lstm_ohlc_1hr.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/beinghorizontal/Quantext/blob/main/lstm_ohlc_1hr.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "initial_id",
      "metadata": {
        "collapsed": true,
        "id": "initial_id"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "outputs": [],
      "source": [
        "\n",
        "!pip install yfinance\n",
        "!pip install pandas_ta\n",
        "!pip install torch\n",
        "!pip install keras==3.0.0\n",
        "!pip install livelossplot\n"
      ],
      "metadata": {
        "id": "9dc217f781c7c0ba"
      },
      "id": "9dc217f781c7c0ba"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Set Torch as backend"
      ],
      "metadata": {
        "id": "AsQSUbbPxLlb"
      },
      "id": "AsQSUbbPxLlb"
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import torch\n",
        "os.environ[\"KERAS_BACKEND\"] = \"torch\""
      ],
      "metadata": {
        "id": "JZGlYakBxOks"
      },
      "id": "JZGlYakBxOks",
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import essential libraries"
      ],
      "metadata": {
        "collapsed": false,
        "id": "d23ec81ad7522a42"
      },
      "id": "d23ec81ad7522a42"
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "outputs": [],
      "source": [
        "import yfinance as yf\n",
        "import datetime\n",
        "import keras\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from pandas_datareader import data\n",
        "# from datetime import datetime\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from keras.models import Sequential\n",
        "from keras.layers import LSTM, Dense, Dropout\n",
        "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
        "from keras.utils import plot_model"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-03-08T08:55:53.441015Z",
          "start_time": "2024-03-08T08:55:52.501832Z"
        },
        "id": "df7283778863139d"
      },
      "id": "df7283778863139d"
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Download the historical data from Yahoo finance for 15 years data, replace  'days=15' to change the years accordingly"
      ],
      "metadata": {
        "collapsed": false,
        "id": "d88345d162a0dc09"
      },
      "id": "d88345d162a0dc09"
    },
    {
      "cell_type": "code",
      "source": [
        "ticker = yf.Ticker(\"^NSEI\")\n",
        "df1 = ticker.history(period=\"2y\", interval=\"60m\")\n"
      ],
      "metadata": {
        "id": "Op5Kjv1oXFRv"
      },
      "id": "Op5Kjv1oXFRv",
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tabulate import tabulate\n",
        "print(tabulate(df1, headers='keys', tablefmt='fancy_grid'))\n"
      ],
      "metadata": {
        "id": "wuNn11FKXtsf"
      },
      "id": "wuNn11FKXtsf",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        }
      ],
      "source": [
        "# ticker_symbol = \"RELIANCE.NS\"\n",
        "# start_date = datetime.datetime.now() - datetime.timedelta(days=15 * 365)\n",
        "# end_date = datetime.datetime.now()\n",
        "\n",
        "# df1 = yf.download(ticker_symbol, start=start_date, end=end_date)"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-03-08T08:55:57.228321Z",
          "start_time": "2024-03-08T08:55:57.208284Z"
        },
        "id": "41c61a5c9d638a5b",
        "outputId": "5b8972e3-1d43-43bf-c962-f495d94365de",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "41c61a5c9d638a5b"
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "outputs": [],
      "source": [
        "df = df1.copy()"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-03-08T08:56:04.291635Z",
          "start_time": "2024-03-08T08:56:02.654355Z"
        },
        "id": "f7d7ca20a88f90a3"
      },
      "id": "f7d7ca20a88f90a3"
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "print(f\"Using device: {device}\")"
      ],
      "metadata": {
        "id": "aw6VexOP8i3h",
        "outputId": "9976f587-8e88-48c4-f1cb-1634ae7e2290",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "aw6VexOP8i3h",
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using device: cuda\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# To experiment with various features keep original copy safe so you don't need to download the file from yahoo server everytime you want to change the features."
