRioU8YvmjVOPk8LPZnFrwCJtO01Lf8gV90axJEzGz58vMnDmsHRxruL9XLjTGgMze5fmU37UyKcOrRPmnM257XoZ5eLs2tdpLRzFyIyKi8MUBEXFgsi70OEt1C8GrsOa4W2YC1LCwZPxzTW6owuq68/ztVeDYJRN5Rqsho7GWIU2ueJI39LafNwh3vnsf8zXENw1pRuNywv1M0b5kkP+4KECknooEkNmDDE9MEwI8+LbB4PpsXtqOqpAAv/utNfPHRP2mpRxgmhQevhkZz/iG/c5sPSmrsg03xZa1s9DVSXBYg0vcOS0m7FvQ0okDl0bE0jaq1vnDlOPzzjQO48/+8pJaRwDhWaYNDIQ7MIlkn4Rz7uOHW2Au1lfwzdrsx+Wnsc3+7pwLR53T7ljmH89L0Tgef0OwIsCqwaSKD18ce6A0bRxJly/jY6xpVcW/M2ts4P2N5+jH7GuffcUf2H8ykYN+eILwIzQkcUVvTisoCjOuc3f7/qSMzkRdk8vCuOa1o0+iHbhTB/qXljHaB4pV2+jlMH3Wb4iOrVwT1tQAwLPDHNe2kebrL1V1n8Ly68kKcNrdNLRMwtE+GYpEc6tU3AMSC5+kp0zSi73PejU3/qY6hmbd8zrEXtNJxW8e5y9KxKWm5Nw/Us3Sa4nMJ7WY7V1Qa+zjPsY/bpMZyfXaf7hB2DSC/Fo/S+iOtkGBPKBHVuBJZONFg14fM5HvFmgm465wuLXnliFLT6n5R2Vk1qV5/YRFmyLuwld0ASPoufFwae672lBQOZAh7Tl85oxMfPX6Kcjkq7Z2rolPYSQZCQrJCOe7j7ory81BgO3pCzBRbIC1/0lTB7Rct2FasdxvXxrnL99xvE8tvQzLutU6fFYHYINemmedMl0U2NHK/TJXoHHv2377vOtf3Jm5hsFoaiUKCPaGEdlNi3vduTMGoBhP8bS2Wb11ZEeaOqhWvkA0VgeDoqY1orCxWKt+Pr505R2t+OvpjkPmmG16BnZXKP+iRLf8gjT1XyYzrbBfOaa/BMdOa0FxdIpWXX7fS7cvvRtpUV7lcPrNIN1NbqtBSE6+VTK7uGc0vGR3ZudtVbxUHMnOb4r//6EnSZfGO/auPkS8jDkTeFRnFI8qY7WIpzG2uvB1uJxx9jC9OjmYrKhc87a/bSiC4LO93Jk5DkYuKr6fs2H3sg/72k+tdf8cVUHYoQII9kSi0mJu581TPMrFEEYgmbRPpF07vxKSmiljKjrutwsoPPGfeno+QJnAgcZAQJtNX3WO3rrwIt5w2CwvGSAZVjOu4O9nrYupQVgJ0HyxTfNWnF2SNI5SPW2MVmFasPIcpfk+vMd9jFvNH1+Ldi0cbe/pxv4u5z7EXsRCEwsady6w4SABO6gkx9k1Rns1f0+6Q7jq8092Lf795MPgaiToNbY295fjMs3GjI76Qd96N+y2VTEiwJ5SQGVh15UVoHyamDdK1kEqTJiIJuBcbfpy3bIzWct0aVNMaVTsqfUS0llq0h478gjNU2en3tbCzfQ72sZfdodc3aN3PZ2ZbNUoLM5g3apjRJQJPu/vdp0rP939mwfXRNU+q1D0nyDgEey1+xup5eLq5oRfL1JZqz3dOf1YxATSMx//87760g/Q9ye1jz6l17EtrKWgfnc/SrcHnyUEFVSHbAtDbG5rMEexNV9fitPgGAHwhJPCuTJ3i1NjHHSvKq323f/bb3HF+Lz1mAi4brPOWKBQVn1BCdiCdNq8NH/vZH/RWhoPBtsMXxd3wlFFTWiB2gWESqtyIfceZ97gneR97w9oYxezdguEPzl2AQ71ZFOYb3uOW1egZ6h9h7aiihbSjwwTWsj2aXtX8FIQwZzZ6BWp3+gcuWYKH//AazlzQjoM9HJITT74JmJdNINIjTGhZfa3aOIpya+xFA0XqSqOCQ2PPsZHol6YnzBE+YfBaf5gg7rEcpH3n3XDR4xKlnsdghAR7QgnecaV7yg7cteO4ft/bh7TVZTAzFCfOuHfDRXG8VEPS8vorMzVTHOXrXjdns1lp31Jmfq6ZKC/PQmEEdo3yR/2ZKTdUuE3QENC9WaTbSob1tyrjGiowrqHPnajbJtiz6l5RlI/9B8LfZyJ1TNDj14pyVHwRVb4geZbl0mzzWPnED5+P/cBnv/GsS7CXOh9doiHjNIc3vYHe0VSJLQvbMaO12ieF5fo08Le/xt71dwIs+AYrZIpPBFJbVmgg1xh3Zg/PCa/tfye+OmhEZX7nnVi5tAKGJ9uo/Qul2zWmCMG8+cmYDw7kHW5ip/24O7hcDRT7WXxR8cPTMDcwFMuVb3M9XvY6mtvedsoKe+gRhtyLV53moWHzsls79tB7l+ILp88KzZfXWsdTSIIRraWqKb5fWtmxYu/PfX2TrdlOqn+9ZVmc59j7/TGAsjVOcPb6kShI1zrJ9D3m5QEnzW7t31z0lK9BY69jY0TERXcoQYI94cs3Ns/Gf1+8JDBNXNpNET961vvitf0HNNdocMK7wNfdDVTf8YldCLnaUkezifjc8u70izSfQxujWY2h+zHG1S2kx4cGH1jZYvWY4qtc23exvc/qWPybMAG1YGl7F4pqtuori3H01KbwfA26D8SJiLuHysYmwG6XPMtiCimiJcmMuSTsv3C1v93FwCeJqMeJb6C+iNokTs2x7vesKEEWS/4nsTi/N+FjT/RBpvgEk/+5Yjlaa0tDzaNkjWn8Mbtwy/326r7BobEfVl4kfa29mQrz83DwEOPNqmmBP1QQD56nt3HDNfZ8+bAEKJ6aBkbFl5gtsllXwYrNFbQGNdnP4zqeSCTAlON3/VWRxqG51JOjhhxEhGR9ralicSBy3WCd83l97H2fme940tOnnD72HBvqCTDX57Gg5xH8emLcjZdxA4hTtjY9PkWCq/ZtSIVv3JjRrg/SiUoR0tgTTFpr+6LWhw0bualY/CrdAtBH10/Vml9cLBpbh3OWjFbPSOM7VceTSqjCPRTRtYkWjb1AJtwae8kHYMIUn5mXrOlrTD1LhxuCTsIXbnrEUR3B83RGxddliWAiEJSJvOyIbC4ZO+4u5ondRFT8oO/DcASey3MFz5PMM0os8I1xnjgwvdp87MWvkbEEklqTanqoshvFd7x7npbygywPeaPim7D2TVt8JFOQYE8oEccwCtc2BWkN+9jQ2YJdV6/CqXPblOpy4YqxaK0tUcpDBcsCrjp6EuoUNPeAv8Aj83yTIJQrn3dtqGN78tVcTli9eU34WO3nr/21QtMAKppGjYvd2DqnnGbNstSEIVlz1SQtj5zn2Guw6FLOQUz7pB4AUQ+D0MVeGBORzPvO8WZ/L5QPnO3u7Pd+ZQsVYQS7PO5XH55o/6Jac//NF/FGkTl4Is62ly26uVrPWjUuH3sKnMcHCfZEIKGTVwrGmfM4loEKDysvUl90WRZqy9SEatXy+xi4x2UThgvn4yc8+C1aoiYJmwU8xKURzhF+jj1fPrKaVlUfVk89tOYWlp+5ji7bLKoLGVHNo+M6Dc2hdI794X/1Bs/T4wvv1T4pZ+mbNyOBVLlCafmTChH3u8REVHzL53tRLJevPk+WCXg1CwfP89Poaguex9Eoi8fVobhgQPw51Csu2csJpnqQ0dhn8iy015VxpQ17Fh5TfNud+bWLR6ch+07kVCQMZUiwJwJJimmL5fM5NHEEJKOFBuCtD6/vqqhwkYT20GECLFeuWHrdbRWWH6/Giqmx98nd3taBPvbSL3L7Z7UWi6tfJGQa7SfMckNXVHwdOzP2Z67j+RnR2GtyXWDnrSnfBMzMgTEufOpXmJ+HJeOHY+XEeu81grfE62M/c2QN83u2Zl7PM3JvEMT/tMKxLHC99HhM8bUdd8eRZt2MZjz74TX9f0vI9ZwlmUFmU+GkzhbutM++vC/w9+CNTT4rMRNxZ9IwZqKABHtCCe6BZGA93VDJ1pQHmkUKpOUlCSZZjmNzJCrkt2B2mwdGRdy+mKbw7lrrbdxQU3xuH3v+B8Db96TPcpe6ik1c/YrvZAkLR09tdH2nWG7SnPsV0fH8dAy5uIIhuisv9HwNafd1EWTt9M2z5uLrm+ewrxPoEzwa+1PntmLZeH6rtz6NJeN7jmvtdc9zbQ5xaex5AuwZfpj25uc5EtWvYbQFz+O8X3tfkNHYx7pZK1G41vo6Hief9ZIOKyf3WEvoayp2SLAnlIhDo58r8/6LluBzp84MTW96MZ/WyYVbs2+0FpykRNKPo5ZWwEvWjZKlvM+19gW5blN8zipwE1cv4m2Wm06agY3zB+J+iN7vloXtXOnC8tWlhdSNjtgZOjTXpkzgufKTrL9Q8LwkPXyNgzY/E35fqzsaAwRU1nea3Dss9zPSa6liBovPhJ7jHSUaPE/WzYiVRip4nvAV+sZV3FvkXgXFwGff4HkMKyfCDCTYEwD4F4RxwZoDasoKsWhsHSNtgNbQgJljnBNUrmj7a+l/X90vnI9qcB79baC2kEnsOsiF9kV/SH68grdI+9nXY7pN8bPZLNuvVbLdYtPYc1a4pDCDpeMHTI4tS2xRHxStmO8Hrp+5UfKxZ1ycFI190MJWN7pM6GM++jqUKFwFMhyuSKLPUnSMBmGvHp/GXkuxSjgstnzS8FgiaLLEl+JQj8xxd/E1vkzZWuOAuC2HbE/YtMIoMFBfAsZDEiDBngAAbF8+FqfMaRW+Tm6xLn6No0yfz3zX6h35fmZ4UWNfWBxgnUfPwOm76pOGUxuRhDbQialFpveFqCFPgZcqb1T8kcNKveX4aextnScJC80ggjQzRoUzW97zR9fi3u0LjZTvfry8ZpHsNOro1jSqBtjS9YiDFrbeMsVKDesHPP7KMvnKpjVOSF1EApZyKOxDFAOMTUbftOFluWtuwsfe9KPkOseeo89q87HnuGH3/o5M2bG6YEqUrbO6lusPh8beR6p0j504N0YGOyTYE0po19Pq0MgIpVW7g2w25gmeUX/uHVNebTx/dQ7n63/F+46aCAC4YMVYwVzFiCs6vXDwPP12uoHwyPXHTGtCUX5GNOu+NAGJpGI/cJabdOzjNKyPOLVbYnfv3riRFTr6TsNIXsvrGNV6rLTM5MtblgxJOMdeCo1TuVtj/8AlSzxphO/csoSOBw3IRnjsx/2ceK0VHJvPPvclumnne5Qnx7WFGef7Tca/X6bt43xaOuenoA1k3ucif1IMEQYJ9gSA5A8WP1Mu0QWWicVX3C9XwJzvsIyvbdCL/tylY/D8dWtw5ORG3zQqxC2LiG4oaNHYc7xUc/CYolYU5Uu3o/Zd+JS4VIThOLIN+vrpmOFlqCzO7/+b148xrPj4ZzT2o1e1AOibz9TvzmMBqvGdExyAUi1v/jqYyTdu3FHxxzVUeNIEzWGsXywAhRm5pXRVSUH/54xlOcrmEXxMPafPnjKDK12e5Q6e6pPO1jx+96VPYx/eKIX5zufVVuu1UAsvR/gSbcgUrXOdGmiK79cHLPffOuZhvo3soQYJ9oQSSdTquAk8yk2x+paFWGcT05oj3dp/oM+POAz3+n2QyHfGF+ahPvYKBfqNdfuzEV0U85XrLUv2NgJN8X2+b6oqRlNVMb54+iy5QuGqbzbEdDtk49LOLy5ZinybUMG7WApLJ7Ohx0L3uI3aossPd/vpHMamNl1E3tW8KW88YSruv3ixQC0kHmDQpgkssaj4HLb4Mps0xQUZ3H7WXHxj82z+ygCoLSvE1zbNxre2zkV+Js/Rr3S9G0Tz+eLps7BuRjPu2bYgPG9YXJvZPIKfrqj4XBr7w4L9PdsW4LOnzMC0lmrxciSej8g1QRs7vC51smWH5uX67Hxn+Wwme3ad9dWHcJIfnoQYCsgK6HGMTadPsbcGUWvQkzA/OQPY6K2RaN+INZjg4X+V1wiytxDDDoT7JRuEaS1QUP6y+erszzL94tjpI/D+oyfhb/95S7pc+5jw21zwu0t76s0L2nHbr//S/3eeFfz8425z3cEKdbjY6Ill4BLsA4XPZMAz9r+1da5QnqfMbQtPpIrOqPgcjRCy5eX5JpflUtcRebxjaFVHw0Be9uB5EcXBsDNyWCmOmtoEgG8TOC+Pb4w7s2LnKxoVn68sNjkLi1ltNZjVViNXjuGRnWdxnjggkJ8uvP7y7M/Oa/TUJw3KxLghjT0BwPziQ3VB5hjMMWik/a+3EmG2KGOiylXtBNxbmgh7Ct6+Eu0mjMxOf3/eGsoXRXesBJX8VO7NbXXAqxV0Lx6vPW4yrl8/xbdOvJrkcB/7ZA599cCrlpYFuUhXEN8YDf7NGWBNRAsfnraxsji0DmmGR5iIcyPbYdJveBOWmZ9weqfwybUZ4aexFz1K3k+A5KiD2xRfBtNjJMnB5YLWMSZ97N3l7vp//3H9ntw2ixIS7Il0wxjHUfrYZ5GN1ceeVXIcc5vxjSHOBX1uYh9Kx92JRFLmO+5OMHCara0DNfb8OWq60ItMv9AR2d0ZPC84Px6zVUf6IG2JpI89b9lhqGyksNopqcHzTL4DdM3nfNY6fYl03s85S0YD6IuvYgKRPsEz/0Udo8dOvqSvvi5Ex5dl8V3D847Spp3meEZFGgR7qSPnRDbiJPrhlOZKwRrJ4bASs6zgd9BAQlceshr7gc//eeugVB6DHRLsCQAKJpsxCJHFIZOyu0rZgB1lLYGU0rpJyKO94Jx8dWqlgPT61Os+3kuUsHZVOcvaL2/7HQctnJOwm67ydFRqv37miNA69DdPmHFS4JF9nLXk8LHXgXZTfA0uNjpuzRM8SuOGlq7Fvzctv1ArEuchjPcdNREPXroEV66ZoJaRBrgEe8E8Za1iWBTYYgDos5BSF6CCyucyxXddw0JX8Dwe0q6x95sj6sqL/K/RWN9AyzBfSwr99TnIebTzUIN87AlFolms2yeB0kL/KNCiKEdZjtsU/3DZWe9XerKPXxZLFaFHmRkMutWXX3COPFHxlXzsAwV78fx0HycZ9HxMbTyMbyjH2OHlXHUAGAu60ECSA+l1BXXr60fJG/xaNs50aOzVs/DPO3CTQP59w3MdS5iwoLghZlkYW++NPu9Np1AIJzxa1sA5TDJPXgpsGnv7fOTX7fW/P8TTOxQnfkJd2GYlxIPn+W6ocFwre4qBaDkqyMSrCXy/aayxx2KJwz0o6BpZDrr8N2i92gdp7AkAZk0JdVMaFlU9YlO6RLSd48gZTi27oaroQHYBn7sn5ZgOSlfz8/lTZyrnIVJXjqDQviIdTz9Psl9gHwP94hMbpvFdcfgS2VurKS10tEuoj739M5fWbOBz0PnCdsIUl9o09nqy0ZZfX99WvzmhM+EVi9P1fuHyL8/9a0trckyf2NmCy1aPx88vWixtjSHyruCR54TvlvOC6a3VoWl4gvuZRPQR5Fl8VhB5HP1p2zI9rho83VWPxl78WYlcovuEGZ3D2O1aYc/aN3iex1pWrmx7kEXS2LMhwZ7ow6AWwE0W/juLPO9o+3FpPMUHZam8ULTi3SU0vanAm7vpNojbxN0EC8YMw7zRw/RmGvIcVILn+eF0dfFHXnusLzd7N3rXnFa+a6