LSTM Stock trend model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3fce58ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import csv as csv\n",
    "import json as js\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas_datareader as pd_data\n",
    "import pickle\n",
    "import requests\n",
    "import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6d16f7f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Enter your Alpha Vantage API key\n",
    "api_key = 'XO2YT4XMHPUERBBP'\n",
    "\n",
    "# Specify the API endpoint and query parameters\n",
    "url = 'https://www.alphavantage.co/query'\n",
    "params = {\n",
    "    'function': 'TIME_SERIES_DAILY_ADJUSTED',\n",
    "    'symbol': 'IBM',\n",
    "    'apikey': api_key,\n",
    "    'outputsize': 'full'\n",
    "}\n",
    "\n",
    "# Send the API request and parse the JSON response\n",
    "response = requests.get(url, params=params)\n",
    "data_IBM = js.loads(response.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "70b1cb1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "daily_data = data_IBM['Time Series (Daily)']\n",
    "dates = list(daily_data.keys())[:2000][::-1]  # Slice the most recent 600 dates and reverse the order\n",
    "header = ['Date', '1. open', '2. high', '3. low', '4. close', '5. adjusted close', '6. volume', '7. dividend amount', '8. split coefficient']\n",
    "rows = []\n",
    "for date in dates:\n",
    "    row = [date] + [float(daily_data[date][col]) for col in header[1:]]\n",
    "    rows.append(row)\n",
    "# Write the data to a CSV file\n",
    "with open('data_IBM.csv', 'w', newline='') as csvfile:\n",
    "    writer = csv.writer(csvfile)\n",
    "    writer.writerow(header)\n",
    "    for row in rows:\n",
    "        writer.writerow(row)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a0d08b29",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Date  1. open  2. high  3. low  4. close  5. adjusted close   \n",
      "0  2015-05-13   171.24   172.74  170.75    172.28         116.544929  \\\n",
      "1  2015-05-14   173.50   174.40  173.22    174.05         117.742309   \n",
      "2  2015-05-15   173.91   174.41  172.60    173.26         117.207885   \n",
      "3  2015-05-18   173.44   173.49  172.30    173.06         117.072588   \n",
      "4  2015-05-19   172.97   173.75  171.93    173.48         117.356712   \n",
      "\n",
      "   6. volume  7. dividend amount  8. split coefficient  \n",
      "0  2457521.0                 0.0                   1.0  \n",
      "1  2438669.0                 0.0                   1.0  \n",
      "2  2916115.0                 0.0                   1.0  \n",
      "3  1970630.0                 0.0                   1.0  \n",
      "4  2523002.0                 0.0                   1.0  \n",
      "         Date    open    high     low   close  5. adjusted close     volume   \n",
      "0  2015-05-13  171.24  172.74  170.75  172.28         116.544929  2457521.0  \\\n",
      "1  2015-05-14  173.50  174.40  173.22  174.05         117.742309  2438669.0   \n",
      "2  2015-05-15  173.91  174.41  172.60  173.26         117.207885  2916115.0   \n",
      "3  2015-05-18  173.44  173.49  172.30  173.06         117.072588  1970630.0   \n",
      "4  2015-05-19  172.97  173.75  171.93  173.48         117.356712  2523002.0   \n",
      "\n",
      "   d_amt   sc  \n",
      "0    0.0  1.0  \n",
      "1    0.0  1.0  \n",
      "2    0.0  1.0  \n",
      "3    0.0  1.0  \n",
      "4    0.0  1.0  \n",
      "(1400, 1)\n",
      "(600, 1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.88908006],\n",
       "       [0.90938289],\n",
       "       [0.90032117],\n",
       "       ...,\n",
       "       [0.339298  ],\n",
       "       [0.32977747],\n",
       "       [0.32564808]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "# Read CSV file into a pandas DataFrame\n",
    "df = pd.read_csv('data_IBM.csv')\n",
    "\n",
    "# Print the first 5 rows of the DataFrame\n",
    "print(df.head())\n",
    "df.head()\n",
    "df = df.rename(columns={'4. close':'close','6. volume':'volume','7. dividend amount':'d_amt','8. split coefficient':'sc'})\n",
    "df = df.rename(columns={'1. open':'open','2. high':'high','3. low':'low'})\n",
    "print(df.head())\n",
    "df = df.drop(['A'])\n",
    "plt.plot(df.close)\n",
    "weekma = df.close.rolling(100).mean()\n",
    "plt.plot(weekma)\n",
    "data_training = pd.DataFrame(df['close'][0:int(len(df)*0.70)])\n",
    "data_testing =  pd.DataFrame(df['close'][int(len(df)*0.70):int(len(df))])\n",