      ],
      "metadata": {
        "id": "yj63QE1m8HyV"
      },
      "id": "yj63QE1m8HyV"
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Alternate method to download historical data You need Alpha Vantage free API key. Store that key in the text folder and replace the path. For this script I have Used  the Yahoo finance data the only difference will be the column name which will be capitalized Or in small letters"
      ],
      "metadata": {
        "collapsed": false,
        "id": "f06eee203466f08"
      },
      "id": "f06eee203466f08"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "outputs": [],
      "source": [
        "#!pip install pandas-dataloader\n",
        "#from pandas_datareader import data\n",
        "# Download stock market data (e.g., Apple Inc.)\n",
        "# with open('G:/anaconda/Scripts/data/alpha_vantage/key.txt', 'r') as f:\n",
        "#     key = f.read()\n",
        "# symbol = 'AAPL'  # Ticker symbol for Apple Inc.\n",
        "# df = data.DataReader(symbol, \"av-daily\", start=datetime(2017, 2, 9),\n",
        "#                     end=datetime(2024, 2, 26),\n",
        "#                     api_key=key)"
      ],
      "metadata": {
        "id": "ac3091694c200fab"
      },
      "id": "ac3091694c200fab"
    },
    {
      "cell_type": "markdown",
      "source": [
        "# We will try to normalize the values without calculating the returns. It just for the demonstration to see how it will be difficult for the LSTM model to learn effectively"
      ],
      "metadata": {
        "collapsed": false,
        "id": "4c894feaee84b881"
      },
      "id": "4c894feaee84b881"
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Let's plot unscaled  data 1st"
      ],
      "metadata": {
        "collapsed": false,
        "id": "37e1c4d83951501c"
      },
      "id": "37e1c4d83951501c"
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "df['Close'].plot()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "8fc02961dc36558",
        "outputId": "c8ff9477-10a1-43d2-9d12-e4e89da72a83",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 433
        }
      },
      "id": "8fc02961dc36558"
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Now we will normalize it without calculating the returns and plot the normalized closed value.This is the most frequent error I have seen traders do as you can see even after transforming the data only the scale will be different but you are not essentially removing any underlying trend.Model is still highly sensitive to the price itself so imagine if the price 0.2 in the skilled format has never come what is the point of the model to train on that kind of data"
      ],
      "metadata": {
        "collapsed": false,
        "id": "3cddb5cdea98ee7a"
      },
      "id": "3cddb5cdea98ee7a"
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x791798fe5840>]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "scaler = MinMaxScaler(feature_range=(0, 1))\n",
        "df_test = df.copy()\n",
        "scaled_data = scaler.fit_transform(df_test[['Open', 'High', 'Low', 'Close','Volume']])\n",
        "plt.plot(scaled_data[:, 3])"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-03-08T07:38:51.863191700Z",
          "start_time": "2024-03-08T07:38:51.833851200Z"
        },
        "id": "666e0abae777abdf",
        "outputId": "c15bcc2f-23d4-4108-fc0e-71880ec1e4b7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        }
      },
      "id": "666e0abae777abdf"
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Create Various columns with indicators based on pandas_ta  library, binary output method that is less noise but less opportunities to learn the pattern due to loss of information"
      ],
      "metadata": {
        "collapsed": false,
        "id": "d17ce6d6fd0e52b0"
      },
      "id": "d17ce6d6fd0e52b0"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "outputs": [],
