BOA_Stock.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import pandas_datareader as web\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, LSTM\n",
    "from keras.layers import Dropout\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing Stock data from yahoo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the stock quote\n",
    "df=web.DataReader('BAC',data_source='yahoo',start='2010-01-01', end='2020-5-12')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 Open       Volume\n",
      "Date                              \n",
      "2010-01-04  15.240000  180845200.0\n",
      "2010-01-05  15.740000  209521300.0\n",
      "2010-01-06  16.209999  205257900.0\n",
      "2010-01-07  16.680000  320868400.0\n",
      "2010-01-08  16.980000  220104700.0\n",
      "...               ...          ...\n",
      "2020-05-06  23.000000   53874600.0\n",
      "2020-05-07  22.660000   51884300.0\n",
      "2020-05-08  23.469999   45250000.0\n",
      "2020-05-11  23.110001   68718200.0\n",
      "2020-05-12  22.709999   69316900.0\n",
      "\n",
      "[2607 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "df=df[['Open','Volume']]\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualising our data\n",
    "plt.figure(figsize=[14,8])\n",
    "plt.title(\"Close price History\")\n",
    "plt.plot(df['Open'])\n",
    "plt.xlabel('Date',fontsize=16)\n",
    "plt.ylabel('Stock Price',fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# seperating training and testing data\n",
    "# We are going to test 60 days of latest stock price\n",
    "# The rest data before the latest 60 days will be trained\n",
    "Training_data=df[:2547].values\n",
    "Test_data=df[2547:].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of Test_data  (60, 2)\n",
      " Shape of Training data  (2547, 2)\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape of Test_data \",Test_data.shape)\n",
    "print(\" Shape of Training data \", Training_data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Scaling the data between one and 0\n",
    "sc=MinMaxScaler(feature_range=(0,1))\n",
    "Training_data_scaled=sc.fit_transform(Training_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating Time Steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train=[]\n",
    "y_train=[]\n",
    "\n",
    "#Creating a 60 day timesteps \n",
    "for i in range(60,Training_data.shape[0]):\n",
    "    #Takes data form 0 to 59. The process continues \n",
    "    x_train.append(Training_data_scaled[i-60:i])\n",
    "    # takes the data of 60th position\n",
    "    y_train.append(Training_data_scaled[i,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# converting x_train and y_train to array\n",
    "x_train=np.array(x_train)\n",
    "y_train=np.array(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of x_train is: (2487, 60, 2)\n",
      "The shape of y_train is: (2487,)\n"
     ]
    }
   ],
   "source": [
    "print(\"The shape of x_train is:\",x_train.shape)\n",
    "print(\"The shape of y_train is:\",y_train.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Initialising the RNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "regressor=Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\aayus\\anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n"
     ]
    }
   ],
   "source": [
    "# First layer of LSTM and Dropout\n",
    "#Dropout is important feature of LSTM wher it drops some portion of the data\n",
    "regressor.add(LSTM(units=50, return_sequences=True, input_shape=(x_train.shape[1],2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# adding dropout of 20%\n",
    "regressor.add(Dropout(0.2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding the second layer of the LSTM\n",
    "regressor.add(LSTM(units=50, return_sequences=True))\n",
    "regressor.add(Dropout(0.2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding the Third layer of the LSTM\n",
    "regressor.add(LSTM(units=50, return_sequences=True))\n",
    "regressor.add(Dropout(0.2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Adding fourth Layer of LSTM\n",
    "# we remove return sequence as we are not returning any sequence\n",
    "regressor.add(LSTM(units=50))\n",
    "regressor.add(Dropout(0.2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Output layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "regressor.add(Dense(units=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adding Optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for the optimer we are using adam which is a socratic gradient descent\n",
    "regressor.compile(optimizer='adam', loss='mean_squared_error')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training our RNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\aayus\\anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Epoch 1/100\n",
      "2487/2487 [==============================] - 8s 3ms/step - loss: 0.0152\n",
      "Epoch 2/100\n",
      "2487/2487 [==============================] - 6s 3ms/step - loss: 0.0037\n",
      "Epoch 3/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 0.0034A: 0s - loss: \n",
      "Epoch 4/100\n",
      "2487/2487 [==============================] - 6s 3ms/step - loss: 0.0034\n",
      "Epoch 5/100\n",
      "2487/2487 [==============================] - 6s 3ms/step - loss: 0.0030\n",
      "Epoch 6/100\n",
      "2487/2487 [==============================] - 6s 3ms/step - loss: 0.0030\n",
      "Epoch 7/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 0.0027\n",
      "Epoch 8/100\n",
      "2487/2487 [==============================] - 6s 3ms/step - loss: 0.0027\n",
      "Epoch 9/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 0.0026\n",
      "Epoch 10/100\n",
      "2487/2487 [==============================] - 6s 3ms/step - loss: 0.0026\n",
      "Epoch 11/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 0.0024\n",
      "Epoch 12/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 0.0023\n",
      "Epoch 13/100\n",
      "2487/2487 [==============================] - 6s 3ms/step - loss: 0.0023\n",
      "Epoch 14/100\n",
      "2487/2487 [==============================] - 6s 3ms/step - loss: 0.0023\n",
      "Epoch 15/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 0.0021\n",
      "Epoch 16/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 0.0021\n",
      "Epoch 17/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 0.0021\n",
      "Epoch 18/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 0.0021\n",
      "Epoch 19/100\n",
      "2487/2487 [==============================] - 6s 3ms/step - loss: 0.0019\n",
      "Epoch 20/100\n",
      "2487/2487 [==============================] - 6s 3ms/step - loss: 0.0018\n",
      "Epoch 21/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 0.0020\n",
      "Epoch 22/100\n",
      "2487/2487 [==============================] - 6s 3ms/step - loss: 0.0019\n",
      "Epoch 23/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 0.0018\n",
      "Epoch 24/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 0.0017\n",
      "Epoch 25/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 0.0016\n",
      "Epoch 26/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 0.0016\n",
      "Epoch 27/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 0.0015A: 0s - loss: 0\n",
      "Epoch 28/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 0.0014\n",
      "Epoch 29/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 0.0014\n",
      "Epoch 30/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 0.0017\n",
      "Epoch 31/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 0.0013\n",
      "Epoch 32/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 0.0013A: 1s - \n",
      "Epoch 33/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 0.0013\n",
      "Epoch 34/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 0.0015\n",
      "Epoch 35/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 0.0013\n",
      "Epoch 36/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 0.0014\n",
      "Epoch 37/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 0.0013\n",
      "Epoch 38/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 0.0014\n",
      "Epoch 39/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 0.0014A: \n",
      "Epoch 40/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 0.0012\n",
      "Epoch 41/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 0.0012\n",
      "Epoch 42/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 0.0011\n",
      "Epoch 43/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 0.0011\n",
      "Epoch 44/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 0.0012\n",
      "Epoch 45/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 0.0011\n",
      "Epoch 46/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 0.0011\n",
      "Epoch 47/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 0.0011\n",
