Bollinger_+_RSI,_Double_Strategy.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPopSaHQJ1s60LCrH/2aVtW",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/VinayChaudhari1996/SwingProfit/blob/main/Bollinger_%2B_RSI%2C_Double_Strategy.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import yfinance as yf\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Specify the stock symbol (e.g., TCS.BO for Tata Consultancy Services)\n",
        "stock_symbol = \"TCS.BO\"\n",
        "\n",
        "# Set the start and end dates for the data\n",
        "start_date = \"2022-01-01\"\n",
        "end_date = \"2023-01-01\"\n",
        "\n",
        "# Fetch historical stock data\n",
        "stock_data = yf.download(stock_symbol, start=start_date, end=end_date)\n",
        "\n",
        "# Display the fetched data\n",
        "print(stock_data.head())\n",
        "\n",
        "# Plot the closing prices\n",
        "stock_data['Close'].plot(figsize=(10, 6), label=stock_symbol)\n",
        "plt.title(f\"{stock_symbol} Stock Price\")\n",
        "plt.xlabel(\"Date\")\n",
        "plt.ylabel(\"Closing Price\")\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 815
        },
        "id": "6WtDz7zXiN9J",
        "outputId": "2bc7e774-78a2-41b2-b751-55079d339758"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n",
            "                   Open         High          Low        Close    Adj Close  \\\n",
            "Date                                                                          \n",
            "2022-01-03  3744.000000  3829.000000  3743.949951  3818.149902  3686.580078   \n",
            "2022-01-04  3829.050049  3888.000000  3810.000000  3882.699951  3748.905762   \n",
            "2022-01-05  3865.000000  3870.100098  3811.000000  3861.050049  3728.001953   \n",
            "2022-01-06  3816.000000  3834.750000  3771.350098  3806.850098  3675.669678   \n",
            "2022-01-07  3809.100098  3864.449951  3796.000000  3854.850098  3722.015625   \n",
            "\n",
            "            Volume  \n",
            "Date                \n",
            "2022-01-03   67010  \n",
            "2022-01-04  342652  \n",
            "2022-01-05   54550  \n",
            "2022-01-06  111898  \n",
            "2022-01-07   41904  \n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import yfinance as yf\n",
        "import pandas as pd\n",
        "import plotly.graph_objects as go\n",
        "\n",
        "# Function to calculate RSI\n",
        "def calculate_rsi(data, window=6):\n",
        "    delta = data['Close'].diff(1)\n",
        "    gain = delta.where(delta > 0, 0)\n",
        "    loss = -delta.where(delta < 0, 0)\n",
        "\n",
        "    avg_gain = gain.rolling(window=window, min_periods=1).mean()\n",
        "    avg_loss = loss.rolling(window=window, min_periods=1).mean()\n",
        "\n",
        "    rs = avg_gain / avg_loss\n",
        "    rsi = 100 - (100 / (1 + rs))\n",
        "\n",
        "    return rsi\n",
        "\n",
        "# Function to calculate Bollinger Bands\n",
        "def calculate_bollinger_bands(data, window=200, multiplier=2):\n",
        "    basis = data['Close'].rolling(window=window, min_periods=1).mean()\n",
        "    dev = data['Close'].rolling(window=window, min_periods=1).std()\n",
        "\n",
        "    upper_band = basis + multiplier * dev\n",
        "    lower_band = basis - multiplier * dev\n",
        "\n",
        "    return upper_band, lower_band\n",
        "\n",
        "# Download historical stock data\n",
        "symbol = 'TCS.BO'  # Replace with the desired stock symbol\n",
        "data = yf.download(symbol, start='2022-01-01', end='2023-01-01')\n",
        "\n",
        "# Calculate RSI and Bollinger Bands\n",
        "data['RSI'] = calculate_rsi(data, window=6)\n",
        "data['BB_Upper'], data['BB_Lower'] = calculate_bollinger_bands(data, window=200, multiplier=2)\n",
        "\n",
        "# Identify buy and sell signals\n",