RKcuIYH7592fKkZY3vYKsDd45+rRfcfhYjrySgJd6BDo09Q/ukq7lE8tGdNidMiG4u8eJ+fCUFGZy/YhwmNUn6BgvWjSfSe5DAptIWPEK7Q2PPk6nBMcoXCM/CZ0+ZicbKYtx00vSAvOzXeH/fedUKtNSInyXPLiu83mkwxZddW/vmJ10TL0EbyP7H3anXx4KFHttNujX2RB8k2BNKRLX2s5cTprEPPCNaU31yJEXeNFUNqV1prjRmek6uunE9F5FidWnCRPxh+aJCi33Pncb1Y0VxuCdYnym+vr6i0i9U+qzDrDYkr7DgeW7h1rlo5tOKhDWpNkE1aT720PMOcOcRtOGg2n91LIhZ+fCmiSo2hn8MD41HfikGz2Om15jOaYrPk6feZyOT25TmKjz+/pXY0Nnie31YYDyeDRdeItPYa7qmvoLtFy+zwRQ0D1UUFwAAlk8Y7puGF/eaw94P/TX2/nmI0NM7MfKYegAAXyNJREFUIMy7bzeBe9GxQIL9EOSIjgZ89Pgpju+i1s6olFfi8LH3ZiSSt5aFosLNfP3M2XjvEeMVyub7jhhg9siauKtgjLBHb8Ks1j6EdOcf5XF3YTWXjdrsvircx16qmMPX8l0clkrXU9T9/ETia31t02zMba/FJ04ccLnIzwie+OCDO4/ebFafAByQjacIgSJFNvWC3Dt0Yi8nd9SeKEJR8QXcEdi/ya83eNo/3+YrZZ8n/MaR8marIrxZH7INXG3DhFMzzEKLj72BjXk7wSfMiJW9cOwwbF08CgDwX6fNwtfPnC10vRtPLBcO5YL7e9m1AinpwyHBfghSUZSPlRMbtOQlO7fxrZHZs0VpQYiPfVCOrgor+2JbaovglZMaHK4FsvAEsHGjc1Gg3RJC8rqwF15pYSYwcmx/PpIdOw5LAYe/W0i9+XxMzQSElJVJdFZF7ri7vn+7Na0oQucc1w17NgbcyQOEML+2C9fYR6epFUFkvl7V0YC7zu1CW+2AiW8mL0+Pxt6Vic7FZqibhLTKPjwJyxTfpI+9PeePrJ+CIzoaMHp4mSuNvvJ5NPai9+sfLEzc1MKusY8ySjwLqcfuc43dF5rZXhq7GJcpvgbBXteGl199A33sfTX23u/mttfijnfPR3lRn0KsrCgfKyc51/9NVcW45bRZfBVGyOaX34aL28eeuzQndo09wYYE+yEI63Uhv1Ywufhjv9hKi4J97D3fZf1/06OxV89DuuzD/ybEI4CbsDZTfS5xtYduDaUoQc06qakSjVUl8nnbMr/6mEn9n+3PKui5SvkM6jblVrhWV6Ce3t5gSxunKb43YVCbBGlSnN+HCY/xC/Ws+5TpD/Y7yVj+m1bVpQVSeQJmF5u6noSYxt7WBzWVzy5vIPf6ymJ8ddNsLB5b50ijYmXjxsQ59jo3v+3H3R3iEOzjH6V82DdFLUuPObgKOmLNSK19BfrPv970P6Pdb35mjhWNih5W+W5FgOxmMi9Bm6hJeG8lATruboiiy4Sb9xqdC/TQqPgRYiF5L1d+c1yORY5qZTTB3X2SUmEOTGwCsB59Y2Ux1kxpxHuWjEZTVTEuWjkOk5r8j59SeTea0O7pzFJlHior0vO6DI2Kz7FIcqQP8MlXigsgfeUA+s+xF8/QvtjL5Fn+GlbJPAHNGnuRBbZArXlkmX6NveX9LrEIdAk+U/wATSnrO42Ck11jf4ijU9nzvPGEqVg9uRGzrn/AmYavenJwZt5ti3hmWcDXz5yDp/6+F+tv+ZX2oqPqrhF43kRGNis6/7n/HvjCb77wxn+Ru3P7JqrbpZjogwR7AkDfIEtCIDhfXzLb5xKbKT57g4LfhEn1lrMh5fEQ1y6jVv88hzm4XH0GA3GPIdaitG1YKa49bnL/35dwxHRg+pIqbgSZPEeXF5nNlNw1I6pL8PENU1FWlI/z7/y9XAUQLpyqCFIy5r/+eclfmyMJx93Z7yM/z/JtE5F2dwvJomdwi+B9P8g9GL4o54wSDM7nou9vVVSD5/HUbdWkBjzx1/9g5cR657UcDWmPnN9t09jzdK+igjzUlhWGJzSI3z3aNfZ5loW8PAvDbUHjEnFksCBSijDGfeqcOVj9xETLeizD7J/9rMQ0lW2fa0+fN1JTroMLEuyHINlsVtrf1Y3MdapHFmXygj1IvMK7WWkrzldSbqERt0Bph6cuptZu/dkGHKeYlI0HE4sZHffGpSW2FdTriO/Av6nGgzuCvPJxZwJHxbE4eU7bYd9XecEe8Ns48daDVaegFuC1ME28FtYHZVP8PH9TfBVNeW+I2fS3ts7F/ncOYdsd/5cjb7nfwuDpG/33xaGBM4XJo015jpwTvV13+3x1Uyd6s3xm/96yB67h0tjbrXViWInwlukInuf6VzfRneKgpxzVOU01r/5rBdLau3ZeHp+Fj1fLL1CgjSAXlXS+1fRDgj2hBO/kxjtp8Lwo8jgmEd/8XelV1xAWzAmKP7twMf7++ts4+5u/C01r37zgvSe+autMxY97cSf6nOL2dechqXXkiTbst2YNjuQrWx/JCxnE0uKuQsP6suX6LCLoBGlSHN+LCHkKqAhprPEhM2bs99pniu+Xjv9+Ldfecpg/9OJx/D7F4fEP2J/DMw5Pkhu/FuM7E0QtjHJp7BXrZFkWMowsQvQRHg71hPd1nr6QhD08t4+9SaK6XV3lyMxpImPShLus2/RexBqo/2/JGAVhm6gEBc8bkmQBz6yUhMkf4DvWJWwnXOxWdEwSYiVOaHD6N/td3TGiEkd0BJ9ekLuW52gcGUzGXTBBrmwdGzZpJDL/Qt/yzVZANX8ZQVN/AL/gDJ2LdYbpZkCFPFHxY57YtZviS2U40AZ9GnufjSuBHN0bKGkInse3+Pamcd/r0vF9mxRTmivV68Sokmif1X3cnagAzltfUUGGJ3ieTD348xNP43dNtyPwqJ56xr2RoWttJKWx9ylcZe0ncpyrc+PPaQXlq7F3PXeZ9jt6SpPwuBiKkMZ+COI3fpMi3A/gpznUZyYYR1T8iU0VuOld09EgeW5vGEkyy/cjbJFj8haCntcVayYo5S0iOBoxxTe0aJrWUuXyoxv4i9tCRKJqvnOVeFYAgKZq+VMBVMvO4ecOkmtTpQU6r8ZePKtEILOm49fYC+Tp+lvvcXdm0vIFzzucb4Am+NS5bbhgxVhMahIX7D1HNbLSGHyBCbkjsH7TNzRDOcSxWRSVZYUqB13B8+z/uj+rElUz6HJR0Smnsn3s/dbRboUQP86ApHKWI6Ktd/26yTh6aiN+8+K/AuolmOkghQT7IYr3TMn0jAj7rjt7gRxhZSA+QWWzwJTmKj1la9oB9s1fX1ZK6HTl8GPLwnZsWzZW+npRooqKr8oXT5+F5e5AUFJWdOIXbVnYLl5QANuXj8U/9h/AMdOatOYbhPs598UN8Me5gSK44HL/7VNQVH7TSj6fzGslTPFtn4N87EXaxJ1URNsliq5HxbNh1L+5ZPfddl2XZwGz22s11Sna61WD5zHzNDSUujlM8R1BaxPztvZi36QwXcvIfOwF07fU+G0qq7kXheXkK2i70ou5fNnzt1zzBV8eoo9p1sgaWJZFGnsOyBR/CMI6bkl2LozO9NeupYftM6sCzu+C5ivWbxevGidWt4gXJyxUo0UPNvzaI0iY1iHsxP3K0fFI3c0wtr4cxQWZyM26P3vKDCwcW6e1n5YX5eNTJ03H8gn1nt+iur0wITDsDHGRc+yj4JJV4/H+oydGUtbYev9jGv0wcdydm0O9vfpM5gMycmvZRODSVjO0qUZ97Bk3K2yKL9AgXKb4Ie3v+Y67dDHs7h08lkuyz+lzp870yZtjEyTk7xx2U/y43YO0IXAbm7pG4o53zxOez4WLlvSTBwRN8V1rcC5TfLc1mWA/yPVHk5uogwUS7Icg01vY2mI5Xx/FygTCrpB9191E8Rev8h4HVm87msVO3D7o/S9fQ3Mdt/+g4D2Z6jdcfoE+vUbHAlYoAI0JU3ymCYdEPo48Gd9x5mM/mlL0dk25qqii3Hf9TPFd//oV5t6cChLCVPxQee/zolXjcNz0Zr7EAtjL/+G2Bbj0iPHY1CV+vJFDY2/5a+yFgue5OrNOU3wRhOrMJdTmNPa26xKsCRaFL1J9Mu5XVDEpOy8dN32ELQ8z9263PhgI0Ki2jvO7JqqnJ7KJet26KRg5rIz5m1zYEHnrov7v3fUQqIj93t0niPI+F9HnlCsyOKhkMsZu3JBgP8TYumgUzlzQrq37j5fQoKhin1RUTfHDzKE3L2jHh4+bjFPmtvmmEV34mNhwXHvYtHhqcxW3BkP1XHI7PMcIBXH9usmOc229kcTFGk3GTzNqTYIRU3wteeiT+tbPHBD4hF/kjPrknmuUj0q3z29Ybo75TTBvb1T8aBpK9/Nori5xzLkz22pw4cpxKMiIL1ncPvYm6O3N6jOZ9wSZcv8tmS/Xpqc3rdmo+OHoHH48zz/QYoJRY+6TgTjv4+zFo9BSU4JT5/ivOQbKDq+HyBxgKr7BQXtUfMNzUnRWpBLXMCono4H2K1t2TZFFVlApMUCfxl5iXEkqg3pIYx8KCfZDjM0L2n0XRzIT4rSWaiweVyd0TdCwLC8qYH7vt9BQj5Id/PuExgqcuaA9UHBVNsVXuxwAcP36KfjEidNw+1lzIzUHP2/ZGExvqcK6GWoauzO62vGb969Uro9KW+oI5hP3EXY6FjXePCzP9/YkfovBo6Y0Ko3VgWByQpdJY2rB6W6esMVz2P26L7d8/1Bruzg1tTsuX4bKYva7QBT7feRnAnzsFVZDWhebAoKlyBPi0TAOaOztm+cxS/YhCEXF5xDsRd1ZdG98fOCYDvzPFctRVRre/3m03lFugooIeObelVFtZuopR6elrGjwPDtipvh2jb2zBH8LAbVN51z6HvKxD4WC5w1RdGkBAODIyY34nz/+MzAN707whSvH4pm/78UJs5rx4POvMtOIHnensuayXP8y08Rp/XO47LKifLxrdisA5/2OritDZUkB/vaft/DPNw4yrw3MPiTNlWu8vrUimiHndTbNbHgW2lk2fsDvurasUCqPuDeT4/Bf9I1nMEjfv8qbiX25cJbF2BgIqI9OH/s45zWd/dihsbeCfOz5y3RXLw2LTZ67sxgvPJUNj9DyeKzGNPZDnvEh/q6Xq2CQQsTd/3l6l46x74jvobHdq0sL8Ppb3Vzl8uJroZBgjT0LnVYSYsHzLMcVYufYD3zOy+Nrc143sbAyg4LnDZbwDaqQxp4AIK+dkR1ISycMZ35fXVqIu87tOmyG6Tdxhwj2ApXijrYuaJ4nAk/wWxHsO+GdI2vwo+0LMb2lWiqvNPhXXrhiIJJ97tkHCpuMW7pn2wJ0jRnW//fFK8dj1aR63HLaLI01TQdW//8O/23l/hUTID2+4O5yQscx+7qoMXEWe5ALkdMywoz2UPuZ11pzM0dwVHz5fHVGaha12NCVr18ak+8AnjolyxRfHvd8+JUzZivk1ocOKzNd5QPs9lk+YThuOH6q5xqZfjWqju2nHlYHE8hspLAukeneJoKkilhQ2J+dd7OUb8PF/uf16ybjB+d1cZXdm4JN1LghwX6IkZTF8sTGSqdPdQj2+mZieYEFbDIo1ueQQuSl0EVgQLq4+4AOfnbhYmawQ1FmtdU4/q4qLcDXzpwjfCya7lfOA5cswWWr1e4vrmP13AKs+xqT51UnmbDnERZDJKjZ3IKLigAfq8ZeZ152jX3gOfbypbIWm9cc2yGdny8KWi8eQYoVPC9uH/vg6y0hwZ8nREOQ0MSO4s9ffo7q0gKUFGbCE4bgfE7JfKPfumUuRlQPHPWmMs78Aj/biawZJMph9lWNUfGF3qmuTETkZY+LHVdbuN9NA58rSwrQOTL4CM1ccjruLhwS7AkA8pOhyhzaPqxU6rqws2jdvwZNAzpkC9X3yMFDekMq2+8p91n2ZWrqJakr344RlcxTEgaLzDiuoQJF+eoLQBF4tDD27/zaWvUR5MoYNMcjHSablTH3tV0vcGTj4Go5ORzapTz/QE8q3Yxlin/8TLm4I2HVkK0nTxwA1pjTOf7inpdNCL8yORppB5+KyN4xn0udhNZaoi45/J5fU1X0J6jo6kpSwfN8yhbJybNOFhHsbWuuPIvPcsS7rhBrwNz1QRp7et/1QYL9EEXn+y3qdbfOlzOvNtNe5JjhZa7f1OrTraKxZ3xnv6P+KOLSJcSDtDaXazGSLmIxsRRoJZkx1Pd3wp6E32JJcRHuvtz/TGrL8W9AlXzxCPYKTSy08ErYo7TDrbFXKENn8DyVY/eCsJ+L7gf7uDuOesgqBhK4dhA1xee9B+eGu55TXhy+zgmYT3mEOlY9eWvud9Tx3ed22b6Pph10tbfpvS6ejcxsVv58eHfwPN96BJTPR98FpLEPhwT7IYb/kSiy+Sn4/EkGKwqbUIUmDM662+u6cf5I129iuIvsNjhR5XJmvkwNLQTi9MuPe2kTt0ZKB97Amt5W5fKx90Rvt1y/80WHZ1sMxP2k5RGJii86RHlPgzMprOnA1MZzsI+9fKEsjX3S+mjw+c99sOI8mNws1dFGIq5GPMeyivaDWN93dsuKWMrnTGffrFSoqN/js7sgRTVv6SpGKiq+T+kq6w+RS+1jxG0F5btxqmhN1q+xHwyLLMOQYD9E0fkyEh1muXEp6/sbtng1fk6q+2/F4roVTPFZQhdLM+BXx2OnjwAArO5o8MlfvE58zzU4Y14tJ+/1Koi1wRB56XC1iR5j/LgVUbpjFGQRvKnm8F9kNHTQWIjrHPu0kLGiM8WXJagaFuQX7zyaLtYRk3ybQHKNF7nGXjF4HqsxpDT2fJcIoVtjb2ruUMmVN/jhF06fZdw8X2ptxBi8Uu8XP+sy/qTCG+2Oa12bzzIae9EGzKVeObHv9KK6cm+MrrjXCkmBBPshBu9umhAR7KCZOldXT1R8MdzpDyqY4rPJej75Bc/7+Iap+NLGWfjMyTM01yEecn1jwyx//1bR/pMEE8co8WxcSebjnhbcO+2JM8UXQKXqvdmsjwtN/2j1+T4cb/A8wcrZrxVKm9xnabdAzw84u03luLthZYWIRWcqUCRLsN+ysN0nW7H3rWyAvahbzMQ59jKYWDIlYTrlOjNdYXPE4thxsmDh6KlNeOSyZXyZSqJrzpPT2PNnxuvzLht3L8/yt4IKKk9cY993xRld7fjKGZ24/+LFgjkMHUiwH6ooDrL+6yyLa9nJ3knkLzXjCNYRfJ13wlJ/izreSxymykF4TPF1+9izguf5tHVpYT7WTGlCWVG+T/7iPSMJi/yN80Yyv5fpCSJ3Y2bBFnF7WuELLfvPQcHz7H2BxxTYUYaV+9dp9mcKUzm7558wxamoGbQdCp7nxb6hlPE5UmV6a7WSYPS+oyd6v5QVdsPGnmS+rNNX3n/0JLxrdotyGbxz/oy2arWC3OVagkG/eHzsBX+T2QjQdSIIX9Ayg3OmRNYq64OMryupoImJBnQ1q5Rg71O4Sq8SMXH3mOJLKN7E55iB8lZPbmRq7N0s8zlWGwBaakp8f0s7JNgTysi+o8I0UWcu6BPOFo2tQ37GPpHIlcesA6+fb8ALNGx++szJ0wN/79Z8kH2W9VlylzwJWgDA2VeOnMx2GwAG6stjcsmLSBsk0f1LtU5Maw/bl37Zu8dWN0fwrjD8FnYmCWu/rtHDhPLr6c0y23RgE24Aca1G8N8qeUWJqUjsfv3ne++ZL13mybNbUV+hz+w3TPCx349IlWe0Vnu+K8jkYX5I/+UqgrMezdUl2HH5MtHLfBGd26pKCsITBVnn+Zm+cZBlWNKp4gy0qXfAmhr/uXxlTNB5XutRTVtybopepKLi++XPyMovrft7EW8ijyl+BBs8XOtVV55BJ5P84pIlQuWnCRLshxjM4Dj9f8tNiabOo148bjh+/b4VuG3LHCGNvQjcpviOF6jnx0COn+nUiLiTq2jsWdifR1Kj4oc9wqAu1VIjd0xif9nC6ZPWembhuV+ZFhHW2DO+07lhw0uY36LodMTSnNpxnmPvzdw939r/dqfP/eTWTvDVmf/GkrIByMIesZ5l8VFbVojiggyuWDOBO0/7GImjT8owrLwIv7t6Fea2O8+LDnt987xvRZpg5LCBU2Wi7jdj68tD04gHzxPHRMyYeLqhoJbDJwnvO5Zn840VJ8IE2kzxteSSy0vET965OS/mY++/HudtFftlPEXrXIc1VhajtJBtpToYIMGeUMakknJEdQnyM3mOaLY6o+LzzmXBPvZiE46oKf73z+3CRSvHoZxhLs/U/DE+y26GJHHBHnmVRPqTgdHgPl4xCpxaY28D8PQLd0voOKYmE9GizSRhgdZUNr08m4aHy/rO2fNx3rIxHLUbfPSGCPa5b5ZPqMeT16wWzt8v0rpsHw181yh2/LryIhQVOJd9LI2h8xz78Hyl3y8ck2vQaBExxT9r4SjOOsn9FoYzeJ4JU/x0TIoDRyqK19cR/R5ifVQ3ujZSZBRjIvfr1y+WjK8TLpdFdD72ghcEYGKdliRIsB+iuMeISlTbKMyPM7agR6KLCO3V8/jYq2V38FBwDWe31+KSI8Y73BGCyDIk+5S885UxcZsieZoYC0vHD8f166foz9gH0Ze0r4+96/swTbW3DG9FkqgdFdbY92aZi9qB0HlO6yChoEaubHNnl7fWluLKNQw/cIG80kqvbSMlrPtwmWq7yAkbcbSXjiJFImn7kYYAo1IB2jjykFk76XpP2EvWPTXymIjz3rqs+4gbEVN846cjaXruMvvdfuNNxBT/hhOmuerBXxH7ZkTGsqTa2sSUIdMfByMk2BM25Hp7FOdK2rUiOo+7C6s5U1tp/2ypL654TfHZk3Zw6bmdSab5G9dL0szLMbZloFSgGuff39o6F221pfjO2fP11Cm0fAtnzGcHA4wLZ7/ga1QdGnuTcr1vQCLWuLNrNAV7c09vFjVl/gJkWECsQA2mqy5+8TuSL4bpo9chVOi5c3s2UQu1qsV5+jOzf4vlKVslvndQtATNMWHrgSDszRynUMPLvrcPGamDyppC+bhCjegqhhW8MrRsn8JFluLuTUzZ17PlWo/7Bbh1P3fRuTgFe4eJgQT7IYq2BU5Er12HCVaEI7xfMA4wTVStjkkf+1y8srScM5zDbSrF+8JqqAwPYiW+aHVesHjccPzyiuXoGiMWNC0tWAjvL7ym+PZ0ov2cVQRPVHxek1tdhJrOu/7uyWZRUVyAe7YtwJ3vnheYX1CQPZ66iMY1cOQlmfbWzXMwtbkKd57tvbc40HnGPAteSypeRFwxwsbpCMZZ3u65lWmKLxjlWtrtgCNN1Mo1E0G93AhvBvk0Ao8pvuyz0TluHH2OqWXgyyfPZx3mULYwfjeBrvyvWzcFU5orteTFtL4xoMW2p82zLBTm5+HUuW04dvoIY9Hm4zjmOq2QYE/YkBs4UZi1ODT2gkJHUP14/ZuCSlTd3FA67i5EoxfkSzSYgsJ99z3zsXhcHb5w+izteQvFbNCUT5zo2rhyjy3RhSKr3AH/TH9WddQLlSNDvsJGY65ZZrXVYEpLle177yaiKO4r1eYWuXpMaa7CTy5YhAVj9PhwqlJamDGav5+QloTh/uF1Xhce9ysvTBjguQ9ZFxnVOdFIGwdp7Bm/ydRBl5VHYFBfqfwGmDeqFsdOH8GdnrcOKrfOcypKVIofKVN8xmgrLsjgqClNOqqkhJApvu0+cs/khhOm4vOnzuRuF4c7H4eozdW3uEo2F/A7KZBgP0TRNfVZFt+gdJ6tLj6oHFHxdR53F/J77sXpXOi4TYrU6nBQ83F3dnJNLbv7LnNrrPZwLxDCJn/RLjJ/9DB8a+s8jB4eHvVYFF1jxb2Yi+rdYqIYR0RdzgJ0HOvIo7HXv2HlrfewssL+zyruAWHKK7YvPn/0PB3uD2lnWksVtixsxzXHdmjL097MOo9f7cvbv0OJdrXCfG/lPIJ9iF9uEs3l7Vy0chzz+7vO6cKjly0Tzk90PHMLeJr8zP3QsVlg7wp5eRY+f+pMZtBe4XxDFPa8+D4bxkZUEjbWeBF9dP4+9nIRM1TWIvLWOuYtY4YqJNgTykSxdrSbO+qMis9L0EJHtbyOJj1mWP1kvR9Zgr1MJFMZhpUV4rMnz1DPSBOitySyMx/0gozjDHYZRI+787tl5eB5jHrwCPZRRLytLbcL9no3qdym+B6X6ACNq7vNRNvcUQ+RtAnu25Zl4ZpjJ2OLIReNXGDXOFogrMwilmDvdnMKGS88j1b+1BX1VnvXnFZcfqT3qMJh5YVor7MdrWeoTlFp7D++Yaq3bIv9OSo81pFc13grylt3LsuQ5E5Fvpi0nuRt2+NCrDPsuE3xZVB1iVRhkCvsSbAfauQ6tK6XQFRzqEhUfKEJgHOAO4NlOcv6x/4D/OXBu6t61dETcd6yMZg9skYoH9/8HWX1/WvazzSIltpS5Wjmcc7DIjUPdH3QvCFkkrCq8fnYO9tCT/A8cxp7katqy4r6P6toytxnCavn5/w7Do19kvs1C9X6+m3YmYprIpLtjNZq1JYVYnprdf937kUtq4sEWaix6xTvQ2f59crWSPg6iYJEX4dZZHHynDY88aEjMKqOffypr0uIQFmqT7HXZ75xWAJIFrKpa6TTxz4kfdx9komu0xAM3FprbSkKOc2PpJ6ne/3DeVl/ei5FFF+ug1yuJ8GeUCdyH/uYj7wS8eHnobK4AFeumYiJTRXKdemrj9PLHugL2CWHmbZO4CvXF10v0bhM8UXhM70VN+871Ct63J33u9y6Q/RIKt1EZYrPIjgqvhM/H3uTAdGS2q/9kKmvvf10m+IHl+t/dNjYeq8bUnFBBv/n/Svxw/MW9H/nud0Q810+jX14GhasvLctGyNwfdA8IFepoI01praZ821m3+iU3byrLi10WQ/qnexYrhsi+G0k2tckufYSsay6/ay5uG7dFK6I62mMHaTrMYocd8eikvO4T3s5sutxh489R1fQ+VTJx54YlOia/CwrmuPunFHxg9N6f/evH+/LJahM8WM7zL54WBp7v530pGKqtllkzT6vgIon8Ah2aeyuMUEvSfstuyO0ywwDngWx7umIlV916cACSPtxZw7zWubOnf+lrvRKwfNSuECOg4zOoC+QX8B+5+z5+NjxU1FXXuT4viCT57SYMhA8T6efbX1lMVpr+SJrBwWcdM+3vHUUNhHmTC9ymkFwgeyvVeahm0+egbbaUtx8ygzBqjjL5AlUKVPNnEuJiPtgmmavkgKxAJ++PvbSK6fcOOJNbduokbVMivEJpWs1LA4J9kQqsGvsdS6keYUAxy65RPC8lRMHInXL7hbKXJe7QtYkNynWbHFusGqS67ULgMsmDMd7lozWmifQ19f9qnrWwlFYObEenTa3EV4f+2UT1KPVR31mOMDue/magnmytBYqka7JFF8cZVN83cHzJCs0vKIIp81rCxWuPMfdseKvOCsUWrZuP1sdi34TgoOMhQ0zH01yvVN7L5/n+pnN+OUVyzGxUS3eT2lhPu7ZtsBzRjrbJsS/wqfNa3Ol7cPu9uLfd9LHu2a3Ym57LdPqhoXfPbLeVSJvgCjbzv78pjZX+Sc8jM432SBX2CdbsL/hhhswZ84cVFRUoL6+HuvXr8fu3bsdad555x1s374dw4YNQ3l5OTZs2IBXX33Vkeall17CMcccg9LSUtTX1+Pyyy/HoUOHoryVxGEfVFsWtsvnAysSs5aMgCm+yOQUWvVcZgEqDJ7b/6/T9B/D1lcV792yTiBgnlXM5a9shtCiEzTx6moD0YXXqkn+gnBFcT5u2zIX7z96Umg+wuMzoKIfOrYDX988x7GQ9/erdH5/5OQGfOfs+UrV4IqKH8HqxH7/Sj7xjN4l7H8bEHG6prQQLEQ0X6Lwdrd1M/iDNSUNe9NEedydJ06HRCl8x92JbS7p3nDjt6TzL1e2SqL3Imd5JH7NQHls4TaOTU9WkbPaahwxHXiucVNZXIALV4zt/ztnccITR6BfY59ACd+vV5cUZnDXuV3YvpzPDcXv3lj5i7z+ZaxPZFk4tg6/et8K/HDbAoxrCHdDHezCuE4SLdjv2LED27dvx+OPP44HHngA3d3dWL16Nd58883+NJdccgl+8pOf4O6778aOHTvw8ssv44QTTuj/vaenB8cccwwOHjyIX//617j99ttx22234UMf+lAct5RIRvsEY+FFdMDJjM98R/C84LRaJ/R+LZotf1dZPIJTieGzlO3YF0VBUfHTStR3ost1QiSA4I/PX4hbTmdvBl111EQ8duUKLXXigXX79q94NfaWZaFrzDClupiMryEUZEpAsBftr57+xiGIsVg8rg5ndrULlh4ds9pqwhOlgHzNfVI1tzChOOyUBXcdTApI0WoI+UoLdrtjfMftYz+ACSHcrxsmwaXGufnIuTnL2FzSFf8lqbD6RX1Fkec7vw1bVSVbVG133wWLMKmpEs3VJZjJ+R7QedrNYPexVz+g0iD333+/4+/bbrsN9fX12LVrF5YsWYK9e/fi61//Ou68806sWNG30L311lsxadIkPP7445g/fz5+8Ytf4LnnnsODDz6IhoYGzJgxA9dffz2uvPJKXHvttSgsZA+QIYWKxsmKRtDKCBx35yZoDKv62GezKoHpnIyuEz+DPcwFNyfPswT7BsYLw5t/Ml6SOid1YfNmgbRBLwx3vw1q2mkt1b6/jW+s8Jg66kT4iQc8GpX+w9Zmh+en3ceecYN282s17RvjO4U87J9vPnlGpBuKrDoMBeIO5iqKe47S4WMv647CY4rPswjX+QSC8tLVt3WtYRxR4mPohjJFcsc6cFzT95eIxVZS1i6y3LNtAb73m79i4/yROPa/HgPQt6n/+tvdmDuqViAn/hdiVJt44zk09G64AuxFaHGQZBIt2LvZu3cvAKC2tq9T79q1C93d3Vi1alV/mokTJ6KtrQ07d+7E/PnzsXPnTkydOhUNDQ39aY488kicd955ePbZZzFz5kxPOQcOHMCBAwNHmO3bt8/ULSUCZQ1BxFHxw1+KGmen3EvCESXYmb8uZfgZXSPx7zcPYvG4OqV8nMHzDkfFt1XyK2d04oV/vMH1cghqSRVNlcrOsMrTlYuArVCgDXdzxWE6yUvY8+Gpul9T15UX4Z9vHEDX6GH4+TN7hOrF48+cJlN8J3wBjDym1I6//ecpURLcPROFnyWOdEA5IcsR73dhc5xXYx98gfMEAAsLxnrfT9I+9j7zjOhGLiu17BGrgVHxGfWVMsVXicth+2yPYp9sQVbtRIDcJc7j7rwa/aQTNjbt9ze2vhwfP3Ea/vPmwf7vjp/VjPqKYv/8Jcq0w9uOMhrvZPfPwUVqBPve3l5cfPHFWLhwIaZMmQIA2LNnDwoLC1FdXe1I29DQgD179vSnsQv1ud9zv7G44YYb8OEPf1jzHSSD3HDUOcZ0alP9yAgI9u6fl0+sR/uwUkxhBOgIm5/GDPdq0XlN8a9YMwFHTm4MLsBGQSYPlx05gTu9uy79MKpjtypYPbkRq4VKYfPApUs15MImij7Fi8gLSSR4nuzGiOnXo6i5o++pGD5f/3DbAvz4yZexcd7IQMGeVQ+eRWGxYITh/vIEWtZhiq/bFNuWdxbesaBjbCRhiZXmdZ697tpN8QUFSzehgn3gxlCuDvYyB3j62tUoLfQuG2VbgC/WQ1B7BFwnkNZZHmfC/vR8F7COfJPBXlyRXbDnSB8XQXFAWFhg90GeuTYJ9yuLc+NCAtXgedx9WSBTZjni12gNnqcxrySSGsF++/bteOaZZ/DYY48ZL+uqq67CpZde2v/3vn370NraarzcuFCdCKNw3xZZPGVcN1RckMHD713GfCn4Vf3H5y/Ei/98sz/6t5+5K+Dvv75t2Vjm91GSm4Blfez9+saU5kqMUozNEBuiCzeBtEEvPLdQ6jYrvG3LHHzgh8/gkydNEyiRo05ac+PP308Aba0txfblcmMjSLBvqSnBx46f6ljs6oD1TO1zTHEBX3l5lneuZJri275jbRraTyQIulZ1fUsaFj5yfTItzeXR2DPGqZ/gWehjMmOyr0Ruii8YkI/b/Nf2Wcl9B/a5Z2ATM57gefLa99B0tvvM3Rtfu6VkIDIIC4YYtiGk+q6PqguZKsZdfxmT/8FAooPn5Tj//PNx33334ZFHHkFLS0v/942NjTh48CBef/11R/pXX30VjY2N/WncUfJzf+fSuCkqKkJlZaXjv8GG04xJZffYkt6+WzuNPyqyXQgK01ix/LD8dnr9qj6tpRrrZjT3/+3W0tu/99VYRgDrxeoMnuc1xRfKX6JvcEXbV5jZ07rbGmaKv2xCPX71vhVYMEbNFUMVC1b4m9chfLKTmNjVD/KxXDyuDkvGD1crlBN7NY7oaMQCjqCAYf6hA8fdBbN8Qj2+ckYndly+LLRMwjy558ozV16yajw6msyuJ0IFYdfvzFeDz0a239we96ZGxwhvm8ZdpyCEfextn+2X2jcVk3y/zvrzVZSlTLFvqOo4Sz1q3GvX+aOd7pB5PuPO73rP74bWou66qG8giD8v0Xu7/ay5mOQz1w724HmJFuyz2SzOP/98/PCHP8TDDz+MUaNGOX7v7OxEQUEBHnroof7vdu/ejZdeegldXV0AgK6uLjz99NN47bXX+tM88MADqKysREdHRzQ3knBU50TZIbJx/kh8a+tcnDw73BrCHhU/TEY1Mcf7Bs8D0Nurvzy/srjSZb2fo4yKzzNpCp+koLH6opsVIouwoGq6X2b5GbmOGvUihlWeQ6uscavlcpsrCjN4XoBw3C8Ya2ieT5wYbDVhr0dhJg93nj3fX5N+uGIswZ7tsxt8A5ZlYfXkRowc5rWY0aq51JhXHPmbxO13zkzDuMOLVo3D98/rMlYvHtyjtabUG4jTuZEdbh4sf469nl4wZng57tm2AD85f9FA3u7aaiiKlYVMtrqm8KL8cI19GsdZbZkzqHXu3g4cGlhsvfDaG8xrUyLjAwC+duYc1zd6FG52RNZjvH1IedNe4pphZeHBnu0sDdjkH0SHRDFJtGC/fft2fPvb38add96JiooK7NmzB3v27MHbb78NAKiqqsLWrVtx6aWX4pFHHsGuXbuwZcsWdHV1Yf78vvOSV69ejY6ODpxxxhl48skn8d///d+4+uqrsX37dhQViXWUwYQuk00LEoP8cPpMnoXF44ajmrGwcOPQ2IcUKBalmDMqfsCEG6vGnvGdM3he37+ydYziJfkBxlnsQdVVrVJdudhJGCJtEBgV3zXbJjV4Hp+P/QCCLvaBLBkXrHF3u9k4ytM4DN9l22wMixqem24OhawW8hkRs9hxBHhqOIDfxopq90po99RCiWQcBhaiUfF1jnu2Vi8Y9zg5YVYLTpjZjE+dNN2Wb7gw76iH6+8FY4bh1s1uoSX8OhVmtdWgtbZkIO+ozIo5y7G3u64+wOMGxIoTFDW8c/NnT5mB46aPwMb5I5l9Y/er+/s/K/ujJ4DyIqc3dJjGXsYUX8zH3u975w+qm/ki3f9X71uB/7liOdcJL2k+6lAniRbsv/jFL2Lv3r1YtmwZmpqa+v/73ve+15/mM5/5DNauXYsNGzZgyZIlaGxsxD333NP/eyaTwX333YdMJoOuri5s3LgRmzZtwnXXXRfHLSUSdY29+e07u4992G5b0OJfGj/TRIgLzcZ9nm31ETHFv2z1eADelw2LoAlUVAuzobMlNM3aaU0AgMkMk0tRWmpLhdLr6k1hPvZJQbRW/qb4MpFz2Z9zBEXFz41DWQF/mMCGj32OyWnve0JMd0JN8Q//6zzqy9CpI5rnyLSZNv7u6lW2v9TaQvR9o9L0lhV+fXhUfGeCgkwePn3yDJxom4f9ivAr2z233XLaLCyfWB9ckYD8dCCa9eqOBjx5TUhYWQm/Zxa6BHuHxt41v9RXFOHk2a24cYPemC0mWTejGZ87dSaKCzLMd4G9j/p18zjeqlcdNZErXdjYdGyoHf5oP/mgMCR+DCt/HVHxtWvsBfr/iKpitAqu2cJIUnBmEyQ6eB7PYqG4uBi33HILbrnlFt80I0eOxM9+9jOdVUs9uiY/y5Iwa3EVvnHeSHx5x59x9FT/CPLOl1ZwgUJRzDnrHpRjT4LniKBz7N2cv2IcTp3bhm/86kXc8sifDNdsgDCrAwAYOawMT3zoCJQX5ePGn/9BuqwsgDZRwV7TIswj2Muar+qoTFgZIYU4I7ez+5bosDhnyejQNEGBylTN6yY1VeIDR09CY5XzOCHWHOE87q7v30M+E0HuW1YAUJblgy5hR1V7kTbth0i7lQVsXm5dNApff+xF7rxEj7tTFeqcEca9eYX74YqVZ9LHXncPc9ybYOZ1FUWoKgm3HnQjc++i1zieuU/gTneW01qq8fEQlyJV+K0VZDZ57XNs3+dZbTX930XpXhgVDo394SdaVpSPG0+Yit4spPqnmMae/UD72t+uMBJHdqybcD1M2T60MInW2BPRkJtAIjO9dA2q1tpS/OH6NbjltFlcl4dq7AU0oTrGt+hLS2czh5piHv4jzEw4x7DyIqfbgaE+IbNjWl1aiPxMnvIzG1YmZoovgr1uZS7TMXdbmjhnWQcTXQFnWKVxmeILPKgRVcW46uhJoS5CQWM716dUmufsJaNx7PTwoJ4sYae7J1hjzzqGT/eiJa4gUmkJXsXDB9d24A/Xr+FOL3rcHSv1RSvHCeWhAs+4tBgCRnB6t6kuHyb7jbveOjaqwubCIOxtojKH26/k8bFPAurB1rzf2S0lw26dd20pS9D9nTKH/0Qth3uB7Z5OmduG0+a1cdTDWxOR9alvM+pW2RuCe6PJbDVihwT7IYrFWEHLjFVL8jo3faZXfKNyRHVJ4O8mLJydJlLOApJnim8vq++P3gTvbke9HikIsudmIFu/0S7fRtVz7E/sbMH0lipPJN0weLvnzy5cjE+cOA2rJoWb0DryF/yeRU7oDVt8By5eDxcYxZrDvsGQq1OYBulLGzsxoqoYnz1lRmA6+z1mkUWlgJbG8v0jIJ1fGoHuKWWKn2BBhLUJ44foiSusPrxsgr7THMJN8cMR3dyVfefy5F1RzN//VYZ+lBpFRrgNgfIGPhcF+NhHMbyisuphjRm/+Zbpm66pmkdNacTY+nK8x2VdFjTmRFwhnLFbxCut/O5L7pSsl+Quh7WQaFN8IhpUx3JU/pX3XbAIr7/VjeYwwV5EY89Zd8vnMwCEKOqMErao6I+KL7Jry2F6qYrSEYuKZRcIRqMXagJbM3/u1Jk479u78Ic9fQF/3Kb3ohp7e3ArE3SMqOw/NiqsZvZbURn/U5ur8PTf92L9zGZPvszAcgFtZiqIJUsLwjTFDxHsp7ZU4ddXrXR8x9QAOtoWuG3LHFzx/adwFSPIpElYdWusLMbaaU34moCp+mBHh4+9bM+VC54XXlrYOHTjFkJ4W4Qn3be2zuXOwD4HmNngV7jYUTc9lbNvQB1yxfhIkoymuu/Huhe/6ZZ50oh48UxKC/Px4KVLNeXmRTRoZY5JTZW4cMVYfOSnz0uVOxDfhU1CwwFJM9h97EljP8TIvdQdgmqA32oYlmVFNkSmNFdh0bjwM75NBM/zW+hYVrIDR+VqtnnBKADACp6ARj6fdRLXeayAhMZeoBXs9zWqrgz3X7xkIB9XNkaCPEaEvU185dlsNnROuePsebhtyxxsWzYGgHvB670412asZ5Krh/ZmZfrY2z/3/eHnYy+Ku/rTWqpx/8VLAo/v6b9Ww71XBPift9aW4Mgp/rFQhiJ+ljd+z8K024KOqdM5CsPrK73w52iLaS3Vklm7Nhs0NHvYRhx3Piqm+LZLi22m+AcPmdEwBNU0qlcYq73sVohxxwPxW8+4x0XY0AyLiu/Hzy9ajKOmNvFf4INvDA3PSVAyecvUSLAM80WkAtLYE6nR2POi69xxO0E+h5EFcZEoJqfBOGfJaMwbXYuOJvWo8rqJ8qWczWYjM8X35uPMKKlR8XWRRbiQUVlcgGUTBjabwto69+iaXAHucuW5WTWpAZsXtAdnKgErejFvHAtnPgOf+zddBTucXxvL9Nv7LliE1prDwSVZ2mAe/2zOsgZL75eNlWGnslhuKebjBBB4DdfrWlTASMgmpemlCGtsykXFV6iDrTy79Zk76GdCHgkAOQ1pmDuIn4WWSVN80ftQOQpTZvOHtRbXERXfTX1luo8KT5jIoh0S7AcZH1zbgevvew7vPWI8bnrgf33T6RNW+AfJOUtG48u//DM+uLZDT+E+iPiv8UfF928wETP3qMlVLS/PckSUDcQngAsvOrqWyRbNd5ni11cEv6SENooCKu7eT0izYG9q0ei0JPL+nnsWmxaMxIv/ehMrJtTj3d/8HQD2cXdfPqPTSDuzfezZGrOgPsFavNmrKxzBnHP0+T2/Kc1Vodfq8OE2QVyjKdcXVMbE2PoKXLhiLIaHzEU64BFORIVVaR97uct8sd9b0mZXleB5jvtybLpYeOJDR+BgTy9KC5OznHffXW5eqRDYwAo3xfcR7AW+1YVIPI0gTNRSZDPCr7ru8X1iZwv++OobWDBmmELN4sOU215SIFP8QcbWRaPw2w+swgUCUXZVF+m8iqr3HTURv/3AKrxLIEqoDCImzlIae7d5VYLniARXbYCIV2Bujf3d53YFphepXlBfSMs59rrIZsXnFr/001qqkMmz0HV4IVGUn8HHjp+KVR0NtgIZ+YkVz4T1SO2PLvcctZniO4LnKeTD+C4XePHIyeHm9Oyj1PriIvBy4wlTudPKktsovvmUmVLXq77/dI3jS1dPwBld7YFpeLR4oRsvglHxebDPbXXlhdwBH/3KkT6G1nadO28dT4mVh8zj1zn1V5cWor7Ca8EUBX634X58lcUFePKa1fjtB1ZJ5c0SkB3rzpD2tCzgo8dPAWA+Vo0dt5tOqHWrYr9g5R5UZHVp3zid2157uHh2BbrGOF1g8/PycP36KULm/1FYZrLmx1lt1QCACQ0VxstPCiTYD0J4dv1ZpqQyiFxqWVYkGgnTx764c0/07p/M+bGOz4NP+LSbL165ZiJGDisLvsCQKX6SjyeyI2vaKBegxmJ8An60bSGe/fCRgRGydRx3x4vzjOW+fzOCQRnd6JhFwu79O2fPxx+uX4O6cp53hPe7bDaLksJM4BFO9stOmduG569jHx2n6zltXTQKz1+3BsdxHFPIQnX6Fj3dQidS5rqC6XnmKXuKnVet5N7sUH2/uO/FKesJaktlrQ44r/M7i56HMbYTVnivNPXuPp3j2DUWVSUFQqdN2GE1l6gL5OnzRuL569bgxM4WrvTvmt2CqpICnLPUFgFfcPCIuumorglE57Ifb1+ES48Yjxs39G3A+hX/yROnOY4JTpNO4ktndOKSVeNx+1kDQTgTvGLXAgn2hIaXa7KGiYmo+Hbc/vaR+dhLoHz6SSRCkvky7Ng19jybMkIa+4AWd3fLqASCuHqnzhPQ8vKs0EWhjyW8ETKOjdG+z1/bNDu6CnDA9Ae2wtsxiNwjLcr3Xzq4H3tJIV95IpYAbnjL0MkxU5swq60ak0f01TspllxaouLbPvOcImIXSERjmDDLl5was07JXjvaXBg50927fSFuOmk65o+2mTxH/MJcK7FhpsU6wpaJ2HF3jHnv8L8i88SI6hL83w8egauOkj+NRMXHXhdBw71tWCkuXDkO1aV9QrvfhlNNWSEutFkBmw4AqpP6imJctGqcIwZFknVxOiDBfoiRG8B2lMaoZQ36QQL4T2QlhZlE378u4WrH5ctwy2mz1Ct0GB3morLYBWqeTRldLzHV4+6SBM9moMwzVAkeJOK/qYo9jkeuzrPba3HPtgXSebLaK87ApEy/VkObmF8/czauOmqikbzDkB3et5w+C/dsWxipS427JFbJYX2GK3aerVF47k+3tlvWFN/PFz2oLFV45yl73XgFuOmt1djAqWH21kvqMg8fWtuBm0+eIXSNFuujkHeMvZ+H3avsO9zd9/0PgGH/InryTXGBmkimqmQLqq2jvZVKMUdS6xU1JNgPEe6/eDHuu2ARygOOMpIim01cVHwTWK6/Pri2A0vHD8eJnS2RmeLLlKJroh85rAzHTOPzpxL2qxZLroz9Jc8l2AvkHRwozfl3ko+7C2sVPlN8cSyfz0F89pQZmD+6Fles8QqGOpqYNb+xzrEHfAQthTEoeqXs/c4bVYsvbQzfuMtF/g9aKEs3eUzDobm6xEi+cQ3v0D4j6GOfzxGNVlqwl7vMF4e5u+a8+/JkWcGI5yMS4NdbB850mhqgpDCD9TOb+//WtdrJ+XcDQAnDisihsWdsLokELRZpisLDFidLOI4XzeEbPI9zYyDH1OYqHDd9BM47fPyrKMzNYQM2ewleuhCgqPhDhomN/secqWgke3qTZogvBrdmwHJ+3rpoFLYuGgVA4ri7CBtMSmOPEElFQ5lJWfRymeLrMr90ZeSOzj/YUHVz4WXdjGasm9EcnlAjTh/7ZD5HkVp97xxvAEnWe4FnrpOd3ioD4ieY4O5zu/DlHX/GNcfqOaWlJXdMYMzoOLXA/uS5NPaSYrTuoVNr8wNmCYsmCLv3ZROG49Hd/8CWBaPw3rufBIDwuC5B5SVouvGrC08Viwsy2HH5MliwQt03mNZDPh1ZNiZMjv/z/pX4++tvM08IEX2nibrbWZaFz506U+iaMESqzLvhlCZT/KEICfaE0s52Tzbd/iq82xKOM1VdvyXYxR4TGsUjgfLM2arzelL6DI+gInTcXcBvHlP8FL8cubVGogGsQs4u5iGKvuX0sYfts/wzld0ilQ0YNr21Gk/+9XXfQHqsnHr6NfYCFfTBXdcpzVXYtmwMmgxp0N3Maa/FnMPRoHVw4cpxePpve7Hzz//SlmcYMs+By8ferrHn8bGX1EDrDvBWXJDBbz+wCnkWkK/B19+DRHW/umk2/t+/3sLY+nI0VRfjnv/7d1zJsC7SXYU4A9/yzmRBGxxhAZ5NmYbXlBWipszrshpGbVkh/v3mQcd3Ub/jmVHxBa4P6jOpEOZTUMUoIFN8Qone3qzvgnQNx5FKacQ9wQlrJiOcfK5ZO1nper+JXsTkPLSMGF8YIuZ8PLCy27ygHQDw3tXjHd+bWHeagG1+auaZ6c5WRz3DjruzL94KdT9Uhe4Zdutf3tiJzQvacdc587nzzI2XwAUgd25erlgzEWfMH6mQQ3yUF+XjEydOi7saWnzs7U+RR+soK8C01Kht4rDObh9eUYRhHKc+6CLs1gsyeRhb3xfVfsGYOnzqpOmo4jwOUK1iZrJVie4vQqkt0J1Q8DxN7hIiZAH84pIlnu891i6GN52ZQz8hSpSZbdWwLGD0cHlrFYIP0tgTSpPeod6sr8b6yqMm4v5n98hnHgEypvhuhIVDyYlWxrS5qlR8ATGUNj1NBQOzc+1xk3H5kRNQ5opvkVFxtDSMjuN2VfdMkmLey7oPP1N8VvAjlXYQ1eKL3HtjVTGuPc5/40/HEVO8JMWCJ+l4A8J5H1J4VHyxckzMU9/eOg+7X92PrjHDwhMHUCoQ5VyHBpt1QkDU78vr1k3Bhi/+2hGlPGnoaBN7MNQg6yEgfINBl/WCe+icMqcVP33qFZw6tw0FjHESZWDNPtQmUpPVLSvKx/PXrdFyagYRDLUwoTTp9WazvguFwSQgWj6fAbZwOH+0PhPPHG9392jPMwxfHzqNDzfOftLDcUSamDaKPRjcQj0AROZiH5PUJGNanpaTAuwLNnuVi1T8ejU/JhOmuId62Kb4tWWFWDyuDovG1jn8nINIg2WnKIm4p1Afe/1+wqIa+0Xj6rB10ShljW9pUbTHHF6/bgoaK4vxYduGWNTPfEpzFZ798JHYvnxsYLooquVXho6pzB7omdVPhPYYDTXGjRum4fcfOgLDK9gWIlG/zlSD562Y2AAA3HO4KMUFGaObHUmYfpMAaeyJ0BfTjsuX4am/7cUF3/m957e+XdOBiWPuqFr85sV/a66hOWSD59lhvWC+vFH/mdZx+PLHNVEOM/RicT9v3cHzRGTo6Hfz9WHs6KgIytCBIxK+XWMfcL57FOjdcGMtpnOm+O5yLXzzrLn9n4cqzdUlWN3RgNLCDIojCt4mCpfG3vaZZ56KaypjmeKbZPTwcuy8agUsy8I1P34WQDy+7DzxA9I+DitswTRZt+L37jZ526wjVYOehftECdOhppmW+AJFnrdsDNqGlWDBmDptdSKihzT2ROhraeSwMhw7fQTzt0O9To39CTOjjU6tCv9E6x/I5ayF7Z7UMibwaWB6S1+k2JMkz9ZlYW/Pu87pQufIGnxz61xt+QfBd469mbLTop1mRxkOr7vUiQwJbBLWbTiOYrJ91i3M8bSh84gvs6Y0QcHzLMtKvTChimVZ+Mqm2bj5lJmx1UGPKf7Ac+QT7PU+99Pn9cVZWBBiql8mYIpfremd7O7jQ7nLV5eyN+B1NIldYx/mYx9Wnmp9bjppOuaNqsWlR0wQui7qdzzLXVPkNVyYn4fjZ7agobJYX6WIyCGNPaH0YurtDTDFH0QvvKB7uXDlOCwePxyf/O/dqbJW4MG9iLnz7Pl4/pV9