      "source": [
        "# df['sma_small_delta'] = np.where(df.ta.sma(length=5) > df.ta.sma(length=10),1,0)\n",
        "# df['wma_small_delta'] = np.where(df.ta.wma(length=10) > df.ta.wma(length=20),1,0)\n",
        "# df['sma_long_delta'] = np.where(df.ta.sma(length=20) > df.ta.sma(length=50),1,0)\n",
        "# df['rsi_delta'] = np.where(df.ta.rsi(length=7) > df.ta.rsi(length=14),1,0)\n",
        "# df['vwma_small_delta'] = np.where(df.ta.vwma(length=5) > df.ta.vwma(length=10),1,0)\n",
        "# df['vwma_long_delta'] = np.where(df.ta.vwma(length=10) > df.ta.vwma(length=20),1,0)\n",
        "#df['percent_return_20'] = df.ta.percent_return(length=20)\n",
        "# df_vortex = df.ta.vortex(length=14)  # Vortex indicator has two oscillators positive and negative. We calculate the delta\n",
        "# df_vortex['plus_delta'] = np.where(df_vortex['VTXP_14'] > df_vortex['VTXM_14'],1,0)\n",
        "# df['vortex_plus_delta'] = df_vortex['plus_delta']\n"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-03-08T09:01:49.835217Z",
          "start_time": "2024-03-08T09:01:49.810991Z"
        },
        "id": "36bf30c6c7035383"
      },
      "id": "36bf30c6c7035383"
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Create columns best on pandas_ta library, non binary method that is more noise but more opportunities to learn the pattern"
      ],
      "metadata": {
        "collapsed": false,
        "id": "1ec33e3025847641"
      },
      "id": "1ec33e3025847641"
    },
    {
      "cell_type": "code",
      "outputs": [],
      "source": [
        "import pandas_ta as ta\n",
        "df['sma_small_delta'] = (df.ta.sma(length=5) - df.ta.sma(length=10))/df.ta.sma(length=10)\n",
        "df['sma_long_delta'] = (df.ta.sma(length=20) - df.ta.sma(length=50))/df.ta.sma(length=50)\n",
        "df['rsi_delta'] = (df.ta.rsi(length=7) - df.ta.rsi(length=14)) / df.ta.rsi(length=14)\n",
        "df['percent_return_20'] = df.ta.percent_return(length=20)\n",
        "df_vortex = df.ta.vortex(length=14)  # Vortex indicator has two oscillators positive and negative. We calculate the delta\n",
        "df_vortex['plus_delta'] = (df_vortex['VTXP_14'] - df_vortex['VTXM_14'])/df_vortex['VTXM_14']\n",
        "df['vortex_plus_delta'] = df_vortex['plus_delta']\n",
        "df['atr'] = df.ta.atr()/df['Close']\n",
        "df['roc5'] = df.ta.roc(length=5)\n",
        "df['roc'] = df.ta.roc()\n",
        "df['qclose'] = 100 * ((df['Close'] - df['Low']) / (df['High'] - df['Low']))\n",
        "df= df.dropna()\n"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-03-08T11:37:54.441214Z",
          "start_time": "2024-03-08T11:37:54.408883Z"
        },
        "id": "8f0231de7f836460"
      },
      "id": "8f0231de7f836460",
      "execution_count": 62
    },
    {
      "cell_type": "markdown",
      "source": [
        "# plot any one indicator to confirm stationary properties"
      ],
      "metadata": {
        "collapsed": false,
        "id": "f392100af22e15b8"
      },
      "id": "f392100af22e15b8"
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Datetime'>"
            ]
          },
          "metadata": {},
          "execution_count": 63
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "df[['sma_small_delta']].tail(200).plot()\n",
        "# Zoom in view to see if there is randomness or some cyclical pattern\n"
      ],
      "metadata": {
        "id": "30ff6d23611cf146",
        "outputId": "81bffc44-90a2-4580-c10d-82509a2759b3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        }
      },
      "id": "30ff6d23611cf146"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "outputs": [],
      "source": [
        "#df.ta.percent_return(length=20)"
      ],
      "metadata": {
        "id": "59823321b443b3ac"
      },