        "data['Buy_Signal'] = (data['RSI'] < 50) & (data['Close'] < data['BB_Lower'])\n",
        "data['Sell_Signal'] = (data['RSI'] > 50) & (data['Close'] > data['BB_Upper'])\n",
        "\n",
        "# Plot using Plotly\n",
        "fig = go.Figure()\n",
        "\n",
        "fig.add_trace(go.Scatter(x=data.index, y=data['Close'], mode='lines', name='Close Price'))\n",
        "fig.add_trace(go.Scatter(x=data[data['Buy_Signal']].index, y=data[data['Buy_Signal']]['Close'],\n",
        "                         mode='markers', marker=dict(color='green'), name='Buy Signal'))\n",
        "fig.add_trace(go.Scatter(x=data[data['Sell_Signal']].index, y=data[data['Sell_Signal']]['Close'],\n",
        "                         mode='markers', marker=dict(color='red'), name='Sell Signal'))\n",
        "\n",
        "fig.add_trace(go.Scatter(x=data.index, y=data['BB_Upper'], line=dict(color='black'), name='Bollinger Bands Upper'))\n",
        "fig.add_trace(go.Scatter(x=data.index, y=data['BB_Lower'], line=dict(color='black'), name='Bollinger Bands Lower'))\n",
        "\n",
        "# Add a range slider with animation\n",
        "fig.update_layout(\n",
        "    xaxis=dict(\n",
        "        rangeslider=dict(visible=True),\n",
        "        type='date'\n",
        "    )\n",
        ")\n",
        "\n",
        "fig.show()\n"
      ],
      "metadata": {
        "id": "nrvvPLHubGuh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 559
        },
        "outputId": "158b504c-d2ae-4567-c805-c03e8001d219"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
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    {
      "cell_type": "code",
      "source": [
        "import yfinance as yf\n",
        "import pandas as pd\n",
        "import plotly.graph_objects as go\n",
        "\n",
        "# Function to calculate RSI\n",
        "def calculate_rsi(data, window=6):\n",
        "    delta = data['Close'].diff(1)\n",
        "    gain = delta.where(delta > 0, 0)\n",
        "    loss = -delta.where(delta < 0, 0)\n",
        "\n",
        "    avg_gain = gain.rolling(window=window, min_periods=1).mean()\n",
        "    avg_loss = loss.rolling(window=window, min_periods=1).mean()\n",
        "\n",
        "    rs = avg_gain / avg_loss\n",
        "    rsi = 100 - (100 / (1 + rs))\n",
        "\n",
        "    return rsi\n",
        "\n",
        "# Function to calculate Bollinger Bands\n",
        "def calculate_bollinger_bands(data, window=200, multiplier=2):\n",
        "    basis = data['Close'].rolling(window=window, min_periods=1).mean()\n",
        "    dev = data['Close'].rolling(window=window, min_periods=1).std()\n",
        "\n",
        "    upper_band = basis + multiplier * dev\n",
        "    lower_band = basis - multiplier * dev\n",
        "\n",
        "    return upper_band, lower_band\n",
        "\n",
        "# Function to simulate the trading strategy\n",
        "def simulate_strategy(data, initial_capital=50000):\n",
        "    capital = initial_capital\n",
        "    position = 0\n",
        "    buy_price = 0\n",
        "    sell_price = 0\n",
        "    trades = []\n",
        "\n",
        "    for i in range(1, len(data)):\n",
        "        if data['Buy_Signal'][i]:\n",
        "            position = capital / data['Close'][i]\n",
        "            buy_price = data['Close'][i]\n",
        "            trades.append(('Buy', data.index[i], buy_price))\n",
        "\n",
        "        elif data['Sell_Signal'][i] and position > 0:\n",
        "            sell_price = data['Close'][i]\n",
        "            capital = position * sell_price\n",
        "            position = 0\n",
        "            trades.append(('Sell', data.index[i], sell_price))\n",
        "\n",
        "    # Sell remaining position at the end\n",
        "    if position > 0:\n",
        "        sell_price = data['Close'].iloc[-1]\n",
        "        capital = position * sell_price\n",
        "        trades.append(('Sell', data.index[-1], sell_price))\n",
