src/LSTM-model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7b4a7fc3",
   "metadata": {},
   "source": [
    "# LSTM Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "036774cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "%matplotlib inline\n",
    "plt.style.use('fivethirtyeight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1decaaf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "def calculate_rmse(y_true, y_pred):\n",
    "    \"\"\"\n",
    "    Calculate the Root Mean Squared Error (RMSE)  \n",
    "    \"\"\" \n",
    "    return np.sqrt(mean_squared_error(y_true, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d34b0f0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>adjclose</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1999-11-01</th>\n",
       "      <td>136.500000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>135.562500</td>\n",
       "      <td>135.562500</td>\n",
       "      <td>88.217185</td>\n",
       "      <td>4006500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-11-02</th>\n",
       "      <td>135.968704</td>\n",
       "      <td>137.250000</td>\n",
       "      <td>134.593704</td>\n",
       "      <td>134.593704</td>\n",
       "      <td>87.586742</td>\n",
       "      <td>6516900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-11-03</th>\n",
       "      <td>136.000000</td>\n",
       "      <td>136.375000</td>\n",
       "      <td>135.125000</td>\n",
       "      <td>135.500000</td>\n",
       "      <td>88.176513</td>\n",
       "      <td>7222300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-11-04</th>\n",
       "      <td>136.750000</td>\n",
       "      <td>137.359299</td>\n",
       "      <td>135.765594</td>\n",
       "      <td>136.531204</td>\n",
       "      <td>88.847568</td>\n",
       "      <td>7907500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-11-05</th>\n",
       "      <td>138.625000</td>\n",
       "      <td>139.109299</td>\n",
       "      <td>136.781204</td>\n",
       "      <td>137.875000</td>\n",
       "      <td>89.722043</td>\n",
       "      <td>7431500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  open        high         low       close   adjclose   volume\n",
       "date                                                                          \n",
       "1999-11-01  136.500000  137.000000  135.562500  135.562500  88.217185  4006500\n",
       "1999-11-02  135.968704  137.250000  134.593704  134.593704  87.586742  6516900\n",
       "1999-11-03  136.000000  136.375000  135.125000  135.500000  88.176513  7222300\n",
       "1999-11-04  136.750000  137.359299  135.765594  136.531204  88.847568  7907500\n",
       "1999-11-05  138.625000  139.109299  136.781204  137.875000  89.722043  7431500"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_cleaned_data=pd.read_csv('../data/stock_cleaned_data.csv', index_col = 0)\n",
    "stock_cleaned_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5cbd762",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.DataFrame(stock_cleaned_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "3830d3ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>adjclose</th>\n",
       "      <th>MA100</th>\n",
       "      <th>RSI</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1999-11-22</th>\n",
       "      <td>142.437500</td>\n",
       "      <td>143.000000</td>\n",
       "      <td>141.5000</td>\n",
       "      <td>142.468704</td>\n",
       "      <td>92.711392</td>\n",
       "      <td>138.830063</td>\n",
       "      <td>79.387602</td>\n",
       "      <td>91.897955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-11-23</th>\n",
       "      <td>142.843704</td>\n",
       "      <td>142.843704</td>\n",
       "      <td>140.3750</td>\n",
       "      <td>141.218704</td>\n",
       "      <td>91.897955</td>\n",
       "      <td>138.970571</td>\n",
       "      <td>71.264847</td>\n",
       "      <td>92.386017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-11-24</th>\n",
       "      <td>140.750000</td>\n",
       "      <td>142.437500</td>\n",
       "      <td>140.0000</td>\n",
       "      <td>141.968704</td>\n",
       "      <td>92.386017</td>\n",
       "      <td>139.137134</td>\n",
       "      <td>73.038272</td>\n",
       "      <td>92.040336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-11-26</th>\n",
       "      <td>142.468704</td>\n",
       "      <td>142.875000</td>\n",
       "      <td>141.2500</td>\n",
       "      <td>141.437500</td>\n",
       "      <td>92.040336</td>\n",
       "      <td>139.258206</td>\n",
       "      <td>69.770625</td>\n",
       "      <td>91.714962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-11-29</th>\n",
       "      <td>140.875000</td>\n",
       "      <td>141.921799</td>\n",
       "      <td>140.4375</td>\n",
       "      <td>140.937500</td>\n",
       "      <td>91.714962</td>\n",
       "      <td>139.342171</td>\n",
       "      <td>66.758564</td>\n",