      "Epoch 48/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 0.0010\n",
      "Epoch 49/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 0.0011\n",
      "Epoch 50/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 0.0011\n",
      "Epoch 51/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 0.0011\n",
      "Epoch 52/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 0.0010\n",
      "Epoch 53/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 0.0011\n",
      "Epoch 54/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 0.0012\n",
      "Epoch 55/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 0.0011\n",
      "Epoch 56/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 0.0010\n",
      "Epoch 57/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 9.5260e-04\n",
      "Epoch 58/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 0.0010\n",
      "Epoch 59/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 0.0011A: 0s - loss:\n",
      "Epoch 60/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 9.0988e-04\n",
      "Epoch 61/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 9.0031e-04\n",
      "Epoch 62/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 9.5658e-04\n",
      "Epoch 63/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 9.8459e-04\n",
      "Epoch 64/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 9.6275e-04\n",
      "Epoch 65/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 9.8461e-04\n",
      "Epoch 66/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 0.0011\n",
      "Epoch 67/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 0.0011\n",
      "Epoch 68/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 8.6769e-04\n",
      "Epoch 69/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 0.0010\n",
      "Epoch 70/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 9.4049e-04\n",
      "Epoch 71/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 9.4140e-04\n",
      "Epoch 72/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 8.9597e-04\n",
      "Epoch 73/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 9.1678e-04\n",
      "Epoch 74/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 9.7900e-04\n",
      "Epoch 75/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 8.8701e-04\n",
      "Epoch 76/100\n",
      "2487/2487 [==============================] - 6s 3ms/step - loss: 9.1655e-04\n",
      "Epoch 77/100\n",
      "2487/2487 [==============================] - 6s 3ms/step - loss: 0.0010\n",
      "Epoch 78/100\n",
      "2487/2487 [==============================] - 6s 3ms/step - loss: 8.6415e-04\n",
      "Epoch 79/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 8.1995e-04\n",
      "Epoch 80/100\n",
      "2487/2487 [==============================] - 6s 3ms/step - loss: 8.9037e-04\n",
      "Epoch 81/100\n",
      "2487/2487 [==============================] - 6s 3ms/step - loss: 8.6051e-04\n",
      "Epoch 82/100\n",
      "2487/2487 [==============================] - 6s 3ms/step - loss: 9.3322e-04\n",
      "Epoch 83/100\n",
      "2487/2487 [==============================] - 6s 3ms/step - loss: 8.4816e-04\n",
      "Epoch 84/100\n",
      "2487/2487 [==============================] - 6s 3ms/step - loss: 8.3724e-04\n",
      "Epoch 85/100\n",
      "2487/2487 [==============================] - 6s 3ms/step - loss: 8.2642e-04\n",
      "Epoch 86/100\n",
      "2487/2487 [==============================] - 6s 3ms/step - loss: 8.7937e-04\n",
      "Epoch 87/100\n",
      "2487/2487 [==============================] - 6s 3ms/step - loss: 9.6608e-04\n",
      "Epoch 88/100\n",
      "2487/2487 [==============================] - 6s 3ms/step - loss: 8.3930e-04\n",
      "Epoch 89/100\n",
      "2487/2487 [==============================] - 6s 3ms/step - loss: 9.1055e-04\n",
      "Epoch 90/100\n",
      "2487/2487 [==============================] - 6s 3ms/step - loss: 8.5653e-04A: 1\n",
      "Epoch 91/100\n",
      "2487/2487 [==============================] - 7s 3ms/step - loss: 8.6722e-04\n",
      "Epoch 92/100\n",
      "2487/2487 [==============================] - 6s 3ms/step - loss: 7.7997e-04\n",
      "Epoch 93/100\n",
      "2487/2487 [==============================] - 6s 3ms/step - loss: 8.1029e-04\n",
      "Epoch 94/100\n",
      "2487/2487 [==============================] - 6s 3ms/step - loss: 8.2072e-04\n",
      "Epoch 95/100\n",
      "2487/2487 [==============================] - 6s 3ms/step - loss: 8.2153e-04\n",
      "Epoch 96/100\n",
      "2487/2487 [==============================] - 6s 3ms/step - loss: 8.6215e-04\n",
      "Epoch 97/100\n",
      "2487/2487 [==============================] - 6s 3ms/step - loss: 9.0974e-04\n",
      "Epoch 98/100\n",
      "2487/2487 [==============================] - 6s 3ms/step - loss: 8.2166e-04\n",
      "Epoch 99/100\n",
      "2487/2487 [==============================] - 6s 3ms/step - loss: 8.1097e-04\n",
      "Epoch 100/100\n",
      "2487/2487 [==============================] - 6s 3ms/step - loss: 7.6225e-04\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x24fd041b3c8>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Batch size of 32 is a standard batch size most of the people use\n",
    "regressor.fit(x_train,y_train,epochs=100,batch_size=32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Making Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "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>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-11-19</th>\n",
       "      <td>33.119999</td>\n",
       "      <td>33183700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-20</th>\n",
       "      <td>32.790001</td>\n",
       "      <td>43612200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-21</th>\n",
       "      <td>32.779999</td>\n",
       "      <td>42787600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-22</th>\n",
       "      <td>32.959999</td>\n",
       "      <td>38783900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-25</th>\n",
       "      <td>33.330002</td>\n",
       "      <td>50238800.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Open      Volume\n",
       "Date                             \n",
       "2019-11-19  33.119999  33183700.0\n",
       "2019-11-20  32.790001  43612200.0\n",
       "2019-11-21  32.779999  42787600.0\n",
       "2019-11-22  32.959999  38783900.0\n",
       "2019-11-25  33.330002  50238800.0"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We are going to concat both training and testing dataset\n",
    "Train_60_days=df[:2547].tail(60)\n",
    "Test_60_days=df[2547:]\n",
    "Train_60_days.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "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>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33.119999</td>\n",
       "      <td>33183700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32.790001</td>\n",
       "      <td>43612200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>32.779999</td>\n",
       "      <td>42787600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32.959999</td>\n",
       "      <td>38783900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33.330002</td>\n",
       "      <td>50238800.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Open      Volume\n",
       "0  33.119999  33183700.0\n",
       "1  32.790001  43612200.0\n",
       "2  32.779999  42787600.0\n",
       "3  32.959999  38783900.0\n",
       "4  33.330002  50238800.0"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df=Train_60_days.append(Test_60_days, ignore_index=True)\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(120, 2)"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "120"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs=sc.transform(new_df)\n",
    "inputs.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_x_test=[]\n",
    "new_y_test=[]\n",
    "\n",
    "for i in range(60,inputs.shape[0]):\n",
    "    new_x_test.append(inputs[i-60:i])\n",
    "    new_y_test.append(inputs[i,0])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60, 60, 2)"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_x_test=np.array(new_x_test)\n",
    "new_y_test=np.array(new_y_test)\n",
    "new_x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_prediction=regressor.predict(new_x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.26904210e-02, 1.18225949e-09])"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc.scale_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30.590000661049917"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler=(1/3.26904210e-02)\n",
    "scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_prediction=final_prediction*scaler\n",
    "final_prediction=(final_prediction+5.11)\n",
    "new_y_test=new_y_test*scaler\n",
    "new_y_test=(new_y_test+5.11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[34.626045],\n",
       "       [34.62489 ],\n",
       "       [34.557053],\n",