        "\n",
        "    return capital, trades\n",
        "\n",
        "# Download historical stock data\n",
        "symbol = 'TCS.BO'  # Replace with the desired stock symbol\n",
        "data = yf.download(symbol, start='2022-01-01', end='2023-01-01' )\n",
        "\n",
        "# Calculate RSI and Bollinger Bands\n",
        "data['RSI'] = calculate_rsi(data, window=6)\n",
        "data['BB_Upper'], data['BB_Lower'] = calculate_bollinger_bands(data, window=200, multiplier=2)\n",
        "\n",
        "# Identify buy and sell signals\n",
        "data['Buy_Signal'] = (data['RSI'] < 50) & (data['Close'] < data['BB_Lower'])\n",
        "data['Sell_Signal'] = (data['RSI'] > 50) & (data['Close'] > data['BB_Upper'])\n",
        "\n",
        "# Simulate the trading strategy\n",
        "final_capital, trades = simulate_strategy(data)\n",
        "\n",
        "# Print results\n",
        "print(f\"Initial Capital: 50,000 INR\")\n",
        "print(f\"Final Capital: {final_capital:.2f} INR\")\n",
        "print(f\"Profit/Loss: {final_capital - 50000:.2f} INR\")\n",
        "\n",
        "# Plot using Plotly\n",
        "fig = go.Figure()\n",
        "\n",
        "fig.add_trace(go.Scatter(x=data.index, y=data['Close'], mode='lines', name='Close Price'))\n",
        "fig.add_trace(go.Scatter(x=data[data['Buy_Signal']].index, y=data[data['Buy_Signal']]['Close'],\n",
        "                         mode='markers', marker=dict(color='green'), name='Buy Signal'))\n",
        "fig.add_trace(go.Scatter(x=data[data['Sell_Signal']].index, y=data[data['Sell_Signal']]['Close'],\n",
        "                         mode='markers', marker=dict(color='red'), name='Sell Signal'))\n",
        "\n",
        "fig.add_trace(go.Scatter(x=data.index, y=data['BB_Upper'], line=dict(color='black'), name='Bollinger Bands Upper'))\n",
        "fig.add_trace(go.Scatter(x=data.index, y=data['BB_Lower'], line=dict(color='black'), name='Bollinger Bands Lower'))\n",
        "\n",
        "# Display trade events\n",
        "for trade_type, trade_date, trade_price in trades:\n",
        "    if trade_type == 'Buy':\n",
        "        fig.add_trace(go.Scatter(x=[trade_date], y=[trade_price], mode='markers', marker=dict(color='blue'),\n",
        "                                 text=f'Buy at {trade_price}', name='Buy Event'))\n",
        "    elif trade_type == 'Sell':\n",
        "        fig.add_trace(go.Scatter(x=[trade_date], y=[trade_price], mode='markers', marker=dict(color='orange'),\n",
        "                                 text=f'Sell at {trade_price}', name='Sell Event'))\n",
        "\n",
        "fig.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 611
        },
        "id": "kEbRPQNahSyB",
        "outputId": "4332a5cb-778f-4b57-e271-8c9cacffb76d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n",
            "Initial Capital: 50,000 INR\n",
            "Final Capital: 54437.87 INR\n",
            "Profit/Loss: 4437.87 INR\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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    },
    {
      "cell_type": "code",
      "source": [
        "import yfinance as yf\n",
        "import pandas as pd\n",
        "import math\n",
        "\n",
        "# Download historical stock data with 15-minute interval for the last 60 days\n",
        "symbol = 'TCS.BO'  # Replace with the desired stock symbol\n",
        "data = yf.download(symbol, period='5d', interval='30m')\n",
        "\n",
        "# Function to calculate RSI\n",
        "def calculate_rsi(data, window=6):\n",
        "    delta = data['Close'].diff(1)\n",
        "    gain = delta.where(delta > 0, 0)\n",
        "    loss = -delta.where(delta < 0, 0)\n",
        "\n",
        "    avg_gain = gain.rolling(window=window, min_periods=1).mean()\n",
        "    avg_loss = loss.rolling(window=window, min_periods=1).mean()\n",
        "\n",
        "    rs = avg_gain / avg_loss\n",
        "    rsi = 100 - (100 / (1 + rs))\n",
        "\n",
