Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime as dt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pandas_datareader as dr\n",
    "import matplotlib.pyplot as plt\n",
    "import fix_yahoo_finance as yf\n",
    "\n",
    "yf.pdr_override()  ## for fixing pandas data reader error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 downloaded\n"
     ]
    }
   ],
   "source": [
    "stock_name =[\"TCS\",\"SBIN\"]\n",
    "stock_name =[ x+\".NS\" for x in stock_name]\n",
    "\n",
    "start =dt.date.today()-dt.timedelta(days=2000)\n",
    "\n",
    "df= dr.data.get_data_yahoo(stock_name[0],start,end =dt.date.today())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\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>Adj Close</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>2016-01-15</th>\n",
       "      <td>1144.43</td>\n",
       "      <td>1145.85</td>\n",
       "      <td>1127.50</td>\n",
       "      <td>1130.55</td>\n",
       "      <td>1012.47</td>\n",
       "      <td>3269408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-18</th>\n",
       "      <td>1131.00</td>\n",
       "      <td>1145.95</td>\n",
       "      <td>1130.00</td>\n",
       "      <td>1138.82</td>\n",
       "      <td>1019.88</td>\n",
       "      <td>2213922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-19</th>\n",
       "      <td>1138.50</td>\n",
       "      <td>1150.00</td>\n",
       "      <td>1134.15</td>\n",
       "      <td>1139.65</td>\n",
       "      <td>1020.62</td>\n",
       "      <td>967910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-20</th>\n",
       "      <td>1136.90</td>\n",
       "      <td>1142.50</td>\n",
       "      <td>1121.50</td>\n",
       "      <td>1140.05</td>\n",
       "      <td>1020.98</td>\n",
       "      <td>1615954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-21</th>\n",
       "      <td>1142.50</td>\n",
       "      <td>1144.95</td>\n",
       "      <td>1122.88</td>\n",
       "      <td>1127.25</td>\n",
       "      <td>1011.96</td>\n",
       "      <td>2675536</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Open     High      Low    Close  Adj Close   Volume\n",
       "Date                                                              \n",
       "2016-01-15  1144.43  1145.85  1127.50  1130.55    1012.47  3269408\n",
       "2016-01-18  1131.00  1145.95  1130.00  1138.82    1019.88  2213922\n",
       "2016-01-19  1138.50  1150.00  1134.15  1139.65    1020.62   967910\n",
       "2016-01-20  1136.90  1142.50  1121.50  1140.05    1020.98  1615954\n",
       "2016-01-21  1142.50  1144.95  1122.88  1127.25    1011.96  2675536"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Open         False\n",
       "High         False\n",
       "Low          False\n",
       "Close        False\n",
       "Adj Close    False\n",
       "Volume       False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1348 entries, 2016-01-15 to 2021-07-06\n",
      "Data columns (total 6 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   Open       1348 non-null   float64\n",
      " 1   High       1348 non-null   float64\n",
      " 2   Low        1348 non-null   float64\n",
      " 3   Close      1348 non-null   float64\n",
      " 4   Adj Close  1348 non-null   float64\n",
      " 5   Volume     1348 non-null   int64  \n",
      "dtypes: float64(5), int64(1)\n",
      "memory usage: 73.7 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1130.55\n",
       "1       1138.82\n",
       "2       1139.65\n",
       "3       1140.05\n",
       "4       1127.25\n",
       "         ...   \n",
       "1343    3345.75\n",
       "1344    3341.50\n",
       "1345    3325.45\n",
       "1346    3321.00\n",
       "1347    3262.30\n",
       "Name: Close, Length: 1348, dtype: float64"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1=df.reset_index()[\"Close\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20ea6bb9108>"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1.plot(figsize=(16,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1348,)"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler =MinMaxScaler(feature_range=(0,1))\n",
    "df1= scaler.fit_transform(np.array(df1).reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.03432279],\n",
       "       [0.0378718 ],\n",
       "       [0.03822799],\n",
       "       ...,\n",
       "       [0.97624698],\n",
       "       [0.9743373 ],\n",
       "       [0.94914665]])"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "## spliting the data in train test\n",
    "\n",
    "size = int(len(df1)*.75)\n",
    "train_data= df1[0:size,:]\n",
    "test_data=df1[size:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_dataset(data,time_step=3):\n",
    "    x_data,y_data=[],[]\n",
    "    \n",
    "    for i in range(len(data)-time_step-1):\n",