    "print(data_training.shape)\n",
    "print(data_testing.shape)\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "data_training_array = scaler.fit_transform(data_training)\n",
    "data_training_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7898193a",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = []\n",
    "y_train = []\n",
    "\n",
    "for i in range(100, data_training_array.shape[0]):\n",
    "    x_train.append(data_training_array[i-100: i])\n",
    "    y_train.append(data_training_array[i,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "69f6bae7",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, y_train = np.array(x_train), np.array(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c380020d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import Dense, Dropout, LSTM\n",
    "from keras.models import Sequential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "62997d66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " lstm_4 (LSTM)               (None, 100, 50)           10400     \n",
      "                                                                 \n",
      " dropout_4 (Dropout)         (None, 100, 50)           0         \n",
      "                                                                 \n",
      " lstm_5 (LSTM)               (None, 100, 60)           26640     \n",
      "                                                                 \n",
      " dropout_5 (Dropout)         (None, 100, 60)           0         \n",
      "                                                                 \n",
      " lstm_6 (LSTM)               (None, 100, 80)           45120     \n",
      "                                                                 \n",
      " dropout_6 (Dropout)         (None, 100, 80)           0         \n",
      "                                                                 \n",
      " lstm_7 (LSTM)               (None, 120)               96480     \n",
      "                                                                 \n",
      " dropout_7 (Dropout)         (None, 120)               0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 1)                 121       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 178,761\n",
      "Trainable params: 178,761\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(LSTM(units = 50, activation = 'relu', return_sequences = True, input_shape = (x_train.shape[1], 1))) \n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(LSTM(units = 60, activation = 'relu', return_sequences = True)) \n",
    "model.add(Dropout(0.3))\n",
    "\n",
    "model.add(LSTM(units = 80, activation = 'relu', return_sequences = True)) \n",
    "model.add(Dropout(0.4))\n",
    "\n",
    "model.add(LSTM(units = 120, activation = 'relu')) \n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "model.add(Dense(units = 1))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "05eb1b4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam', loss='mean_squared_error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "32a3d768",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "41/41 [==============================] - 34s 435ms/step - loss: 0.0781\n",
      "Epoch 2/50\n",
      "41/41 [==============================] - 18s 434ms/step - loss: 0.0195\n",
      "Epoch 3/50\n",
      "41/41 [==============================] - 19s 456ms/step - loss: 0.0150\n",
      "Epoch 4/50\n",
      "41/41 [==============================] - 18s 438ms/step - loss: 0.0154\n",
      "Epoch 5/50\n",
      "41/41 [==============================] - 18s 440ms/step - loss: 0.0136\n",
      "Epoch 6/50\n",
      "41/41 [==============================] - 18s 433ms/step - loss: 0.0123\n",
      "Epoch 7/50\n",
      "41/41 [==============================] - 18s 437ms/step - loss: 0.0121\n",
      "Epoch 8/50\n",
      "41/41 [==============================] - 18s 450ms/step - loss: 0.0108\n",
      "Epoch 9/50\n",
      "41/41 [==============================] - 18s 441ms/step - loss: 0.0112\n",
      "Epoch 10/50\n",
      "41/41 [==============================] - 18s 434ms/step - loss: 0.0096\n",
      "Epoch 11/50\n",
      "41/41 [==============================] - 21s 525ms/step - loss: 0.0094\n",
      "Epoch 12/50\n",
      "41/41 [==============================] - 29s 710ms/step - loss: 0.0092\n",
      "Epoch 13/50\n",
      "41/41 [==============================] - 28s 695ms/step - loss: 0.0090\n",
      "Epoch 14/50\n",