       "       [34.47809 ],\n",
       "       [34.444893],\n",
       "       [34.18036 ],\n",
       "       [33.75022 ],\n",
       "       [33.132183],\n",
       "       [32.19146 ],\n",
       "       [30.977228],\n",
       "       [30.065296],\n",
       "       [29.896666],\n",
       "       [29.877855],\n",
       "       [29.491169],\n",
       "       [28.493668],\n",
       "       [26.65459 ],\n",
       "       [25.07263 ],\n",
       "       [24.19003 ],\n",
       "       [23.364492],\n",
       "       [22.988367],\n",
       "       [22.406584],\n",
       "       [21.999256],\n",
       "       [21.648727],\n",
       "       [21.372227],\n",
       "       [21.588625],\n",
       "       [21.367048],\n",
       "       [20.840479],\n",
       "       [20.960278],\n",
       "       [21.464901],\n",
       "       [22.007946],\n",
       "       [22.322363],\n",
       "       [22.42404 ],\n",
       "       [21.895092],\n",
       "       [21.05413 ],\n",
       "       [20.545113],\n",
       "       [20.6388  ],\n",
       "       [21.546076],\n",
       "       [22.484581],\n",
       "       [23.43402 ],\n",
       "       [24.336924],\n",
       "       [24.829172],\n",
       "       [24.275396],\n",
       "       [23.30908 ],\n",
       "       [22.600386],\n",
       "       [22.426546],\n",
       "       [22.411308],\n",
       "       [22.455004],\n",
       "       [22.429342],\n",
       "       [22.386305],\n",
       "       [22.461126],\n",
       "       [23.149   ],\n",
       "       [24.148542],\n",
       "       [24.71414 ],\n",
       "       [24.556948],\n",
       "       [23.90516 ],\n",
       "       [23.470537],\n",
       "       [23.289375],\n",
       "       [23.185854],\n",
       "       [23.329817],\n",
       "       [23.475609]], dtype=float32)"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "plt.plot(new_y_test, color='red', label=\"Real BOA Stock\")\n",
    "plt.plot(final_prediction, color='blue', label=\"Predicted BOA Stock\")\n",
    "plt.title(\"BOA stock prediction\")\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel('Stock Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Table of Actual price vs Predicted price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aayus\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "C:\\Users\\aayus\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "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>Volume</th>\n",
       "      <th>Open_before_pred</th>\n",
       "      <th>Actual_Prediction</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-02-18</th>\n",
       "      <td>34.770000</td>\n",
       "      <td>40318000.0</td>\n",
       "      <td>34.770000</td>\n",
       "      <td>34.626045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-19</th>\n",
       "      <td>34.450001</td>\n",
       "      <td>29632100.0</td>\n",
       "      <td>34.450001</td>\n",
       "      <td>34.624889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-20</th>\n",
       "      <td>34.459999</td>\n",
       "      <td>42604100.0</td>\n",
       "      <td>34.459999</td>\n",
       "      <td>34.557053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-21</th>\n",
       "      <td>34.529999</td>\n",
       "      <td>46675500.0</td>\n",
       "      <td>34.529999</td>\n",
       "      <td>34.478088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-24</th>\n",
       "      <td>33.180000</td>\n",
       "      <td>79159600.0</td>\n",
       "      <td>33.180000</td>\n",
       "      <td>34.444893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-25</th>\n",
       "      <td>32.689999</td>\n",
       "      <td>91586800.0</td>\n",
       "      <td>32.689999</td>\n",
       "      <td>34.180359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>31.389999</td>\n",
       "      <td>80012800.0</td>\n",
       "      <td>31.389999</td>\n",
       "      <td>33.750221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-27</th>\n",
       "      <td>29.549999</td>\n",
       "      <td>121148300.0</td>\n",
       "      <td>29.549999</td>\n",
       "      <td>33.132183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-28</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>171263600.0</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>32.191460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-02</th>\n",
       "      <td>28.350000</td>\n",
       "      <td>147776300.0</td>\n",
       "      <td>28.350000</td>\n",
       "      <td>30.977228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-03</th>\n",
       "      <td>29.370001</td>\n",
       "      <td>138698500.0</td>\n",
       "      <td>29.370001</td>\n",
       "      <td>30.065296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-04</th>\n",
       "      <td>28.250000</td>\n",
       "      <td>111239800.0</td>\n",
       "      <td>28.250000</td>\n",
       "      <td>29.896666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-05</th>\n",
       "      <td>27.240000</td>\n",
       "      <td>110878700.0</td>\n",
       "      <td>27.240000</td>\n",
       "      <td>29.877855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-06</th>\n",
       "      <td>25.469999</td>\n",
       "      <td>119121200.0</td>\n",
       "      <td>25.469999</td>\n",
       "      <td>29.491169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-09</th>\n",
       "      <td>22.230000</td>\n",
       "      <td>145196000.0</td>\n",
       "      <td>22.229999</td>\n",
       "      <td>28.493668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-10</th>\n",
       "      <td>23.250000</td>\n",
       "      <td>134863600.0</td>\n",
       "      <td>23.250000</td>\n",
       "      <td>26.654591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-11</th>\n",
       "      <td>22.840000</td>\n",
       "      <td>127347000.0</td>\n",
       "      <td>22.840000</td>\n",
       "      <td>25.072630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-12</th>\n",
       "      <td>20.590000</td>\n",
       "      <td>139552600.0</td>\n",
       "      <td>20.590000</td>\n",
       "      <td>24.190029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-13</th>\n",
       "      <td>22.190001</td>\n",
       "      <td>128357400.0</td>\n",
       "      <td>22.190000</td>\n",
       "      <td>23.364492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-16</th>\n",
       "      <td>19.850000</td>\n",
       "      <td>121688800.0</td>\n",
       "      <td>19.850000</td>\n",
       "      <td>22.988367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-17</th>\n",
       "      <td>21.059999</td>\n",
       "      <td>120065700.0</td>\n",
       "      <td>21.059999</td>\n",
       "      <td>22.406584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-18</th>\n",
       "      <td>20.270000</td>\n",
       "      <td>148272500.0</td>\n",
       "      <td>20.270000</td>\n",
       "      <td>21.999256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-19</th>\n",
       "      <td>20.260000</td>\n",
       "      <td>122461300.0</td>\n",
       "      <td>20.260000</td>\n",
       "      <td>21.648727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-20</th>\n",
       "      <td>21.760000</td>\n",
       "      <td>150910400.0</td>\n",
       "      <td>21.760000</td>\n",
       "      <td>21.372227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-23</th>\n",
       "      <td>19.260000</td>\n",
       "      <td>181898500.0</td>\n",
       "      <td>19.260000</td>\n",
       "      <td>21.588625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-24</th>\n",
       "      <td>19.420000</td>\n",
       "      <td>147882400.0</td>\n",
       "      <td>19.420000</td>\n",
       "      <td>21.367048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-25</th>\n",
       "      <td>21.709999</td>\n",
       "      <td>146991300.0</td>\n",
       "      <td>21.709999</td>\n",
       "      <td>20.840479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-26</th>\n",
       "      <td>21.309999</td>\n",
       "      <td>116111400.0</td>\n",
       "      <td>21.309999</td>\n",
       "      <td>20.960278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-27</th>\n",
       "      <td>21.680000</td>\n",
       "      <td>91522800.0</td>\n",
       "      <td>21.680000</td>\n",
       "      <td>21.464901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-30</th>\n",
       "      <td>21.650000</td>\n",
       "      <td>80449100.0</td>\n",
       "      <td>21.650000</td>\n",
       "      <td>22.007946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-31</th>\n",
       "      <td>21.820000</td>\n",
       "      <td>95213700.0</td>\n",
       "      <td>21.820000</td>\n",
       "      <td>22.322363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-01</th>\n",
       "      <td>19.930000</td>\n",
       "      <td>83961300.0</td>\n",