      "id": "59823321b443b3ac"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "outputs": [],
      "source": [
        "#df.ta.indicators()"
      ],
      "metadata": {
        "id": "10faaa5702a76674"
      },
      "id": "10faaa5702a76674"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "outputs": [],
      "source": [
        "#help(ta.vwma)\n"
      ],
      "metadata": {
        "id": "52fbb2a9ad0712f"
      },
      "id": "52fbb2a9ad0712f"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "outputs": [],
      "source": [
        "# df.ta.roc().tail(200).plot()\n",
        "# plt.show()"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-03-08T09:01:58.320357Z",
          "start_time": "2024-03-08T09:01:58.033339Z"
        },
        "id": "b90f1b00b2445db7"
      },
      "id": "b90f1b00b2445db7"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "outputs": [],
      "source": [
        "df = df.dropna()"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-03-08T11:38:03.925367Z",
          "start_time": "2024-03-08T11:38:03.915367Z"
        },
        "id": "5ec35e89dac95f85"
      },
      "id": "5ec35e89dac95f85"
    },
    {
      "cell_type": "markdown",
      "source": [
        "# check what dataframe looks like because we are going to normalize the remaining data later\n",
        "# Most of the values have positive and negative numbers. So we will use minmax scaler range is equal to -1, 1"
      ],
      "metadata": {
        "collapsed": false,
        "id": "b2240803c95ce304"
      },
      "id": "b2240803c95ce304"
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "outputs": [],
      "source": [
        "scaler = MinMaxScaler(feature_range=(0, 1))"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-03-08T11:38:34.526219Z",
          "start_time": "2024-03-08T11:38:34.509220Z"
        },
        "id": "64a8263100287c9f"
      },
      "id": "64a8263100287c9f"
    },
    {
      "cell_type": "code",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Open', 'High', 'Low', 'Close', 'Volume', 'Dividends', 'Stock Splits', 'sma_small_delta', 'sma_long_delta', 'rsi_delta', 'percent_return_20', 'vortex_plus_delta', 'atr', 'roc5', 'roc', 'qclose']\n"
          ]
        }
      ],
      "source": [
        "print(df.columns.to_list())"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-03-08T11:39:17.966195Z",
          "start_time": "2024-03-08T11:39:17.961194Z"
        },
        "id": "2817bcddaf640bea",
        "outputId": "bab5bc75-c2c7-4001-9cc5-d1ad8259de5b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "2817bcddaf640bea",
      "execution_count": 65
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "outputs": [],
      "source": [
        "columns_to_norm = ['sma_small_delta', 'sma_long_delta', 'rsi_delta', 'percent_return_20', 'vortex_plus_delta', 'atr', 'roc5', 'roc','qclose']\n",
        "#columns_to_norm = ['All_Candle_Patterns']\n",
        "\n",
        "for col in columns_to_norm:\n",
        "    df[[col]] = scaler.fit_transform(df[[col]])\n"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-03-08T11:39:21.808683Z",
          "start_time": "2024-03-08T11:39:21.768380Z"
        },
        "id": "c18f3474b3023e8a"
      },
      "id": "c18f3474b3023e8a"
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Create 5 day sma for target"
      ],
      "metadata": {
        "collapsed": false,
        "id": "b10e0987a2fffa2f"
      },
      "id": "b10e0987a2fffa2f"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "outputs": [],
      "source": [
        "df['5sma_lag'] = df['Close'].rolling(5).mean()\n",
        "df['5sma_ahead'] =  df['5sma_lag'].shift(-5)\n",
        "df['target_close'] = df['Close'].shift(-1)\n",
        "# create binary target\n",
        "#df['target'] = (df['target_close'] > df['5sma_lag']).astype(int)\n",
        "df['target'] = (df['5sma_ahead'] > df['5sma_lag']).astype(int)\n",
