lstm.ipynb

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   "source": [
    "## Using Long Short Term Memory (LSTM) neural networks for cryptocurrency price movement prediction\n",
    "* Forget gate: decides what information to throw away and what past information to remember\n",
    "* Update/input gate: what new information is added and how much information is added\n",
    "* Output gate: conditionally decided what to output from Memory \n",
    "<br> *Source: https://medium.com/@ahmetozlu93/long-short-term-memory-lstm-networks-in-a-nutshell-363cd470ccac*  \n",
    " \n",
    "    \n",
    "\n",
    "\n",
    "*Source code: https://www.youtube.com/watch?v=H6du_pfuznE&ab_channel=KrishNaik*"
   ],
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   "cell_type": "code",
   "execution_count": 20,
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   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.style.use('fivethirtyeight')\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the file in from device\n",
    "df = pd.read_csv('BTC_INR.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change the index to the date\n",
    "df = df.set_index(pd.DatetimeIndex(df['Date'].values)) \n",
    "df.index.name = 'Date'\n",
    "df.drop(columns=['Date'], inplace=True)"
   ]
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   "cell_type": "code",
   "execution_count": 4,
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     "data": {
      "text/plain": [
       "                   Open         High          Low        Close    Adj Close  \\\n",
       "Date                                                                          \n",
       "2019-10-24  531125.5625  534905.8750  528807.6250  532109.6250  532109.6250   \n",
       "2019-10-25  531911.8125  615708.6875  529882.0625  613524.0625  613524.0625   \n",
       "2019-10-26  614011.1875  709940.3750  613660.1875  654913.8125  654913.8125   \n",
       "2019-10-27  654682.5000  690656.6250  645532.4375  676643.4375  676643.4375   \n",
       "2019-10-28  677591.8125  693664.0625  654827.1250  654827.1250  654827.1250   \n",
       "\n",
       "                  Volume  \n",
       "Date                      \n",
       "2019-10-24  1.155234e+12  \n",
       "2019-10-25  2.033467e+12  \n",
       "2019-10-26  3.152115e+12  \n",
       "2019-10-27  2.308897e+12  \n",
       "2019-10-28  2.189437e+12  "
      ],
      "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>2019-10-24</th>\n      <td>531125.5625</td>\n      <td>534905.8750</td>\n      <td>528807.6250</td>\n      <td>532109.6250</td>\n      <td>532109.6250</td>\n      <td>1.155234e+12</td>\n    </tr>\n    <tr>\n      <th>2019-10-25</th>\n      <td>531911.8125</td>\n      <td>615708.6875</td>\n      <td>529882.0625</td>\n      <td>613524.0625</td>\n      <td>613524.0625</td>\n      <td>2.033467e+12</td>\n    </tr>\n    <tr>\n      <th>2019-10-26</th>\n      <td>614011.1875</td>\n      <td>709940.3750</td>\n      <td>613660.1875</td>\n      <td>654913.8125</td>\n      <td>654913.8125</td>\n      <td>3.152115e+12</td>\n    </tr>\n    <tr>\n      <th>2019-10-27</th>\n      <td>654682.5000</td>\n      <td>690656.6250</td>\n      <td>645532.4375</td>\n      <td>676643.4375</td>\n      <td>676643.4375</td>\n      <td>2.308897e+12</td>\n    </tr>\n    <tr>\n      <th>2019-10-28</th>\n      <td>677591.8125</td>\n      <td>693664.0625</td>\n      <td>654827.1250</td>\n      <td>654827.1250</td>\n      <td>654827.1250</td>\n      <td>2.189437e+12</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "df.head()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "            Open  High  Low  Close  Adj Close  Volume\n",
       "Date                                                 \n",
       "2020-08-04   NaN   NaN  NaN    NaN        NaN     NaN"
      ],
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     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "# Check of null values in the data\n",
    "filt = df['Open'].isnull()\n",
    "df[filt]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# There is 1 day of missing values. Using linear interpolate to fill that value\n",
    "df = df.interpolate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                 Open          High           Low         Close     Adj Close  \\\n",
       "Date                                                                            \n",
       "2020-08-04  834176.75  872190.40625  831134.90625  861993.21875  861993.21875   \n",
       "\n",
       "                  Volume  \n",
       "Date                      \n",