       "      <td>19.930000</td>\n",
       "      <td>22.424040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-02</th>\n",
       "      <td>19.650000</td>\n",
       "      <td>70007400.0</td>\n",
       "      <td>19.650000</td>\n",
       "      <td>21.895092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-03</th>\n",
       "      <td>20.400000</td>\n",
       "      <td>68293500.0</td>\n",
       "      <td>20.400000</td>\n",
       "      <td>21.054131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-06</th>\n",
       "      <td>21.030001</td>\n",
       "      <td>97766200.0</td>\n",
       "      <td>21.030001</td>\n",
       "      <td>20.545113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-07</th>\n",
       "      <td>22.799999</td>\n",
       "      <td>94560300.0</td>\n",
       "      <td>22.799999</td>\n",
       "      <td>20.638800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-08</th>\n",
       "      <td>22.410000</td>\n",
       "      <td>81212300.0</td>\n",
       "      <td>22.410000</td>\n",
       "      <td>21.546076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-09</th>\n",
       "      <td>24.070000</td>\n",
       "      <td>125414800.0</td>\n",
       "      <td>24.070000</td>\n",
       "      <td>22.484581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-13</th>\n",
       "      <td>24.780001</td>\n",
       "      <td>83164700.0</td>\n",
       "      <td>24.780001</td>\n",
       "      <td>23.434019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-14</th>\n",
       "      <td>24.500000</td>\n",
       "      <td>88991100.0</td>\n",
       "      <td>24.500000</td>\n",
       "      <td>24.336924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-15</th>\n",
       "      <td>22.160000</td>\n",
       "      <td>99082200.0</td>\n",
       "      <td>22.160000</td>\n",
       "      <td>24.829172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-16</th>\n",
       "      <td>22.150000</td>\n",
       "      <td>89213300.0</td>\n",
       "      <td>22.150000</td>\n",
       "      <td>24.275396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-17</th>\n",
       "      <td>22.340000</td>\n",
       "      <td>92800300.0</td>\n",
       "      <td>22.340000</td>\n",
       "      <td>23.309080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-20</th>\n",
       "      <td>22.570000</td>\n",
       "      <td>74446300.0</td>\n",
       "      <td>22.570000</td>\n",
       "      <td>22.600386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-21</th>\n",
       "      <td>21.900000</td>\n",
       "      <td>78871300.0</td>\n",
       "      <td>21.900000</td>\n",
       "      <td>22.426546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-22</th>\n",
       "      <td>22.160000</td>\n",
       "      <td>49759100.0</td>\n",
       "      <td>22.160000</td>\n",
       "      <td>22.411308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-23</th>\n",
       "      <td>21.959999</td>\n",
       "      <td>50614800.0</td>\n",
       "      <td>21.959999</td>\n",
       "      <td>22.455004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-24</th>\n",
       "      <td>22.110001</td>\n",
       "      <td>47675700.0</td>\n",
       "      <td>22.110000</td>\n",
       "      <td>22.429342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-27</th>\n",
       "      <td>22.520000</td>\n",
       "      <td>66916700.0</td>\n",
       "      <td>22.520000</td>\n",
       "      <td>22.386305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-28</th>\n",
       "      <td>24.450001</td>\n",
       "      <td>65504900.0</td>\n",
       "      <td>24.450001</td>\n",
       "      <td>22.461126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-29</th>\n",
       "      <td>24.910000</td>\n",
       "      <td>73493900.0</td>\n",
       "      <td>24.910000</td>\n",
       "      <td>23.149000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-30</th>\n",
       "      <td>24.209999</td>\n",
       "      <td>55244100.0</td>\n",
       "      <td>24.209999</td>\n",
       "      <td>24.148542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-01</th>\n",
       "      <td>23.379999</td>\n",
       "      <td>51772600.0</td>\n",
       "      <td>23.379999</td>\n",
       "      <td>24.714140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-04</th>\n",
       "      <td>22.700001</td>\n",
       "      <td>45886100.0</td>\n",
       "      <td>22.700001</td>\n",
       "      <td>24.556948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-05</th>\n",
       "      <td>23.480000</td>\n",
       "      <td>45691900.0</td>\n",
       "      <td>23.479999</td>\n",
       "      <td>23.905161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-06</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>53874600.0</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>23.470537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-07</th>\n",
       "      <td>22.660000</td>\n",
       "      <td>51884300.0</td>\n",
       "      <td>22.660000</td>\n",
       "      <td>23.289375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-08</th>\n",
       "      <td>23.469999</td>\n",
       "      <td>45250000.0</td>\n",
       "      <td>23.469999</td>\n",
       "      <td>23.185854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-11</th>\n",
       "      <td>23.110001</td>\n",
       "      <td>68718200.0</td>\n",
       "      <td>23.110000</td>\n",
       "      <td>23.329817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-12</th>\n",
       "      <td>22.709999</td>\n",
       "      <td>69316900.0</td>\n",
       "      <td>22.709999</td>\n",
       "      <td>23.475609</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Open       Volume  Open_before_pred  Actual_Prediction\n",
       "Date                                                                   \n",
       "2020-02-18  34.770000   40318000.0         34.770000          34.626045\n",
       "2020-02-19  34.450001   29632100.0         34.450001          34.624889\n",
       "2020-02-20  34.459999   42604100.0         34.459999          34.557053\n",
       "2020-02-21  34.529999   46675500.0         34.529999          34.478088\n",
       "2020-02-24  33.180000   79159600.0         33.180000          34.444893\n",
       "2020-02-25  32.689999   91586800.0         32.689999          34.180359\n",
       "2020-02-26  31.389999   80012800.0         31.389999          33.750221\n",
       "2020-02-27  29.549999  121148300.0         29.549999          33.132183\n",
       "2020-02-28  28.000000  171263600.0         28.000000          32.191460\n",
       "2020-03-02  28.350000  147776300.0         28.350000          30.977228\n",
       "2020-03-03  29.370001  138698500.0         29.370001          30.065296\n",
       "2020-03-04  28.250000  111239800.0         28.250000          29.896666\n",
       "2020-03-05  27.240000  110878700.0         27.240000          29.877855\n",
       "2020-03-06  25.469999  119121200.0         25.469999          29.491169\n",
       "2020-03-09  22.230000  145196000.0         22.229999          28.493668\n",
       "2020-03-10  23.250000  134863600.0         23.250000          26.654591\n",
       "2020-03-11  22.840000  127347000.0         22.840000          25.072630\n",
       "2020-03-12  20.590000  139552600.0         20.590000          24.190029\n",
       "2020-03-13  22.190001  128357400.0         22.190000          23.364492\n",
       "2020-03-16  19.850000  121688800.0         19.850000          22.988367\n",
       "2020-03-17  21.059999  120065700.0         21.059999          22.406584\n",
       "2020-03-18  20.270000  148272500.0         20.270000          21.999256\n",
       "2020-03-19  20.260000  122461300.0         20.260000          21.648727\n",
       "2020-03-20  21.760000  150910400.0         21.760000          21.372227\n",
       "2020-03-23  19.260000  181898500.0         19.260000          21.588625\n",
       "2020-03-24  19.420000  147882400.0         19.420000          21.367048\n",
       "2020-03-25  21.709999  146991300.0         21.709999          20.840479\n",
       "2020-03-26  21.309999  116111400.0         21.309999          20.960278\n",
       "2020-03-27  21.680000   91522800.0         21.680000          21.464901\n",
       "2020-03-30  21.650000   80449100.0         21.650000          22.007946\n",
       "2020-03-31  21.820000   95213700.0         21.820000          22.322363\n",
       "2020-04-01  19.930000   83961300.0         19.930000          22.424040\n",
       "2020-04-02  19.650000   70007400.0         19.650000          21.895092\n",
       "2020-04-03  20.400000   68293500.0         20.400000          21.054131\n",
       "2020-04-06  21.030001   97766200.0         21.030001          20.545113\n",
       "2020-04-07  22.799999   94560300.0         22.799999          20.638800\n",