      "41/41 [==============================] - 29s 695ms/step - loss: 0.0097\n",
      "Epoch 15/50\n",
      "41/41 [==============================] - 28s 691ms/step - loss: 0.0089\n",
      "Epoch 16/50\n",
      "41/41 [==============================] - 29s 696ms/step - loss: 0.0082\n",
      "Epoch 17/50\n",
      "41/41 [==============================] - 28s 688ms/step - loss: 0.0077\n",
      "Epoch 18/50\n",
      "41/41 [==============================] - 28s 675ms/step - loss: 0.0076\n",
      "Epoch 19/50\n",
      "41/41 [==============================] - 27s 668ms/step - loss: 0.0077\n",
      "Epoch 20/50\n",
      "41/41 [==============================] - 28s 678ms/step - loss: 0.0069\n",
      "Epoch 21/50\n",
      "41/41 [==============================] - 28s 672ms/step - loss: 0.0069\n",
      "Epoch 22/50\n",
      "41/41 [==============================] - 28s 677ms/step - loss: 0.0079\n",
      "Epoch 23/50\n",
      "41/41 [==============================] - 28s 672ms/step - loss: 0.0068\n",
      "Epoch 24/50\n",
      "41/41 [==============================] - 28s 678ms/step - loss: 0.0064\n",
      "Epoch 25/50\n",
      "41/41 [==============================] - 28s 677ms/step - loss: 0.0063\n",
      "Epoch 26/50\n",
      "41/41 [==============================] - 28s 677ms/step - loss: 0.0070\n",
      "Epoch 27/50\n",
      "41/41 [==============================] - 28s 685ms/step - loss: 0.0068\n",
      "Epoch 28/50\n",
      "41/41 [==============================] - 28s 677ms/step - loss: 0.0067\n",
      "Epoch 29/50\n",
      "41/41 [==============================] - 29s 698ms/step - loss: 0.0062\n",
      "Epoch 30/50\n",
      "41/41 [==============================] - 28s 675ms/step - loss: 0.0059\n",
      "Epoch 31/50\n",
      "41/41 [==============================] - 28s 681ms/step - loss: 0.0064\n",
      "Epoch 32/50\n",
      "41/41 [==============================] - 28s 672ms/step - loss: 0.0053\n",
      "Epoch 33/50\n",
      "41/41 [==============================] - 28s 677ms/step - loss: 0.0055\n",
      "Epoch 34/50\n",
      "41/41 [==============================] - 28s 678ms/step - loss: 0.0051\n",
      "Epoch 35/50\n",
      "41/41 [==============================] - 28s 682ms/step - loss: 0.0052\n",
      "Epoch 36/50\n",
      "41/41 [==============================] - 28s 678ms/step - loss: 0.0053\n",
      "Epoch 37/50\n",
      "41/41 [==============================] - 28s 677ms/step - loss: 0.0049\n",
      "Epoch 38/50\n",
      "41/41 [==============================] - 28s 680ms/step - loss: 0.0054\n",
      "Epoch 39/50\n",
      "41/41 [==============================] - 27s 650ms/step - loss: 0.0051\n",
      "Epoch 40/50\n",
      "41/41 [==============================] - 27s 651ms/step - loss: 0.0050\n",
      "Epoch 41/50\n",
      "41/41 [==============================] - 26s 643ms/step - loss: 0.0051\n",
      "Epoch 42/50\n",
      "41/41 [==============================] - 26s 642ms/step - loss: 0.0047\n",
      "Epoch 43/50\n",
      "41/41 [==============================] - 27s 648ms/step - loss: 0.0046\n",
      "Epoch 44/50\n",
      "41/41 [==============================] - 26s 643ms/step - loss: 0.0045\n",
      "Epoch 45/50\n",
      "41/41 [==============================] - 26s 646ms/step - loss: 0.0043\n",
      "Epoch 46/50\n",
      "41/41 [==============================] - 26s 643ms/step - loss: 0.0043\n",
      "Epoch 47/50\n",
      "41/41 [==============================] - 26s 642ms/step - loss: 0.0040\n",
      "Epoch 48/50\n",
      "41/41 [==============================] - 26s 641ms/step - loss: 0.0041\n",
      "Epoch 49/50\n",
      "41/41 [==============================] - 26s 646ms/step - loss: 0.0039\n",
      "Epoch 50/50\n",
      "41/41 [==============================] - 27s 654ms/step - loss: 0.0041\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x171b4e19650>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train, epochs = 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5612919f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/150\n",
      "41/41 [==============================] - 11s 272ms/step - loss: 0.0038\n",
      "Epoch 2/150\n",
      "41/41 [==============================] - 11s 270ms/step - loss: 0.0040\n",
      "Epoch 3/150\n",
      "41/41 [==============================] - 11s 272ms/step - loss: 0.0037\n",
      "Epoch 4/150\n",
      "41/41 [==============================] - 11s 278ms/step - loss: 0.0039\n",
      "Epoch 5/150\n",