        "    return rsi\n",
        "\n",
        "# Function to calculate Bollinger Bands\n",
        "def calculate_bollinger_bands(data, window=200, multiplier=2):\n",
        "    basis = data['Close'].rolling(window=window, min_periods=1).mean()\n",
        "    dev = data['Close'].rolling(window=window, min_periods=1).std()\n",
        "\n",
        "    upper_band = basis + multiplier * dev\n",
        "    lower_band = basis - multiplier * dev\n",
        "\n",
        "    return upper_band, lower_band\n",
        "\n",
        "# Function to simulate the trading strategy\n",
        "def simulate_strategy(data, initial_capital=50000):\n",
        "    capital = initial_capital\n",
        "    position = 0\n",
        "    buy_price = 0\n",
        "    sell_price = 0\n",
        "    trades = []\n",
        "\n",
        "    for i in range(1, len(data)):\n",
        "        if data['Buy_Signal'][i]:\n",
        "            position = capital // data['Close'][i]  # Use integer division\n",
        "            buy_price = data['Close'][i]\n",
        "            trades.append(('Buy', data.index[i], buy_price, position))\n",
        "        elif data['Sell_Signal'][i] and position > 0:\n",
        "            sell_price = data['Close'][i]\n",
        "            capital = position * sell_price\n",
        "            position = 0\n",
        "            trades.append(('Sell', data.index[i], sell_price, position))\n",
        "\n",
        "    # Sell remaining position at the end\n",
        "    if position > 0:\n",
        "        sell_price = data['Close'].iloc[-1]\n",
        "        capital = position * sell_price\n",
        "        trades.append(('Sell', data.index[-1], sell_price, position))\n",
        "\n",
        "    return capital, pd.DataFrame(trades, columns=['Action', 'Timestamp', 'Price', 'Quantity'])\n",
        "\n",
        "# Calculate RSI and Bollinger Bands\n",
        "data['RSI'] = calculate_rsi(data, window=6)\n",
        "data['BB_Upper'], data['BB_Lower'] = calculate_bollinger_bands(data, window=200, multiplier=2)\n",
        "\n",
        "# Identify buy and sell signals\n",
        "data['Buy_Signal'] = (data['RSI'] < 50) & (data['Close'] < data['BB_Lower'])\n",
        "data['Sell_Signal'] = (data['RSI'] > 50) & (data['Close'] > data['BB_Upper'])\n",
        "\n",
        "# Simulate the trading strategy\n",
        "final_capital, trades_df = simulate_strategy(data)\n",
        "\n",
        "# Print results\n",
        "print(f\"Initial Capital: 50,000 INR\")\n",
        "print(f\"Final Capital: {final_capital:.2f} INR\")\n",
        "print(f\"Profit/Loss: {final_capital - 50000:.2f} INR\")\n",
        "\n",
        "# Display the DataFrame with buy and sell information\n",
        "print(\"\\nTrade Details:\")\n",
        "trades_df\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 529
        },
        "id": "uunr5p4UjxRS",
        "outputId": "687183e8-4517-4eb7-96b1-da1d5e3bafac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n",
            "Initial Capital: 50,000 INR\n",
            "Final Capital: 48541.50 INR\n",
            "Profit/Loss: -1458.50 INR\n",
            "\n",
            "Trade Details:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Action           Timestamp        Price  Quantity\n",
              "0     Buy 2023-11-22 09:30:00  3506.000000      14.0\n",
              "1     Buy 2023-11-24 09:30:00  3489.699951      14.0\n",
              "2     Buy 2023-11-24 10:00:00  3497.100098      14.0\n",
              "3     Buy 2023-11-24 10:30:00  3484.899902      14.0\n",
              "4     Buy 2023-11-24 11:00:00  3482.100098      14.0\n",
              "5     Buy 2023-11-24 11:30:00  3477.550049      14.0\n",
              "6     Buy 2023-11-24 12:00:00  3476.000000      14.0\n",
              "7     Buy 2023-11-24 12:30:00  3475.949951      14.0\n",
              "8     Buy 2023-11-24 13:00:00  3474.149902      14.0\n",
              "9     Buy 2023-11-24 13:30:00  3466.949951      14.0\n",