    "        a=data[i:i+time_step,0]\n",
    "        x_data.append(a)\n",
    "        y_data.append(data[i+time_step,0])\n",
    "        \n",
    "    return np.array(x_data),np.array(y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_step=100\n",
    "\n",
    "x_train,y_train = create_dataset(train_data,time_step)\n",
    "x_test,y_test =create_dataset(test_data,time_step)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(910, 100)\n",
      "(910,)\n"
     ]
    }
   ],
   "source": [
    "print(x_train.shape)\n",
    "print(y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reshaping input for LSTM input type [samples,time_steps,fetures]\n",
    "x_train=x_train.reshape(x_train.shape[0],x_train.shape[1],1)\n",
    "x_test =x_test.reshape(x_test.shape[0],x_test.shape[1],1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense,LSTM\n",
    "from keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "## stacked Lstm model\n",
    "\n",
    "model =Sequential()\n",
    "model.add(LSTM(50,return_sequences=True,input_shape=(100,1)))\n",
    "model.add(LSTM(50,return_sequences=True))\n",
    "model.add(LSTM(50))\n",
    "model.add(Dense(1))\n",
    "model.compile(loss=\"mse\",optimizer='adam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_9 (LSTM)                (None, 100, 50)           10400     \n",
      "_________________________________________________________________\n",
      "lstm_10 (LSTM)               (None, 100, 50)           20200     \n",
      "_________________________________________________________________\n",
      "lstm_11 (LSTM)               (None, 50)                20200     \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 51        \n",
      "=================================================================\n",
      "Total params: 50,851\n",
      "Trainable params: 50,851\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "15/15 [==============================] - 3s 189ms/step - loss: 0.0195 - val_loss: 0.0871\n",
      "Epoch 2/10\n",
      "15/15 [==============================] - 1s 85ms/step - loss: 0.0035 - val_loss: 0.0232\n",
      "Epoch 3/10\n",
      "15/15 [==============================] - 1s 86ms/step - loss: 0.0013 - val_loss: 0.0084\n",
      "Epoch 4/10\n",
      "15/15 [==============================] - 1s 88ms/step - loss: 0.0011 - val_loss: 0.0088\n",
      "Epoch 5/10\n",
      "15/15 [==============================] - 1s 87ms/step - loss: 8.8495e-04 - val_loss: 0.0080\n",
      "Epoch 6/10\n",
      "15/15 [==============================] - 1s 90ms/step - loss: 8.6214e-04 - val_loss: 0.0075\n",
      "Epoch 7/10\n",
      "15/15 [==============================] - 1s 91ms/step - loss: 8.5128e-04 - val_loss: 0.0102\n",
      "Epoch 8/10\n",
      "15/15 [==============================] - 1s 86ms/step - loss: 8.5532e-04 - val_loss: 0.0089\n",
      "Epoch 9/10\n",
      "15/15 [==============================] - 1s 95ms/step - loss: 8.3163e-04 - val_loss: 0.0071\n",
      "Epoch 10/10\n",
      "15/15 [==============================] - 1s 91ms/step - loss: 8.9417e-04 - val_loss: 0.0095\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x20ea78b5f88>"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train,y_train,validation_data=(x_test,y_test),epochs=10,batch_size=64,verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_predict=model.predict(x_train)\n",
    "test_predict=model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.10762651], dtype=float32)"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_predict[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_predict=scaler.inverse_transform(train_predict)\n",
    "test_predict=scaler.inverse_transform(test_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1697.3244909027399"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "from sklearn.metrics import mean_squared_error\n",
    "math.sqrt(mean_squared_error(y_train,train_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2691.7496496466624"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math.sqrt(mean_squared_error(y_test,test_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Plotting \n",
    "# shift train predictions for plotting\n",
    "look_back=100\n",
    "trainPredictPlot = np.empty_like(df1)\n",
    "trainPredictPlot[:, :] = np.nan\n",
    "trainPredictPlot[look_back:len(train_predict)+look_back, :] = train_predict\n",
    "# shift test predictions for plotting\n",
    "testPredictPlot = np.empty_like(df1)\n",
    "testPredictPlot[:, :] = np.nan\n",
    "testPredictPlot[len(train_predict)+(look_back*2)+1:len(df1)-1, :] = test_predict\n",
    "# plot baseline and predictions\n",
    "plt.plot(scaler.inverse_transform(df1))\n",
    "plt.plot(trainPredictPlot)\n",
    "plt.plot(testPredictPlot)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}