      "41/41 [==============================] - 11s 280ms/step - loss: 0.0037\n",
      "Epoch 6/150\n",
      "41/41 [==============================] - 11s 271ms/step - loss: 0.0035\n",
      "Epoch 7/150\n",
      "41/41 [==============================] - 11s 270ms/step - loss: 0.0038\n",
      "Epoch 8/150\n",
      "41/41 [==============================] - 11s 270ms/step - loss: 0.0039\n",
      "Epoch 9/150\n",
      "41/41 [==============================] - 11s 272ms/step - loss: 0.0035\n",
      "Epoch 10/150\n",
      "41/41 [==============================] - 11s 275ms/step - loss: 0.0034\n",
      "Epoch 11/150\n",
      "41/41 [==============================] - 11s 267ms/step - loss: 0.0034\n",
      "Epoch 12/150\n",
      "41/41 [==============================] - 11s 270ms/step - loss: 0.0035\n",
      "Epoch 13/150\n",
      "41/41 [==============================] - 11s 272ms/step - loss: 0.0036\n",
      "Epoch 14/150\n",
      "41/41 [==============================] - 11s 276ms/step - loss: 0.0034\n",
      "Epoch 15/150\n",
      "41/41 [==============================] - 11s 272ms/step - loss: 0.0032\n",
      "Epoch 16/150\n",
      "41/41 [==============================] - 11s 273ms/step - loss: 0.0032\n",
      "Epoch 17/150\n",
      "41/41 [==============================] - 11s 270ms/step - loss: 0.0031\n",
      "Epoch 18/150\n",
      "41/41 [==============================] - 11s 269ms/step - loss: 0.0033\n",
      "Epoch 19/150\n",
      "41/41 [==============================] - 11s 269ms/step - loss: 0.0030\n",
      "Epoch 20/150\n",
      "41/41 [==============================] - 11s 273ms/step - loss: 0.0032\n",
      "Epoch 21/150\n",
      "41/41 [==============================] - 11s 274ms/step - loss: 0.0035\n",
      "Epoch 22/150\n",
      "41/41 [==============================] - 11s 270ms/step - loss: 0.0031\n",
      "Epoch 23/150\n",
      "41/41 [==============================] - 11s 271ms/step - loss: 0.0030\n",
      "Epoch 24/150\n",
      "41/41 [==============================] - 11s 275ms/step - loss: 0.0029\n",
      "Epoch 25/150\n",
      "41/41 [==============================] - 11s 270ms/step - loss: 0.0032\n",
      "Epoch 26/150\n",
      "41/41 [==============================] - 11s 274ms/step - loss: 0.0030\n",
      "Epoch 27/150\n",
      "41/41 [==============================] - 11s 271ms/step - loss: 0.0031\n",
      "Epoch 28/150\n",
      "41/41 [==============================] - 11s 267ms/step - loss: 0.0029\n",
      "Epoch 29/150\n",
      "41/41 [==============================] - 11s 270ms/step - loss: 0.0029\n",
      "Epoch 30/150\n",
      "41/41 [==============================] - 11s 273ms/step - loss: 0.0031\n",
      "Epoch 31/150\n",
      "41/41 [==============================] - 11s 270ms/step - loss: 0.0029\n",
      "Epoch 32/150\n",
      "41/41 [==============================] - 11s 274ms/step - loss: 0.0030\n",
      "Epoch 33/150\n",
      "41/41 [==============================] - 11s 271ms/step - loss: 0.0029\n",
      "Epoch 34/150\n",
      "41/41 [==============================] - 11s 268ms/step - loss: 0.0028\n",
      "Epoch 35/150\n",
      "41/41 [==============================] - 11s 268ms/step - loss: 0.0030\n",
      "Epoch 36/150\n",
      "41/41 [==============================] - 11s 270ms/step - loss: 0.0029\n",
      "Epoch 37/150\n",
      "41/41 [==============================] - 11s 275ms/step - loss: 0.0027\n",
      "Epoch 38/150\n",
      "41/41 [==============================] - 11s 271ms/step - loss: 0.0029\n",
      "Epoch 39/150\n",
      "41/41 [==============================] - 11s 270ms/step - loss: 0.0030\n",
      "Epoch 40/150\n",
      "41/41 [==============================] - 11s 271ms/step - loss: 0.0028\n",
      "Epoch 41/150\n",
      "41/41 [==============================] - 11s 269ms/step - loss: 0.0027\n",
      "Epoch 42/150\n",
      "41/41 [==============================] - 11s 271ms/step - loss: 0.0027\n",
      "Epoch 43/150\n",
      "41/41 [==============================] - 11s 270ms/step - loss: 0.0030\n",
      "Epoch 44/150\n",
      "41/41 [==============================] - 11s 270ms/step - loss: 0.0025\n",
      "Epoch 45/150\n",
      "41/41 [==============================] - 11s 269ms/step - loss: 0.0027\n",
      "Epoch 46/150\n",
      "41/41 [==============================] - 11s 268ms/step - loss: 0.0027\n",
      "Epoch 47/150\n",
      "41/41 [==============================] - 11s 271ms/step - loss: 0.0026\n",
      "Epoch 48/150\n",