mNVWo5Sv38tm7qha/OC8BUp5i8ATH0FEWBJ5iSb5HHsRKorysf/AIc/3WUgEUkyJMZ29nk4fe6+gwbvBoTvukYnulVtM61j42as3tcV7tBThhWd8yMRi8ZYzAE9UfN197T1LRmN2ew2mjAjuF6UMFyc3nzppOh7+w6vYGBKUUTqmh9RV5jFXryw+/a7peOC5V/sDw3pTqFNmc7Ng3UuULqAbOluwQUKZkQR38kM8/oaDhKG+sZwjAd2OMMVFh4OrXHWU/NEqYfRk/aPipwFuU3zHZ+fkkZ/Jw5z2WkweUamvYgrknvfFq9SD67inybKifMxur9W6Ex2nMNdWG372tKl3RVSm+KZG566rV+Hxq1Zq9XMNcnlJFA6NvZh2U3tVTLUZo+McOizYn9E1EifPbsWXNnYqFfHkNavx2JXLSUMUQs41ae6o8Ngt4Rp7wePueM6x1zxYM3kW5rTXoiREI8+jsT+xswVfOL0z1JqmtVYyQn+iJyoznDCrBV/c2Bn6fFSwa+wPMoRTHlP8XB4zWqu11o0X9+a96XA3rOwPHNIj2A/Bbp5aSGM/iLl41TicOrcNjVVhiyaF4Hkujf2QGPw+9/je1RNgwcIx05qirY+Lc5aOwboZzWiojO7In7Rx59nz8PDzr/lqHOyY6tKJ9rHnWIDkjpTyW6zIaA51zB+j6spQmMnT5g4Tdh9WzNvjUcZGzAUKLcrP4OMCR7v5xV6pKimI5uivlPOr963A2wd7UFNWiJf+/VZg2rD+wNNd7JutPJZFafaxv/Pd8/DI7tdwJse7gEWCZ/HY0NEmlcUFqCsvQm82y4y5Y7e28+uiv7t6Fd7p7vF1GTBN9Kb43u9YmyKDlTDXytHDy/Dnf7yJWW3V0VQoJkiwH8RYlsUh1A9MijJTUE/AcXdpMKvl9rC3vTn87qq8KB8fOraDo0zzK3Ge586DjKAl+tyj3AzKaZ8WjKnjDxAjUEEeYfbIyQ347V/+gyM6GrjzVUG1ecOu97tjmV5u7zuyWsCi/AyeunZ1ZBsnuvyLmRGNBRvR9B0fkozg+d2z5+PIm3+JQ71ZvPjPNwHo98sezBQX6AvGJ3rcHVegtpje9TM1LNAXjK3DgrHywcImNlYo18EEpoYXV9wPDeXk5VnYedUK9PRmmX3Q3wXU6RoVZxDLqN3tWOuPg5o09mngUG/wvX576zx89zcvYWNXsFtO2iHBnlB6JQuf4Z5SnNG6aUGaNr60cRau/tEz+Pyps4SvFXnaPKPhSxs7fRcrOvj5RYvxu7/8Gx+891nuOrkpsp3Hzjqqz47vFJAVn1t0DS3Tizl7NU3uH6hsAqq2Jats2fk+L8/Cf1+8BFkAH7r3Gez807+wdnq8lk1J4Yuni89JQYT1GdE+xRU8LwarlevXT8FxPkF9o+An5y/CU39/HWumNMZWh8FOQSYPCT1cwkNlST4Wj6tDd08vHv9zn3WSe+yYWC5fuHLA5fKYaU34zm/+igkNFdj96n4AQ0yw7wlu4BHVJbh0tVgAxDRCgj2hJKj29Dp97NOgpXcgcdydKklvI3uTJL2uvKyZ0oQjJzdK9XXd+ziWZXEFpJJlUlMlJjVV9gv2MhQXZPC998xHbzZcsPdDTmPP/hwnYfcRpnmOcuvT9J5jr8KZmzmz1I8ePxXZbDZVG6SfOmk6bv/1X/D03/dqzXfN5EYcNTXaDQ4e4cLuvxyHjz0PZ4QEwzPN1JaqRAd9jHN0RV12EtYp9iM/R131MwB8gSdVWWg7OeJDayejc2Qtlk8Yjs6PPAhAn499GugOEeyHCiTYE/1TosyQ6El5VHxe7YUjQFVEZSaCBD5D2daTXXyKmAwPJgOWeaODj5oagH3Tcj72A22dlqZM6jxnYrEra4rvJk1CfUHGwomdLbj3ib9rz9tEM+jwsbf7qsZx3B2hTpxjLM65O9YNDVebl0RgbmAvs6QwgxNd0fu7h5SP/dC51yAoKj6hdtxdNqvleJ244I+Kb/f9NVSZEL551lwsnzAcH98wNZ4KaCRNfUa3Kf5gw/0oc7EDti4aJZxXIsUDxkO1L6bcQs1nT5khFT+BteHHN0ycNjZEejAi2If9bkJjH54lQURDgjqj2yWs0kCg0LH15YG/kyn+0IM09gR38Ly7zunCu7680/Fdnym+7W/bgsDup5sGTupswcpJPgtyh8Ze7c0he/2S8cOxZPxw7PjffyiVL0IUmxjiZ51HS9qVUVEfsfOF02fhT/94AxMaKvDRnz4vlJddSE7L5o9bsF83oxnrZjSj/X0/FcqnukQ9cnPa+2oSMWnmayTv0GETPq7sC+QMRyyQJB/wMVQx9Ui4gi8aKtu3vIT2P7dgP6e9Rlvev//gEXiruwe1jBMD7GizstKSi1m6SWMPgAR7wkbYzt/cUbUYM7wMf/rHm/3f9fQ6J/rX9h3o/1xXlvzj1uxT3idPms51jWqgoDSZ4qdhMjeNyOI7LcKoSQoyeZjYWCl3sa2p09KUYUJNWJ/4zMnT8bd/v830142zCdLS/lGRlvYIDZ7HcR89KfCxJ4IZn9Bo/aYpzk9OtD23Kb5lWTh1biu+85u/KuddU1YIfdsE4aRh+iONfR8k2BP9gsvG+SOx/51DWBhw9Iv7Bd6bzTrM9l7Z+3b/56jP8JSBVxCz30kUflNxMr21GkCfwFIgEbldXAOf8H6S8OqFYXrNHTSGhPtCAts6TFBSFWqOn9kSnogTnc0X5zFRSSL3/NO0IRsEz13YFV88fWp6SxV++Hv9MQgIcX60fSF27P4Hzloo7go1GCgpTM68VZwyq9W0o8s6Ie2QYE/0v7kLMnmOozNYrJxUjxdee6P/7x7XQHqnu0d79cIY31CO/331jfCEDGTOsR/sC96l44fjy2d0wsLgv1ceEihrCmFa01hamI//vNWtJa8ktnUhY3MrsnpyPLzSwnwAB72XKgqiJYUZfHXTbGSzWbznW7uU8hoMGBlHcQTP47iRsPOg3Ww8HKG+a4z8efCEHma0VmPG4c15EyR9g6s0QYL9YFICJfHdTLCh7SRCaMBesmo8PnnitP6/e7LOaf7iVeMxZngZbjwhugBvd54933gZ9jYaCsLukZMbsXpyNOfzJlFLayfp9YubL5/RidF1ZfjSxk7lvJJk0nv9uskYV1+OK4+aGHdVAvnC6bMOt/8sKQubII7oaIhsHkgqOYuiXEDEunL1WAgDeesnNHgeRx69grsY+Zk8bF44ChOGqPk3kRySJEwXJ2iTgRg6kMaeEFpMFxdkcNLsVlz+/acAAD09WYfPeXtdGR567zLNNQymrrwIqzsa8IvnXhW+ljsqvq2JyLwqXpZNqMfXHnsxsucwVI+742VKcxUevmyZlrzsLR13W57R1Y4zutqlr5/WUoWn/rbXc/yQCDxNYG//bDaL9TNGoKwoH0UJ8jVNM7mt601d7WipKcXMtupY6xM274Vp5DfNH4nPPfwCFo/z166Tryrhpqa0AP95qxur/AIMx4j9Fd1nwZQM4vT3r68owmv7D4QnJAYdyRkBRGyoaA16sllY2fi1bKZda+wuB7I7whMbK/CHPfu1+tQORRaNq8MPzluA9mGlkZQnokQ+ZW4rHnvhn7Ev/tOKIyp+gk0+efrEnWfPxzN/34u57bXC+Rfm5+HgoV4sGDNMsF4Wbj5lpnB5RDiZPEvqGEPddDRV4tS5rWisLJG6/sKV49A1pi7QXHtay8BvyR2FRJQ8ctkyvPDaG+gcGWXINnESZYrPqEtUG9bffvc8fOSnz+OSVcHutdwkyJqOCIYEe0KJ3t4sRg4PjqYfBbLRyHmvOmA7C1TWFP9H2xfipX+/hfENg9tcMTf9Hz21ET97eg/OXTKaK70ISV1cHDO1CeMursDIiDYdeEiygOzGvnaIW2OvSnlRPuaPFhPMc/zu6lX41xsHMaquTHOtCFGMHncnsVi2LAs3nDDN9/ewYZOfyUNXyIZRY1Uxfnn5clQU5+P+Z/cI15EYfFSXFmI25ybl6OFlkR7LaydJwfPidAsY31CBb541V1+GaX8hDyHIpngI01rbt+Ovol3syWZxwYqx2LygHd97j9fXvaWmr4xZhjWY25aPBQAcP7NZ6LrLV08AAGyc3xaYzh4UsChfbtgUF2S0CPU6fTxNkJv+/+vUWfj1+1bgqKlNvmnSgIgpvmVZmNBYMSTiMPCQJJ95ndSVmz3Ks7K4gIR6Qgpd6++2YaWoCTkjmyBYXHrEeJzZNRLfP7dLe94fO74vftPVx0zq/86++ZYEH/uOpr7jXpeMHx5zTdJJTm5orpazShrqkMZ+CPPwe5ehu6dXySdpRms1yoryce1xk5m/f+fs+fjOb17C5gXt0mXw0DmyBk9duxoVRWL3smhcHdd1do193MLK5BFVuObYDoxI+KSXl2clvo48zGqrwf/88Z9xV0OaOI8TnCgYTMuhsddcF51MaqrEtcd2oLEq/f2biA8TI/MrZ3TiPd/ahRsiDGBLEHYqigvw4XVTjOR92rw2HDOtCVUlBf3f2a3SkmCK/+PzF+LAoV6UMdaV5y4dg+/+9q84fV6wMmkoU16Uj+euO1J7MNihAgn2Q5iCTJ70wHnw0qV4dPdr/cfc+NFaW4or1kQTVbqyuCA8keR1dsE+CWxJ8Bm1SdHRLhw7DL964V/Ku+bnLRuDiuJ8LJtQr6lm0RKnKf76Gc3Y93Y3twlnnJsQomxO8BjUTVF+XuLmQILN6smN+N+PHIVCScsyN5m89IxJYmhgF+oBIGPbER5eYdaaiof8TB7yfdbW7XVlWsdnZESs0EpSEMS0QS1HSDG2vhxj6+P3rY+KAzZTfCIY0c0iUxYQXzitEz9/5hWmK4AIxQUZvHtxcJwAgk1eniUkAA9Sy/3Uc+fZ83DpXU/i2mPZllmEGIWZPBzs6TVmqqtTaDhu+gh8a+f/C/XLJ4i4yM/k4Ttnz8eh3l5UlybffSR1Qj2Aqc1VcVeB4IQEe4LggHymw7lgxVg89be9WDYhfLFaGIGJVVVpAU6ZO3TN3TqaKvHcK/tSdQqD87i7JBvjDy06R9Zix+XL465GLDRWFWvP83+uXI4n//p6Io8Oc1NckMFPLlgUdzUA9MW2+OcbdIQX4YU2nswyo7Ua3zxrLlprkxMY2I+hbmREgj1BcHBiZwt2/O8/uITWocp7Dwci5KG1thQb57ehoriATD0N8b1z5uPpv+/FvFHpWfDYAxWmUatBDB5+cF4Xbn7wj7jm2A7teTdUFmP15Ebt+Q52vv3uufjoT58XetcQBKEH08EAF42tw2Mv/BNndAW7+IYxrn5wnzwVhpUltUgo+/btQ1VVFfbu3YvKysq4q0MQBDFo+eKjf8L+d7oji81BEARBEES8HDjUgxdeewMdTZVSLppP/20vvrTjT7hyzUS0JejIYR2IyKEk2HNAgj1BEARBEARBEAQRJSJyKNk6EgRBEARBEARBEESKIcGeIAiCIAiCIAiCIFIMCfYEQRAEQRAEQRAEkWJIsCcIgiAIgiAIgiCIFEOCPUEQBEEQBEEQBEGkGBLsCYIgCIIgCIIgCCLFkGBPEARBEARBEARBECmGBHuCIAiCIAiCIAiCSDEk2BMEQRAEQRAEQRBEiiHBniAIgiAIgiAIgiBSDAn2BEEQBEEQBEEQBJFiSLAnCIIgCIIgCIIgiBRDgj1BEARBEARBEARBpBgS7AmCIAiCIAiCIAgixZBgTxAEQRAEQRAEQRAphgR7giAIgiAIgiAIgkgxJNgTBEEQBEEQBEEQRIohwZ4gCIIgCIIgCIIgUkx+3BVIA9lsFgCwb9++mGtCEARBEARBEARBDAVy8mdOHg2CBHsO9u/fDwBobW2NuSYEQRAEQRAEQRDEUGL//v2oqqoKTGNlecT/IU5vby9efvllVFRUwLKsuKsTyL59+9Da2oq//vWvqKysjLs6xCCF+hkRBdTPiKigvkZEBfU1Igqonw0estks9u/fjxEjRiAvL9iLnjT2HOTl5aGlpSXuaghRWVlJA5kwDvUzIgqonxFRQX2NiArqa0QUUD8bHIRp6nNQ8DyCIAiCIAiCIAiCSDEk2BMEQRAEQRAEQRBEiiHBfpBRVFSEa665BkVFRXFXhRjEUD8jooD6GREV1NeIqKC+RkQB9bOhCQXPIwiCIAiCIAiCIIgUQxp7giAIgiAIgiAIgkgxJNgTBEEQBEEQBEEQRIohwZ4gCIIgCIIgCIIgUgwJ9gRBEARBEARBEASRYkiwTxg33HAD5syZg4qKCtTX12P9+vXYvXu3I80777yD7du3Y9iwYSgvL8eGDRvw6quvOtJceOGF6OzsRFFREWbMmMEs66mnnsLixYtRXFyM1tZWfOITnzB1W0TCiKqfvfPOO9i8eTOmTp2K/Px8rF+/3uBdEUkkqr726KOPYt26dWhqakJZWRlmzJiBO+64w+StEQkjqr62e/duLF++HA0NDSguLsbo0aNx9dVXo7u72+TtEQkhynVajhdeeAEVFRWorq7WfDdEUomqn/3lL3+BZVme/x5//HGTt0cYggT7hLFjxw5s374djz/+OB544AF0d3dj9erVePPNN/vTXHLJJfjJT36Cu+++Gzt27MDLL7+ME044wZPXWWedhZNPPplZzr59+7B69WqMHDkSu3btwic/+Ulce+21+MpXvmLs3ojkEFU/6+npQUlJCS688EKsWrXK2P0QySWqvvbrX/8a06ZNww9+8AM89dRT2LJlCzZt2oT77rvP2L0RySKqvlZQUIBNmzbhF7/4BXbv3o2bb74ZX/3qV3HNNdcYuzciOUTVz3J0d3fj1FNPxeLFi7XfC5Fcou5nDz74IF555ZX+/zo7O7XfExEBWSLRvPbaa1kA2R07dmSz2Wz29ddfzxYUFGTvvvvu/jTPP/98FkB2586dnuuvueaa7PTp0z3ff+ELX8jW1NRkDxw40P/dlVdemZ0wYYL+myASj6l+ZufMM8/Mrlu3Tme1iRQSRV/LcfTRR2e3bNmipd5E+oiyr11yySXZRYsWaak3kS5M97Mrrrgiu3Hjxuytt96araqq0l19IiWY6mcvvvhiFkD297//vamqExFCGvuEs3fvXgBAbW0tAGDXrl3o7u52aD8nTpyItrY27Ny5kzvfnTt3YsmSJSgsLOz/7sgjj8Tu3bvxn//8R1PtibRgqp8RhJso+9revXv7yyGGHlH1tRdeeAH3338/li5dqlZhIpWY7GcPP/ww7r77btxyyy36KkykEtPz2XHHHYf6+nosWrQIP/7xj/VUmogcEuwTTG9vLy6++GIsXLgQU6ZMAQDs2bMHhYWFHj+rhoYG7NmzhzvvPXv2oKGhwZNH7jdi6GCynxGEnSj72l133YXf/va32LJli0qViZQSRV9bsGABiouLMW7cOCxevBjXXXedjqoTKcJkP/vXv/6FzZs347bbbkNlZaXOahMpw2Q/Ky8vx0033YS7774bP/3pT7Fo0SKsX7+ehPuUkh93BQh/tm/fjmeeeQaPPfZY3FUhBjHUz4ioiKqvPfLII9iyZQu++