              "10    Buy 2023-11-24 14:00:00  3467.250000      14.0\n",
              "11   Sell 2023-11-24 14:00:00  3467.250000      14.0"
            ],
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Action</th>\n",
              "      <th>Timestamp</th>\n",
              "      <th>Price</th>\n",
              "      <th>Quantity</th>\n",
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              "      <th>0</th>\n",
              "      <td>Buy</td>\n",
              "      <td>2023-11-22 09:30:00</td>\n",
              "      <td>3506.000000</td>\n",
              "      <td>14.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Buy</td>\n",
              "      <td>2023-11-24 09:30:00</td>\n",
              "      <td>3489.699951</td>\n",
              "      <td>14.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Buy</td>\n",
              "      <td>2023-11-24 10:00:00</td>\n",
              "      <td>3497.100098</td>\n",
              "      <td>14.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Buy</td>\n",
              "      <td>2023-11-24 10:30:00</td>\n",
              "      <td>3484.899902</td>\n",
              "      <td>14.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Buy</td>\n",
              "      <td>2023-11-24 11:00:00</td>\n",
              "      <td>3482.100098</td>\n",
              "      <td>14.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Buy</td>\n",
              "      <td>2023-11-24 11:30:00</td>\n",
              "      <td>3477.550049</td>\n",
              "      <td>14.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Buy</td>\n",
              "      <td>2023-11-24 12:00:00</td>\n",
              "      <td>3476.000000</td>\n",
              "      <td>14.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Buy</td>\n",
              "      <td>2023-11-24 12:30:00</td>\n",
              "      <td>3475.949951</td>\n",
              "      <td>14.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Buy</td>\n",
              "      <td>2023-11-24 13:00:00</td>\n",
              "      <td>3474.149902</td>\n",
              "      <td>14.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Buy</td>\n",
              "      <td>2023-11-24 13:30:00</td>\n",
              "      <td>3466.949951</td>\n",
              "      <td>14.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Buy</td>\n",
              "      <td>2023-11-24 14:00:00</td>\n",
              "      <td>3467.250000</td>\n",
              "      <td>14.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Sell</td>\n",
              "      <td>2023-11-24 14:00:00</td>\n",
              "      <td>3467.250000</td>\n",
              "      <td>14.0</td>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-3b688ea5-aefe-4c9c-90ec-154e002e6357');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "    width: 32px;\n",
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              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-01c25eb2-8565-41a7-8af5-87e99babc42e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import yfinance as yf\n",
        "import pandas as pd\n",
        "import plotly.graph_objects as go\n",
        "\n",
        "# Function to calculate RSI\n",
        "def calculate_rsi(data, window=6):\n",
        "    delta = data['Close'].diff(1)\n",
        "    gain = delta.where(delta > 0, 0)\n",
        "    loss = -delta.where(delta < 0, 0)\n",
        "\n",
        "    avg_gain = gain.rolling(window=window, min_periods=1).mean()\n",
        "    avg_loss = loss.rolling(window=window, min_periods=1).mean()\n",
        "\n",
        "    rs = avg_gain / avg_loss\n",
        "    rsi = 100 - (100 / (1 + rs))\n",
        "\n",
        "    return rsi\n",
        "\n",
        "# Function to calculate Bollinger Bands\n",
        "def calculate_bollinger_bands(data, window=200, multiplier=2):\n",
        "    basis = data['Close'].rolling(window=window, min_periods=1).mean()\n",
