predicting-stockprice-neuralnetwork.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting stock of Tongaat Hullets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "Epoch 1/50\n",
      "141/141 [==============================] - 19s 99ms/step - loss: 0.0090\n",
      "Epoch 2/50\n",
      "141/141 [==============================] - 15s 104ms/step - loss: 0.0022\n",
      "Epoch 3/50\n",
      "141/141 [==============================] - 15s 108ms/step - loss: 0.0022\n",
      "Epoch 4/50\n",
      "141/141 [==============================] - 15s 108ms/step - loss: 0.0019\n",
      "Epoch 5/50\n",
      "141/141 [==============================] - 16s 111ms/step - loss: 0.0016\n",
      "Epoch 6/50\n",
      "141/141 [==============================] - 15s 109ms/step - loss: 0.0017\n",
      "Epoch 7/50\n",
      "141/141 [==============================] - 15s 110ms/step - loss: 0.0016\n",
      "Epoch 8/50\n",
      "141/141 [==============================] - 18s 125ms/step - loss: 0.0014\n",
      "Epoch 9/50\n",
      "141/141 [==============================] - 16s 114ms/step - loss: 0.0014\n",
      "Epoch 10/50\n",
      "141/141 [==============================] - 16s 114ms/step - loss: 0.0013\n",
      "Epoch 11/50\n",
      "141/141 [==============================] - 16s 110ms/step - loss: 0.0011\n",
      "Epoch 12/50\n",
      "141/141 [==============================] - 16s 112ms/step - loss: 0.0011\n",
      "Epoch 13/50\n",
      "141/141 [==============================] - 16s 116ms/step - loss: 0.0010\n",
      "Epoch 14/50\n",
      "141/141 [==============================] - 18s 126ms/step - loss: 0.0011\n",
      "Epoch 15/50\n",
      "141/141 [==============================] - 17s 118ms/step - loss: 0.0011\n",
      "Epoch 16/50\n",
      "141/141 [==============================] - 16s 116ms/step - loss: 9.3174e-04\n",
      "Epoch 17/50\n",
      "141/141 [==============================] - 17s 122ms/step - loss: 9.5053e-04\n",
      "Epoch 18/50\n",
      "141/141 [==============================] - 16s 116ms/step - loss: 9.2172e-04\n",
      "Epoch 19/50\n",
      "141/141 [==============================] - 16s 111ms/step - loss: 9.0585e-04\n",
      "Epoch 20/50\n",
      "141/141 [==============================] - 19s 137ms/step - loss: 9.2391e-04\n",
      "Epoch 21/50\n",
      "141/141 [==============================] - 17s 121ms/step - loss: 8.6285e-04\n",
      "Epoch 22/50\n",
      "141/141 [==============================] - 18s 128ms/step - loss: 9.6573e-04\n",
      "Epoch 23/50\n",
      "141/141 [==============================] - 18s 127ms/step - loss: 7.9824e-04\n",
      "Epoch 24/50\n",
      "141/141 [==============================] - 18s 125ms/step - loss: 7.7118e-04\n",
      "Epoch 25/50\n",
      "141/141 [==============================] - 17s 121ms/step - loss: 7.8825e-04\n",
      "Epoch 26/50\n",
      "141/141 [==============================] - 17s 119ms/step - loss: 7.5984e-04\n",
      "Epoch 27/50\n",
      "141/141 [==============================] - 16s 115ms/step - loss: 7.7866e-04\n",
      "Epoch 28/50\n",
      "141/141 [==============================] - 17s 122ms/step - loss: 8.5325e-04\n",
      "Epoch 29/50\n",
      "141/141 [==============================] - 16s 111ms/step - loss: 7.4261e-04\n",
      "Epoch 30/50\n",
      "141/141 [==============================] - 16s 111ms/step - loss: 7.0304e-04\n",
      "Epoch 31/50\n",
      "141/141 [==============================] - 16s 113ms/step - loss: 7.1608e-04\n",
      "Epoch 32/50\n",
      "141/141 [==============================] - 16s 114ms/step - loss: 7.3588e-04\n",
      "Epoch 33/50\n",
      "141/141 [==============================] - 16s 112ms/step - loss: 6.9343e-04\n",
      "Epoch 34/50\n",
      "141/141 [==============================] - 16s 116ms/step - loss: 6.5438e-04\n",
      "Epoch 35/50\n",
      "141/141 [==============================] - 16s 115ms/step - loss: 7.9032e-04\n",
      "Epoch 36/50\n",
      "141/141 [==============================] - 16s 116ms/step - loss: 7.5234e-04\n",
      "Epoch 37/50\n",
      "141/141 [==============================] - 16s 113ms/step - loss: 6.4095e-04\n",
      "Epoch 38/50\n",
      "141/141 [==============================] - 16s 112ms/step - loss: 7.6411e-04\n",
      "Epoch 39/50\n",
      "141/141 [==============================] - 16s 112ms/step - loss: 7.3232e-04\n",
      "Epoch 40/50\n",
      "141/141 [==============================] - 17s 121ms/step - loss: 6.9140e-04\n",
      "Epoch 41/50\n",
      "141/141 [==============================] - 17s 118ms/step - loss: 6.7933e-04\n",
      "Epoch 42/50\n",
      "141/141 [==============================] - 16s 111ms/step - loss: 6.5145e-04\n",
      "Epoch 43/50\n",
      "141/141 [==============================] - 17s 118ms/step - loss: 6.8632e-04\n",
      "Epoch 44/50\n",
      "141/141 [==============================] - 16s 116ms/step - loss: 5.7334e-04\n",
      "Epoch 45/50\n",
      "141/141 [==============================] - 17s 119ms/step - loss: 6.2035e-04\n",
      "Epoch 46/50\n",
      "141/141 [==============================] - 17s 121ms/step - loss: 6.1441e-04\n",
      "Epoch 47/50\n",
      "141/141 [==============================] - 16s 113ms/step - loss: 6.0797e-04\n",
      "Epoch 48/50\n",
      "141/141 [==============================] - 17s 118ms/step - loss: 6.1127e-04\n",
      "Epoch 49/50\n",
      "141/141 [==============================] - 18s 127ms/step - loss: 6.1833e-04\n",
      "Epoch 50/50\n",
      "141/141 [==============================] - 17s 121ms/step - loss: 5.9376e-04\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2872670f520>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from keras.models import Sequential, load_model\n",
    "from keras.layers import LSTM, Dense, Dropout\n",
    "import stonks\n",
    "\n",
    "def create_dataset(df):\n",
    "    x = []\n",
    "    y = []\n",
    "    for i in range(50, df.shape[0]):\n",
    "        x.append(df[i-50:i, 0])\n",
    "        y.append(df[i, 0])\n",
    "    x = np.array(x)\n",
    "    y = np.array(y)\n",
    "    return x,y \n",
    "\n",
    "df = stonks.GetStockHistory('TON')\n",
    "\n",
    "df = df['Open'].values\n",
    "df = df.reshape(-1, 1)\n",
    "\n",
    "dataset_train = np.array(df[:int(df.shape[0]*0.8)])\n",
    "dataset_test = np.array(df[int(df.shape[0]*0.8):])\n",
    "\n",
    "scaler = MinMaxScaler(feature_range=(0,1))\n",
    "dataset_train = scaler.fit_transform(dataset_train)\n",
    "\n",
    "dataset_test = scaler.transform(dataset_test)\n",
    "\n",
    "x_train, y_train = create_dataset(dataset_train)\n",
    "x_test, y_test = create_dataset(dataset_test)\n",
    "\n",
    "model = Sequential()\n",
    "model.add(LSTM(units=96, return_sequences=True, input_shape=(x_train.shape[1], 1)))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(LSTM(units=96, return_sequences=True))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(LSTM(units=96, return_sequences=True))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(LSTM(units=96))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(units=1))\n",
    "\n",
    "x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))\n",
    "x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))\n",
    "\n",
    "model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "\n",
    "model.fit(x_train, y_train, epochs=50, batch_size=32)\n",
    "model.save('stock_prediction.h5')\n",
    "\n",
    "model = load_model('stock_prediction.h5')\n",
    "\n",
    "predictions = model.predict(x_test)\n",
    "predictions = scaler.inverse_transform(predictions)\n",
    "y_test_scaled = scaler.inverse_transform(y_test.reshape(-1, 1))\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(16,8))\n",
    "ax.set_facecolor('#000041')\n",
    "ax.plot(y_test_scaled, color='red', label='Original price')\n",
    "plt.plot(predictions, color='cyan', label='Predicted price')\n",
    "plt.legend()"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "3c5e62c22b15a649a0ef82ba1642c8d242063c96ec794d0d64bb4d1890e0fb9e"
  },
  "kernelspec": {
   "display_name": "Python 3.9.10 64-bit (windows store)",
   "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": "3.9.10"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}