       "2020-04-08  22.410000   81212300.0         22.410000          21.546076\n",
       "2020-04-09  24.070000  125414800.0         24.070000          22.484581\n",
       "2020-04-13  24.780001   83164700.0         24.780001          23.434019\n",
       "2020-04-14  24.500000   88991100.0         24.500000          24.336924\n",
       "2020-04-15  22.160000   99082200.0         22.160000          24.829172\n",
       "2020-04-16  22.150000   89213300.0         22.150000          24.275396\n",
       "2020-04-17  22.340000   92800300.0         22.340000          23.309080\n",
       "2020-04-20  22.570000   74446300.0         22.570000          22.600386\n",
       "2020-04-21  21.900000   78871300.0         21.900000          22.426546\n",
       "2020-04-22  22.160000   49759100.0         22.160000          22.411308\n",
       "2020-04-23  21.959999   50614800.0         21.959999          22.455004\n",
       "2020-04-24  22.110001   47675700.0         22.110000          22.429342\n",
       "2020-04-27  22.520000   66916700.0         22.520000          22.386305\n",
       "2020-04-28  24.450001   65504900.0         24.450001          22.461126\n",
       "2020-04-29  24.910000   73493900.0         24.910000          23.149000\n",
       "2020-04-30  24.209999   55244100.0         24.209999          24.148542\n",
       "2020-05-01  23.379999   51772600.0         23.379999          24.714140\n",
       "2020-05-04  22.700001   45886100.0         22.700001          24.556948\n",
       "2020-05-05  23.480000   45691900.0         23.479999          23.905161\n",
       "2020-05-06  23.000000   53874600.0         23.000000          23.470537\n",
       "2020-05-07  22.660000   51884300.0         22.660000          23.289375\n",
       "2020-05-08  23.469999   45250000.0         23.469999          23.185854\n",
       "2020-05-11  23.110001   68718200.0         23.110000          23.329817\n",
       "2020-05-12  22.709999   69316900.0         22.709999          23.475609"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Visualise_Test_data=df[2547:]\n",
    "Visualise_Test_data['Open_before_pred']=new_y_test\n",
    "Visualise_Test_data['Actual_Prediction']=final_prediction\n",
    "Visualise_Test_data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting single day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting updated data for today\n",
    "new_df=web.DataReader('BAC',data_source='yahoo',start='2020-01-01', end='2020-5-13')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df=new_df[['Open','Volume']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "Last_30_days=new_df.tail(60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 Open       Volume\n",
      "Date                              \n",
      "2020-02-19  34.450001   29632100.0\n",
      "2020-02-20  34.459999   42604100.0\n",
      "2020-02-21  34.529999   46675500.0\n",
      "2020-02-24  33.180000   79159600.0\n",
      "2020-02-25  32.689999   91586800.0\n",
      "2020-02-26  31.389999   80012800.0\n",
      "2020-02-27  29.549999  121148300.0\n",
      "2020-02-28  28.000000  171263600.0\n",
      "2020-03-02  28.350000  147776300.0\n",
      "2020-03-03  29.370001  138698500.0\n",
      "2020-03-04  28.250000  111239800.0\n",
      "2020-03-05  27.240000  110878700.0\n",
      "2020-03-06  25.469999  119121200.0\n",
      "2020-03-09  22.230000  145196000.0\n",
      "2020-03-10  23.250000  134863600.0\n",
      "2020-03-11  22.840000  127347000.0\n",
      "2020-03-12  20.590000  139552600.0\n",
      "2020-03-13  22.190001  128357400.0\n",
      "2020-03-16  19.850000  121688800.0\n",
      "2020-03-17  21.059999  120065700.0\n",
      "2020-03-18  20.270000  148272500.0\n",
      "2020-03-19  20.260000  122461300.0\n",
      "2020-03-20  21.760000  150910400.0\n",
      "2020-03-23  19.260000  181898500.0\n",
      "2020-03-24  19.420000  147882400.0\n",
      "2020-03-25  21.709999  146991300.0\n",
      "2020-03-26  21.309999  116111400.0\n",
      "2020-03-27  21.680000   91522800.0\n",
      "2020-03-30  21.650000   80449100.0\n",
      "2020-03-31  21.820000   95213700.0\n",
      "2020-04-01  19.930000   83961300.0\n",
      "2020-04-02  19.650000   70007400.0\n",
      "2020-04-03  20.400000   68293500.0\n",
      "2020-04-06  21.030001   97766200.0\n",
      "2020-04-07  22.799999   94560300.0\n",
      "2020-04-08  22.410000   81212300.0\n",
      "2020-04-09  24.070000  125414800.0\n",
      "2020-04-13  24.780001   83164700.0\n",
      "2020-04-14  24.500000   88991100.0\n",
      "2020-04-15  22.160000   99082200.0\n",
      "2020-04-16  22.150000   89213300.0\n",
      "2020-04-17  22.340000   92800300.0\n",
      "2020-04-20  22.570000   74446300.0\n",
      "2020-04-21  21.900000   78871300.0\n",
      "2020-04-22  22.160000   49759100.0\n",
      "2020-04-23  21.959999   50614800.0\n",
      "2020-04-24  22.110001   47675700.0\n",
      "2020-04-27  22.520000   66916700.0\n",
      "2020-04-28  24.450001   65504900.0\n",
      "2020-04-29  24.910000   73493900.0\n",
      "2020-04-30  24.209999   55244100.0\n",
      "2020-05-01  23.379999   51772600.0\n",
      "2020-05-04  22.700001   45886100.0\n",
      "2020-05-05  23.480000   45691900.0\n",
      "2020-05-06  23.000000   53874600.0\n",
      "2020-05-07  22.660000   51884300.0\n",
      "2020-05-08  23.469999   45250000.0\n",
      "2020-05-11  23.110001   68718200.0\n",
      "2020-05-12  22.709999   69316900.0\n",
      "2020-05-13  21.719999  110265602.0\n"
     ]
    }
   ],
   "source": [
    "print(Last_30_days)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs_single=sc.transform(Last_30_days)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "Single_data=[]\n",
    "for i in range(60,61):\n",
    "    Single_data.append(inputs_single[i-60:i])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "Single_data=np.array(Single_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0.95913697, 0.01871127],\n",
       "        [0.95946382, 0.03404754],\n",
       "        [0.96175214, 0.03886099],\n",
       "        [0.91762012, 0.07726562],\n",
       "        [0.90160176, 0.0919578 ],\n",
       "        [0.85910424, 0.07827433],\n",
       "        [0.79895386, 0.12690716],\n",
       "        [0.74828373, 0.18615645],\n",
       "        [0.75972539, 0.15838837],\n",
       "        [0.79306964, 0.14765605],\n",
       "        [0.75645634, 0.11519274],\n",
       "        [0.72343901, 0.11476583],\n",
       "        [0.66557695, 0.1245106 ],\n",
       "        [0.55965999, 0.15533778],\n",
       "        [0.59300423, 0.14312221],\n",
       "        [0.57960117, 0.13423563],\n",
       "        [0.50604772, 0.14866582],\n",
       "        [0.5583524 , 0.13543019],\n",
       "        [0.48185681, 0.12754617],\n",
       "        [0.52141219, 0.12562725],\n",
       "        [0.49558679, 0.158975  ],\n",
       "        [0.49525988, 0.12845947],\n",
       "        [0.54429551, 0.16209369],\n",
       "        [0.46256946, 0.19872966],\n",
       "        [0.46779992, 0.15851381],\n",
       "        [0.54266095, 0.15746029],\n",
       "        [0.5295848 , 0.12095224],\n",
       "        [0.54168028, 0.09188213],\n",
       "        [0.54069955, 0.07879015],\n",
       "        [0.54625692, 0.09624573],\n",
       "        [0.48447205, 0.08294248],\n",
       "        [0.4753187 , 0.06644535],\n",
       "        [0.49983652, 0.06441907],\n",
       "        [0.52043152, 0.09926345],\n",
       "        [0.57829352, 0.09547325],\n",
       "        [0.56554427, 0.07969245],\n",
       "        [0.61981037, 0.13195127],\n",
       "        [0.6430206 , 0.08200069],\n",
       "        [0.63386726, 0.08888901],\n",
       "        [0.55737167, 0.10081931],\n",
       "        [0.55704476, 0.08915171],\n",
       "        [0.56325596, 0.09339247],\n",
       "        [0.57077474, 0.07169328],\n",
       "        [0.54887215, 0.07692478],\n",
       "        [0.55737167, 0.0425066 ],\n",
       "        [0.55083356, 0.04351826],\n",
       "        [0.55573717, 0.04004348],\n",
       "        [0.56914024, 0.06279134],\n",
       "        [0.63223276, 0.06112222],\n",
       "        [0.64727033, 0.0705673 ],\n",
       "        [0.62438701, 0.0489913 ],\n",
       "        [0.59725396, 0.04488708],\n",
       "        [0.57502453, 0.03792771],\n",
       "        [0.60052302, 0.03769812],\n",
       "        [0.58483163, 0.04737219],\n",
       "        [0.57371688, 0.04501914],\n",
       "        [0.6001961 , 0.03717568],\n",
       "        [0.58842759, 0.06492118],\n",
       "        [0.57535138, 0.065629  ],\n",
       "        [0.54298787, 0.11404099]]])"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Single_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 60, 2)"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Single_data.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "Single_final_prediction=regressor.predict(Single_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "Single_final_prediction=Single_final_prediction*scaler\n",