       "2020-08-04  1.674818e+12  "
      ],
      "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>2020-08-04</th>\n      <td>834176.75</td>\n      <td>872190.40625</td>\n      <td>831134.90625</td>\n      <td>861993.21875</td>\n      <td>861993.21875</td>\n      <td>1.674818e+12</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "source": [
    "# Missing values filled in using interpolate()\n",
    "df[filt]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df['Close']"
   ]
  },
  {
   "source": [
    "## Visualise the data"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "plt.figure(figsize=(12.2, 4.5))\n",
    "plt.plot(df1, label='Close')\n",
    "plt.title('BTC/INR Close Price History')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Price INR')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# LSTMs are sensitive to scale of data\n",
    "scaler = MinMaxScaler(feature_range=(0,1)) # The values we need to scale is between 0 and 1\n",
    "df1 = scaler.fit_transform(np.array(df1).reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 594,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting data into train-test split\n",
    "training_size = int(len(df1)*0.65)\n",
    "test_size = len(df1)-training_size\n",
    "train_data, test_data = df1[0:training_size,:], df1[training_size:len(df1),:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 595,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_dataset(dataset, time_step):\n",
    "    dataX, dataY = [], []\n",
    "    for i in range(len(dataset) - time_step -1):\n",
    "        a = dataset[i:(i+time_step),0]\n",
    "        dataX.append(a)\n",
    "        dataY.append(dataset[1+time_step,0])\n",
    "    return np.array(dataX), np.array(dataY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 683,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_step = 90\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": 684,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(147, 90)"
      ]
     },
     "metadata": {},
     "execution_count": 684
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 685,
   "metadata": {},
   "outputs": [],
   "source": [
    "# LSTMs need to reshaped into 3D (samples, time_step, features) -> add 1 to the existing shape \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": 686,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.layers import LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 687,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(LSTM(50, return_sequences=True, input_shape=(X_train.shape[1],1)))\n",
    "model.add(LSTM(50, return_sequences=True))\n",
    "model.add(LSTM(50))\n",
    "model.add(Dense(1))\n",
    "model.compile(loss='mean_squared_error', optimizer='adam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 688,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Model: \"sequential_7\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\nlstm_21 (LSTM)               (None, 90, 50)            10400     \n_________________________________________________________________\nlstm_22 (LSTM)               (None, 90, 50)            20200     \n_________________________________________________________________\nlstm_23 (LSTM)               (None, 50)                20200     \n_________________________________________________________________\ndense_7 (Dense)              (None, 1)                 51        \n=================================================================\nTotal params: 50,851\nTrainable params: 50,851\nNon-trainable params: 0\n_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 689,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x19ea9d64808>"
      ]
     },
     "metadata": {},
     "execution_count": 689
    }
   ],
   "source": [
    "model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=100, batch_size=64, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 690,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_predict = model.predict(X_train)\n",
    "test_predict = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 691,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transform back from MinMax Scaler\n",
    "train_predict = scaler.inverse_transform(train_predict)\n",
    "test_predict = scaler.inverse_transform(test_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 692,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "599656.1634183621"
      ]
     },
     "metadata": {},
     "execution_count": 692
    }
   ],
   "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": 693,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "598496.8467540113"