      "41/41 [==============================] - 11s 269ms/step - loss: 0.0025\n",
      "Epoch 49/150\n",
      "41/41 [==============================] - 11s 261ms/step - loss: 0.0024\n",
      "Epoch 50/150\n",
      "41/41 [==============================] - 11s 260ms/step - loss: 0.0025\n",
      "Epoch 51/150\n",
      "41/41 [==============================] - 11s 260ms/step - loss: 0.0025\n",
      "Epoch 52/150\n",
      "41/41 [==============================] - 11s 262ms/step - loss: 0.0026\n",
      "Epoch 53/150\n",
      "41/41 [==============================] - 11s 267ms/step - loss: 0.0025\n",
      "Epoch 54/150\n",
      "41/41 [==============================] - 11s 263ms/step - loss: 0.0025\n",
      "Epoch 55/150\n",
      "41/41 [==============================] - 11s 259ms/step - loss: 0.0027\n",
      "Epoch 56/150\n",
      "41/41 [==============================] - 11s 262ms/step - loss: 0.0024\n",
      "Epoch 57/150\n",
      "41/41 [==============================] - 11s 263ms/step - loss: 0.0024\n",
      "Epoch 58/150\n",
      "41/41 [==============================] - 11s 268ms/step - loss: 0.0024\n",
      "Epoch 59/150\n",
      "41/41 [==============================] - 11s 269ms/step - loss: 0.0027\n",
      "Epoch 60/150\n",
      "41/41 [==============================] - 11s 262ms/step - loss: 0.0025\n",
      "Epoch 61/150\n",
      "41/41 [==============================] - 11s 257ms/step - loss: 0.0027\n",
      "Epoch 62/150\n",
      "41/41 [==============================] - 11s 257ms/step - loss: 0.0025\n",
      "Epoch 63/150\n",
      "41/41 [==============================] - 11s 260ms/step - loss: 0.0026\n",
      "Epoch 64/150\n",
      "41/41 [==============================] - 11s 272ms/step - loss: 0.0023\n",
      "Epoch 65/150\n",
      "41/41 [==============================] - 11s 264ms/step - loss: 0.0023\n",
      "Epoch 66/150\n",
      "41/41 [==============================] - 11s 262ms/step - loss: 0.0025\n",
      "Epoch 67/150\n",
      "41/41 [==============================] - 11s 270ms/step - loss: 0.0025\n",
      "Epoch 68/150\n",
      "41/41 [==============================] - 11s 260ms/step - loss: 0.0026\n",
      "Epoch 69/150\n",
      "41/41 [==============================] - 11s 263ms/step - loss: 0.0024\n",
      "Epoch 70/150\n",
      "41/41 [==============================] - 11s 265ms/step - loss: 0.0024\n",
      "Epoch 71/150\n",
      "41/41 [==============================] - 11s 260ms/step - loss: 0.0026\n",
      "Epoch 72/150\n",
      "41/41 [==============================] - 11s 260ms/step - loss: 0.0025\n",
      "Epoch 73/150\n",
      "41/41 [==============================] - 11s 258ms/step - loss: 0.0024\n",
      "Epoch 74/150\n",
      "41/41 [==============================] - 11s 259ms/step - loss: 0.0022\n",
      "Epoch 75/150\n",
      "41/41 [==============================] - 11s 263ms/step - loss: 0.0023\n",
      "Epoch 76/150\n",
      "41/41 [==============================] - 11s 260ms/step - loss: 0.0024\n",
      "Epoch 77/150\n",
      "41/41 [==============================] - 11s 259ms/step - loss: 0.0022\n",
      "Epoch 78/150\n",
      "41/41 [==============================] - 11s 263ms/step - loss: 0.0024\n",
      "Epoch 79/150\n",
      "41/41 [==============================] - 11s 262ms/step - loss: 0.0024\n",
      "Epoch 80/150\n",
      "41/41 [==============================] - 11s 262ms/step - loss: 0.0023\n",
      "Epoch 81/150\n",
      "41/41 [==============================] - 11s 268ms/step - loss: 0.0024\n",
      "Epoch 82/150\n",
      "41/41 [==============================] - 11s 260ms/step - loss: 0.0021\n",
      "Epoch 83/150\n",
      "41/41 [==============================] - 11s 260ms/step - loss: 0.0023\n",
      "Epoch 84/150\n",
      "41/41 [==============================] - 11s 258ms/step - loss: 0.0023\n",
      "Epoch 85/150\n",
      "41/41 [==============================] - 11s 260ms/step - loss: 0.0023\n",
      "Epoch 86/150\n",
      "41/41 [==============================] - 11s 266ms/step - loss: 0.0022\n",
      "Epoch 87/150\n",
      "41/41 [==============================] - 11s 266ms/step - loss: 0.0023\n",
      "Epoch 88/150\n",
      "41/41 [==============================] - 11s 262ms/step - loss: 0.0023\n",
      "Epoch 89/150\n",
      "41/41 [==============================] - 11s 258ms/step - loss: 0.0023\n",
      "Epoch 90/150\n",
      "41/41 [==============================] - 11s 259ms/step - loss: 0.0021\n",
      "Epoch 91/150\n",
