From a2bcecefe85555f1d0e23e1551d606f294809210 Mon Sep 17 00:00:00 2001 From: PRIYANSHU TIWARI Date: Sun, 21 Jul 2024 00:13:37 +0530 Subject: [PATCH] here is my Nasdaq --- .../nasdaq stock forecasting-checkpoint.ipynb | 1062 +++++++++++++ NYSE/Nasdaq stock forecasting/IKNA.csv | 835 +++++++++++ .../nasdaq stock forecasting.ipynb | 1312 +++++++++++++++++ 3 files changed, 3209 insertions(+) create mode 100644 NYSE/Nasdaq stock forecasting/.ipynb_checkpoints/nasdaq stock forecasting-checkpoint.ipynb create mode 100644 NYSE/Nasdaq stock forecasting/IKNA.csv create mode 100644 NYSE/Nasdaq stock forecasting/nasdaq stock forecasting.ipynb diff --git a/NYSE/Nasdaq stock forecasting/.ipynb_checkpoints/nasdaq stock forecasting-checkpoint.ipynb b/NYSE/Nasdaq stock forecasting/.ipynb_checkpoints/nasdaq stock forecasting-checkpoint.ipynb new file mode 100644 index 00000000..c377c1c1 --- /dev/null +++ b/NYSE/Nasdaq stock forecasting/.ipynb_checkpoints/nasdaq stock forecasting-checkpoint.ipynb @@ -0,0 +1,1062 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "0716dcc9", + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-17T07:33:08.838984Z", + "iopub.status.busy": "2023-07-17T07:33:08.838455Z", + "iopub.status.idle": "2023-07-17T07:33:21.323080Z", + "shell.execute_reply": "2023-07-17T07:33:21.322029Z" + }, + "papermill": { + "duration": 12.501222, + "end_time": "2023-07-17T07:33:21.326001", + "exception": false, + "start_time": "2023-07-17T07:33:08.824779", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "\n", + "import tensorflow as tf\n", + "from tensorflow.keras.layers import LSTMCell\n", + "tf.compat.v1.disable_eager_execution()\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import pandas as pd\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "from datetime import datetime\n", + "from datetime import timedelta\n", + "from tqdm import tqdm\n", + "sns.set()\n", + "tf.compat.v1.random.set_random_seed(1234)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "39a0fb82", + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-17T07:33:21.353092Z", + "iopub.status.busy": "2023-07-17T07:33:21.352296Z", + "iopub.status.idle": "2023-07-17T07:33:21.397655Z", + "shell.execute_reply": "2023-07-17T07:33:21.396315Z" + }, + "papermill": { + "duration": 0.062096, + "end_time": "2023-07-17T07:33:21.400747", + "exception": false, + "start_time": "2023-07-17T07:33:21.338651", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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tickerdateopenhighlowclose
0IKNA2021-03-2622.4037.6119.010132.00
1IKNA2021-03-2928.2533.6924.520028.30
2IKNA2021-03-3028.8030.2423.810025.60
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dateopenhighlowclose
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self.logits))\n", + " self.optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate).minimize(\n", + " self.cost\n", + " )\n", + " \n", + "def calculate_accuracy(real, predict):\n", + " mse = np.mean(np.square(real - predict))\n", + " return mse\n", + "\n", + "def anchor(signal, weight):\n", + " buffer = []\n", + " last = signal[0]\n", + " for i in signal:\n", + " smoothed_val = last * weight + (1 - weight) * i\n", + " buffer.append(smoothed_val)\n", + " last = smoothed_val\n", + " return buffer" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "0c043f17", + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-17T07:33:21.724448Z", + "iopub.status.busy": "2023-07-17T07:33:21.724006Z", + "iopub.status.idle": "2023-07-17T07:33:21.730244Z", + "shell.execute_reply": "2023-07-17T07:33:21.728618Z" + }, + "papermill": { + "duration": 0.024716, + "end_time": "2023-07-17T07:33:21.733256", + "exception": false, + "start_time": "2023-07-17T07:33:21.708540", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "num_layers = 1\n", + "size_layer = 128\n", + "timestamp = 5\n", + "epoch = 300\n", + "dropout_rate = 0.8\n", + "future_day = test_size\n", + "learning_rate = 0.01" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "0cc20f1c", + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-17T07:33:21.763299Z", + "iopub.status.busy": "2023-07-17T07:33:21.762839Z", + "iopub.status.idle": "2023-07-17T07:33:22.174412Z", + "shell.execute_reply": "2023-07-17T07:33:22.172887Z" + }, + "papermill": { + "duration": 0.432044, + "end_time": "2023-07-17T07:33:22.178981", + "exception": false, + "start_time": "2023-07-17T07:33:21.746937", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def forecast():\n", + " tf.compat.v1.reset_default_graph()\n", + " modelnn = Model(\n", + " learning_rate, num_layers, df_log.shape[1], size_layer, df_log.shape[1], dropout_rate\n", + " )\n", + " sess = tf.compat.v1.InteractiveSession()\n", + " sess.run(tf.compat.v1.global_variables_initializer())\n", + " date_ori = pd.to_datetime(df.iloc[:, 1]).tolist()\n", + "\n", + " pbar = tqdm(range(epoch), desc = 'train loop')\n", + " for i in pbar:\n", + " init_value = np.zeros((1, num_layers * 2 * size_layer))\n", + " total_loss, total_acc = [], []\n", + " for k in range(0, df_train.shape[0] - 1, timestamp):\n", + " index = min(k + timestamp, df_train.shape[0] - 1)\n", + " batch_x = np.expand_dims(\n", + " df_train.iloc[k : index, :].values, axis = 0\n", + " )\n", + " batch_y = df_train.iloc[k + 1 : index + 1, :].values\n", + " logits, last_state, _, loss = sess.run(\n", + " [modelnn.logits, modelnn.last_state, modelnn.optimizer, modelnn.cost],\n", + " feed_dict = {\n", + " modelnn.X: batch_x,\n", + " modelnn.Y: batch_y,\n", + " modelnn.hidden_layer: init_value,\n", + " },\n", + " ) \n", + " init_value = last_state\n", + " total_loss.append(loss)\n", + " total_acc.append(calculate_accuracy(batch_y[:, 0], logits[:, 0]))\n", + " pbar.set_postfix(cost = np.mean(total_loss), acc = np.mean(total_acc))\n", + " \n", + " future_day = test_size\n", + "\n", + " output_predict = np.zeros((df_train.shape[0] + future_day, df_train.shape[1]))\n", + " output_predict[0] = df_train.iloc[0]\n", + " upper_b = (df_train.shape[0] // timestamp) * timestamp\n", + " init_value = np.zeros((1, num_layers * 2 * size_layer))\n", + "\n", + " for k in range(0, (df_train.shape[0] // timestamp) * timestamp, timestamp):\n", + " out_logits, last_state = sess.run(\n", + " [modelnn.logits, modelnn.last_state],\n", + " feed_dict = {\n", + " modelnn.X: np.expand_dims(\n", + " df_train.iloc[k : k + timestamp], axis = 0\n", + " ),\n", + " modelnn.hidden_layer: init_value,\n", + " },\n", + " )\n", + " init_value = last_state\n", + " output_predict[k + 1 : k + timestamp + 1] = out_logits\n", + "\n", + " if upper_b != df_train.shape[0]:\n", + " out_logits, last_state = sess.run(\n", + " [modelnn.logits, modelnn.last_state],\n", + " feed_dict = {\n", + " modelnn.X: np.expand_dims(df_train.iloc[upper_b:], axis = 0),\n", + " modelnn.hidden_layer: init_value,\n", + " },\n", + " )\n", + " output_predict[upper_b + 1 : df_train.shape[0] + 1] = out_logits\n", + " future_day -= 1\n", + " date_ori.append(date_ori[-1] + timedelta(days = 1))\n", + "\n", + " init_value = last_state\n", + " \n", + " for i in range(future_day):\n", + " o = output_predict[-future_day - timestamp + i:-future_day + i]\n", + " out_logits, last_state = sess.run(\n", + " [modelnn.logits, modelnn.last_state],\n", + " feed_dict = {\n", + " modelnn.X: np.expand_dims(o, axis = 0),\n", + " modelnn.hidden_layer: init_value,\n", + " },\n", + " )\n", + " init_value = last_state\n", + " output_predict[-future_day + i] = out_logits[-1]\n", + " date_ori.append(date_ori[-1] + timedelta(days = 1))\n", + " \n", + " output_predict = minmax.inverse_transform(output_predict)\n", + " deep_future = anchor(output_predict[:, 0], 0.3)\n", + " \n", + " return deep_future[-test_size:]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "92325e52", + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-17T07:33:22.209078Z", + "iopub.status.busy": "2023-07-17T07:33:22.207586Z", + "iopub.status.idle": "2023-07-17T08:10:25.385123Z", + "shell.execute_reply": "2023-07-17T08:10:25.384092Z" + }, + "papermill": { + "duration": 2223.195532, + "end_time": "2023-07-17T08:10:25.388252", + "exception": false, + "start_time": "2023-07-17T07:33:22.192720", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "simulation 1\n", + "WARNING:tensorflow:: Using a concatenated state is slower and will soon be deprecated. Use state_is_tuple=True.\n", + "WARNING:tensorflow:`tf.nn.rnn_cell.MultiRNNCell` is deprecated. This class is equivalent as `tf.keras.layers.StackedRNNCells`, and will be replaced by that in Tensorflow 2.0.\n", + "WARNING:tensorflow:From /var/folders/qh/215l544s06x3_jp987f9gdgh0000gn/T/ipykernel_13667/3070950353.py:26: dynamic_rnn (from tensorflow.python.ops.rnn) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Please use `keras.layers.RNN(cell)`, which is equivalent to this API\n", + "WARNING:tensorflow:From /Users/shikarichacha/anaconda3/lib/python3.11/site-packages/keras/src/layers/rnn/legacy_cells.py:1043: calling Zeros.