tWvYvLkyUbLIpJJFH3te9/7Hvbv348nn3wSl19+OT71qU/hiiuuMFYekTxM9rOzzz4bp512GpYsWaI9byJdmOxndXV1uPTSS/v/njNnDl5++WV88pOfxHHHHae9PMIspLFPKOeffz7uu+8+PPLII2hpaen/vrGxEQcPHsTrr7/uSP/qq6+isbGRO//GxkZP5Mzc3yL5EOnGdD8jiBxR9bUdO3bg2GOPxWc+8xls2rRJtdpEComqr7W2tqKjowOnnnoqbrzxRlx77bXo6elRrT6REkz3s4cffhif+tSnkJ+fj/z8fGzduhV79+5Ffn4+vvGNb+i6DSLhxLFOmzdvHl544QWlPIh4IME+YWSzWZx//vn44Q9/iIcffhijRo1y/N7Z2YmCggI89NBD/d/t3r0bL730Erq6urjL6erqwi9/+UvH8TwPPPAAJkyYgJqaGvUbIRJNVP2MIKLsa48++iiOOeYYfPzjH8d73vMeLfUn0kOc81pvby+6u7vR29urlA+RfKLqZzt37sQTTzzR/991112HiooKPPHEEzj++OO13Q+RTOKcz5544gk0NTUp5UHEA5niJ4zt27fjzjvvxL333ouKiop+P5mqqiqUlJSgqqoKW7duxaWXXora2lpUVlbiggsuQFdXF+bPn9+fzwsvvIA33ngDe/bswdtvv40nnngCANDR0YHCwkKcdtpp+PCHP4ytW7fiyiuvxDPPPIPPfvaz+MxnPhPHbRMRE1U/A4DnnnsOBw8exL///W/s37+/P03Yub3E4CCqvvbII49g7dq1uOiii7Bhw4b+cgoLCymA3hAhqr52xx13oKCgAFOnTkVRURF+97vf4aqrrsLJJ5+MgoKCOG6diJCo+tmkSZMc5f7ud79DXl5ev481MbiJqp/dfvvtKCwsxMyZMwEA99xzD77xjW/ga1/7WuT3TGggzpD8hBcAzP9uvfXW/jRvv/12dtu2bdmamppsaWlp9vjjj8++8sorjnyWLl3KzOfFF1/sT/Pkk09mFy1alC0qKso2Nzdnb7zxxojukoibKPvZyJEjmWmIoUFUfe3MM89k/r506dLobpaIlaj62ne/+93srFmzsuXl5dmysrJsR0dH9mMf+1j27bffjvBuibiI8v1ph467G1pE1c9uu+227KRJk7KlpaXZysrK7Ny5cx1H6BHpwspms1m5LQGCIAiCIAiCIAiCIOKGfOwJgiAIgiAIgiAIIsWQYE8QBEEQBEEQBEEQKYYEe4IgCIIgCIIgCIJIMSTYEwRBEARBEARBEESKIcGeIAiCIAiCIAiCIFIMCfYEQRAEQRAEQRAEkWJIsCcIgiAIgiAIgiCIFEOCPUEQBEEQBEEQBEGkGBLsCYIgCIIgCIIgCCLFkGBPEARBEEQomzdvhmVZsCwLBQUFaGhowBFHHIFvfOMb6O3t5c7ntttuQ3V1tbmKEgRBEMQQhAR7giAIgiC4WLNmDV555RX85S9/wc9//nMsX74cF110EdauXYtDhw7FXT2CIAiCGLKQYE8QBEEQBBdFRUVobGxEc3MzZs2ahfe///2499578fOf/xy33XYbAODTn/40pk6dirKyMrS2tmLbtm144403AACPPvootmzZgr179/Zr/6+99loAwIEDB3DZZZehubkZZWVlmDdvHh599NF4bpQgCIIgUgYJ9gRBEARBSLNixQpMnz4d99xzDwAgLy8Pn/vc5/Dss8/i9ttvx8MPP4wrrrgCALBgwQLcfPPNqKysxCuvvIJXXnkFl112GQDg/PPPx86dO/Hd734XTz31FE466SSsWbMGf/zjH2O7N4IgCIJIC1Y2m83GXQmCIAiCIJLN5s2b8frrr+NHP/qR57dTTjkFTz31FJ577jnPb9///vdx7rnn4p///CeAPh/7iy++GK+//np/mpdeegmjR4/GSy+9hBEjRvR/v2rVKsydOxcf+9jHtN8PQRAEQQwm8uOuAEEQBEEQ6SabzcKyLADAgw8+iBtuuAF/+MMfsG/fPhw6dAjvvPMO3nrrLZSWljKvf/rpp9HT04Px48c7vj9w4ACGDRtmvP4EQRAEkXZIsCcIgiAIQonnn38eo0aNwl/+8hesXbsW5513Hj760Y+itrYWjz32GLZu3YqDBw/6CvZvvPEGMpkMdu3ahUwm4/itvLw8ilsgCIIgiFRDgj1BEARBENI8/PDDePrpp3HJJZdg165d6O3txU033YS8vL4wPnfddZcjfWFhIXp6ehzfzZw5Ez09PXjttdewePHiyOpOEARBEIMFEuwJgiAIguDiwIED2LNnD3p6evDqq6/i/vvvxw033IC1a9di06ZNeOaZZ9Dd3Y3Pf/7zOPbYY/GrX/0KX/rSlxx5tLe344033sBDDz2E6dOno7S0FOPHj8fpp5+OTZs24aabbsLMmTPxj3/8Aw899BCmTZuGY445JqY7JgiCIIh0QFHxCYIgCILg4v7770dTUxPa29uxZs0aPPLII/jc5z6He++9F5lMBtOnT8enP/1pfPzjH8eUKVNwxx134IYbbnDksWDBApx77rk4+eSTMXz4cHziE58AANx6663YtGkT3vve92LChAlYv349fvvb36KtrS2OWyUIgiCIVEFR8QmCIAiCIAiCIAgixZDGniAIgiAIgiAIgiBSDAn2BEEQBEEQBEEQBJFiSLAnCIIgCIIgCIIgiBRDgj1BEARBEARBEARBpBgS7AmCIAiCIAiCIAgixZBgTxAEQRAEQRAEQRAphgR7giAIgiAIgiAIgkgxJNgTBEEQBEEQBEEQRIohwZ4gCIIgCIIgCIIgUgwJ9gRBEARBEARBEASRYkiwJwiCIAiCIAiCIIgU8/8BK/mPON1SKn4AAAAASUVORK5CYII=\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Plot the volatility\n", + "plt.figure(figsize=(12, 6))\n", + "plt.plot(data['Volatility'], label='Volatility')\n", + "plt.title('Volatility over Time')\n", + "plt.xlabel('Date')\n", + "plt.ylabel('Volatility')\n", + "plt.legend()\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 493 + }, + "id": "FWrLwCiQPdNq", + "outputId": "39a73418-37cc-4a19-f69e-fd3f60993efa" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Pairplot\n", + "sns.pairplot(data)\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 758 + }, + "id": "fvfyc3iwPe4k", + "outputId": "04e32faa-e56b-425d-a417-abc59e9d6886" + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Load the dataset\n", + "data = pd.read_csv('market_regime_detection.csv', parse_dates=['Date'], index_col='Date')\n", + "\n", + "# Fitness function: We will use a simple moving average crossover strategy to define the fitness\n", + "def fitness(individual, data):\n", + " short_window = int(individual[0])\n", + " long_window = int(individual[1])\n", + "\n", + " data['Short_MA'] = data['Price'].rolling(window=short_window).mean()\n", + " data['Long_MA'] = data['Price'].rolling(window=long_window).mean()\n", + "\n", + " data['Signal'] = 0\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + " data['Position'] = data['Signal'].diff()\n", + "\n", + " # Calculate the returns\n", + " data['Strategy_Return'] = data['Price'].pct_change().shift(-1) * data['Position']\n", + " return data['Strategy_Return'].sum()\n", + "\n", + "# Population initialization\n", + "def initialize_population(pop_size, param_range):\n", + " population = []\n", + " for _ in range(pop_size):\n", + " individual = [np.random.randint(param_range[0], param_range[1]),\n", + " np.random.randint(param_range[0], param_range[1])]\n", + " population.append(individual)\n", + " return np.array(population)\n", + "\n", + "# Selection\n", + "def selection(population, fitness_scores, num_parents):\n", + " parents = population[np.argsort(fitness_scores)][-num_parents:]\n", + " return parents\n", + "\n", + "# Crossover\n", + "def crossover(parents, offspring_size):\n", + " offspring = []\n", + " crossover_point = np.uint8(offspring_size[1]/2)\n", + "\n", + " for k in range(offspring_size[0]):\n", + " parent1_idx = k % parents.shape[0]\n", + " parent2_idx = (k + 1) % parents.shape[0]\n", + " offspring.append(np.concatenate((parents[parent1_idx, :crossover_point],\n", + " parents[parent2_idx, crossover_point:])))\n", + " return np.array(offspring)\n", + "\n", + "# Mutation\n", + "def mutation(offspring, param_range):\n", + " for idx in range(offspring.shape[0]):\n", + " random_value = np.random.randint(param_range[0], param_range[1], 1)\n", + " offspring[idx, np.random.randint(0, offspring.shape[1])] = random_value\n", + " return offspring\n", + "\n", + "# Genetic Algorithm\n", + "def genetic_algorithm(data, pop_size, param_range, num_generations, num_parents):\n", + " population = initialize_population(pop_size, param_range)\n", + " best_outputs = []\n", + "\n", + " for generation in range(num_generations):\n", + " fitness_scores = np.array([fitness(ind, data.copy()) for ind in population])\n", + " best_outputs.append(np.max(fitness_scores))\n", + "\n", + " parents = selection(population, fitness_scores, num_parents)\n", + " offspring_crossover = crossover(parents, (pop_size - parents.shape[0], len(parents[0])))\n", + " offspring_mutation = mutation(offspring_crossover, param_range)\n", + "\n", + " population[0:parents.shape[0], :] = parents\n", + " population[parents.shape[0]:, :] = offspring_mutation\n", + "\n", + " print(f'Generation {generation}: Best Fitness = {best_outputs[-1]}')\n", + "\n", + " return population, best_outputs\n", + "\n", + "# Parameters\n", + "pop_size = 20\n", + "param_range = [5, 50]\n", + "num_generations = 50\n", + "num_parents = 10\n", + "\n", + "# Run the genetic algorithm\n", + "population, best_outputs = genetic_algorithm(data, pop_size, param_range, num_generations, num_parents)\n", + "\n", + "# Plot the fitness over generations\n", + "plt.plot(best_outputs)\n", + "plt.xlabel('Generation')\n", + "plt.ylabel('Best Fitness')\n", + "plt.title('Fitness over Generations')\n", + "plt.show()\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "_h4vDUTSPgk6", + "outputId": "f56238dc-6ec3-4ace-d3cc-8fee19f456e3" + }, + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":54: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " offspring[idx, np.random.randint(0, offspring.shape[1])] = random_value\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":54: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " offspring[idx, np.random.randint(0, offspring.shape[1])] = random_value\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Generation 