        "    dev = data['Close'].rolling(window=window, min_periods=1).std()\n",
        "\n",
        "    upper_band = basis + multiplier * dev\n",
        "    lower_band = basis - multiplier * dev\n",
        "\n",
        "    return upper_band, lower_band\n",
        "\n",
        "# Function to simulate the trading strategy\n",
        "def simulate_strategy(data, initial_capital=50000):\n",
        "    capital = initial_capital\n",
        "    position = 0\n",
        "    buy_price = 0\n",
        "    sell_price = 0\n",
        "    trades = []\n",
        "\n",
        "    for i in range(1, len(data)):\n",
        "        if data['Buy_Signal'][i]:\n",
        "            position = capital / data['Close'][i]\n",
        "            buy_price = data['Close'][i]\n",
        "            trades.append(('Buy', data.index[i], buy_price))\n",
        "\n",
        "        elif data['Sell_Signal'][i] and position > 0:\n",
        "            sell_price = data['Close'][i]\n",
        "            capital = position * sell_price\n",
        "            position = 0\n",
        "            trades.append(('Sell', data.index[i], sell_price))\n",
        "\n",
        "    # Sell remaining position at the end\n",
        "    if position > 0:\n",
        "        sell_price = data['Close'].iloc[-1]\n",
        "        capital = position * sell_price\n",
        "        trades.append(('Sell', data.index[-1], sell_price))\n",
        "\n",
        "    return capital, trades\n",
        "\n",
        "# Download historical stock data\n",
        "symbol = 'TCS.BO'  # Replace with the desired stock symbol\n",
        "data = yf.download(symbol, start='2022-01-01', end='2023-01-01')\n",
        "\n",
        "# Calculate RSI and Bollinger Bands\n",
        "data['RSI'] = calculate_rsi(data, window=6)\n",
        "data['BB_Upper'], data['BB_Lower'] = calculate_bollinger_bands(data, window=200, multiplier=2)\n",
        "\n",
        "# Identify buy and sell signals\n",
        "data['Buy_Signal'] = (data['RSI'] < 50) & (data['Close'] < data['BB_Lower'])\n",
        "data['Sell_Signal'] = (data['RSI'] > 50) & (data['Close'] > data['BB_Upper'])\n",
        "\n",
        "# Simulate the trading strategy\n",
        "final_capital, trades = simulate_strategy(data)\n",
        "\n",
        "# Print results\n",
        "print(f\"Initial Capital: 50,000 INR\")\n",
        "print(f\"Final Capital: {final_capital:.2f} INR\")\n",
        "print(f\"Profit/Loss: {final_capital - 50000:.2f} INR\")\n",
        "\n",
        "# Plot using Plotly\n",
        "fig = go.Figure()\n",
        "\n",
        "# Close price line plot\n",
        "fig.add_trace(go.Scatter(x=data.index, y=data['Close'], mode='lines', name='Close Price'))\n",
        "\n",
        "# Buy signals\n",
        "fig.add_trace(go.Scatter(x=[trade[1] for trade in trades if trade[0] == 'Buy'],\n",
        "                         y=[trade[2] for trade in trades if trade[0] == 'Buy'],\n",
        "                         mode='markers',\n",
        "                         marker=dict(color='green', symbol='triangle-up', size=10),\n",
        "                         name='Buy Signal'))\n",
        "\n",
        "# Sell signals\n",
        "fig.add_trace(go.Scatter(x=[trade[1] for trade in trades if trade[0] == 'Sell'],\n",
        "                         y=[trade[2] for trade in trades if trade[0] == 'Sell'],\n",
        "                         mode='markers',\n",
        "                         marker=dict(color='red', symbol='triangle-down', size=10),\n",
        "                         name='Sell Signal'))\n",
        "\n",
        "# Display the figure\n",
        "fig.show()\n"
      ],
      "metadata": {
        "id": "T2YpjMT8kRNQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 611
        },
        "outputId": "0c410106-b5bd-48ca-dc63-bf20305b91c4"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n",
            "Initial Capital: 50,000 INR\n",