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Call initializer instance with the dtype argument instead of passing it to the constructor\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/qh/215l544s06x3_jp987f9gdgh0000gn/T/ipykernel_13667/3070950353.py:12: UserWarning: `tf.nn.rnn_cell.LSTMCell` is deprecated and will be removed in a future version. This class is equivalent as `tf.keras.layers.LSTMCell`, and will be replaced by that in Tensorflow 2.0.\n", + " return tf.compat.v1.nn.rnn_cell.LSTMCell(size_layer, state_is_tuple = False)\n", + "/var/folders/qh/215l544s06x3_jp987f9gdgh0000gn/T/ipykernel_13667/3070950353.py:29: UserWarning: `tf.layers.dense` is deprecated and will be removed in a future version. Please use `tf.keras.layers.Dense` instead.\n", + " self.logits = tf.compat.v1.layers.dense(self.outputs[-1], output_size)\n", + "2024-07-21 00:00:52.670346: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:388] MLIR V1 optimization pass is not enabled\n", + "train loop: 100%|█| 300/300 [01:02<00:00, 4.79it/s, acc=0.000826, cost=0.000826" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "simulation 2\n", + "WARNING:tensorflow:: Using a concatenated state is slower and will soon be deprecated. Use state_is_tuple=True.\n", + "WARNING:tensorflow:`tf.nn.rnn_cell.MultiRNNCell` is deprecated. This class is equivalent as `tf.keras.layers.StackedRNNCells`, and will be replaced by that in Tensorflow 2.0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "/var/folders/qh/215l544s06x3_jp987f9gdgh0000gn/T/ipykernel_13667/3070950353.py:12: UserWarning: `tf.nn.rnn_cell.LSTMCell` is deprecated and will be removed in a future version. This class is equivalent as `tf.keras.layers.LSTMCell`, and will be replaced by that in Tensorflow 2.0.\n", + " return tf.compat.v1.nn.rnn_cell.LSTMCell(size_layer, state_is_tuple = False)\n", + "/var/folders/qh/215l544s06x3_jp987f9gdgh0000gn/T/ipykernel_13667/3070950353.py:29: UserWarning: `tf.layers.dense` is deprecated and will be removed in a future version. Please use `tf.keras.layers.Dense` instead.\n", + " self.logits = tf.compat.v1.layers.dense(self.outputs[-1], output_size)\n", + "/Users/shikarichacha/anaconda3/lib/python3.11/site-packages/tensorflow/python/client/session.py:1793: UserWarning: An interactive session is already active. This can cause out-of-memory errors in some cases. You must explicitly call `InteractiveSession.close()` to release resources held by the other session(s).\n", + " warnings.warn('An interactive session is already active. This can '\n", + "train loop: 60%|▌| 179/300 [00:37<00:24, 4.84it/s, acc=0.000771, cost=0.000771" + ] + } + ], + "source": [ + "results = []\n", + "for i in range(simulation_size):\n", + " print('simulation %d'%(i + 1))\n", + " results.append(forecast())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d71f1bb4", + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-17T08:10:26.529873Z", + "iopub.status.busy": "2023-07-17T08:10:26.529400Z", + "iopub.status.idle": "2023-07-17T08:10:26.543166Z", + "shell.execute_reply": "2023-07-17T08:10:26.541525Z" + }, + "papermill": { + "duration": 0.591327, + "end_time": "2023-07-17T08:10:26.546303", + "exception": false, + "start_time": "2023-07-17T08:10:25.954976", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "df = data.copy()\n", + "df.drop(['ticker'], axis=1, inplace=True)\n", + "\n", + "# Calculate returns instead of prices\n", + "df_returns = df['close'].pct_change().fillna(0)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9223d91d", + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-17T08:10:27.684858Z", + "iopub.status.busy": "2023-07-17T08:10:27.683924Z", + "iopub.status.idle": "2023-07-17T08:10:28.321260Z", + "shell.execute_reply": "2023-07-17T08:10:28.319649Z" + }, + "papermill": { + "duration": 1.214362, + "end_time": "2023-07-17T08:10:28.324979", + "exception": false, + "start_time": "2023-07-17T08:10:27.110617", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "mse_scores = [calculate_accuracy(df_returns.iloc[-test_size:].values, r) for r in results]\n", + "\n", + "plt.figure(figsize=(15, 5))\n", + "for no, r in enumerate(results[:]):\n", + " plt.plot(r, label='forecast %d' % (no + 1))\n", + "plt.plot(df_returns.iloc[-test_size:].values, label='true trend', c='black')\n", + "plt.legend()\n", + "plt.title('average MSE: %.4f' % (np.mean(mse_scores)))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8ec1a5d4", + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-17T08:10:29.589925Z", + "iopub.status.busy": "2023-07-17T08:10:29.588847Z", + "iopub.status.idle": "2023-07-17T08:10:30.063053Z", + "shell.execute_reply": "2023-07-17T08:10:30.061777Z" + }, + "papermill": { + "duration": 1.053548, + "end_time": "2023-07-17T08:10:30.065694", + "exception": false, + "start_time": "2023-07-17T08:10:29.012146", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "mse_scores = [calculate_accuracy(df_returns.iloc[-test_size:].values, r) for r in results]\n", + "\n", + "plt.figure(figsize=(15, 5))\n", + "for no, r in enumerate(results[:2]):\n", + " plt.plot(r, label='forecast %d' % (no + 1))\n", + "plt.plot(df_returns.iloc[-test_size:].values, label='true trend', c='black')\n", + "plt.legend()\n", + "plt.title('average MSE: %.4f' % (np.mean(mse_scores)))\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1732288f", + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-17T08:10:31.224605Z", + "iopub.status.busy": "2023-07-17T08:10:31.223861Z", + "iopub.status.idle": "2023-07-17T08:10:31.716316Z", + "shell.execute_reply": "2023-07-17T08:10:31.714969Z" + }, + "papermill": { + "duration": 1.079892, + "end_time": "2023-07-17T08:10:31.719313", + "exception": false, + "start_time": "2023-07-17T08:10:30.639421", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "mse_scores = [calculate_accuracy(df_returns.iloc[-test_size:].values, r) for r in results]\n", + "\n", + "plt.figure(figsize=(15, 5))\n", + "for no, r in enumerate(results[2:3]):\n", + " plt.plot(r, label='forecast %d' % (no + 1))\n", + "plt.plot(df_returns.iloc[-test_size:].values, label='true trend', c='black')\n", + "plt.legend()\n", + "plt.title('average MSE: %.4f' % (np.mean(mse_scores)))\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a3b29d1a", + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-17T08:10:32.986304Z", + "iopub.status.busy": "2023-07-17T08:10:32.985889Z", + "iopub.status.idle": "2023-07-17T08:10:33.474685Z", + "shell.execute_reply": "2023-07-17T08:10:33.472894Z" + }, + "papermill": { + "duration": 1.181438, + "end_time": "2023-07-17T08:10:33.478241", + "exception": false, + "start_time": "2023-07-17T08:10:32.296803", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "mse_scores = [calculate_accuracy(df_returns.iloc[-test_size:].values, r) for r in results]\n", + "\n", + "plt.figure(figsize=(15, 5))\n", + "for no, r in enumerate(results[3:7]):\n", + " plt.plot(r, label='forecast %d' % (no + 1))\n", + "plt.plot(df_returns.iloc[-test_size:].values, label='true trend', c='black')\n", + "plt.legend()\n", + "plt.title('average MSE: %.4f' % (np.mean(mse_scores)))\n", + "plt.show()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6ff79c11", + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-17T08:10:34.632323Z", + "iopub.status.busy": "2023-07-17T08:10:34.631260Z", + "iopub.status.idle": "2023-07-17T08:10:35.094137Z", + "shell.execute_reply": "2023-07-17T08:10:35.092593Z" + }, + "papermill": { + "duration": 1.05015, + "end_time": "2023-07-17T08:10:35.097176", + "exception": false, + "start_time": "2023-07-17T08:10:34.047026", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "mse_scores = [calculate_accuracy(df_returns.iloc[-test_size:].values, r) for r in results]\n", + "\n", + "plt.figure(figsize=(15, 5))\n", + "for no, r in enumerate(results[7:9]):\n", + " plt.plot(r, label='forecast %d' % (no + 1))\n", + "plt.plot(df_returns.iloc[-test_size:].values, label='true trend', c='black')\n", + "plt.legend()\n", + "plt.title('average MSE: %.4f' % (np.mean(mse_scores)))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f729578f", + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-17T08:10:36.238467Z", + "iopub.status.busy": "2023-07-17T08:10:36.238044Z", + "iopub.status.idle": "2023-07-17T08:10:36.703857Z", + "shell.execute_reply": "2023-07-17T08:10:36.702907Z" + }, + "papermill": { + "duration": 1.038518, + "end_time": "2023-07-17T08:10:36.706306", + "exception": false, + "start_time": "2023-07-17T08:10:35.667788", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "mse_scores = [calculate_accuracy(df_returns.iloc[-test_size:].values, r) for r in results]\n", + "\n", + "plt.figure(figsize=(15, 5))\n", + "for no, r in enumerate(results[9:]):\n", + " plt.plot(r, label='forecast %d' % (no + 1))\n", + "plt.plot(df_returns.iloc[-test_size:].values, label='true trend', c='black')\n", + "plt.legend()\n", + "plt.title('average MSE: %.4f' % (np.mean(mse_scores)))\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + }, + "papermill": { + "default_parameters": {}, + "duration": 2267.614901, + "end_time": "2023-07-17T08:10:40.316291", + "environment_variables": {}, + "exception": null, + "input_path": "__notebook__.ipynb", + "output_path": "__notebook__.ipynb", + "parameters": {}, + "start_time": "2023-07-17T07:32:52.701390", + "version": "2.4.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/NYSE/Nasdaq stock forecasting/IKNA.csv b/NYSE/Nasdaq stock forecasting/IKNA.csv new file mode 100644 index 00000000..92ea5016 --- /dev/null +++ b/NYSE/Nasdaq stock 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"2023-07-17T07:33:21.322029Z" + }, + "papermill": { + "duration": 12.501222, + "end_time": "2023-07-17T07:33:21.326001", + "exception": false, + "start_time": "2023-07-17T07:33:08.824779", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "\n", + "import tensorflow as tf\n", + "from tensorflow.keras.layers import LSTMCell\n", + "tf.compat.v1.disable_eager_execution()\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import pandas as pd\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "from datetime import datetime\n", + "from datetime import timedelta\n", + "from tqdm import tqdm\n", + "sns.set()\n", + "tf.compat.v1.random.set_random_seed(1234)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "39a0fb82", + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-17T07:33:21.353092Z", + "iopub.status.busy": "2023-07-17T07:33:21.352296Z", + "iopub.status.idle": 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tickerdateopenhighlowclose