0: Best Fitness = 0.17778347703687392\n", + "Generation 1: Best Fitness = 0.17778347703687392\n", + "Generation 2: Best Fitness = 0.24669753010536022\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":54: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " offspring[idx, np.random.randint(0, offspring.shape[1])] = random_value\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":54: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " offspring[idx, np.random.randint(0, offspring.shape[1])] = random_value\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Generation 3: Best Fitness = 0.24669753010536022\n", + "Generation 4: Best Fitness = 0.24669753010536022\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":54: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " offspring[idx, np.random.randint(0, offspring.shape[1])] = random_value\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":54: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " offspring[idx, np.random.randint(0, offspring.shape[1])] = random_value\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":54: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " offspring[idx, np.random.randint(0, offspring.shape[1])] = random_value\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Generation 5: Best Fitness = 0.24669753010536022\n", + "Generation 6: Best Fitness = 0.24669753010536022\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":54: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " offspring[idx, np.random.randint(0, offspring.shape[1])] = random_value\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":54: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " offspring[idx, np.random.randint(0, offspring.shape[1])] = random_value\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Generation 7: Best Fitness = 0.24669753010536022\n", + "Generation 8: Best Fitness = 0.24669753010536022\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":54: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " offspring[idx, np.random.randint(0, offspring.shape[1])] = random_value\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":54: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " offspring[idx, np.random.randint(0, offspring.shape[1])] = random_value\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Generation 9: Best Fitness = 0.24669753010536022\n", + "Generation 10: Best Fitness = 0.4548053255119535\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":54: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " offspring[idx, np.random.randint(0, offspring.shape[1])] = random_value\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":54: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " offspring[idx, np.random.randint(0, offspring.shape[1])] = random_value\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Generation 11: Best Fitness = 0.4548053255119535\n", + "Generation 12: Best Fitness = 0.4548053255119535\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":54: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " offspring[idx, np.random.randint(0, offspring.shape[1])] = random_value\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":54: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " offspring[idx, np.random.randint(0, offspring.shape[1])] = random_value\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Generation 13: Best Fitness = 0.4548053255119535\n", + "Generation 14: Best Fitness = 0.4548053255119535\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":54: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " offspring[idx, np.random.randint(0, offspring.shape[1])] = random_value\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Generation 15: Best Fitness = 0.4548053255119535\n", + "Generation 16: Best Fitness = 0.4548053255119535\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + ":54: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " offspring[idx, np.random.randint(0, offspring.shape[1])] = random_value\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":54: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " offspring[idx, np.random.randint(0, offspring.shape[1])] = random_value\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Generation 17: Best Fitness = 0.4548053255119535\n", + "Generation 18: Best Fitness = 0.49564337263458047\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":54: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " offspring[idx, np.random.randint(0, offspring.shape[1])] = random_value\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":54: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " offspring[idx, np.random.randint(0, offspring.shape[1])] = random_value\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Generation 19: Best Fitness = 0.49564337263458047\n", + "Generation 20: Best Fitness = 0.49564337263458047\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":54: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " offspring[idx, np.random.randint(0, offspring.shape[1])] = random_value\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":54: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " offspring[idx, np.random.randint(0, offspring.shape[1])] = random_value\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Generation 21: Best Fitness = 0.49564337263458047\n", + "Generation 22: Best Fitness = 0.49564337263458047\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":54: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " offspring[idx, np.random.randint(0, offspring.shape[1])] = random_value\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":54: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " offspring[idx, np.random.randint(0, offspring.shape[1])] = random_value\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":54: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " offspring[idx, np.random.randint(0, offspring.shape[1])] = random_value\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Generation 23: Best Fitness = 0.49564337263458047\n", + "Generation 24: Best Fitness = 0.49564337263458047\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":54: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " offspring[idx, np.random.randint(0, offspring.shape[1])] = random_value\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":54: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " offspring[idx, np.random.randint(0, offspring.shape[1])] = random_value\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Generation 25: Best Fitness = 0.49564337263458047\n", + "Generation 26: Best Fitness = 0.49564337263458047\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":54: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " offspring[idx, np.random.randint(0, offspring.shape[1])] = random_value\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Generation 27: Best Fitness = 0.49564337263458047\n", + "Generation 28: Best Fitness = 0.49564337263458047\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":54: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " offspring[idx, np.random.randint(0, offspring.shape[1])] = random_value\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":54: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " offspring[idx, np.random.randint(0, offspring.shape[1])] = random_value\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Generation 