        "df = df.dropna()\n"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-03-08T11:39:28.025611Z",
          "start_time": "2024-03-08T11:39:28.017119Z"
        },
        "id": "b33b790f9726f29b"
      },
      "id": "b33b790f9726f29b"
    },
    {
      "cell_type": "markdown",
      "source": [
        "# drop unwanted columns"
      ],
      "metadata": {
        "collapsed": false,
        "id": "d43b73565e06bd49"
      },
      "id": "d43b73565e06bd49"
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "outputs": [],
      "source": [
        "df = df.drop(['Open', 'High','Low', 'Close','Dividends', 'Stock Splits',  'Volume','5sma_lag','5sma_ahead','target_close'],axis=1)"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-03-08T11:40:37.989664Z",
          "start_time": "2024-03-08T11:40:37.978665Z"
        },
        "id": "c7cbe55f7a05f8ad"
      },
      "id": "c7cbe55f7a05f8ad"
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "outputs": [],
      "source": [
        "df = df.dropna()"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-03-08T11:41:02.895414Z",
          "start_time": "2024-03-08T11:41:02.882414Z"
        },
        "id": "ad64184d035e6747"
      },
      "id": "ad64184d035e6747"
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: tabulate print df fancygrid\n",
        "\n",
        "print(tabulate(df, headers='keys', tablefmt='fancy_grid'))\n"
      ],
      "metadata": {
        "id": "Tcd6Gnt5lMJE"
      },
      "id": "Tcd6Gnt5lMJE",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Create target and features"
      ],
      "metadata": {
        "collapsed": false,
        "id": "2eda59574e7365d9"
      },
      "id": "2eda59574e7365d9"
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "outputs": [],
      "source": [
        "features = df.drop('target', axis=1).values\n",
        "target = df['target'].values"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-03-08T11:41:05.114920Z",
          "start_time": "2024-03-08T11:41:05.104919Z"
        },
        "id": "46a977f111f7d888"
      },
      "id": "46a977f111f7d888"
    },
    {
      "cell_type": "code",
      "source": [
        "print(tabulate(features, headers='keys', tablefmt='fancy_grid'))\n"
      ],
      "metadata": {
        "id": "JISoXobLlq93"
      },
      "id": "JISoXobLlq93",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "p8ebrKmd9V5y"
      },
      "id": "p8ebrKmd9V5y"
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "outputs": [],
      "source": [
        "def create_sequences(features, target, sequence_length):\n",
        "    X = []\n",
        "    y = []\n",
        "    for i in range(len(features) - sequence_length):\n",
        "        X.append(features[i:i+sequence_length])\n",
        "        y.append(target[i+(sequence_length-1)])\n",
        "    return np.array(X), np.array(y)\n",
        "\n",
        "sequence_length = 10\n",
        "X, y = create_sequences(features, target, sequence_length)"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-03-08T11:42:03.390256Z",
          "start_time": "2024-03-08T11:42:03.367256Z"
        },
        "id": "654eb5a943f22991"
      },
      "id": "654eb5a943f22991"
    },
    {
      "cell_type": "markdown",
      "source": [
        "# split X and Y into training and validation sets"
      ],
      "metadata": {
        "collapsed": false,
        "id": "d6dc44a26c1dc982"
      },
      "id": "d6dc44a26c1dc982"
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.4,shuffle=False)\n"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-03-08T11:42:06.469871Z",
          "start_time": "2024-03-08T11:42:06.463853Z"
        },
        "id": "e555a4a424d364f0"
      },
      "id": "e555a4a424d364f0"
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training shape: (2030, 10, 9)\n",
            "Validation shape: (1354, 10, 9)\n"