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self.logits))\n", + " self.optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate).minimize(\n", + " self.cost\n", + " )\n", + " \n", + "def calculate_accuracy(real, predict):\n", + " mse = np.mean(np.square(real - predict))\n", + " return mse\n", + "\n", + "def anchor(signal, weight):\n", + " buffer = []\n", + " last = signal[0]\n", + " for i in signal:\n", + " smoothed_val = last * weight + (1 - weight) * i\n", + " buffer.append(smoothed_val)\n", + " last = smoothed_val\n", + " return buffer" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "0c043f17", + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-17T07:33:21.724448Z", + "iopub.status.busy": "2023-07-17T07:33:21.724006Z", + "iopub.status.idle": "2023-07-17T07:33:21.730244Z", + "shell.execute_reply": "2023-07-17T07:33:21.728618Z" + }, + "papermill": { + "duration": 0.024716, + "end_time": "2023-07-17T07:33:21.733256", + "exception": false, + "start_time": "2023-07-17T07:33:21.708540", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "num_layers = 1\n", + "size_layer = 128\n", + "timestamp = 5\n", + "epoch = 300\n", + "dropout_rate = 0.8\n", + "future_day = test_size\n", + "learning_rate = 0.01" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "0cc20f1c", + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-17T07:33:21.763299Z", + "iopub.status.busy": "2023-07-17T07:33:21.762839Z", + "iopub.status.idle": "2023-07-17T07:33:22.174412Z", + "shell.execute_reply": "2023-07-17T07:33:22.172887Z" + }, + "papermill": { + "duration": 0.432044, + "end_time": "2023-07-17T07:33:22.178981", + "exception": false, + "start_time": "2023-07-17T07:33:21.746937", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def forecast():\n", + " tf.compat.v1.reset_default_graph()\n", + " modelnn = Model(\n", + " learning_rate, num_layers, df_log.shape[1], size_layer, df_log.shape[1], dropout_rate\n", + " )\n", + " sess = tf.compat.v1.InteractiveSession()\n", + " sess.run(tf.compat.v1.global_variables_initializer())\n", + " date_ori = pd.to_datetime(df.iloc[:, 1]).tolist()\n", + "\n", + " pbar = tqdm(range(epoch), desc = 'train loop')\n", + " for i in pbar:\n", + " init_value = np.zeros((1, num_layers * 2 * size_layer))\n", + " total_loss, total_acc = [], []\n", + " for k in range(0, df_train.shape[0] - 1, timestamp):\n", + " index = min(k + timestamp, df_train.shape[0] - 1)\n", + " batch_x = np.expand_dims(\n", + " df_train.iloc[k : index, :].values, axis = 0\n", + " )\n", + " batch_y = df_train.iloc[k + 1 : index + 1, :].values\n", + " logits, last_state, _, loss = sess.run(\n", + " [modelnn.logits, modelnn.last_state, modelnn.optimizer, modelnn.cost],\n", + " feed_dict = {\n", + " modelnn.X: batch_x,\n", + " modelnn.Y: batch_y,\n", + " modelnn.hidden_layer: init_value,\n", + " },\n", + " ) \n", + " init_value = last_state\n", + " total_loss.append(loss)\n", + " total_acc.append(calculate_accuracy(batch_y[:, 0], logits[:, 0]))\n", + " pbar.set_postfix(cost = np.mean(total_loss), acc = np.mean(total_acc))\n", + " \n", + " future_day = test_size\n", + "\n", + " output_predict = np.zeros((df_train.shape[0] + future_day, df_train.shape[1]))\n", + " output_predict[0] = df_train.iloc[0]\n", + " upper_b = (df_train.shape[0] // timestamp) * timestamp\n", + " init_value = np.zeros((1, num_layers * 2 * size_layer))\n", + "\n", + " for k in range(0, (df_train.shape[0] // timestamp) * timestamp, timestamp):\n", + " out_logits, last_state = sess.run(\n", + " [modelnn.logits, modelnn.last_state],\n", + " feed_dict = {\n", + " modelnn.X: np.expand_dims(\n", + " df_train.iloc[k : k + timestamp], axis = 0\n", + " ),\n", + " modelnn.hidden_layer: init_value,\n", + " },\n", + " )\n", + " init_value = last_state\n", + " output_predict[k + 1 : k + timestamp + 1] = out_logits\n", + "\n", + " if upper_b != df_train.shape[0]:\n", + " out_logits, last_state = sess.run(\n", + " [modelnn.logits, modelnn.last_state],\n", + " feed_dict = {\n", + " modelnn.X: np.expand_dims(df_train.iloc[upper_b:], axis = 0),\n", + " modelnn.hidden_layer: init_value,\n", + " },\n", + " )\n", + " output_predict[upper_b + 1 : df_train.shape[0] + 1] = out_logits\n", + " future_day -= 1\n", + " date_ori.append(date_ori[-1] + timedelta(days = 1))\n", + "\n", + " init_value = last_state\n", + " \n", + " for i in range(future_day):\n", + " o = output_predict[-future_day - timestamp + i:-future_day + i]\n", + " out_logits, last_state = sess.run(\n", + " [modelnn.logits, modelnn.last_state],\n", + " feed_dict = {\n", + " modelnn.X: np.expand_dims(o, axis = 0),\n", + " modelnn.hidden_layer: init_value,\n", + " },\n", + " )\n", + " init_value = last_state\n", + " output_predict[-future_day + i] = out_logits[-1]\n", + " date_ori.append(date_ori[-1] + timedelta(days = 1))\n", + " \n", + " output_predict = minmax.inverse_transform(output_predict)\n", + " deep_future = anchor(output_predict[:, 0], 0.3)\n", + " \n", + " return deep_future[-test_size:]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "92325e52", + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-17T07:33:22.209078Z", + "iopub.status.busy": "2023-07-17T07:33:22.207586Z", + "iopub.status.idle": "2023-07-17T08:10:25.385123Z", + "shell.execute_reply": "2023-07-17T08:10:25.384092Z" + }, + "papermill": { + "duration": 2223.195532, + "end_time": "2023-07-17T08:10:25.388252", + "exception": false, + "start_time": "2023-07-17T07:33:22.192720", + "status": "completed" + }, + "scrolled": false, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "simulation 1\n", + "WARNING:tensorflow:: Using a concatenated state is slower and will soon be deprecated. Use state_is_tuple=True.\n", + "WARNING:tensorflow:`tf.nn.rnn_cell.MultiRNNCell` is deprecated. This class is equivalent as `tf.keras.layers.StackedRNNCells`, and will be replaced by that in Tensorflow 2.0.\n", + "WARNING:tensorflow:From /var/folders/qh/215l544s06x3_jp987f9gdgh0000gn/T/ipykernel_13667/3070950353.py:26: dynamic_rnn (from tensorflow.python.ops.rnn) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Please use `keras.layers.RNN(cell)`, which is equivalent to this API\n", + "WARNING:tensorflow:From /Users/shikarichacha/anaconda3/lib/python3.11/site-packages/keras/src/layers/rnn/legacy_cells.py:1043: calling Zeros.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Call initializer instance with the dtype argument instead of passing it to the constructor\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/qh/215l544s06x3_jp987f9gdgh0000gn/T/ipykernel_13667/3070950353.py:12: UserWarning: `tf.nn.rnn_cell.LSTMCell` is deprecated and will be removed in a future version. This class is equivalent as `tf.keras.layers.LSTMCell`, and will be replaced by that in Tensorflow 2.0.\n", + " return tf.compat.v1.nn.rnn_cell.LSTMCell(size_layer, state_is_tuple = False)\n", + "/var/folders/qh/215l544s06x3_jp987f9gdgh0000gn/T/ipykernel_13667/3070950353.py:29: UserWarning: `tf.layers.dense` is deprecated and will be removed in a future version. Please use `tf.keras.layers.Dense` instead.\n", + " self.logits = tf.compat.v1.layers.dense(self.outputs[-1], output_size)\n", + "2024-07-21 00:00:52.670346: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:388] MLIR V1 optimization pass is not enabled\n", + "train loop: 100%|█| 300/300 [01:02<00:00, 4.79it/s, acc=0.000826, cost=0.000826" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "simulation 2\n", + "WARNING:tensorflow:: Using a concatenated state is slower and will soon be deprecated. Use state_is_tuple=True.