    "Single_final_prediction=(Single_final_prediction+5.11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " The Stock price for next Day is [[22.952244]]\n"
     ]
    }
   ],
   "source": [
    "print(\" The Stock price for next Day is\", Single_final_prediction)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Finding the trend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "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>Volume</th>\n",
       "      <th>Open_before_pred</th>\n",
       "      <th>Actual_Prediction</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-02-18</th>\n",
       "      <td>34.770000</td>\n",
       "      <td>40318000.0</td>\n",
       "      <td>34.770000</td>\n",
       "      <td>34.626045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-19</th>\n",
       "      <td>34.450001</td>\n",
       "      <td>29632100.0</td>\n",
       "      <td>34.450001</td>\n",
       "      <td>34.624889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-20</th>\n",
       "      <td>34.459999</td>\n",
       "      <td>42604100.0</td>\n",
       "      <td>34.459999</td>\n",
       "      <td>34.557053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-21</th>\n",
       "      <td>34.529999</td>\n",
       "      <td>46675500.0</td>\n",
       "      <td>34.529999</td>\n",
       "      <td>34.478088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-24</th>\n",
       "      <td>33.180000</td>\n",
       "      <td>79159600.0</td>\n",
       "      <td>33.180000</td>\n",
       "      <td>34.444893</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Open      Volume  Open_before_pred  Actual_Prediction\n",
       "Date                                                                  \n",
       "2020-02-18  34.770000  40318000.0         34.770000          34.626045\n",
       "2020-02-19  34.450001  29632100.0         34.450001          34.624889\n",
       "2020-02-20  34.459999  42604100.0         34.459999          34.557053\n",
       "2020-02-21  34.529999  46675500.0         34.529999          34.478088\n",
       "2020-02-24  33.180000  79159600.0         33.180000          34.444893"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Visualise_Test_data[:5]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([34.626045], dtype=float32)"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_prediction[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "Predict_trend=[]\n",
    "Actual_trend=[]\n",
    "for i in range(59):\n",
    "    Predict_trend.append(final_prediction[i]-final_prediction[i+1])\n",
    "    Actual_trend.append(new_y_test[i]-new_y_test[i+1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "Trend_df=pd.DataFrame({'Actual_Trend':Actual_trend,'Predict_trend':Predict_trend})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "def color_negative_red(val):\n",
    "    \"\"\"\n",
    "    Takes a scalar and returns a string with\n",
    "    the css property `'color: red'` for negative\n",
    "    strings, black otherwise.\n",
    "    \"\"\"\n",
    "    color = 'green' if val < 0 else 'red'\n",
    "    return 'color: %s' % color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row0_col0 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row0_col1 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row1_col0 {\n",
       "            color:  green;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row1_col1 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row2_col0 {\n",
       "            color:  green;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row2_col1 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row3_col0 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row3_col1 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row4_col0 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row4_col1 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row5_col0 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row5_col1 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row6_col0 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row6_col1 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row7_col0 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row7_col1 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row8_col0 {\n",
       "            color:  green;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row8_col1 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row9_col0 {\n",
       "            color:  green;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row9_col1 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row10_col0 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row10_col1 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row11_col0 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row11_col1 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row12_col0 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row12_col1 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row13_col0 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row13_col1 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row14_col0 {\n",
       "            color:  green;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row14_col1 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row15_col0 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row15_col1 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row16_col0 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row16_col1 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row17_col0 {\n",
       "            color:  green;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row17_col1 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row18_col0 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row18_col1 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row19_col0 {\n",
       "            color:  green;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row19_col1 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row20_col0 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row20_col1 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row21_col0 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row21_col1 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row22_col0 {\n",
       "            color:  green;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row22_col1 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row23_col0 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row23_col1 {\n",
       "            color:  green;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row24_col0 {\n",
       "            color:  green;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row24_col1 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row25_col0 {\n",
       "            color:  green;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row25_col1 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row26_col0 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row26_col1 {\n",
       "            color:  green;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row27_col0 {\n",
       "            color:  green;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row27_col1 {\n",
       "            color:  green;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row28_col0 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row28_col1 {\n",
       "            color:  green;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row29_col0 {\n",
       "            color:  green;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row29_col1 {\n",
       "            color:  green;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row30_col0 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row30_col1 {\n",
       "            color:  green;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row31_col0 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row31_col1 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row32_col0 {\n",
       "            color:  green;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row32_col1 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row33_col0 {\n",
       "            color:  green;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row33_col1 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row34_col0 {\n",
       "            color:  green;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row34_col1 {\n",
       "            color:  green;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row35_col0 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row35_col1 {\n",
       "            color:  green;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row36_col0 {\n",
       "            color:  green;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row36_col1 {\n",
       "            color:  green;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row37_col0 {\n",