       "      <td>90.637129</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  open        high       low       close   adjclose  \\\n",
       "date                                                                  \n",
       "1999-11-22  142.437500  143.000000  141.5000  142.468704  92.711392   \n",
       "1999-11-23  142.843704  142.843704  140.3750  141.218704  91.897955   \n",
       "1999-11-24  140.750000  142.437500  140.0000  141.968704  92.386017   \n",
       "1999-11-26  142.468704  142.875000  141.2500  141.437500  92.040336   \n",
       "1999-11-29  140.875000  141.921799  140.4375  140.937500  91.714962   \n",
       "\n",
       "                 MA100        RSI     target  \n",
       "date                                          \n",
       "1999-11-22  138.830063  79.387602  91.897955  \n",
       "1999-11-23  138.970571  71.264847  92.386017  \n",
       "1999-11-24  139.137134  73.038272  92.040336  \n",
       "1999-11-26  139.258206  69.770625  91.714962  \n",
       "1999-11-29  139.342171  66.758564  90.637129  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop(['volume'], axis=1, inplace = True)\n",
    "data['MA100'] = MA(dataset, len(dataset), 100)\n",
    "data['MA100'].shift(-1)\n",
    "\n",
    "import pandas_ta as ta\n",
    "data['RSI']=ta.rsi(data['adjclose'], length=15)\n",
    "data['target'] = data['adjclose'].shift(-1)\n",
    "data.dropna(inplace = True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "a4bed9c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.18111582 0.17805747 0.18192489 ... 0.31620353 0.8936042  0.09647095]\n",
      " [0.1821035  0.17767624 0.17917401 ... 0.31809466 0.77558104 0.09764265]\n",
      " [0.17701267 0.17668545 0.17825705 ... 0.32033647 0.80134881 0.09681277]\n",
      " ...\n",
      " [0.82969825 0.83867506 0.83355878 ... 0.99898    0.66414476 0.86605357]\n",
      " [0.83852457 0.84172399 0.83861502 ... 0.9994884  0.64145845 0.86960665]\n",
      " [0.83745471 0.83896775 0.84112138 ... 1.         0.66193279 0.87025484]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "sc = MinMaxScaler(feature_range=(0,1))\n",
    "data_set_scaled = sc.fit_transform(data)\n",
    "scale = np.max(data['target']) - np.min(data['target'])\n",
    "print(data_set_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "151e088b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5887\n"
     ]
    }
   ],
   "source": [
    "X = []\n",
    "\n",
    "backcandles = 60\n",
    "print(data_set_scaled.shape[0])\n",
    "for j in range(7):\n",
    "    X.append([])\n",
    "    for i in range(backcandles, data_set_scaled.shape[0]):\n",
    "        X[j].append(data_set_scaled[i-backcandles:i, j])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "abf89584",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5827, 60, 7)\n",
      "(5827, 1)\n"
     ]
    }
   ],
   "source": [
    "X=np.moveaxis(X, [0], [2])\n",
    "\n",
    "X, yi =np.array(X), np.array(data_set_scaled[backcandles:,-1])\n",
    "y=np.reshape(yi,(len(yi),1))\n",
    "\n",
    "print(X.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "4516438c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4661\n",
      "(4661, 60, 7)\n",
      "(1166, 60, 7)\n",
      "(4661, 1)\n",
      "(1166, 1)\n"
     ]
    }
   ],
   "source": [
    "train_data_len = int(len(X)*0.8)\n",
    "print(train_data_len)\n",
    "X_train, X_test = X[:train_data_len], X[train_data_len:]\n",
    "y_train, y_test = y[:train_data_len], y[train_data_len:]\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "06981e1f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "132/132 [==============================] - 19s 91ms/step - loss: 0.0016 - val_loss: 1.4242e-04\n",
      "Epoch 2/30\n",
      "132/132 [==============================] - 10s 79ms/step - loss: 2.5315e-04 - val_loss: 2.3158e-04\n",
      "Epoch 3/30\n",
      "132/132 [==============================] - 10s 79ms/step - loss: 1.8188e-04 - val_loss: 3.3421e-04\n",
      "Epoch 4/30\n",
      "132/132 [==============================] - 10s 78ms/step - loss: 1.3220e-04 - val_loss: 3.4817e-04\n",
      "Epoch 5/30\n",
      "132/132 [==============================] - 10s 76ms/step - loss: 1.3076e-04 - val_loss: 4.8226e-04\n",
      "Epoch 6/30\n",
      "132/132 [==============================] - 12s 90ms/step - loss: 1.1219e-04 - val_loss: 7.0059e-05\n",
      "Epoch 7/30\n",
      "132/132 [==============================] - 10s 77ms/step - loss: 9.7121e-05 - val_loss: 4.9701e-05\n",
      "Epoch 8/30\n",
      "132/132 [==============================] - 10s 75ms/step - loss: 9.4055e-05 - val_loss: 2.9331e-04\n",
      "Epoch 9/30\n",
      "132/132 [==============================] - 10s 74ms/step - loss: 8.5728e-05 - val_loss: 7.0889e-05\n",
      "Epoch 10/30\n",
      "132/132 [==============================] - 10s 75ms/step - loss: 8.5049e-05 - val_loss: 2.8452e-04\n",
      "Epoch 11/30\n",
      "132/132 [==============================] - 12s 91ms/step - loss: 8.2950e-05 - val_loss: 6.4206e-05\n",
      "Epoch 12/30\n",