\n", + "WARNING:tensorflow:`tf.nn.rnn_cell.MultiRNNCell` is deprecated. This class is equivalent as `tf.keras.layers.StackedRNNCells`, and will be replaced by that in Tensorflow 2.0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "/var/folders/qh/215l544s06x3_jp987f9gdgh0000gn/T/ipykernel_13667/3070950353.py:12: UserWarning: `tf.nn.rnn_cell.LSTMCell` is deprecated and will be removed in a future version. This class is equivalent as `tf.keras.layers.LSTMCell`, and will be replaced by that in Tensorflow 2.0.\n", + " return tf.compat.v1.nn.rnn_cell.LSTMCell(size_layer, state_is_tuple = False)\n", + "/var/folders/qh/215l544s06x3_jp987f9gdgh0000gn/T/ipykernel_13667/3070950353.py:29: UserWarning: `tf.layers.dense` is deprecated and will be removed in a future version. Please use `tf.keras.layers.Dense` instead.\n", + " self.logits = tf.compat.v1.layers.dense(self.outputs[-1], output_size)\n", + "/Users/shikarichacha/anaconda3/lib/python3.11/site-packages/tensorflow/python/client/session.py:1793: UserWarning: An interactive session is already active. This can cause out-of-memory errors in some cases. You must explicitly call `InteractiveSession.close()` to release resources held by the other session(s).\n", + " warnings.warn('An interactive session is already active. This can '\n", + "train loop: 100%|██| 300/300 [01:03<00:00, 4.75it/s, acc=0.00228, cost=0.00228]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "simulation 3\n", + "WARNING:tensorflow:: Using a concatenated state is slower and will soon be deprecated. Use state_is_tuple=True.\n", + "WARNING:tensorflow:`tf.nn.rnn_cell.MultiRNNCell` is deprecated. This class is equivalent as `tf.keras.layers.StackedRNNCells`, and will be replaced by that in Tensorflow 2.0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "/var/folders/qh/215l544s06x3_jp987f9gdgh0000gn/T/ipykernel_13667/3070950353.py:12: UserWarning: `tf.nn.rnn_cell.LSTMCell` is deprecated and will be removed in a future version. This class is equivalent as `tf.keras.layers.LSTMCell`, and will be replaced by that in Tensorflow 2.0.\n", + " return tf.compat.v1.nn.rnn_cell.LSTMCell(size_layer, state_is_tuple = False)\n", + "/var/folders/qh/215l544s06x3_jp987f9gdgh0000gn/T/ipykernel_13667/3070950353.py:29: UserWarning: `tf.layers.dense` is deprecated and will be removed in a future version. Please use `tf.keras.layers.Dense` instead.\n", + " self.logits = tf.compat.v1.layers.dense(self.outputs[-1], output_size)\n", + "/Users/shikarichacha/anaconda3/lib/python3.11/site-packages/tensorflow/python/client/session.py:1793: UserWarning: An interactive session is already active. This can cause out-of-memory errors in some cases. You must explicitly call `InteractiveSession.close()` to release resources held by the other session(s).\n", + " warnings.warn('An interactive session is already active. This can '\n", + "train loop: 100%|█| 300/300 [01:03<00:00, 4.72it/s, acc=0.000821, cost=0.000821\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "simulation 4\n", + "WARNING:tensorflow:: Using a concatenated state is slower and will soon be deprecated. Use state_is_tuple=True.\n", + "WARNING:tensorflow:`tf.nn.rnn_cell.MultiRNNCell` is deprecated. This class is equivalent as `tf.keras.layers.StackedRNNCells`, and will be replaced by that in Tensorflow 2.0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/qh/215l544s06x3_jp987f9gdgh0000gn/T/ipykernel_13667/3070950353.py:12: UserWarning: `tf.nn.rnn_cell.LSTMCell` is deprecated and will be removed in a future version. This class is equivalent as `tf.keras.layers.LSTMCell`, and will be replaced by that in Tensorflow 2.0.\n", + " return tf.compat.v1.nn.rnn_cell.LSTMCell(size_layer, state_is_tuple = False)\n", + "/var/folders/qh/215l544s06x3_jp987f9gdgh0000gn/T/ipykernel_13667/3070950353.py:29: UserWarning: `tf.layers.dense` is deprecated and will be removed in a future version. Please use `tf.keras.layers.Dense` instead.\n", + " self.logits = tf.compat.v1.layers.dense(self.outputs[-1], output_size)\n", + "/Users/shikarichacha/anaconda3/lib/python3.11/site-packages/tensorflow/python/client/session.py:1793: UserWarning: An interactive session is already active. This can cause out-of-memory errors in some cases. You must explicitly call `InteractiveSession.close()` to release resources held by the other session(s).\n", + " warnings.warn('An interactive session is already active. This can '\n", + "train loop: 100%|█| 300/300 [01:02<00:00, 4.77it/s, acc=0.000853, cost=0.000853" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "simulation 5\n", + "WARNING:tensorflow:: Using a concatenated state is slower and will soon be deprecated. Use state_is_tuple=True.\n", + "WARNING:tensorflow:`tf.nn.rnn_cell.MultiRNNCell` is deprecated. This class is equivalent as `tf.keras.layers.StackedRNNCells`, and will be replaced by that in Tensorflow 2.0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "/var/folders/qh/215l544s06x3_jp987f9gdgh0000gn/T/ipykernel_13667/3070950353.py:12: UserWarning: `tf.nn.rnn_cell.LSTMCell` is deprecated and will be removed in a future version. This class is equivalent as `tf.keras.layers.LSTMCell`, and will be replaced by that in Tensorflow 2.0.\n", + " return tf.compat.v1.nn.rnn_cell.LSTMCell(size_layer, state_is_tuple = False)\n", + "/var/folders/qh/215l544s06x3_jp987f9gdgh0000gn/T/ipykernel_13667/3070950353.py:29: UserWarning: `tf.layers.dense` is deprecated and will be removed in a future version. Please use `tf.keras.layers.Dense` instead.\n", + " self.logits = tf.compat.v1.layers.dense(self.outputs[-1], output_size)\n", + "/Users/shikarichacha/anaconda3/lib/python3.11/site-packages/tensorflow/python/client/session.py:1793: UserWarning: An interactive session is already active. This can cause out-of-memory errors in some cases. You must explicitly call `InteractiveSession.close()` to release resources held by the other session(s).\n", + " warnings.warn('An interactive session is already active. This can '\n", + "train loop: 100%|██| 300/300 [01:05<00:00, 4.61it/s, acc=0.00212, cost=0.00212]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "simulation 6\n", + "WARNING:tensorflow:: Using a concatenated state is slower and will soon be deprecated. Use state_is_tuple=True.\n", + "WARNING:tensorflow:`tf.nn.rnn_cell.MultiRNNCell` is deprecated. This class is equivalent as `tf.keras.layers.StackedRNNCells`, and will be replaced by that in Tensorflow 2.0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "/var/folders/qh/215l544s06x3_jp987f9gdgh0000gn/T/ipykernel_13667/3070950353.py:12: UserWarning: `tf.nn.rnn_cell.LSTMCell` is deprecated and will be removed in a future version. This class is equivalent as `tf.keras.layers.LSTMCell`, and will be replaced by that in Tensorflow 2.0.\n", + " return tf.compat.v1.nn.rnn_cell.LSTMCell(size_layer, state_is_tuple = False)\n", + "/var/folders/qh/215l544s06x3_jp987f9gdgh0000gn/T/ipykernel_13667/3070950353.py:29: UserWarning: `tf.layers.dense` is deprecated and will be removed in a future version. Please use `tf.keras.layers.Dense` instead.\n", + " self.logits = tf.compat.v1.layers.dense(self.outputs[-1], output_size)\n", + "/Users/shikarichacha/anaconda3/lib/python3.11/site-packages/tensorflow/python/client/session.py:1793: UserWarning: An interactive session is already active. This can cause out-of-memory errors in some cases. You must explicitly call `InteractiveSession.close()` to release resources held by the other session(s).\n", + " warnings.warn('An interactive session is already active. This can '\n", + "train loop: 100%|██| 300/300 [01:04<00:00, 4.67it/s, acc=0.00346, cost=0.00346]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "simulation 7\n", + "WARNING:tensorflow:: Using a concatenated state is slower and will soon be deprecated. Use state_is_tuple=True.\n", + "WARNING:tensorflow:`tf.nn.rnn_cell.MultiRNNCell` is deprecated. This class is equivalent as `tf.keras.layers.StackedRNNCells`, and will be replaced by that in Tensorflow 2.0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "/var/folders/qh/215l544s06x3_jp987f9gdgh0000gn/T/ipykernel_13667/3070950353.py:12: UserWarning: `tf.nn.rnn_cell.LSTMCell` is deprecated and will be removed in a future version. This class is equivalent as `tf.keras.layers.LSTMCell`, and will be replaced by that in Tensorflow 2.0.\n", + " return tf.compat.v1.nn.rnn_cell.LSTMCell(size_layer, state_is_tuple = False)\n", + "/var/folders/qh/215l544s06x3_jp987f9gdgh0000gn/T/ipykernel_13667/3070950353.py:29: UserWarning: `tf.layers.dense` is deprecated and will be removed in a future version. Please use `tf.keras.layers.Dense` instead.\n", + " self.logits = tf.compat.v1.layers.dense(self.outputs[-1], output_size)\n", + "/Users/shikarichacha/anaconda3/lib/python3.11/site-packages/tensorflow/python/client/session.py:1793: UserWarning: An interactive session is already active. This can cause out-of-memory errors in some cases. You must explicitly call `InteractiveSession.close()` to release resources held by the other session(s).\n", + " warnings.warn('An interactive session is already active. This can '\n", + "train loop: 100%|█| 300/300 [01:04<00:00, 4.68it/s, acc=0.000986, cost=0.000986" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "simulation 8\n", + "WARNING:tensorflow:: Using a concatenated state is slower and will soon be deprecated. Use state_is_tuple=True.