          ]
        }
      ],
      "source": [
        "print(\"Training shape:\", X_train.shape)\n",
        "print(\"Validation shape:\", X_val.shape)"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-03-08T11:18:01.565514Z",
          "start_time": "2024-03-08T11:18:01.546504Z"
        },
        "id": "8ca4c5fd359407e",
        "outputId": "1188a7ab-ca3a-47e6-de05-3468a24979a5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "8ca4c5fd359407e"
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9\n"
          ]
        }
      ],
      "source": [
        "num_features = X_train.shape[2]\n",
        "print(num_features)"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-03-08T11:43:30.979813Z",
          "start_time": "2024-03-08T11:43:30.970241Z"
        },
        "id": "782d09d5d89c3fa1",
        "outputId": "b8caa50c-e5f7-4666-ddc2-0944bb5770ea",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "782d09d5d89c3fa1"
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/rnn/rnn.py:205: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(**kwargs)\n"
          ]
        }
      ],
      "source": [
        "from keras.optimizers import Adam\n",
        "from keras.regularizers import l1_l2,l1,l2\n",
        "from keras.layers import LeakyReLU\n",
        "# inputs = Input(shape=(sequence_length, X_train.sha))  # Replace with appropriate values\n",
        "\n",
        "\n",
        "# Build the LSTM model\n",
        "model = Sequential()\n",
        "# model.add(LSTM(64, return_sequences=True, input_shape=(sequence_length, num_features), activation='relu', kernel_regularizer=l1_l2(l1=0.01, l2=0.01)))\n",
        "#model.add(LSTM(64, return_sequences=True, input_shape=(sequence_length, num_features), activation='relu', kernel_regularizer=l1(0.001)))\n",
        "# model.add(LSTM(152, return_sequences=True, activation='relu'))  # Add an extra LSTM layer\n",
        "#model.add(Dropout(0.5))\n",
        "#model.add(LSTM(92, return_sequences=True, activation='relu'))  # Add an extra LSTM layer\n",
        "#model.add(Dropout(0.4))\n",
        "model.add(LSTM(32, return_sequences=True, activation='relu',input_shape=(sequence_length, num_features) ))  # Add an extra LSTM layer\n",
        "# # model.add(Dropout(0.4))\n",
        "# model.add(LSTM(32, return_sequences=True, activation='relu'))  # Add an extra LSTM layer\n",
        "# # model.add(Dropout(0.4))\n",
        "# model.add(LSTM(8, return_sequences=True, activation='tanh'))  # Add an extra LSTM layer\n",
        "# # # model.add(Dropout(0.4))\n",
        "model.add(LSTM(8))  # Add an extra LSTM layer\n",
        "# model.add(Dense(1, activation='sigmoid',  kernel_regularizer=l1_l2(l1=0.01, l2=0.01)))\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "optimizer = Adam(learning_rate=0.001) # default is 0.001\n",
        "model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['accuracy'])"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-03-08T11:43:44.643323Z",
          "start_time": "2024-03-08T11:43:44.609073Z"
        },
        "id": "3d8b1598cf5ade1f",
        "outputId": "ebd331f2-ed5a-4749-b73b-1564506d6919",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "3d8b1598cf5ade1f"
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "outputs": [],
      "source": [
        "early_stopping = EarlyStopping(monitor='val_loss',    # Monitor the val loss\n",
        "                               patience=150,           # Num epochs; if no improvement training will be stopped\n",
        "                               verbose=1,\n",
        "                               mode='min',            # The training will stop when the quantity monitored has stopped decreasing\n",
        "                               restore_best_weights=True) # Restores model weights from the epoch with the best value of the monitored quantity."