      "41/41 [==============================] - 11s 260ms/step - loss: 0.0021\n",
      "Epoch 92/150\n",
      "41/41 [==============================] - 11s 269ms/step - loss: 0.0022\n",
      "Epoch 93/150\n",
      "41/41 [==============================] - 11s 260ms/step - loss: 0.0023\n",
      "Epoch 94/150\n",
      "41/41 [==============================] - 11s 259ms/step - loss: 0.0021\n",
      "Epoch 95/150\n",
      "41/41 [==============================] - 11s 272ms/step - loss: 0.0023\n",
      "Epoch 96/150\n",
      "41/41 [==============================] - 11s 260ms/step - loss: 0.0022\n",
      "Epoch 97/150\n",
      "41/41 [==============================] - 11s 261ms/step - loss: 0.0022\n",
      "Epoch 98/150\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "41/41 [==============================] - 11s 267ms/step - loss: 0.0025\n",
      "Epoch 99/150\n",
      "41/41 [==============================] - 11s 262ms/step - loss: 0.0023\n",
      "Epoch 100/150\n",
      "41/41 [==============================] - 11s 260ms/step - loss: 0.0023\n",
      "Epoch 101/150\n",
      "41/41 [==============================] - 11s 259ms/step - loss: 0.0022\n",
      "Epoch 102/150\n",
      "41/41 [==============================] - 11s 261ms/step - loss: 0.0023\n",
      "Epoch 103/150\n",
      "41/41 [==============================] - 11s 267ms/step - loss: 0.0022\n",
      "Epoch 104/150\n",
      "41/41 [==============================] - 11s 260ms/step - loss: 0.0021\n",
      "Epoch 105/150\n",
      "41/41 [==============================] - 11s 260ms/step - loss: 0.0022\n",
      "Epoch 106/150\n",
      "41/41 [==============================] - 11s 261ms/step - loss: 0.0021\n",
      "Epoch 107/150\n",
      "41/41 [==============================] - 11s 267ms/step - loss: 0.0021\n",
      "Epoch 108/150\n",
      "41/41 [==============================] - 11s 267ms/step - loss: 0.0020\n",
      "Epoch 109/150\n",
      "41/41 [==============================] - 11s 268ms/step - loss: 0.0021\n",
      "Epoch 110/150\n",
      "41/41 [==============================] - 11s 262ms/step - loss: 0.0022\n",
      "Epoch 111/150\n",
      "41/41 [==============================] - 11s 260ms/step - loss: 0.0021\n",
      "Epoch 112/150\n",
      "41/41 [==============================] - 11s 258ms/step - loss: 0.0022\n",
      "Epoch 113/150\n",
      "41/41 [==============================] - 11s 260ms/step - loss: 0.0022\n",
      "Epoch 114/150\n",
      "41/41 [==============================] - 11s 265ms/step - loss: 0.0020\n",
      "Epoch 115/150\n",
      "41/41 [==============================] - 11s 267ms/step - loss: 0.0022\n",
      "Epoch 116/150\n",
      "41/41 [==============================] - 11s 263ms/step - loss: 0.0022\n",
      "Epoch 117/150\n",
      "41/41 [==============================] - 11s 259ms/step - loss: 0.0019\n",
      "Epoch 118/150\n",
      "41/41 [==============================] - 11s 258ms/step - loss: 0.0021\n",
      "Epoch 119/150\n",
      "41/41 [==============================] - 11s 261ms/step - loss: 0.0019\n",
      "Epoch 120/150\n",
      "41/41 [==============================] - 11s 265ms/step - loss: 0.0020\n",
      "Epoch 121/150\n",
      "41/41 [==============================] - 11s 258ms/step - loss: 0.0021\n",
      "Epoch 122/150\n",
      "41/41 [==============================] - 11s 258ms/step - loss: 0.0021\n",
      "Epoch 123/150\n",
      "41/41 [==============================] - 11s 264ms/step - loss: 0.0022\n",
      "Epoch 124/150\n",
      "41/41 [==============================] - 11s 268ms/step - loss: 0.0021\n",
      "Epoch 125/150\n",
      "41/41 [==============================] - 11s 274ms/step - loss: 0.0022\n",
      "Epoch 126/150\n",
      "41/41 [==============================] - 11s 263ms/step - loss: 0.0021\n",
      "Epoch 127/150\n",
      "41/41 [==============================] - 11s 262ms/step - loss: 0.0022\n",
      "Epoch 128/150\n",
      "41/41 [==============================] - 11s 271ms/step - loss: 0.0021\n",
      "Epoch 129/150\n",
      "41/41 [==============================] - 11s 264ms/step - loss: 0.0020\n",
      "Epoch 130/150\n",
      "41/41 [==============================] - 11s 262ms/step - loss: 0.0020\n",
      "Epoch 131/150\n",
      "41/41 [==============================] - 11s 264ms/step - loss: 0.0020\n",
      "Epoch 132/150\n",
      "41/41 [==============================] - 11s 258ms/step - loss: 0.0021\n",
      "Epoch 133/150\n",
      "41/41 [==============================] - 11s 258ms/step - loss: 0.0020\n",