       "            color:  green;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row37_col1 {\n",
       "            color:  green;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row38_col0 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row38_col1 {\n",
       "            color:  green;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row39_col0 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row39_col1 {\n",
       "            color:  green;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row40_col0 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row40_col1 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row41_col0 {\n",
       "            color:  green;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row41_col1 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row42_col0 {\n",
       "            color:  green;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row42_col1 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row43_col0 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row43_col1 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row44_col0 {\n",
       "            color:  green;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row44_col1 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row45_col0 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row45_col1 {\n",
       "            color:  green;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row46_col0 {\n",
       "            color:  green;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row46_col1 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row47_col0 {\n",
       "            color:  green;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row47_col1 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row48_col0 {\n",
       "            color:  green;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row48_col1 {\n",
       "            color:  green;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row49_col0 {\n",
       "            color:  green;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row49_col1 {\n",
       "            color:  green;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row50_col0 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row50_col1 {\n",
       "            color:  green;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row51_col0 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row51_col1 {\n",
       "            color:  green;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row52_col0 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row52_col1 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row53_col0 {\n",
       "            color:  green;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row53_col1 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row54_col0 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row54_col1 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row55_col0 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row55_col1 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row56_col0 {\n",
       "            color:  green;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row56_col1 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row57_col0 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row57_col1 {\n",
       "            color:  green;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row58_col0 {\n",
       "            color:  red;\n",
       "        }    #T_2f9b4270_9583_11ea_bf53_e82a44b38c96row58_col1 {\n",
       "            color:  green;\n",
       "        }</style><table id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Actual_Trend</th>        <th class=\"col_heading level0 col1\" >Predict_trend</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row0_col0\" class=\"data row0 col0\" >0.320000</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row0_col1\" class=\"data row0 col1\" >[0.00115585]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row1_col0\" class=\"data row1 col0\" >-0.009998</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row1_col1\" class=\"data row1 col1\" >[0.06783676]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row2_col0\" class=\"data row2 col0\" >-0.070000</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row2_col1\" class=\"data row2 col1\" >[0.07896423]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row3_col0\" class=\"data row3 col0\" >1.349998</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row3_col1\" class=\"data row3 col1\" >[0.0331955]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row4_col0\" class=\"data row4 col0\" >0.490002</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row4_col1\" class=\"data row4 col1\" >[0.264534]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row5_col0\" class=\"data row5 col0\" >1.299999</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row5_col1\" class=\"data row5 col1\" >[0.43013763]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row6_col0\" class=\"data row6 col0\" >1.840000</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row6_col1\" class=\"data row6 col1\" >[0.6180382]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row7_col0\" class=\"data row7 col0\" >1.549999</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row7_col1\" class=\"data row7 col1\" >[0.9407234]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row8_col0\" class=\"data row8 col0\" >-0.350000</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row8_col1\" class=\"data row8 col1\" >[1.2142315]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row9_col0\" class=\"data row9 col0\" >-1.020000</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row9_col1\" class=\"data row9 col1\" >[0.911932]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row10_col0\" class=\"data row10 col0\" >1.120001</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row10_col1\" class=\"data row10 col1\" >[0.1686306]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row11_col0\" class=\"data row11 col0\" >1.010000</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row11_col1\" class=\"data row11 col1\" >[0.01881027]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row12_col0\" class=\"data row12 col0\" >1.770000</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row12_col1\" class=\"data row12 col1\" >[0.38668633]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row13_col0\" class=\"data row13 col0\" >3.240000</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row13_col1\" class=\"data row13 col1\" >[0.9975014]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row14_col0\" class=\"data row14 col0\" >-1.020000</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row14_col1\" class=\"data row14 col1\" >[1.839077]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row15_col0\" class=\"data row15 col0\" >0.410000</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row15_col1\" class=\"data row15 col1\" >[1.5819607]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row16_col0\" class=\"data row16 col0\" >2.250000</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row16_col1\" class=\"data row16 col1\" >[0.8826008]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row17_col0\" class=\"data row17 col0\" >-1.600000</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row17_col1\" class=\"data row17 col1\" >[0.8255367]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row18_col0\" class=\"data row18 col0\" >2.340000</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row18_col1\" class=\"data row18 col1\" >[0.37612534]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row19_col0\" class=\"data row19 col0\" >-1.209999</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row19_col1\" class=\"data row19 col1\" >[0.5817833]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row20_col0\" class=\"data row20 col0\" >0.789999</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row20_col1\" class=\"data row20 col1\" >[0.40732765]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row21_col0\" class=\"data row21 col0\" >0.010000</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row21_col1\" class=\"data row21 col1\" >[0.35052872]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row22_col0\" class=\"data row22 col0\" >-1.500000</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row22_col1\" class=\"data row22 col1\" >[0.2765007]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row23_col0\" class=\"data row23 col0\" >2.500000</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row23_col1\" class=\"data row23 col1\" >[-0.21639824]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row24_col0\" class=\"data row24 col0\" >-0.160000</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row24_col1\" class=\"data row24 col1\" >[0.22157669]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row25_col0\" class=\"data row25 