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-03-08T11:18:10.030170Z",
          "start_time": "2024-03-08T11:18:10.019171Z"
        },
        "id": "9aa67cde5d85bfc8"
      },
      "id": "9aa67cde5d85bfc8"
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "outputs": [],
      "source": [
        "\n",
        "# Model checkpoint callback\n",
        "model_checkpoint = ModelCheckpoint('/content/best_model.keras',   # Path where to save the model\n",
        "                                   monitor='val_loss',   # Monitor the validation loss\n",
        "                                   save_best_only=True,  # The latest best model according to the quantity monitored will not be overwritten\n",
        "                                   mode='min',           # The training will save the model when the quantity monitored has decreased\n",
        "                                   verbose=1)\n"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2024-03-08T11:18:10.973183Z",
          "start_time": "2024-03-08T11:18:10.968181Z"
        },
        "id": "1536e75f7b9f13c5"
      },
      "id": "1536e75f7b9f13c5"
    },
    {
      "cell_type": "code",
      "source": [
        "from livelossplot import PlotLossesKeras\n",
        "plot_losses = PlotLossesKeras()"
      ],
      "metadata": {
        "id": "bOrf0c6Dz8BE"
      },
      "id": "bOrf0c6Dz8BE",
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "accuracy\n",
            "\ttraining         \t (min:    0.524, max:    0.732, cur:    0.732)\n",
            "\tvalidation       \t (min:    0.611, max:    0.716, cur:    0.687)\n",
            "Loss\n",
            "\ttraining         \t (min:    0.523, max:    0.690, cur:    0.523)\n",
            "\tvalidation       \t (min:    0.552, max:    0.686, cur:    0.583)\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-81-43ef6002354b>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m model.fit(X_train, y_train,\n\u001b[0m\u001b[1;32m      2\u001b[0m                     \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m                     \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m                     \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_val\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_val\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m                     \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mearly_stopping\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel_checkpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mplot_losses\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    116\u001b[0m         \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    117\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 118\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    119\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    120\u001b[0m             \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/backend/torch/trainer.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq)\u001b[0m\n\u001b[1;32m    314\u001b[0m                 \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    315\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 316\u001b[0;31m                 \u001b[0mlogs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    317\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    318\u001b[0m                 \u001b[0;31m# Callbacks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/backend/torch/trainer.py\u001b[0m in \u001b[0;36mone_step_on_data\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m    115\u001b[0m             \u001b[0;34m\"\"\"Runs a single training step on a batch of data.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    116\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 117\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    118\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    119\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_should_torch_compile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/backend/torch/trainer.py\u001b[0m in \u001b[0;36mtrain_step\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m     65\u001b[0m             \u001b[0;31m# Call torch.Tensor.backward() on the loss to compute gradients\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m             \u001b[0;31m# for the weights.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m             \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     68\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m             \u001b[0mtrainable_weights\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrainable_weights\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/_tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m    520\u001b[0m                 \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    521\u001b[0m             )\n\u001b[0;32m--> 522\u001b[0;31m         torch.autograd.backward(\n\u001b[0m\u001b[1;32m    523\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    524\u001b[0m         )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m    264\u001b[0m     \u001b[0;31m# some Python versions print out the first line of a multi-line function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    265\u001b[0m     \u001b[0;31m# calls in the traceback and some print out the last line\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 266\u001b[0;31m     Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass\n\u001b[0m\u001b[1;32m    267\u001b[0m         \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    268\u001b[0m         \u001b[0mgrad_tensors_\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ],
      "source": [
        "model.fit(X_train, y_train,\n",
        "                    epochs=1000,\n",
        "                    batch_size=4,\n",
        "                    validation_data=(X_val, y_val),\n",
        "                    callbacks=[early_stopping, model_checkpoint,plot_losses],\n",
        "                    verbose=False)\n"
      ],
      "metadata": {
        "is_executing": true,
        "ExecuteTime": {
          "start_time": "2024-03-08T11:43:49.078451Z"
        },
        "id": "e0525fda238b3308",
        "outputId": "f058e52a-eaa0-49ef-9b0b-757ae3a7d3e9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "id": "e0525fda238b3308"
    },
    {
      "cell_type": "markdown",
      "source": [
        "#plot model architecture"
      ],
      "metadata": {
        "collapsed": false,
        "id": "73272159e33e1d05"
      },
      "id": "73272159e33e1d05"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "outputs": [],
      "source": [
        "plot_model(model, to_file='lstm_model.png', show_shapes=True, show_layer_names=True)\n"
      ],
      "metadata": {
        "id": "814ec0626ef56309"
      },
      "id": "814ec0626ef56309"
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 2
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython2",
      "version": "2.7.6"
    },
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "include_colab_link": true
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 5
}