      "Epoch 134/150\n",
      "41/41 [==============================] - 11s 257ms/step - loss: 0.0022\n",
      "Epoch 135/150\n",
      "41/41 [==============================] - 10s 256ms/step - loss: 0.0021\n",
      "Epoch 136/150\n",
      "41/41 [==============================] - 11s 259ms/step - loss: 0.0020\n",
      "Epoch 137/150\n",
      "41/41 [==============================] - 11s 263ms/step - loss: 0.0020\n",
      "Epoch 138/150\n",
      "41/41 [==============================] - 11s 257ms/step - loss: 0.0021\n",
      "Epoch 139/150\n",
      "41/41 [==============================] - 11s 258ms/step - loss: 0.0019\n",
      "Epoch 140/150\n",
      "41/41 [==============================] - 11s 256ms/step - loss: 0.0021\n",
      "Epoch 141/150\n",
      "41/41 [==============================] - 11s 258ms/step - loss: 0.0020\n",
      "Epoch 142/150\n",
      "41/41 [==============================] - 11s 274ms/step - loss: 0.0019\n",
      "Epoch 143/150\n",
      "41/41 [==============================] - 11s 269ms/step - loss: 0.0022\n",
      "Epoch 144/150\n",
      "41/41 [==============================] - 11s 265ms/step - loss: 0.0022\n",
      "Epoch 145/150\n",
      "41/41 [==============================] - 11s 258ms/step - loss: 0.0020\n",
      "Epoch 146/150\n",
      "41/41 [==============================] - 11s 258ms/step - loss: 0.0021\n",
      "Epoch 147/150\n",
      "41/41 [==============================] - 11s 260ms/step - loss: 0.0022\n",
      "Epoch 148/150\n",
      "41/41 [==============================] - 11s 265ms/step - loss: 0.0020\n",
      "Epoch 149/150\n",
      "41/41 [==============================] - 11s 258ms/step - loss: 0.0021\n",
      "Epoch 150/150\n",
      "41/41 [==============================] - 11s 260ms/step - loss: 0.0021\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x171bb6c3ad0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train, epochs = 150)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "19ed65d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('keras_mode.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c9e8b10d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1400</th>\n",
       "      <td>124.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1401</th>\n",
       "      <td>123.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1402</th>\n",
       "      <td>127.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1403</th>\n",
       "      <td>124.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1404</th>\n",
       "      <td>125.71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       close\n",
       "1400  124.62\n",
       "1401  123.61\n",
       "1402  127.20\n",
       "1403  124.70\n",
       "1404  125.71"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_testing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "69678562",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600\n"
     ]
    }
   ],
   "source": [
    "print(len(data_testing))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "62ddb4ae",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>119.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>120.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1302</th>\n",
       "      <td>123.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1303</th>\n",
       "      <td>124.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>125.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1395</th>\n",
       "      <td>124.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1396</th>\n",
       "      <td>124.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1397</th>\n",
       "      <td>124.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1398</th>\n",
       "      <td>123.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1399</th>\n",
       "      <td>123.16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       close\n",
       "1300  119.20\n",
       "1301  120.60\n",
       "1302  123.00\n",
       "1303  124.01\n",
       "1304  125.11\n",
       "...      ...\n",
       "1395  124.42\n",
       "1396  124.20\n",
       "1397  124.35\n",
       "1398  123.52\n",
       "1399  123.16\n",
       "\n",
       "[100 rows x 1 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_training.tail(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1e95ed88",
   "metadata": {},
   "outputs": [],
   "source": [
    "# here data_training is a data frame\n",