col0\" >-2.289999</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row25_col1\" class=\"data row25 col1\" >[0.52656937]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row26_col0\" class=\"data row26 col0\" >0.400000</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row26_col1\" class=\"data row26 col1\" >[-0.11979866]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row27_col0\" class=\"data row27 col0\" >-0.370001</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row27_col1\" class=\"data row27 col1\" >[-0.5046234]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row28_col0\" class=\"data row28 col0\" >0.030001</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row28_col1\" class=\"data row28 col1\" >[-0.54304504]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row29_col0\" class=\"data row29 col0\" >-0.170000</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row29_col1\" class=\"data row29 col1\" >[-0.3144169]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row30_col0\" class=\"data row30 col0\" >1.889999</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row30_col1\" class=\"data row30 col1\" >[-0.10167694]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row31_col0\" class=\"data row31 col0\" >0.280001</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row31_col1\" class=\"data row31 col1\" >[0.52894783]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row32_col0\" class=\"data row32 col0\" >-0.750000</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row32_col1\" class=\"data row32 col1\" >[0.84096146]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row33_col0\" class=\"data row33 col0\" >-0.630001</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row33_col1\" class=\"data row33 col1\" >[0.50901794]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row34_col0\" class=\"data row34 col0\" >-1.769999</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row34_col1\" class=\"data row34 col1\" >[-0.09368706]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row35_col0\" class=\"data row35 col0\" >0.389999</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row35_col1\" class=\"data row35 col1\" >[-0.90727615]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row36_col0\" class=\"data row36 col0\" >-1.660000</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row36_col1\" class=\"data row36 col1\" >[-0.9385052]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row37_col0\" class=\"data row37 col0\" >-0.710001</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row37_col1\" class=\"data row37 col1\" >[-0.9494381]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row38_col0\" class=\"data row38 col0\" >0.280001</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row38_col1\" class=\"data row38 col1\" >[-0.9029045]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row39_col0\" class=\"data row39 col0\" >2.340000</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row39_col1\" class=\"data row39 col1\" >[-0.49224854]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row40_col0\" class=\"data row40 col0\" >0.010000</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row40_col1\" class=\"data row40 col1\" >[0.5537758]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row41_col0\" class=\"data row41 col0\" >-0.190001</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row41_col1\" class=\"data row41 col1\" >[0.9663162]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row42\" class=\"row_heading level0 row42\" >42</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row42_col0\" class=\"data row42 col0\" >-0.230000</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row42_col1\" class=\"data row42 col1\" >[0.70869446]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row43\" class=\"row_heading level0 row43\" >43</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row43_col0\" class=\"data row43 col0\" >0.670000</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row43_col1\" class=\"data row43 col1\" >[0.17383957]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row44\" class=\"row_heading level0 row44\" >44</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row44_col0\" class=\"data row44 col0\" >-0.260000</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row44_col1\" class=\"data row44 col1\" >[0.01523781]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row45\" class=\"row_heading level0 row45\" >45</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row45_col0\" class=\"data row45 col0\" >0.200001</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row45_col1\" class=\"data row45 col1\" >[-0.04369545]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row46\" class=\"row_heading level0 row46\" >46</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row46_col0\" class=\"data row46 col0\" >-0.150002</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row46_col1\" class=\"data row46 col1\" >[0.02566147]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row47\" class=\"row_heading level0 row47\" >47</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row47_col0\" class=\"data row47 col0\" >-0.410000</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row47_col1\" class=\"data row47 col1\" >[0.04303741]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row48\" class=\"row_heading level0 row48\" >48</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row48_col0\" class=\"data row48 col0\" >-1.930000</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row48_col1\" class=\"data row48 col1\" >[-0.07482147]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row49\" class=\"row_heading level0 row49\" >49</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row49_col0\" class=\"data row49 col0\" >-0.459999</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row49_col1\" class=\"data row49 col1\" >[-0.68787384]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row50\" class=\"row_heading level0 row50\" >50</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row50_col0\" class=\"data row50 col0\" >0.700001</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row50_col1\" class=\"data row50 col1\" >[-0.99954224]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row51\" class=\"row_heading level0 row51\" >51</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row51_col0\" class=\"data row51 col0\" >0.830000</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row51_col1\" class=\"data row51 col1\" >[-0.56559753]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row52\" class=\"row_heading level0 row52\" >52</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row52_col0\" class=\"data row52 col0\" >0.679998</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row52_col1\" class=\"data row52 col1\" >[0.15719223]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row53\" class=\"row_heading level0 row53\" >53</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row53_col0\" class=\"data row53 col0\" >-0.779999</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row53_col1\" class=\"data row53 col1\" >[0.6517868]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row54\" class=\"row_heading level0 row54\" >54</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row54_col0\" class=\"data row54 col0\" >0.480000</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row54_col1\" class=\"data row54 col1\" >[0.43462372]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row55\" class=\"row_heading level0 row55\" >55</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row55_col0\" class=\"data row55 col0\" >0.340000</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row55_col1\" class=\"data row55 col1\" >[0.18116188]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row56\" class=\"row_heading level0 row56\" >56</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row56_col0\" class=\"data row56 col0\" >-0.809999</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row56_col1\" class=\"data row56 col1\" >[0.10352135]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row57\" class=\"row_heading level0 row57\" >57</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row57_col0\" class=\"data row57 col0\" >0.359999</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row57_col1\" class=\"data row57 col1\" >[-0.14396286]</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96level0_row58\" class=\"row_heading level0 row58\" >58</th>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row58_col0\" class=\"data row58 col0\" >0.400002</td>\n",
       "                        <td id=\"T_2f9b4270_9583_11ea_bf53_e82a44b38c96row58_col1\" class=\"data row58 col1\" >[-0.145792]</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x24fd69cf808>"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = Trend_df.style.applymap(color_negative_red)\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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
}