29: Best Fitness = 0.49564337263458047\n", + "Generation 30: Best Fitness = 0.49564337263458047\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":54: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " offspring[idx, np.random.randint(0, offspring.shape[1])] = random_value\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":54: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " offspring[idx, np.random.randint(0, offspring.shape[1])] = random_value\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Generation 31: Best Fitness = 0.49564337263458047\n", + "Generation 32: Best Fitness = 0.49564337263458047\n", + "Generation 33: Best Fitness = 0.49564337263458047\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":54: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " offspring[idx, np.random.randint(0, offspring.shape[1])] = random_value\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":54: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " offspring[idx, np.random.randint(0, offspring.shape[1])] = random_value\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":54: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " offspring[idx, np.random.randint(0, offspring.shape[1])] = random_value\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":54: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " offspring[idx, np.random.randint(0, offspring.shape[1])] = random_value\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Generation 34: Best Fitness = 0.49564337263458047\n", + "Generation 35: Best Fitness = 0.49564337263458047\n", + "Generation 36: Best Fitness = 0.49564337263458047\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":54: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " offspring[idx, np.random.randint(0, offspring.shape[1])] = random_value\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":54: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " offspring[idx, np.random.randint(0, offspring.shape[1])] = random_value\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":54: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " offspring[idx, np.random.randint(0, offspring.shape[1])] = random_value\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Generation 37: Best Fitness = 0.49564337263458047\n", + "Generation 38: Best Fitness = 0.49564337263458047\n", + "Generation 39: Best Fitness = 0.49564337263458047\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":54: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " offspring[idx, np.random.randint(0, offspring.shape[1])] = random_value\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":54: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " offspring[idx, np.random.randint(0, offspring.shape[1])] = random_value\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Generation 40: Best Fitness = 0.49564337263458047\n", + "Generation 41: Best Fitness = 0.49564337263458047\n", + "Generation 42: Best Fitness = 0.49564337263458047\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":54: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " offspring[idx, np.random.randint(0, offspring.shape[1])] = random_value\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":54: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " offspring[idx, np.random.randint(0, offspring.shape[1])] = random_value\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":54: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " offspring[idx, np.random.randint(0, offspring.shape[1])] = random_value\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":54: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " offspring[idx, np.random.randint(0, offspring.shape[1])] = random_value\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Generation 43: Best Fitness = 0.49564337263458047\n", + "Generation 44: Best Fitness = 0.49564337263458047\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":54: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " offspring[idx, np.random.randint(0, offspring.shape[1])] = random_value\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":54: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " offspring[idx, np.random.randint(0, offspring.shape[1])] = random_value\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Generation 45: Best Fitness = 0.49564337263458047\n", + "Generation 46: Best Fitness = 0.49564337263458047\n", + "Generation 47: Best Fitness = 0.49564337263458047\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":54: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " offspring[idx, np.random.randint(0, offspring.shape[1])] = random_value\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":54: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " offspring[idx, np.random.randint(0, offspring.shape[1])] = random_value\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Generation 48: Best Fitness = 0.49564337263458047\n", + "Generation 49: Best Fitness = 0.49564337263458047\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + ":17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)\n", + ":54: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + " offspring[idx, np.random.randint(0, offspring.shape[1])] = random_value\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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+ }, + "metadata": {} + } + ] + } + ] +} \ No newline at end of file diff --git a/Finacial Domain/Market Regime Detection/readme.md b/Finacial Domain/Market Regime Detection/readme.md new file mode 100644 index 00000000..789722d8 --- /dev/null +++ b/Finacial Domain/Market Regime Detection/readme.md @@ -0,0 +1,38 @@ +# Market Regime Detection using Genetic Algorithm + +## Description +This project aims to detect market regimes using a genetic algorithm. Market regimes, such as bull and bear markets, can significantly impact trading strategies. By identifying these regimes, traders can adapt their strategies accordingly. This project involves generating synthetic financial data, applying a genetic algorithm to detect market regimes, and performing exploratory data analysis (EDA). + +## Dataset +The dataset `market_regime_detection.csv` contains synthetic financial data with the following columns: +- Date: The date of the observation +- Price: The price of the asset +- Volume: The trading volume of the asset +- Volatility: The volatility of the asset + +## Key Techniques +1. **Fitness Function**: Defined using a simple moving average crossover strategy. +2. **Population Initialization**: Random initialization of the population. +3. **Selection**: Selecting the best individuals based on fitness scores. +4. **Crossover**: Combining parents to produce offspring. +5. **Mutation**: Introducing randomness to maintain genetic diversity. +6. **Genetic Algorithm**: Combining the above steps to evolve the population. + +## Exploratory Data Analysis (EDA) +- Summary statistics of the dataset. +- Line plots of price, volume, and volatility over time. +- Pairplot of the dataset to visualize relationships between features. + +## Results +The genetic algorithm was run for 50 generations with a population size of 20. The best fitness score was tracked over generations to monitor the algorithm's performance. + +## How to Run +1. Ensure you have the necessary libraries installed: + +numpy +pandas +matplotlib +seaborn + +## Contributor +Ashish Kumar Patel