    "past_100_days = data_training.tail(100) \n",
    "final_df = pd.concat([past_100_days,data_testing],ignore_index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "edcc9c92",
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>119.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>120.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>123.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>124.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>125.11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    close\n",
       "0  119.20\n",
       "1  120.60\n",
       "2  123.00\n",
       "3  124.01\n",
       "4  125.11"
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     "execution_count": 35,
     "metadata": {},
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   ],
   "source": [
    "final_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "6dee94a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_data = scaler.fit_transform(final_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "599689b8",
   "metadata": {},
   "outputs": [
    {
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       "       [0.5274479 ],\n",
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       "       [0.4794981 ],\n",
       "       [0.4371499 ],\n",
       "       [0.42124132],\n",
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       "       [0.40443648],\n",
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       "       [0.43222048],\n",
       "       [0.44633654],\n",
       "       [0.38987228],\n",
       "       [0.3746359 ],\n",
       "       [0.41765629],\n",
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       "       [0.50840242],\n",
       "       [0.51669281],\n",
       "       [0.50571365],\n",
       "       [0.54761371],\n",
       "       [0.56934797],\n",
       "       [0.559041  ],\n",
       "       [0.57114049],\n",
       "       [0.53439391],\n",
       "       [0.54626933],\n",
       "       [0.53260139],\n",
       "       [0.49047726],\n",
       "       [0.47613713],\n",
       "       [0.48151468],\n",
       "       [0.47434461],\n",
       "       [0.47344835],\n",
       "       [0.44073493],\n",
       "       [0.44163119],\n",
       "       [0.42751512]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d617f3d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(700, 1)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_data.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "a60b2d2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test = []\n",
    "y_test = []\n",
    "for i in range(100, input_data.shape[0]):\n",
    "    x_test.append(input_data[i-100: i])\n",
    "    y_test.append(input_data[i,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "1fbdf65a",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test,y_test = np.array(x_test),np.array(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "60e36bdb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 1s 75ms/step\n"
     ]
    }
   ],
   "source": [
    "# Making Predicstion\n",
    "y_predicted =  model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "25bb541e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(600, 1)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicted.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "ab676c2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       0.41765629, 0.50773023, 0.50840242, 0.51669281, 0.50571365,\n",
       "       0.54761371, 0.56934797, 0.559041  , 0.57114049, 0.53439391,\n",
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       "       0.47434461, 0.47344835, 0.44073493, 0.44163119, 0.42751512])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "30d82136",
   "metadata": {},
   "outputs": [
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      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "ded54853",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.02240645])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.scale_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "b9c6b989",
   "metadata": {},
   "outputs": [],
   "source": [
    "scale_factor = 1/0.02240645\n",
    "y_predicted  = y_predicted *  scale_factor\n",
    "y_test = y_test * scale_factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "7f627362",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,6))\n",
    "plt.plot(y_test, 'r', label = 'Original Price')\n",
    "plt.plot(y_predicted, 'g', label = 'Predicted Price')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}