\n", + "WARNING:tensorflow:`tf.nn.rnn_cell.MultiRNNCell` is deprecated. This class is equivalent as `tf.keras.layers.StackedRNNCells`, and will be replaced by that in Tensorflow 2.0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "/var/folders/qh/215l544s06x3_jp987f9gdgh0000gn/T/ipykernel_13667/3070950353.py:12: UserWarning: `tf.nn.rnn_cell.LSTMCell` is deprecated and will be removed in a future version. This class is equivalent as `tf.keras.layers.LSTMCell`, and will be replaced by that in Tensorflow 2.0.\n", + " return tf.compat.v1.nn.rnn_cell.LSTMCell(size_layer, state_is_tuple = False)\n", + "/var/folders/qh/215l544s06x3_jp987f9gdgh0000gn/T/ipykernel_13667/3070950353.py:29: UserWarning: `tf.layers.dense` is deprecated and will be removed in a future version. Please use `tf.keras.layers.Dense` instead.\n", + " self.logits = tf.compat.v1.layers.dense(self.outputs[-1], output_size)\n", + "/Users/shikarichacha/anaconda3/lib/python3.11/site-packages/tensorflow/python/client/session.py:1793: UserWarning: An interactive session is already active. This can cause out-of-memory errors in some cases. You must explicitly call `InteractiveSession.close()` to release resources held by the other session(s).\n", + " warnings.warn('An interactive session is already active. This can '\n", + "train loop: 100%|█| 300/300 [01:17<00:00, 3.87it/s, acc=0.000987, cost=0.000987" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "simulation 9\n", + "WARNING:tensorflow:: Using a concatenated state is slower and will soon be deprecated. Use state_is_tuple=True.\n", + "WARNING:tensorflow:`tf.nn.rnn_cell.MultiRNNCell` is deprecated. This class is equivalent as `tf.keras.layers.StackedRNNCells`, and will be replaced by that in Tensorflow 2.0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "/var/folders/qh/215l544s06x3_jp987f9gdgh0000gn/T/ipykernel_13667/3070950353.py:12: UserWarning: `tf.nn.rnn_cell.LSTMCell` is deprecated and will be removed in a future version. This class is equivalent as `tf.keras.layers.LSTMCell`, and will be replaced by that in Tensorflow 2.0.\n", + " return tf.compat.v1.nn.rnn_cell.LSTMCell(size_layer, state_is_tuple = False)\n", + "/var/folders/qh/215l544s06x3_jp987f9gdgh0000gn/T/ipykernel_13667/3070950353.py:29: UserWarning: `tf.layers.dense` is deprecated and will be removed in a future version. Please use `tf.keras.layers.Dense` instead.\n", + " self.logits = tf.compat.v1.layers.dense(self.outputs[-1], output_size)\n", + "/Users/shikarichacha/anaconda3/lib/python3.11/site-packages/tensorflow/python/client/session.py:1793: UserWarning: An interactive session is already active. This can cause out-of-memory errors in some cases. You must explicitly call `InteractiveSession.close()` to release resources held by the other session(s).\n", + " warnings.warn('An interactive session is already active. This can '\n", + "train loop: 100%|█| 300/300 [01:04<00:00, 4.63it/s, acc=0.000737, cost=0.000737" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "simulation 10\n", + "WARNING:tensorflow:: Using a concatenated state is slower and will soon be deprecated. Use state_is_tuple=True.\n", + "WARNING:tensorflow:`tf.nn.rnn_cell.MultiRNNCell` is deprecated. This class is equivalent as `tf.keras.layers.StackedRNNCells`, and will be replaced by that in Tensorflow 2.0.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "/var/folders/qh/215l544s06x3_jp987f9gdgh0000gn/T/ipykernel_13667/3070950353.py:12: UserWarning: `tf.nn.rnn_cell.LSTMCell` is deprecated and will be removed in a future version. This class is equivalent as `tf.keras.layers.LSTMCell`, and will be replaced by that in Tensorflow 2.0.\n", + " return tf.compat.v1.nn.rnn_cell.LSTMCell(size_layer, state_is_tuple = False)\n", + "/var/folders/qh/215l544s06x3_jp987f9gdgh0000gn/T/ipykernel_13667/3070950353.py:29: UserWarning: `tf.layers.dense` is deprecated and will be removed in a future version. Please use `tf.keras.layers.Dense` instead.\n", + " self.logits = tf.compat.v1.layers.dense(self.outputs[-1], output_size)\n", + "/Users/shikarichacha/anaconda3/lib/python3.11/site-packages/tensorflow/python/client/session.py:1793: UserWarning: An interactive session is already active. This can cause out-of-memory errors in some cases. You must explicitly call `InteractiveSession.close()` to release resources held by the other session(s).\n", + " warnings.warn('An interactive session is already active. This can '\n", + "train loop: 100%|█| 300/300 [01:03<00:00, 4.72it/s, acc=0.000849, cost=0.000849\n" + ] + } + ], + "source": [ + "results = []\n", + "for i in range(simulation_size):\n", + " print('simulation %d'%(i + 1))\n", + " results.append(forecast())" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "d71f1bb4", + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-17T08:10:26.529873Z", + "iopub.status.busy": "2023-07-17T08:10:26.529400Z", + "iopub.status.idle": "2023-07-17T08:10:26.543166Z", + "shell.execute_reply": "2023-07-17T08:10:26.541525Z" + }, + "papermill": { + "duration": 0.591327, + "end_time": "2023-07-17T08:10:26.546303", + "exception": false, + "start_time": "2023-07-17T08:10:25.954976", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "df = data.copy()\n", + "df.drop(['ticker'], axis=1, inplace=True)\n", + "\n", + "# Calculate returns instead of prices\n", + "df_returns = df['close'].pct_change().fillna(0)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "9223d91d", + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-17T08:10:27.684858Z", + "iopub.status.busy": "2023-07-17T08:10:27.683924Z", + "iopub.status.idle": "2023-07-17T08:10:28.321260Z", + "shell.execute_reply": "2023-07-17T08:10:28.319649Z" + }, + "papermill": { + "duration": 1.214362, + "end_time": "2023-07-17T08:10:28.324979", + "exception": false, + "start_time": "2023-07-17T08:10:27.110617", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mse_scores = [calculate_accuracy(df_returns.iloc[-test_size:].values, r) for r in results]\n", + "\n", + "plt.figure(figsize=(15, 5))\n", + "for no, r in enumerate(results[:]):\n", + " plt.plot(r, label='forecast %d' % (no + 1))\n", + "plt.plot(df_returns.iloc[-test_size:].values, label='true trend', c='black')\n", + "plt.legend()\n", + "plt.title('average MSE: %.4f' % (np.mean(mse_scores)))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "8ec1a5d4", + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-17T08:10:29.589925Z", + "iopub.status.busy": "2023-07-17T08:10:29.588847Z", + "iopub.status.idle": "2023-07-17T08:10:30.063053Z", + "shell.execute_reply": "2023-07-17T08:10:30.061777Z" + }, + "papermill": { + "duration": 1.053548, + "end_time": "2023-07-17T08:10:30.065694", + "exception": false, + "start_time": "2023-07-17T08:10:29.012146", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mse_scores = [calculate_accuracy(df_returns.iloc[-test_size:].values, r) for r in results]\n", + "\n", + "plt.figure(figsize=(15, 5))\n", + "for no, r in enumerate(results[:2]):\n", + " plt.plot(r, label='forecast %d' % (no + 1))\n", + "plt.plot(df_returns.iloc[-test_size:].values, label='true trend', c='black')\n", + "plt.legend()\n", + "plt.title('average MSE: %.4f' % (np.mean(mse_scores)))\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "1732288f", + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-17T08:10:31.224605Z", + "iopub.status.busy": "2023-07-17T08:10:31.223861Z", + "iopub.status.idle": "2023-07-17T08:10:31.716316Z", + "shell.execute_reply": "2023-07-17T08:10:31.714969Z" + }, + "papermill": { + "duration": 1.079892, + "end_time": "2023-07-17T08:10:31.719313", + "exception": false, + "start_time": "2023-07-17T08:10:30.639421", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mse_scores = [calculate_accuracy(df_returns.iloc[-test_size:].values, r) for r in results]\n", + "\n", + "plt.figure(figsize=(15, 5))\n", + "for no, r in enumerate(results[2:3]):\n", + " plt.plot(r, label='forecast %d' % (no + 1))\n", + "plt.plot(df_returns.iloc[-test_size:].values, label='true trend', c='black')\n", + "plt.legend()\n", + "plt.title('average MSE: %.4f' % (np.mean(mse_scores)))\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "a3b29d1a", + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-17T08:10:32.986304Z", + "iopub.status.busy": "2023-07-17T08:10:32.985889Z", + "iopub.status.idle": "2023-07-17T08:10:33.474685Z", + "shell.execute_reply": "2023-07-17T08:10:33.472894Z" + }, + "papermill": { + "duration": 1.181438, + "end_time": "2023-07-17T08:10:33.478241", + "exception": false, + "start_time": "2023-07-17T08:10:32.296803", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mse_scores = [calculate_accuracy(df_returns.iloc[-test_size:].values, r) for r in results]\n", + "\n", + "plt.figure(figsize=(15, 5))\n", + "for no, r in enumerate(results[3:7]):\n", + " plt.plot(r, label='forecast %d' % (no + 1))\n", + "plt.plot(df_returns.iloc[-test_size:].values, label='true trend', c='black')\n", + "plt.legend()\n", + "plt.title('average MSE: %.4f' % (np.mean(mse_scores)))\n", + "plt.show()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "6ff79c11", + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-17T08:10:34.632323Z", + "iopub.status.busy": "2023-07-17T08:10:34.631260Z", + "iopub.status.idle": "2023-07-17T08:10:35.094137Z", + "shell.execute_reply": "2023-07-17T08:10:35.092593Z" + }, + "papermill": { + "duration": 1.05015, + "end_time": "2023-07-17T08:10:35.097176", + "exception": false, + "start_time": "2023-07-17T08:10:34.047026", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mse_scores = [calculate_accuracy(df_returns.iloc[-test_size:].values, r) for r in results]\n", + "\n", + "plt.figure(figsize=(15, 5))\n", + "for no, r in enumerate(results[7:9]):\n", + " plt.plot(r, label='forecast %d' % (no + 1))\n", + "plt.plot(df_returns.iloc[-test_size:].values, label='true trend', c='black')\n", + "plt.legend()\n", + "plt.title('average MSE: %.4f' % (np.mean(mse_scores)))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "f729578f", + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-17T08:10:36.238467Z", + "iopub.status.busy": "2023-07-17T08:10:36.238044Z", + "iopub.status.idle": "2023-07-17T08:10:36.703857Z", + "shell.execute_reply": "2023-07-17T08:10:36.702907Z" + }, + "papermill": { + "duration": 1.038518, + "end_time": "2023-07-17T08:10:36.706306", + "exception": false, + "start_time": "2023-07-17T08:10:35.667788", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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u886bpOjoGEnSN9/M1ejRJys3N1cvvzxNDodDdrtdklRUVKg33nhF/v7+SklJ1c6dO7RlyyZdcMFFkqSAgABJUnr6RiUnJ8vPz/94fMkOG6+6ABxXByuyOjYUWb0NKLL+iI/VrKTYECXFhjQec7vdKqmwN87kysyvbJjNVaml6XnKyC3XjWf3bHIbAAAAAG3XxRdfJh8fX82c+Z6ee+5JxcTEasqUy3XxxZce8jYWi0VPP/2CXnjhWd1//92qrq5Whw7JeuSR/2rgwMGSpC5d0jR16pN69dWX9N57bykiIlLnnz9Zl112lcxms/72t1v14Yfv6ZVXXlR0dLROPvlUxcTEasOGdZKkq6/+i5xOp5566r8qLi5SRESkzjnnAl166ZWSpP79B6l795664YardO+9D2vMmFOO/RerGUzuvQs6DeJ0ulRUVGlkhBZltZoVHh6k4uJKORzGbVwHtKSWGNdllXZ9vXiX5q/cV2Qlx4do4ojWV2QdiW1ZpXrx83UqKquVj9WsKWNTdULveI9/Xt6Mn9fwRoxreCPGNbwVY7upujq7CguzFRkZLx8fX6Pj4ChYreY/HNN/9G8dEREki8V8eI/TnFCLFy/WZZdddtDLEhMTNW/evObcHYA2oLTSrq8XZ2jBiizZHfuKrLNGJqtXJ88vsvbqnBCqB64crFdnb9CabYV6Y266Nu8u0aWnpnn0JpYAAAAA0Bo1q9Dq16+ffv755ybHNm/erOuuu07XX399iwYD4NlKK2o1d/EuLVy5f5FlayiyIrymyNpfcICP/n5+b839LUOf/Lhdv67LUUZOuW44u6faRQUZHQ8AAAAAvEazCi1fX19FR0c3fl5XV6epU6fq1FNP1QUXXNDi4QB4noMVWZ3a1RdZPZO9s8jan9lk0pnDOiolIVQvfb5eWQWVevjNZbr89DQN7RFndDwAAAAA8ApHtSn8u+++q+zsbL322mstlQeAh9pbZC1YmaW6/Yqss0cmq0cbKLJ+Ly0pXA9cNVjTv1ivjRnFmv7lBm3OLNVFJ6fIx8oSRAAAAAA4GkdcaNXW1uqll17S5ZdfrpiYmKMLYT28Db88wd7Nyw53EzPAE/zRuC4pr9WcRTs1f8W+IqtzQqjOObGT1y4tPFyRof76vyn99emP2/XFzzu0cGWWdmaX6a/n9VJMeKDR8do8fl7DGzGu4Y0Y1/BWjO2mXK62+7rBm+x9+WcySX92CkKLxXRUfdARn+Vw5syZmjp1qubPn6/w8PAjDuB2u9v0C17AUxWV1ejj+Vv09aKdjUsLu3YI10WndVW/1Gi+r39neXqunnx3hcqr7Aryt+rmC/tpWK92RscCAAAAWoWamhpt27ZdUVFx8vX1MzoOjiG7vVYFBTnq3LmT/P39j/h+jrjQuvjii9W+fXs99thjR/zgkuR0ulRWVn1U99GaWCxm2WwBKiurltPJqVfhHfYf14Ul1Zq9aKcW7Dcjq0ti/Yystri0sDkKS2v0wqdrtSWzVJJ0+pAkTRqTIit/lTMEP6/hjRjX8EaMa3grxnZTdnut8vL2KDIyXj4+vkbHwREymerHttPpOuQMrbo6uwoLsxUT0+6A8tJmCzjsWYtHtOSwqKhIK1eu1F/+8pcjufkBHA7v++Z1Ol1e+bzQdhWWVuu9uRs1f0WWHA3/4aYkhOqsE5LVvUO4TCaTnE63pCPqyNuE0CBf3XFRP338wzZ9s2S3vl68S1syS3TDWT0VYTvyv0zg6PDzGt6IcQ1vxLiGt2Js16t/LQFPt7fEOpypU06n+6jG/hEVWitWrJDJZNLgwYOP+IEBeIY6h0sfLtiq+cszG2dkpSSG6qyR+4osHD6rxazJY7qoS2KYZszZqG1ZZXrg9aW6dkJ39eoUaXQ8AAAAAPAIR1Ropaenq3379goICGjpPABamY9/2KZvl+6WJKW2D9PEER3VjSLrqPVPjVZiTLBe/HSdMnLL9czM1TpzeEedPTJZZjNfWwAAAAD4I0e0cUtBQYHCwsJaOAqA1mbdjsLGMuu2KQN0z2UD1L0j+2S1lJiwAP3z0v4a3S9Bbkmzf92pJz5YqdKKWqOjAQAAADhCOTk5+v77bwx57F9++Uk7dmw/7o+bnb1HI0cO1IoVy47bYx5RofXAAw/oww8/bOksAFqR8iq7ZszeKEk6eUCiRvVPpMg6BnysFl16Wpqum9Bdfj4Wpe8q0QOvL1V6RrHR0QAAAAAcgUceuV+LFy867o+bk5Ot//u/W1VcXHTcH9sInFoLwAHcbrfemJuu0kq74iMDdeEpXYyO5PWG9ojTfVcMVEJUkEor7Xr8g5Wa/etOuY7sRLQAAAAADOI26Hd4ox7XKEe0hxYA7/bD6j1auaVAFrNJf5nYQ34+FqMjtQnxkUH612UD9fa3m/Truhx98uN2bcks1bUTuis4wMfoeAAAAMBx53a7VVVVZdjjBwYGNmulyl//ep1WrVqhVatWaOXK5frooy91/vkTdOKJo7RkyW8qKirSv//9mF57bbri49vpnnseaLztI488oOzsPZo2bbokKT8/T9OmPa3FixfJYrGoZ8/e+utfb1X79kkHPG529h5dcMFESdLf/369rrzyWvXrN0B///v1uuGGv+ndd99SXFycXnnlLRUVFf7h/T7yyANyuZyKiIjS3LmzVV1dpcGDh+r22+9WZGSUJGn79q165pkntGHDOkVFxeiSSy4/0i/xEaPQAtBEdmGlPpi3RZJ03kmdlRQbYnCitsXP16Krz+ymtPZheue7zVq7vVAPvL5EN5zVU50TQo2OBwAAABw3brdb48efqqVLFxuWYfDgofryy28Ou9T6z38e15133qqYmFjdeuudjcc/++xjPfbY0woJCVGnTil/ej/V1dX629/+opSUVD3//HRZLGZ98MG7uu66K/TWWx8oOjqmyfVjYmL1yitv6tprL9cjj/xXgwYNVXr6BknSr7/+rJdffl01NdWy2+2Hdb/z53+vsWNP17Rp05Wbm6MHH/yXpk9/QXfffZ8qKip08803qmfPXpo+/U0VFubrscceOdwvaYthySGARg6nS9O/3CB7nUvdOoTr1MHtjY7UJplMJp3Qp53uuXSAYsMDVFRWq0ffXaFvl+5uc9OIAQAA0LZ52j6+NluorFar/Pz8FB4e3nh86NARGjRoiLp27S5fX98/vZ95875RSUmJHnjgEXXpkqpOnVJ01133Kjg4WF988ekB17dYLAoLq3+8kBCbAgMDGy+76KJL1L59krp0STvs+w0KCtKdd96jjh2TNWTIMJ1++hlas2aVJOn7779RTU2N7rnnQXXq1FmDBg3V3/9+25F+yY4YM7QANPrspx3KyClXkL9V14zvLrOH/efhbZJiQ3TfFYP0+tx0LUvP0wfztmjL7hJdeUY3Bfrz4xsAAADezWQy6csvv/GoJYeHkpjYvMkCmzZtUlVVpcaNG93kuN1uV0bGzmY+9r4liod7vwkJ7WW17nvNERQULIfDIal+uWH79u0VHBzceHmvXr2blakl8IoIgCQpPaNYc3/LkCRdfnpXhYf4GZwIkhTgZ9UNZ/XQ/PZh+mDeFi3fnK/deRW64eye6hDHclAAAAB4N5PJpKCgIKNjHDU/vwNfX/1+9cXewqj+MpeSkjro0UefOuB2AQEBR/zYh3u/Pj4H7uG7f97fZ7dYjn+9xJJDAKqsqdMrszfILemE3vEa2DXmT2+D48dkMunkAYm6+5IBirT5K6+kWo+8vVwLV2WxBBEAAABoZQ5nRpePj48qKyuaHMvM3N34cXJyZ+XkZCsoKFiJie2VmNhecXHxeuml57Vq1Yojftwjud/fS01N065dGSopKWk8tne/ruOJQgto49xut978epOKy2sVEx6gi07pYnQkHEKndjbdf+Ug9ekcKYfTpbe+3qRXZ29Qjd3x5zcGAAAAcFwEBAQqO3uP8vJyD3mdXr36aOnSxfr55x+VlZWpGTNe1vbtWxsvP+20M2Szheqee+7QunVrlZGxU1OnPqhFi35RcnLnQzxu/Qyr7du3qqKi4qDXOZL7/b2TTz5NERGReuCBf2rLls1auXK5nnvuycO6bUui0ALauF/X5WhZep4sZpP+MrGH/H1ZidyaBQf46G/n99YFozrLbDJp0fpcPfzmMmUVVBodDQAAAICks88+Tzt2bNPll18kp9N50OtceOEUjRp1sh5++F5dffUlKiws0KRJFzdeHhwcrGnTpis8PFy33/43XXPNZcrOztZTT01Tp04HL55CQ8N05pkT9cILz+nVV1886HWO5H5/LyAgQM8995J8fHx0441X6+GH79OUKZcf1m1bkslt8HoVp9OloiLveSFmtZoVHh6k4uJKORwuo+MAfyivuEr3v75UtXanzj2xk8YP73jQ6zGuW6dNu4r10hfrVVphl6+PWZef1lXDesYZHctjMK7hjRjX8EaMa3grxnZTdXV2FRZmKzIyXj4+f34WQLReVqv5D8f0H/1bR0QEyWI5vLlXzNAC2iiny6VXvtygWrtTqYmhOmNoB6MjoZnSksL14JWD1a1DuOx1Lr0ye4Pe/DpddY6D/xUIAAAAALwFhRbQRn35y05t21OmAD+LrpnQXWbz0Z+KFsefLchXt03uq4kjOsok6YdVe/TIW8uVW2zcqY0BAAAA4Fij0ALaoK2Zpfry152SpEtPS1NUaPNO+4rWxWw26ewTOunWyX0UHOCjXXkVeuiNpVq+Kc/oaAAAAABwTFBoAW1Mda1D079cL7dbGtYjVkO7s+eSt+iZHKkHrxqslMRQVdc69b9P12ne8kyjYwEAAABAi6PQAtqYd77drILSGkWF+mvK2DSj46CFhYf46c6L+unk/omSpHe/26y5izMMTgUAAAAcHoPPW4fjoKX+jSm0gDbktw05WrQ+RyaTdO2E7gr0txodCceA1WLWxWO7aPzw+o3+Zy3Yps9+2s4vBwAAAGi1LBaLJMlurzU4CY41u71Gkqnx3/xI8WoWaCMKSqv19jebJUkThndUl8QwYwPhmDKZTDr3xM7ytVr0yY/b9cUvO2Wvc+mC0Z1lMnECAAAAALQuZrNFAQHBqqgoliT5+vrxe6uHcrlMcjqb/jHd7XbL5XKqpqZKNTWVCggIltlMoQXgT7hcbr365QZV1zrUuZ1NE0Z0NDoSjpPxwzvK18eiD+Zt0ddLdqnW4dSUsaky88sBAAAAWhmbLUKSGksteCaz2SyXy3WIyyyy2SIVEBB01I9DoQW0AV/9lqHNmaXy87Xo2gndZTGz2rgtOXVQe/n5mPXW15u0YEWW7HVOXTmum8xmSi0AAAC0HiaTSaGhkQoJCZfT6TA6Do6AxWJSaGigSkurDpilZTZbZDabW2zmHYUW4OV2ZJfp8593SJKmnJKqmPBAgxPBCCf1TZCv1aIZczbql7U5qnO4dM347rJaKDcBAADQupjNZpnNvkbHwBGwWs3y9/dXdbVTDsfBZ2m1FF7JAF6sxu7Q9C/Wy+lya2DXGI3oFWd0JBhoWM843XB2D1nMJi3ZmKcXPl2nOofT6FgAAAAA0GwUWoAX+2DeFuUWVys8xE+XnZbGporQgLQY/e28XvKxmrVqa4Ge+2iNausotQAAAAB4FgotwEst35SnH1dnyyTpmvHdFRzgY3QktBK9O0fplvN7y8/HovU7i/X0h6tUXcseBQAAAAA8B4UW4IWKy2v1xtx0SdLpQ5PUrUO4wYnQ2nTrGKHbJvdVgJ9FmzNL9cQHq1RRXWd0LAAAAAA4LBRagJdxud16dfYGVdY41CE2ROec0MnoSGilUhJDdedF/RUc4KMd2WX673srVVZpNzoWAAAAAPwpCi3Ay3y7ZLc2ZhTL12rWdRM5ix3+WIe4EN15cT/ZgnyVmV+hx95boeLyWqNjAQAAAMAf4pUu4EV25Zbr4x+2SZIuPKWL4iODDE4ET5AYHay7pvRXeIifsgur9Oi7y1VQUm10LAAAAAA4JAotwEvU1jn18hfr5XS51a9LlE7q087oSPAgcRGBuntKf0WH+Su/pEZT312hnKIqo2MBAAAAwEFRaAFeYuaCrcourFJokK+uGNdVJpPJ6EjwMFFhAbprygDFRwaquLxWj767Qpn5FUbHAgAAAIADUGgBXmDV1gItWJElSbr6zG4KCfQ1OBE8VXiIn/7v4v5qHxOsskq7Hnt3hXbmlBkdCwAAAACaoNACPFxppV2vf7VRkjR2YHv17BRpcCJ4OluQr+68uJ+S422qrHHo8fdXaktmidGxAAAAAKARhRbgwdxut16bs1HlVXVKjA7S+aM6GR0JXiLI30e3X9hXqYmhqq516skPV2njziKjYwEAAACAJAotwKPNX5GltdsLZbWYdd3EHvKxWoyOBC8S4GfVrZP7qkdyhOx1Lj09a43WbCswOhYAAAAAUGgBniorv0Ifzt8qSZo0urMSo4MNTgRv5Odj0d/P662+KVFyOF16/uO1WpaeZ3QsAAAAAG0chRbggeocTr38xQY5nC717BShkwckGh0JXszHataN5/TU4G4xcrrcevHzdVq0LsfoWAAAAADaMAotwAN9/MN2ZeZXKCTQR1ef0U0mk8noSPByVotZ103ooZG94uV2S6/O3qCFq7KMjgUAAACgjTqiQuuzzz7TGWecoV69eunMM8/U3LlzWzoXgENYt6NQ3y7dLUm68oxuCg32MzgR2gqz2aQrzuiqMf0T5Jb01tebGsciAAAAABxPzS60Pv/8c/3zn//U5MmTNXv2bJ1xxhn6xz/+oZUrVx6LfAD2U15l14zZGyVJo/slqG9KlMGJ0NaYTSZNGZuq04ckSZI+mLdFX/6609hQAAAAANqcZhVabrdbzz77rC6//HJdfvnl6tChg2666SYNHz5cS5YsOVYZAaj++++NuekqrbQrPjJQk8akGB0JbZTJZNIFozrr7JHJkqRPf9yuj3/YJrfbbXAyAAAAAG2FtTlX3r59u7KysjRhwoQmx2fMmNGioQAc6MfVe7RyS4EsZpOum9BDfj4WoyOhDTOZTJo4Mlm+PhbNXLBVcxZlqNbu1EWndGFPNwAAAADHXLMKrZ07d0qSqqqqdPXVV2vDhg1KTEzUDTfcoDFjxhx5CKv37E1vsZibvAdaQnZhpd6ft0WSdMHoFHVODD2uj8+4xqGMH9FR/n4WvfX1Jn2/PFMOl1tXjOsqs7n1l1qMa3gjxjW8EeMa3oqxDW90PMe1yd2MNSKff/657rzzTiUmJuqvf/2runbtqm+++UYvvfSSXn/9dQ0bNqzZAdxuN3/NB/5AncOlO5//UVszS9U7JUoP/2W4R5QFaFu+X7JLz89cKZdbGtU/Ubdc2I9fzgAAAAAcM82aoeXj4yNJuvrqq3XOOedIkrp166YNGzYccaHlcrlVVlbV7Nu1VhaLWTZbgMrKquV0uoyOAy8wc/5Wbc0sVZC/VVed0VWlpcf/+4VxjT8zoEukrj+7p17+fL0WrshURVWtbjynl6ytuNRiXMMbMa7hjRjX8FaMbXijox3XNlvAYf9hvFmFVlxcnCQpNTW1yfGUlBQtXLiwOXfVhMPhfd+8TqfLK58Xjq/0jGLNaTiD3OWnd5Ut0NfQccW4xh8ZmBYj6zlmvfDZWi1Lz9fTH67WTef0lG8r3++NcQ1vxLiGN2Jcw1sxtuGNjse4btafzrt3766goCCtXr26yfHNmzcrKSmpRYMBbV1lTZ1emb1Bbkkn9I7XwK4xRkcC/lTfLlG6+fw+8rWatXZ7oZ6ZtVo1dofRsQAAAAB4mWYVWv7+/rrmmmv0v//9T7Nnz9auXbv04osv6pdfftGVV155rDICbY7b7dabX29ScXmtYsIDdNEpXYyOBBy2HskR+sfkvvL3tSh9V4me/HCVqmrqjI4FAAAAwIs0a8mhJN14440KCAjQ008/rdzcXHXu3FnPP/+8hgwZcizyAW3Sr+tytCw9T2aTSddN6CF/32Z/qwKGSm0fptsv7KenPlylbVllevz9VfrH5D4KCfQ1OhoAAAAAL9CssxweC06nS0VFlUZGaFFWq1nh4UEqLq5kHTSOSF5xle5/falq7U6dc2InTRje0ehIjGscsV255Xryw1Uqr6pTQlSQ/jG5r8JD/IyOJYlxDe/EuIY3YlzDWzG24Y2OdlxHRAQd9qbwrff0U0AbtHepYa3dqS6JoTpzaAejIwFHJSk2RHdN6a+wYF9lFVTqwTeWatOuYqNjAQAAAPBwFFpAK7J6W6E2ZhTLajHrmvHdZTabjI4EHLX4yCDddckAJUYHqazSrsffX6W5izNk8ARhAAAAAB6MQgtoJZwul2Yt2CpJGjswUdFhAQYnAlpOTFiA7rlsoIb1iJPL7dasBds07ZO1qqrhDIgAAAAAmo9CC2glflqTrezCKgUH+OjMYSw1hPfx87HomvHddNlpabJaTFq5pUAPvblUu/MqjI4GAAAAwMNQaAGtQHWtQ5/9tEOSNGFERwX6+xicCDg2TCaTRvVL0N2XDFCkzU95xdV65K1l+mVtttHRAAAAAHgQCi2gFfhmyS6VVdoVEx6g0f0SjI4DHHPJ8Tbdf+Vg9ewUIbvDpRlzNurNr9NV53AaHQ0AAACAB6DQAgxWXF6rr5fskiSdf1JnWQ/zFKWApwsO8NEtF/TR2SOTZZL0w6o9+s87K1RQUm10NAAAAACtHK+cAYN99tN22etc6pxg04C0aKPjAMeV2WTSxJHJunVSHwX5W5WRU64H31iqNdsKjI4GAAAAoBWj0AIMlJlfoZ8b9g6aPLqLTCaTwYkAY/TsFKn7rxyk5PgQVdY49MysNfr0x+1yudxGRwMAAADQClFoAQaatWCb3G5pQFq0UhJDjY4DGCoqNEB3TRnQuI/cl7/u1NMzV6m8ym5wMgAAAACtDYUWYJD1O4u0dnuhLGaTzh/V2eg4QKvgYzXr0tPSdO347vK1mrV+Z7EefGOptu0pNToaAAAAgFaEQgswgMvt1qz5WyVJo/slKDY80OBEQOsyrGec/nXZQMWGB6iorFaPvrNC81dkyu1mCSIAAAAACi3AEIvW5WhXXoUC/CyaMKKj0XGAVikxJlj3XTFIA1Kj5XS59c63m/XKlxtUa3caHQ0AAACAwSi0gOPMXufUJz9ulySNH9ZRIYG+BicCWq8AP6tuPKenJo1Okdlk0m8bcvXvt5Ypu7DS6GgAAAAADEShBRxn3y3breLyWkXa/HTKwESj4wCtnslk0ulDknTnxf0UGuSrrIJKPfTmMi1LzzM6GgAAAACDUGgBx1FZlV1zFmVIks49sbN8rBaDEwGeI7V9mB64cpBS24ep1u7UC5+t0wfztsjhdBkdDQAAAMBxRqEFHEdf/LxDNXanOsSGaEiPWKPjAB4nNNhPd1zUV6cPSZIkfbt0t/77/koVl9canAwAAADA8UShBRwnOUVV+mHVHknSpDH1+wEBaD6L2axJo1N00zm9FOBn0dbMUj34+hKlZxQbHQ0AAADAcUKhBRwnHy3cJqfLrd6dI9WtQ7jRcQCPNyAtWvddPkiJ0UEqq6rT4x+s1Fe/ZcjtdhsdDQAAAMAxRqEFHAebd5doxeZ8mUzSBaNTjI4DeI3YiEDdc9lADe8ZJ7e7vjie9slaVdXUGR0NAAAAwDFEoQUcY263WzMXbJUkndinnRKiggxOBHgXPx+Lrj6zmy47PU1Wi0krtxTooTeWaVduudHRAAAAABwjFFrAMbY0PU/b95TJz8eis0cmGx0H8Eomk0mj+ibo7ksGKNLmr7ySaj3y9nL9vCbb6GgAAAAAjgEKLeAYqnO49NHCbZKkcUOSFBrsZ3AiwLslx9t0/5WD1LNThOocLr321Ua9MTdddQ6n0dEAAAAAtCAKLeAYWrAiUwWlNQoN9tVpg5OMjgO0CcEBPrrlgj46e2SyTJJ+XL1H/3l7hfJLqo2OBgAAAKCFUGgBx0hlTZ2+/HWnJOmcEzrJz9dibCCgDTGbTJo4Mlm3TuqjIH+rMnLL9dAbS7VmW4HR0QAAAAC0AAot4BiZ/etOVdY4lBAdpJG94o2OA7RJPTtF6oErBys53qbKGoeembVGn/y4XS6X2+hoAAAAAI4ChRZwDOSXVGve8kxJ0gWjUmQ2mwxOBLRdkaH+umtKf43unyCpvmx+auYqlVXaDU4GAAAA4EhRaAHHwCc/bpfD6Va3DuHq1SnC6DhAm+djNevSU9N07fju8rWatWFnse57dbHSdxYZHQ0AAADAEaDQAlrYjuwyLd6QK5OkyWNSZDIxOwtoLYb1jNO/Lh+o2IhAFZXX6v+m/aQP52/hLIgAAACAh6HQAlqQ2+3Wh/O3Sqp/4ZwUG2JwIgC/lxgdrPsuH6ihPWLlcktzfs3QA68v1dasUqOjAQAAADhMFFpAC1q1tUCbd5fIx2rWuSd2MjoOgEMI8LPqxnN66Z9XDFJokK+yC6s09e3l+mDeFtXWMVsLAAAAaO0otIAW4nC6NGvBNknSqYPaK8Lmb3AiAH9mWK92+s9fhmpYj1i5JX27dLfuf22JNu8uMToaAAAAgD9AoQW0kJ9W71FOUZWCA3w0bkgHo+MAOEwhgb66dkIP/f383goL9lVecbUee3eF3v1us2rtzNYCAAAAWiMKLaAFVNc69PnPOyRJZ41MVqC/1eBEAJqrb0qU/n3NEI3sFS+3pHnLM3XvjMXamFFsdDQAAAAAv0OhBbSAuYszVFZVp9jwAJ3Ut53RcQAcoUB/H111ZjfdOqmPImx+Kiit0ePvr9Rb32xSda3D6HgAAAAAGjS70MrKylJaWtoBb7NmzToW+YBWr7i8Vt8u2S1JOn9UiqwWemLA0/XqFKmHrx6iUQ0F9cKVWbpvxmKt21FocDIAAAAAktTsdVGbNm2Sn5+fvv/+e5lMpsbjISEhLRoM8BSf/rhddodLKYmh6p8aZXQcAC0kwM+qy07vqoFdY/TG3HQVlNboqQ9Xa2TveF04JkWB/j5GRwQAAADarGYXWps3b1ZycrJiYmKORR7Ao+zKLdcva7MlSZPHpDQpeQF4h+4dI/TQ1YP18Q/bNW95pn5ek631O4p02Wlp6pNCiQ0AAAAYodlrozZt2qSUlJRjkQXwOLMWbpNb0qCuMercLtToOACOEX9fq6aMTdVdU/orJjxAxeW1evajNXrlyw2qqK4zOh4AAADQ5hzRDK3o6GhdfPHF2rlzpzp06KAbb7xRJ5xwwpGHsHrPnkOWhv2TLOyj5PXWbCvU+h1FsphNmnxyileN499jXMMbHcm47p4coUeuG6qPF27TN4t3adH6HG3IKNIV47pqQBozl2E8fl7DGzGu4a0Y2/BGx3Ncm9xut/twr2y329WvXz9169ZNd955pwIDA/XFF1/orbfe0uuvv65hw4Y1O4Db7WaZFjyO0+XWLU8t1M7sMp11Ymddc1ZPoyMBOM7Sdxbp2Q9XKjOvQpJ0Yt8EXXdOL4UG+xmcDAAAAPB+zSq0JKmqqkpWq1W+vr6Nx66++mqZTCa9+uqrzQ7gdLpUVlbd7Nu1VhaLWTZbgMrKquV0uoyOg2Pkx1V79OrsDQr0t+qJm0YoOMC7N4dmXMMbtcS4tjuc+vTH7fpqUYbcbikk0EeXn95Vg7vHtnBa4PDw8xreiHENb8XYhjc62nFtswUc9uyuZi85DAwMPOBYamqqfv755+beVSOHw/u+eZ1Ol1c+L0i1dU59tHCrJGn8sI7y97G0mX9rxjW80dGMa7NMOu/EzurfJVqvzdmorIJKTftkrQasz9Elp6YpNMj3z+8EOAb4eQ1vxLiGt2Jswxsdj3HdrEWN6enp6tevn5YtW9bk+Lp169goHm3Gt0t2qaTCrqhQf508IMHoOABageR4m+67YpDGD+8os8mk5Zvyde+ri/Xb+hw1cyI0AAAAgMPQrEIrNTVVXbp00YMPPqhly5Zp27Ztmjp1qlatWqXrr7/+WGUEWo3SSru+WrxLknTuSZ3kY7UYnAhAa+FjNevcEzvp3ssHqn1MsCqq6zT9yw16/uO1KqmoNToeAAAA4FWaVWiZzWa99NJL6tWrl2655Radc845Wr16tV5//XWlpaUdq4xAq/HFzztUa3eqY1yIBndjjxwAB+oQF6J7Lx+os09IlsVs0qqtBfrXK4v1y9psZmsBAAAALaTZe2hFREToP//5z7HIArRq2YWV+mHVHknS5DEpMnN2TgCHYLWYNXFEsvp3idaMrzYqI6dcM+Zs1JKNebr89DRF2PyNjggAAAB4tGbN0ALaslkLtsnldqtvSpTSksKNjgPAAyTGBOtflw3QeSd1ktVi0trthbp3xmL9sCqL2VoAAADAUaDQAg7Dpl3FWrW1QGaTSReM7mx0HAAexGI268xhHfXAlYPVuZ1N1bVOvfn1Jj354SoVlFQbHQ8AAADwSBRawJ9wud36cP5WSdJJfdspPjLI4EQAPFG7qCDdfckATRqdIh+rWRt2Fuve15Zo/opMuZitBQAAADQLhRbwJ5ZszNXOnHL5+Vo0cWSy0XEAeDCz2aTThyTpwasGq0tiqGrtTr3z7WY98f5K5RVXGR0PAAAA8BgUWsAfqHM49fHC7ZKkM4YkKTTI1+BEALxBXESg/m9Kf118Shf5+piVvqtE9722RN8t3c1sLQAAAOAwUGgBf2De8iwVltUoLNhXpw5OMjoOAC9iNpl0ysD2eujqIeqaFCZ7nUvvz9uiJ95fqaKyGqPjAQAAAK0ahRZwCBXVdZr9605J0jkndpKfj8XYQAC8UkxYgG6/qJ8uPS1t32ytGUu0ZGOu0dEAAACAVotCCziE2b/uVFWtQ4nRQRrRM97oOAC8mNlk0uh+CXrgysHqGBeiqlqHXvp8vWbM3qDqWofR8QAAAIBWh0ILOIi84irNW54pSZo0JkVms8ngRADagriIQP3z0gEaP7yDTCbpl3U5uv+1JdqaWWp0NAAAAKBVodACDuLjH7bL6XKrR3KEeiZHGh0HQBtitZh17omd9X8X91ekzV8FpTWa+u5yffbTdjldLqPjAQAAAK0ChRbwO9uySrU0PU8mSZNGpxgdB0Abldo+TA9eNVhDe8TK7Za++GWnHn1nhfKKq4yOBgAAABiOQgvYj9vt1ocLtkqSRvSKV/uYYIMTAWjLAv2tum5CD103sbsC/KzatqdM97++VD+vyZbb7TY6HgAAAGAYCi1gPys2F2hrZql8rWadc2Ino+MAgCRpaPc4PXjVIKW2D1Ot3anXvtqoFz9bp4rqOqOjAQAAAIag0AIaOJwufbSwfnbWqYOTFB7iZ3AiANgnKjRAd17UT+ed1EkWs0nLNuXr/teWaOPOIqOjAQAAAMcdhRbQ4IdVe5RbXC1boI/GDUkyOg4AHMBsNunMYR31z0sHKDYiUMXltXr8g1X6cP4W1TnYMB4AAABtB4UWIKmqxqHPf94hSTprZLIC/KwGJwKAQ0uOt+mBKwbppL7tJEnfLNmtf7+1TFkFlQYnAwAAAI4PCi1A0tzFGaqorlN8ZKBO6NPO6DgA8Kf8fC26/PSu+tu5vRQc4KPdeRV66I2lmrc8kw3jAQAA4PUotNDmFZXV6NuluyVJ54/qLKuFbwsAnqNfarQeunqweiZHqM7h0rvfbdazH61RaaXd6GgAAADAMcMrd7R5n/y4XXUOl1Lbh6lvSpTRcQCg2cKC/XTLpD666JQuslrMWrOtUPfNWKxVWwuMjgYAAAAcExRaaNN2ZJdp0bocSdLkMSkymUwGJwKAI2M2mTR2YHvdd8VAJUYHq7yqTs99tEZvf7NJtXVOo+MBAAAALYpCC22Wy+XWW99sklvSsB6xSo63GR0JAI5aYnSw7r18gE4d1F6StGBllh56Y6kycsoNTgYAAAC0HAottFkLV2UpI6dcAX5WTRqdYnQcAGgxPlaLLjy5i26b3Fehwb7KLqzSv99apq9+y5DLxYbxAAAA8HwUWmiTSivt+viH7ZKkc0/spNBgP4MTAUDL65EcoYevHqIBqdFyutz6aOE2Pf7+ShWV1RgdDQAAADgqFFpok2bO36rqWoc6xIZodL8Eo+MAwDETHOCjG8/pqSvHdZWfj0WbdpfovhlLtGRjrtHRAAAAgCNGoYU2Z9OuYi1anyOTpEtPS5PZzEbwALybyWTSCX3a6YErByk53qaqWode+ny9Xvlyg6prHUbHAwAAAJqNQgttisPp0tvfbpYkndQvQZ3asRE8gLYjNiJQd1/SXxOGd5TJJC1an6P7X1uiLZklRkcDAAAAmoVCC23Kd0t3a09BpUICfXTeSZ2MjgMAx53VYtY5J3bSXVP6KyrUXwWlNXr03RX69MftcjhdRscDAAAADguFFtqMwtIaff7LDknSpNEpCvL3MTgRABinS2KYHrhysIb1iJPbLX356049+u4K5RZXGR0NAAAA+FMUWmgz3p+3RfY6l1ITQzW8Z5zRcQDAcIH+Vl07obuuP6uHAvys2r6nTA+8tlQ/rd4jt9ttdDwAAADgkCi00Cas3lqgFZvzZTGbdMlpaTKZ2AgeAPYa3C1WD101WGntw1Rb59Trc9P1wqfrVFFdZ3Q0AAAA4KAotOD17HVOvftd/UbwYwe1V2J0sMGJAKD1iQz11x0X9dP5ozrLYjZp+eZ83TdjsVZtLWC2FgAAAFodCi14vTmLMlRQWqPwED9NHNHR6DgA0GqZzSadMbSD7rlsgOIiAlVSYddzH63R1HdXKD2j2Oh4AAAAQCMKLXi1nKIqzV2cIUm6+JQu8ve1GpwIAFq/jnE23X/lIJ0+JEk+VrO2Zpbqv++v1BMfrNS2rFKj4wEAAADi1T28ltvt1jvfbpLD6VavTpHqnxptdCQA8Bh+PhZNGp2isQPba86infph1R5t2FmsDTuXq0/nSJ19Qid1iAsxOiYAAADaKAoteK2l6XnasLNYVotZU8Z2YSN4ADgC4SF+uuTUNJ0+JElf/rJTv6zN0epthVq9rVAD06J11gmdlBAVZHRMAAAAtDEUWvBK1bUOvT9viyRp/LAOigkPNDgRAHi2qNAAXXlGN40b2kFf/LxDizfkatmmfC3flK+hPWI1cWSyYvlZCwAAgOPkiPfQ2rFjh/r166dPPvmkJfMALeKzn3aotMKumPAAjRuaZHQcAPAacRGBum5iDz149WANSI2WW9Ki9bm6Z/pivTF3owpLa4yOCAAAgDbgiGZo1dXV6fbbb1dVVVVL5wGO2q7ccn2/fLck6ZJTU+VjtRicCAC8T2J0sG46t5d25pTps592aM22Qv24Olu/rsvRSX0SdObwDgoL9jM6JgAAALzUERVazz//vIKC2C8DrY/L7dbb326S2y0N7BqjnsmRRkcCAK/WMc6mWy7oo62Zpfr0p+3amFGseSsy9dOaPRozIFHjhiQpJNDX6JgAAADwMs1ecrh06VJ9+OGHeuyxx45FHuCo/LwmW9uyyuTna9FFJ3cxOg4AtBkpiaG646J+uuPCvuqcYJPd4dLXi3fpzpcW6dMft6uqps7oiAAAAPAizZqhVVZWpjvvvFP/+te/FB8f33IhrEe8lVerY7GYm7zH8VNeZdeshdskSeee2EnR4QEGJ/IejGt4I8b1sdErJUo9O0dqzbZCfbxwm3bmlOvLX3dq3opMnTG0g04d3F7+vpyT5lhhXMMbMa7hrRjb8EbHc1w36zfKBx54QH379tWECRNaLIDZbFJ4uPctX7TZKFOOt3e+26LK6jp1jLdp0qldZeU/hhbHuIY3YlwfG6MignXSwCQtWputd79J166ccn20cJu+W7Zb54/ponHDk+Xnwx6HxwrjGt6IcQ1vxdiGNzoe4/qwC63PPvtMy5Yt05dfftmiAVwut8rKvGdzeYvFLJstQGVl1XI6XUbHaTO2ZJbo28UZkqQpY1NVXlZtcCLvwriGN2JcHx/d2ofqoasG67cNufr0h23KLa7WjC/W65MFWzVxZLJO6tuOP0C0IMY1vBHjGt6KsQ1vdLTj2mYLOOzZXSa32+0+nCteeumlWrFihXx9923sWlVVJV9fXyUlJWnOnDnNDipJTqdLRUWVR3Tb1shqNSs8PEjFxZVyOPihdDw4XS499MYy7c6r0Mhe8brqzG5GR/I6jGt4I8b18ed0ufTr2hx98csOFZbVSpIibf6aOLKjhveMk8VMsXW0GNfwRoxreCvGNrzR0Y7riIigwy60DnuG1hNPPKGampomx0499VT9/e9/1xlnnNG8hEALmr88S7vzKhTkb9X5ozsbHQcAcAgWs1kn9GmnoT3i9NOaPfry150qLKvR61+l66tFGTrrhGQN7hYrs8lkdFQAAAC0coddaMXGxh70eGRkpBISElosENAcxeW1+vSn7ZKk80Z1lo1TwwNAq+djNWtM/0SN6BWvBSuy9NVvGcotrtb0LzZozqIMnT2yk/qnRslEsQUAAIBD4DRD8Ggfzt+iGrtTndrZdGKfdkbHAQA0g5+PRacPSdJJfdvp++WZ+mbxLmXlV+p/n65Vh7gQnXNCJ/XqFEGxBQAAgAMcVaG1adOmlsoBNNv6nUVasjFPJpN06alpLFEBAA8V4GfVhOEddXL/BH29ZLe+W7ZbGTnlembWaqUkhOqcEzupW4dwo2MCAACgFWH3VXikOodL73y7WZJ0cv9EdYgLMTgRAOBoBfr76NwTO+mx64fp9MFJ8rGatTWrVI+/v1KPv79SW7NKjY4IAACAVoIlh/BIXy/ZpdyiKoUG+ersEzoZHQcA0IJsgb6aNCZFpw5urzm/ZmjhqixtzCjWxreXq1uHcHVqZ1NcRKBiwwMVGxGg4AAfliUCAAC0MRRa8Dh5JdWa/etOSdLkk1MU6M8wBgBvFBbspymnpuq0Ie01+9ed+nlNTn2xlVHc5HqBflbFRgQqLiKgoeSqL7piwwMV4Mf/EQAAAN6I3/LgUdxut977brPqHC516xCuId0OfvZNAID3iAoN0BXjumnc0A5avaVAucXVyimqUl5xlQrLalVV69CO7DLtyC474La2IF/FhQc0lFz7ZnXFhAXI18diwLMBAABAS6DQgkdZuaVAa7YVymI26ZJTU1liAgBtSGx4oE4dnNTkWG2dU/nF1cotrlJOUZVyi6uVW1Sl3KIqlVXVqazSrrJKuzZnNt1/yyQpwubXpOjaO8MrMtRfVgvbjAIAALRmFFrwGLV2p977vn4j+NOHJCk+MsjgRAAAo/n5WJQYE6zEmOADLquqcSi3uKr+raih6CquUk5RtaprHSosq1VhWa027Gy6hNFiNikq1L9hGWOgYvfO8AoPVLjNj7PqAmg13G63SirsysqvUFmVXbZAX9mCfBUa7KeQAB+Zzfy8AuC9KLTgMb74dYeKymoVafPX+OEdjY4DAGjlAv2tSo63KTne1uS42+1WeXWdcouqGpYu1i9hzC2qVl5xlewOV/1Mr+JqrdlW2OS2PlZzfcG1315d3ZLCFRUWcDyfGoA2qKK6TnsKKpWZX6Gs/Epl5Vcoq6BSlTWOg17fbDIpJMhHoUG+Cg3yq38f7NvwvuHzoPoCzN/XwsoHAB6HQgseIaugUt8u2S1JmjI2VX7sewIAOEImk6l+FkOgr7okhjW5zOV2q6S8tr7s2m/5Yk5xtQpKqlXncCkzv1KZ+ZVNbtc1KUzDe8ZrQFo0G9EDOCq1dU7tKaisL60KKpTZUF6VVNgPen2TSYqLCFRYsJ/Kq+pUVlmr8qo6udxulVbYVVphl1Txh4/p62M+oOja+7mt4eOwYD+FBPqwJBtAq8FvXGj13G633vlmk5wut/qmRKlvlyijIwEAvJTZZFKEzV8RNn9169j0MqfLpYLSmibLF3fnVWhrZqnSd5UofVeJ3vlukwakxmhErzh17RDO8kQAh+RwupRbVKWsgsrG0iqroFL5xdVyH+I2kTZ/JUQHKSE6SIlRwUqIDlJ8ZKB8rE3/2Ot0uVReVVdfaFXaVVpZu9/HdpVV1DZ+XGN3yl7nUn5JjfJLav40d3CAz76ZXgcpwWwNnwf5W5n1BeCYotBCq/fb+lxt2l0iX6tZF5/Sxeg4AIA2ymI21y81DA+UOkc2Hi8srdGv63P069ps5RZXa9H6HC1an6MIm5+G9YjTiF7xiosINDA54Jn27g+VXVSlGkeBXA6H/H0sCvC3KtDPqiB/HwX6W1v9jCGX263C0hpl5TcsFyyoL6+yC6vkdB28ugoO8FFidJASo4MbCqxgJUQFHfYMUIvZrLBgP4UF+/3pdWvtzvrCq9LetPSqrFVJxd6P69+cLrcqqutUUV2nrN/NVP09q8WkoAAfBQf4KMjfR0H+1vqPA/b72N9nv+tYFRTgw0oMAIeNQgutWlVNnT6cv0WSNGFER/YoAQC0OpGh/powvKPGD+ugbXvK9OvabC3emKeislrNWZShOYsy1LmdTcN7xWtwtxgF+fsYHRlodeoLkobldQ2FT1Z+papqD74/1P58rWYF+lsV6O+jQD9rw8fWfR/7+TT5PMjfp7EUC/SzttjG6W63W2WVdmXuXS7Y8Hz2FFSqts550Nv4+VqUGFU/4yohKliJDeWVLci3RTIdDj9fi2J8AxUT/sfFu8vtVmV1XWPhVVqxrwQrq7Q3OV5Z45DDuf+Sx8PnYzU3llvB/r8rwP6gDPOlCAPaHAottGqf/LhdZVV1io8M1Gm/O1U7AACticlkUkpCqFISQnXRKV20ckuBfl2Xo3Xbi7RtT5m27SnT+99vUd8uURrRM049O0XIYm7dM0uAllZd66jfH2q/PaKy8itVWnnw0sNsMikuMlDtooNVWWVXZXWdKmscqqp1qLqh7LI7XLJX2A+5x9SfCfCzKNDPqoCG4iuooez6/UywfWWZjwJ8LSqpsO/boL1hr6uK6rqDPobVYlJ85N7iqr60SowOUqTN32OW5ZlNJoUE+iok0FeJ0X983TqHS2WVdlVU16mypq7hvUOV1XWNxyqrHaqoqav/N2243Olyq87hUskR/Hv6Ws2NhVeQf0PRFbD/xz6KsPkpJSFU/r68DAa8Ad/JaLV25pRpwYosSdIlY1Nb/XRyAAD28rFaNLhbrAZ3i1VpRa0Wrc/Vr+uylZlfqWXpeVqWnqfQIF8N7RGrET3jlRgTbHRkoEXVOZzKLqyqX2ZXsPesfJUqLDv0Hk1Rof77ltg1lD5xEYEK8LcqPDxIxcWVcjhcjdd3udyqtjtUVbP3rU5VtQ0f1zpUWeNQdY1DVbX7lWA1DlU2XM9eV39f1bVOVdc6JdUe9fM2SYoJD2gsrPYuFYwJD2hTv8v6WM2KDPVXZKj/Yd/G7Xarxu5sLLf2FV8HK8Qc+4qyaodcbnd9sVleq+LyP/53tJhNSo63qWuHMHVLClfnhFBmdwEeikILrZLL5dbb32ySW9LQ7rHq1jHC6EgAAByR0GA/nT4kSacNbq9duRX6ZV22flufq9JKu75ZslvfLNmtpNhgjegZryE9YmULPH5LjYCj5XS5lFdc/bv9oSqVW1wl9yF2Ng8N9m1YZhfcWFy1iwps9qwZs9nUsDfTkS3jdThd+wqwhuJr38cHlmT7SrH6YyGBvk02Z0+MDlZcZCB7QB0hk8mkAD+rAvysas4poNxut6prnfXl134l1/4f7y3H9paqW7NKtTWrVLN/zZDVYlZKgk1dk8LVtUO4OrWztanyEfBkFFpolX5YvUc7sssV4GfRpDEpRscBAOComUwmdYgLUYe4EE0anaK12wr1y7ocrd5aoF25FdqVu0UzF2xVr06RGtErTn1SonhRhVZj/43Nswr2FVfZhZVyOA/eXAX5W+tLq+ggJUYFqV1DeRUc0Dr2kbNazLIF+lIieziTydS4b1q0/ny/3fySaqVnFGvjrmKlZxSrpMLeeKZa/bxDvj5mdUkMU9ekMHXrEKEOccEsDwdaKQottDpllXZ9vHCbJOmcEzod1tlZAADwJFaLWf1So9UvNVoV1XVavCFXv6zN1s6ccq3aWqBVWwsU5G/VkO6xGtErXh3jQjxmnx14rr0zXSpq6pRfXF2/qXlDcfWHG5v7WBrKqqB9M6+igxQa5Mu4RasTHRag6LAAndCnndxut3KKqpS+q0QbM4q1aVexyqvqtH5HkdbvKJK0XQF+FqUmhqlrh3B16xCuxJhgmRnXQKtAoYVWZ9aCraqqdSgpNlij+ycYHQcAgGMqOMBHJw9I1MkDEpVVUKlf12Zr0foclVTYNX9FluavyFK7qCAN7xmnYT3iFB7CH3rwx1xut6pr9+435Gjch6hxD6KafUuy6pdl1R+vqqnfi+hQrBaT4iKCGvaGqj8rX0J0kCJD/XmBD49kMtVv1h8fGaTR/RLkcru1J7+ycfbWpl0lqqp1aPW2Qq3eViipfubh3uWJXTuEq11kIMUtYBCT2/0H/2sdB06nS0VFlUZGaFFWq/mgm1bi8GzaVazH3lspk6R/XjZAnduFGh0JYlzDOzGu0Zq5XG5t2FmkX9blaMXmfNU1jFGTSerRMULDe8Wpf5foAzYyZlx7F6fL1bh3U+VBzwzXsE/Q/gVVQzF1NL/g+1rNCrf51xdXUcZvbM64hlFcLrd25ZUrPaN+BtfmzBLV2pvOVLQF+apr0r4ZXDFhAYddcDG24Y2OdlxHRATJcpj/1zBDC62Gw+nSO99uliSd1LcdZRYAoM0ym03q2SlSPTtFqqrGoaXpufplXY62ZpZq3Y4irdtRpAA/iwZ1jdHwnvHqkhjKDAEPU1XjUGZ+hXbnVSinsErl1fYDzuJWXes4qsfw87Uo2N9av3F6gI+C/K0N730U3OTzfceD/K2c8Q1oYDab1DHOpo5xNp0+JEkOp0s7c8qVnlGs9F3F2pJZqrJKu5ZszNOSjXmSpPAQP3XrEK6uSfUFV3PO9AigeZih1cJo2Y/c3MUZmrVgm4IDfPSf64a2mg1DwbiGd2JcwxPlFlfp17U5+nVdjgrLahqPx4QFaHjPOJ3Qt51Sk6MY162Iy+VWXkm1MvMqtCuvQpl59SXW/v9+fybAz9J4Nr/ggP3KpwBr4/HGjwP2lVXeclIBfl6jtapzuLR9T6k2ZtQvUdy2p0xOV9OX19Fh/o0FV9cO4U32B2ZswxsdzxlaFFotjB9KR6aorEb/fOU32etcuuqMbhrZO97oSNgP4xreiHENT+Zyu7V5V4l+WZetZen5TTbr7tYxQl0SQ9UlIVSdE2zy92VC/vFSVVOnzPxK7W4orXbnVSiroEL2uoP/jImw+SmxcQN1PwX5W+vLqP1mTQX6eU8xdaT4eQ1PUVvn1NbMUqXvKtbGjGLtzC4/YF+6+MjA+uWJSeHq0SlCSQnhjG14FQotD8Z/uEfmf5+s1fLN+eqSGKr/m9KfjUVbGcY1vBHjGt6i1u7U8s15+mVtjtIzipvsnWQ2mdQhLlip7cOU2j5MXRLDmAHdApo768rHalZCVJDaxwQrMSZYSTHBSogO5t/iMPHzGp6qutahLZklDTO4SrQrt7zJz2iTpJMGJOrckcn8PIDXoNDyYPyH23xrthXomVlrZDaZ9MCVg5QYE2x0JPwO4xreiHENb1Raade2nAqtTM9RekbJQQuWhOggpbYPU1pDwcVZE//Ykcy6ah9dX1y1b3iLDQ+U2cwf644UP6/hLSqq67R5d0PBtatYWfn1r4P9fS06a2SyTh6Q2OZnZMLzsSk82gx7nVPvfle/EfzYQYmUWQAAHIXIUH+ldIzU4LQoORwuFZbWaHNmiTbvrn/LLqxSVn6lsvIrtWBFliQpJjxAqYn1M7hSk8IUHerfJjeY3zvram9p1ZxZV3vfEmOCFeTPLAsABxcc4KP+qdHqnxotSdqVV6H3vt+szbtK9OH8rfppTbYuGZuqrh3CDU4KeAZmaLUw/oLUPJ/9tF1f/LJT4SF++vc1QxTgR8faGjGu4Y0Y1/BGfzauyyrt2pJZok0NBdfu3Ar9/hfB8BA/dUkMVVrDMsX4qCCv2wqAWVeehZ/X8FZWq1mhoYH6YuEWfTh/qyqq6yRJg7vFaPKYLsyghUdihhbahNyiKn31W4Yk6aKTu1BmAQBwjNmCfDUgLUYD0mIkSVU1Dm3NKm2cwbUju0zF5bVNTkEf5G9t3IMrtX2YkmKDZTG33iUxtXaniitqVVxeq+Lymob39W9FDe/LKu0Hva2v1ayE6CAlRjPrCsDxYTabdFK/BPVJidJnP23XgpVZWrIxT6u3FmriiI4aO6g9yxCBQ6BBgCHcbrfe+W6zHE63eiZHaEBatNGRAABocwL9rerdOVK9O0dKqj9D1449Zdq8u34W17Y9paqscWjllgKt3FIgSfLztSglIbRxH67k+BD5WC3HPKvb7VZ1raOxlNr3VtN4rKS8VpU1jsO6v8iGMwy2jw1uLLCYdQXAKMEBPrrk1DSd0Lud3v1us7ZmlWrWwm36eW22Lh6bqh4dI4yOCLQ6FFowxLJN+Vq/o0hWi1lTTk1tk3t1AADQ2vj5WNS1Q3jj/i0Op0sZueX1M7h2lWhLZqmqah1av6NI63cUSZKsFrM6xYcoNal+BlfndqHNnnXtcrtVXlV34IyqslqVVOydWVVzyCWBB3seETY/hQX7KSLET+E2P4WH+Cs82E/hIX6KCvNn1hWAVqlDXIjuuqS/Fq3L0awFW5VdWKUnP1ilAWnRunBMF0WG+hsdEWg1KLRw3FXXOvT+9/UbwZ8xNEmx4YEGJwIAAAdjtZjVuV2oOrcL1bghHeRyu5WVX9k4g2vz7hKVVdq1ObNUmzNLJWXIbDIpKTa4cQZX54RQOZyufTOrymoalwQWldequKG0croOb1vX4ACf+qLKVl9O7S2p9i+tAvws/LEMgMcym0wa0Ste/bpE6bOfd2j+8iwt35SvtdsLNX5YR502OEk+VpYhAhRaOO4+/3mHSirsigkL0BlDOxgdBwAAHCazydS4t9TJAxLldruVV1zdWG5t3l2igtIa7cwp186ccn27dPdh37dJki3Yt35G1d7ZVPuXVrb6974+x355IwC0BoH+Prr4lNT6ZYjfbtLmzFJ98uN2/dKwDLFXp0ijIwKGotDCcbU7r0LfL8uUJF08NpVfSgEA8GAmk0mxEYGKjQjUiX3aSZKKymoay63NmaXaU1Api9mk8BA/hYU0LAHcW1o1fBwR4idbkC8bHwPAQbSPCdb/Temv3zbkaub8rcotrtbTM1erX5coXXRyF0WFBRgdETAEhRaOm+LyWj3/8Rq53G4NSItu3IAWAAB4jwibv4b2iNPQHnGS6jea97GaZWYJIAAcMZPJpGE94tQ3JUqf/7xD3y/L1MotBVq3o0hnDu2gcUOTjssJOoDWhD+D4bgor7LriQ9WqqC0RjFhAbpkbKrRkQAAwHHg52OhzAKAFhLgZ9WFJ3fRg1cNUtekMNU5XPrs5x3616uLtWprgdHxgOOKQgvHXFWNQ099uFrZhVUKD/HT7Rf2VWiwn9GxAAAAAMAjJUQH646L+un6s3ooPMRP+SU1eu6jNXp21mrlFVcZHQ84LlhyiGOq1u7UMx+tVkZuuUICfXT7hX1Z4w0AAAAAR8lkMmlwt1j17hypL3/ZqW+X7tbqbYVav7NY44Yk6YxhHeTHnsXwYszQwjFT53Bp2qdrtTWzVAF+Vt02ua/iI4OMjgUAAAAAXsPf16oLRqfooasHq0fHcDmcLn35607965XFWrE5X2632+iIwDHR7EKrsLBQd9xxh4YOHap+/frpuuuu09atW49FNngwp8ull79Yr/U7iuTnY9Gtk/ooKTbE6FgAAAAA4JXiI4P0j8l9dePZPRVh81NhWY2mfbJWT89arZwiliHC+zS70Lrhhhu0e/duvfLKK/roo4/k7++vK664QtXV1cciHzyQy+3Wa3PStWJzvqwWk/52Xi+lJIQaHQsAAAAAvJrJZNLArjF65JqhOnNYB1ktJq3bXqT7ZizWxz9sU63daXREoMU0q9AqLi5WYmKiHn74YfXq1UudO3fWjTfeqPz8fG3ZsuVYZYQHcbvdeve7zVq0Pkdmk0k3nN1T3TtGGB0LAAAAANoMP1+Lzjupsx6+eoh6doqQw+nWnEUZuufV37QsPY9liPAKzdoUPjw8XE899VTj5wUFBZoxY4bi4uKUkpLS4uHgeT75cbsWrMiSSdI147upX5dooyMBAAAAQJsUGxGoWy/oo1VbCvT+vC0qKK3RC5+tU/eO4br4lFS1i2KPY3iuIz7L4b333quZM2fK19dXL774ogIDA488hNV79qa3WMxN3rclX/6yQ3MWZUiSLh/XVSP7tDM4EVpKWx7X8F6Ma3gjxjW8EeMa3up4ju1B3WPVp0uUZv+6U3N+zdCGncW6/7UlOm1Iks4amawAvyOuBoAmjue4NrmPcK7h1q1bVVNTo/fff1+zZ8/We++9px49ejT7ftxut0wm05FEQCsy5+fteunTtZKkK8f30LmjmbEHAAAAAK1NdkGlXv18nZZsyJEkRdj8dfXEHjqhbwKvzeFRjrjQ2svlcmnChAnq3bu3pk6d2uzbO50ulZV5z4byFotZNluAysqq5XS6jI5zXPy0eo9e+XKDJOmskck6b1RngxOhpbXFcQ3vx7iGN2JcwxsxruGtjB7bq7YU6J1vNimvpP71eLcO4br0tDQlxgQf9yzwHkc7rm22gMOe3dWseYWFhYVatGiRxo0bJ4vFIkkym83q3Lmz8vLymh10L4fD+/5jcjpdXvm8fm9Zep5enV1fZp0yMFETR3RsE8+7rWor4xptC+Ma3ohxDW/EuIa3Mmps90yO0MPXDNbcxbs0Z1GGNmYU657pv2lAWrTGD++opNiQ454J3uN4jOtmLWrMy8vTbbfdpiVLljQeq6ur04YNG9S5M7Ny2pp12wv18hfr5XZLI3vH68KTuzBFFQAAAAA8hI/VookjkvXINUM0MC1abknLNuXrgdeX6plZq7U1q9ToiMAhNWuGVteuXTVy5Eg9+OCD+ve//y2bzaaXXnpJZWVluuKKK45RRLRGm3eXaNona+V0uTWwa4yuOL2rzJRZAAAAAOBxosICdOM5vZSZX6GvFmVo8cZcrdlWqDXbCtU1KUzjh3d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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mse_scores = [calculate_accuracy(df_returns.iloc[-test_size:].values, r) for r in results]\n", + "\n", + "plt.figure(figsize=(15, 5))\n", + "for no, r in enumerate(results[9:]):\n", + " plt.plot(r, label='forecast %d' % (no + 1))\n", + "plt.plot(df_returns.iloc[-test_size:].values, label='true trend', c='black')\n", + "plt.legend()\n", + "plt.title('average MSE: %.4f' % (np.mean(mse_scores)))\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + }, + "papermill": { + "default_parameters": {}, + "duration": 2267.614901, + "end_time": "2023-07-17T08:10:40.316291", + "environment_variables": {}, + "exception": null, + "input_path": "__notebook__.ipynb", + "output_path": "__notebook__.ipynb", + "parameters": {}, + "start_time": "2023-07-17T07:32:52.701390", + "version": "2.4.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}