      "132/132 [==============================] - 12s 94ms/step - loss: 6.9826e-05 - val_loss: 3.5446e-04\n",
      "Epoch 13/30\n",
      "132/132 [==============================] - 13s 96ms/step - loss: 7.9268e-05 - val_loss: 4.3083e-05\n",
      "Epoch 14/30\n",
      "132/132 [==============================] - 12s 88ms/step - loss: 6.8437e-05 - val_loss: 4.8497e-05\n",
      "Epoch 15/30\n",
      "132/132 [==============================] - 12s 93ms/step - loss: 6.3343e-05 - val_loss: 5.9289e-04\n",
      "Epoch 16/30\n",
      "132/132 [==============================] - 14s 105ms/step - loss: 8.5606e-05 - val_loss: 3.3063e-05\n",
      "Epoch 17/30\n",
      "132/132 [==============================] - 13s 95ms/step - loss: 5.9512e-05 - val_loss: 4.6902e-05\n",
      "Epoch 18/30\n",
      "132/132 [==============================] - 12s 88ms/step - loss: 6.4156e-05 - val_loss: 1.3484e-04\n",
      "Epoch 19/30\n",
      "132/132 [==============================] - 12s 93ms/step - loss: 6.1426e-05 - val_loss: 7.9991e-05\n",
      "Epoch 20/30\n",
      "132/132 [==============================] - 12s 93ms/step - loss: 5.7963e-05 - val_loss: 7.1855e-05\n",
      "Epoch 21/30\n",
      "132/132 [==============================] - 12s 92ms/step - loss: 5.1834e-05 - val_loss: 2.9092e-04\n",
      "Epoch 22/30\n",
      "132/132 [==============================] - 12s 95ms/step - loss: 6.8071e-05 - val_loss: 1.6770e-04\n",
      "Epoch 23/30\n",
      "132/132 [==============================] - 14s 103ms/step - loss: 5.5658e-05 - val_loss: 5.7071e-05\n",
      "Epoch 24/30\n",
      "132/132 [==============================] - 12s 90ms/step - loss: 5.0996e-05 - val_loss: 3.7530e-05\n",
      "Epoch 25/30\n",
      "132/132 [==============================] - 12s 91ms/step - loss: 5.0750e-05 - val_loss: 1.2828e-04\n",
      "Epoch 26/30\n",
      "132/132 [==============================] - 15s 108ms/step - loss: 5.4917e-05 - val_loss: 3.7575e-05\n",
      "Epoch 27/30\n",
      "132/132 [==============================] - 12s 89ms/step - loss: 5.1059e-05 - val_loss: 7.5269e-05\n",
      "Epoch 28/30\n",
      "132/132 [==============================] - 13s 95ms/step - loss: 5.2634e-05 - val_loss: 3.1722e-05\n",
      "Epoch 29/30\n",
      "132/132 [==============================] - 13s 95ms/step - loss: 5.0810e-05 - val_loss: 6.5865e-05\n",
      "Epoch 30/30\n",
      "132/132 [==============================] - 12s 89ms/step - loss: 4.7639e-05 - val_loss: 6.3368e-05\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x2273a434160>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import LSTM\n",
    "from keras.layers import Dropout\n",
    "from keras.layers import Dense\n",
    "from keras.layers import TimeDistributed\n",
    "\n",
    "import tensorflow as tf\n",
    "import keras\n",
    "from keras import optimizers\n",
    "from keras.callbacks import History\n",
    "from keras.models import Model\n",
    "from keras.layers import Dense, Dropout, LSTM, Input, Activation, concatenate\n",
    "\n",
    "np.random.seed(10)\n",
    "\n",
    "lstm_input = Input(shape=(backcandles, 7), name='lstm_input')\n",
    "inputs = LSTM(150, name='first_layer')(lstm_input)\n",
    "inputs = Dropout(0.2)(inputs)\n",
    "inputs = Dense(1, name='dense_layer')(inputs)\n",
    "output = Activation('linear', name='output')(inputs)\n",
    "model = Model(inputs=lstm_input, outputs=output)\n",
    "adam = optimizers.Adam()\n",
    "model.compile(optimizer=adam, loss='mse')\n",
    "model.fit(x=X_train, y=y_train, batch_size=32, epochs=30, shuffle=True, validation_split = 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "23253d61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "146/146 [==============================] - 6s 29ms/step\n",
      "training RMSE: 2.512721630438602\n"
     ]
    }
   ],
   "source": [
    "y_train_pred = model.predict(X_train)\n",
    "print(\"training RMSE:\", calculate_rmse(y_train_pred, y_train) * scale)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf28e807",
   "metadata": {},
   "source": [
    "**Testing the accuracy of the LSTM Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "4eabe8e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "37/37 [==============================] - 1s 29ms/step\n",
      "testing RMSE: 7.298634749821586\n"
     ]
    }
   ],
   "source": [
    "y_test_pred = model.predict(X_test)\n",
    "print(\"testing RMSE:\", calculate_rmse(y_test_pred, y_test) * scale)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "c7a4aa32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "plt.title('LSTM Model')\n",
    "plt.xlabel('Date',fontsize=18)\n",
    "plt.ylabel('Close Price USD ($)',fontsize=18)\n",
    "plt.plot(y_test * scale)\n",
    "plt.plot(y_test_pred * scale)\n",
    "plt.legend(['Test data','LSTM Predictions'],loc='lower right')\n",
    "plt.show"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tensorflow",
   "language": "python",
   "name": "tensorflow"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}