      ]
     },
     "metadata": {},
     "execution_count": 693
    }
   ],
   "source": [
    "math.sqrt(mean_squared_error(y_test, test_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 694,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "129"
      ]
     },
     "metadata": {},
     "execution_count": 694
    }
   ],
   "source": [
    "# Length of the test data\n",
    "len(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 695,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "39"
      ]
     },
     "metadata": {},
     "execution_count": 695
    }
   ],
   "source": [
    "len(test_data)-time_step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 696,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_prediction():\n",
    "    x_input = test_data[:time_step].reshape(1,-1) #Taking the first time_step number of values\n",
    "    x_input_list = x_input[0].tolist() # Use this to draw the last time_step number of values\n",
    "\n",
    "    i=0\n",
    "    while i < (len(test_data)-time_step):\n",
    "        x_input = np.array(x_input_list[-time_step:]).astype('float32') # Taking the last time_step number of values\n",
    "        x_input = x_input.reshape((1, time_step,1))\n",
    "        yHat = model.predict(x_input, verbose=0)\n",
    "        x_input_list.append(yHat.item(0))\n",
    "        i+=1\n",
    "\n",
    "    return scaler.inverse_transform(np.array(x_input_list).reshape(-1, 1))     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 697,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_pred = get_prediction()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 712,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([719070.69, 708232.31, 711612.  , 709423.62, 731276.81, 728123.  ,\n",
       "       705452.88, 699862.62, 692973.12, 684084.81, 691438.19, 694011.06,\n",
       "       690443.94, 696325.69, 683252.19, 678634.87, 682009.19, 677638.  ,\n",
       "       699483.06, 693029.31, 706560.62, 697124.25, 697383.12, 694832.44,\n",
       "       697210.  , 695231.12, 696265.94, 690849.56, 686567.75, 685416.75,\n",
       "       685989.56, 687995.06, 685029.5 , 698836.94, 710687.81, 717179.62,\n",
       "       712641.44, 723119.38, 740510.88, 822141.94, 817375.88, 830489.31,\n",
       "       830777.69, 848466.19, 881145.13, 828115.31, 845338.06, 861993.22,\n",
       "       878648.38, 882346.75, 870409.25, 881853.25, 875972.81, 889387.31,\n",
       "       851413.5 , 866195.56, 881735.69, 880955.25, 888203.31, 890807.19,\n",
       "       915978.56, 894266.  , 881539.69, 890749.  , 868617.06, 875311.  ,\n",
       "       873954.31, 873601.44, 843803.69, 853137.94, 835375.12, 844085.69,\n",
       "       841479.81, 856445.44, 857813.75, 874639.19, 836109.38, 752531.38,\n",
       "       769950.44, 744882.12, 753452.62, 761595.19, 747574.69, 751046.25,\n",
       "       762048.75, 764222.81, 767254.12, 758577.69, 783969.75, 794767.38,\n",
       "       597742.62, 597952.95, 598094.4 , 598186.72, 598263.65, 598354.78,\n",
       "       598479.32, 598645.78, 598853.42, 599095.41, 599361.56, 599640.46,\n",
       "       599920.91, 600192.98, 600448.49, 600681.39, 600887.41, 601064.12,\n",
       "       601210.6 , 601327.27, 601415.46, 601477.16, 601514.88, 601531.5 ,\n",
       "       601529.9 , 601513.01, 601483.68, 601444.44, 601397.73, 601345.66,\n",
       "       601290.12, 601232.68, 601174.66, 601117.13, 601061.02, 601007.01,\n",
       "       600955.48, 600906.94, 600861.46])"
      ]
     },
     "metadata": {},
     "execution_count": 712
    }
   ],
   "source": [
    "final_pred.flatten().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 742,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "plt.figure(figsize=(12.2, 4.5))\n",
    "plt.plot(df.index, scaler.inverse_transform(df1), label='Historical Close Price', alpha=1, linewidth=2) \n",
    "plt.plot(df.index[0:len(scaler.inverse_transform(train_data))], scaler.inverse_transform(train_data), label='Train Data', alpha=0.3, color='green', linewidth=5)\n",
    "plt.plot(df.index[training_size:len(df1)][:time_step], scaler.inverse_transform(test_data[:time_step]), label='x_input', alpha=0.3, color='purple', linewidth=5)\n",
    "plt.plot(df.index[training_size:len(df1)][time_step:], final_pred.flatten()[time_step:], label='predicted price', alpha=0.3, color='red', linewidth=5)\n",
    "plt.title('BTC/INR Prediction')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Price INR')\n",
    "plt.legend(loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}