            "Final Capital: 54437.87 INR\n",
            "Profit/Loss: 4437.87 INR\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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      "cell_type": "code",
      "source": [
        "import yfinance as yf\n",
        "import pandas as pd\n",
        "import plotly.graph_objects as go\n",
        "\n",
        "# Function to calculate RSI\n",
        "def calculate_rsi(data, window=6):\n",
        "    delta = data['Close'].diff(1)\n",
        "    gain = delta.where(delta > 0, 0)\n",
        "    loss = -delta.where(delta < 0, 0)\n",
        "\n",
        "    avg_gain = gain.rolling(window=window, min_periods=1).mean()\n",
        "    avg_loss = loss.rolling(window=window, min_periods=1).mean()\n",
        "\n",
        "    rs = avg_gain / avg_loss\n",
        "    rsi = 100 - (100 / (1 + rs))\n",
        "\n",
        "    return rsi\n",
        "\n",
        "# Function to calculate Bollinger Bands\n",
        "def calculate_bollinger_bands(data, window=200, multiplier=2):\n",
        "    basis = data['Close'].rolling(window=window, min_periods=1).mean()\n",
        "    dev = data['Close'].rolling(window=window, min_periods=1).std()\n",
        "\n",
        "    upper_band = basis + multiplier * dev\n",
        "    lower_band = basis - multiplier * dev\n",
        "\n",
        "    return upper_band, lower_band\n",
        "\n",
        "# Function to simulate the trading strategy\n",
        "def simulate_strategy(data, initial_capital=50000):\n",
        "    capital = initial_capital\n",
        "    position = 0\n",
        "    buy_price = 0\n",
        "    sell_price = 0\n",
        "    trades = []\n",
        "\n",
        "    for i in range(1, len(data)):\n",
        "        if data['Buy_Signal'][i]:\n",
        "            position = capital // data['Close'][i]  # Use integer division\n",
        "            buy_price = data['Close'][i]\n",
        "            trades.append(('Buy', data.index[i], buy_price, position, 0, 0))\n",
        "\n",
        "        elif data['Sell_Signal'][i] and position > 0:\n",
        "            sell_price = data['Close'][i]\n",
        "            profit_loss = (sell_price - buy_price) * position\n",
        "            capital += profit_loss\n",
        "            position = 0\n",
        "            trades.append(('Sell', data.index[i], sell_price, 0, profit_loss, 0))\n",
        "\n",
        "    # Sell remaining position at the end\n",
        "    if position > 0:\n",
        "        sell_price = data['Close'].iloc[-1]\n",
        "        profit_loss = (sell_price - buy_price) * position\n",
        "        capital += profit_loss\n",
        "        trades.append(('Sell', data.index[-1], sell_price, 0, profit_loss, 0))\n",
        "\n",
        "    return capital, pd.DataFrame(trades, columns=['Action', 'Timestamp', 'Price', 'Quantity', 'Profit', 'Loss'])\n",
        "\n",
        "# Download historical stock data\n",
        "symbol = 'TCS.BO'  # Replace with the desired stock symbol\n",
        "data = yf.download(symbol, start='2022-01-01', end='2023-01-01')\n",
        "\n",
        "# Calculate RSI and Bollinger Bands\n",
        "data['RSI'] = calculate_rsi(data, window=6)\n",
        "data['BB_Upper'], data['BB_Lower'] = calculate_bollinger_bands(data, window=200, multiplier=2)\n",
        "\n",
        "# Identify buy and sell signals\n",
        "data['Buy_Signal'] = (data['RSI'] < 50) & (data['Close'] < data['BB_Lower'])\n",
        "data['Sell_Signal'] = (data['RSI'] > 50) & (data['Close'] > data['BB_Upper'])\n",
        "\n",
        "# Simulate the trading strategy\n",
        "final_capital, trades_df = simulate_strategy(data)\n",
        "\n",
        "# Print results\n",
        "print(f\"Initial Capital: 50,000 INR\")\n",
        "print(f\"Final Capital: {final_capital:.2f} INR\")\n",
        "print(f\"Profit/Loss: {final_capital - 50000:.2f} INR\")\n",
        "\n",
        "# Plot using Plotly\n",
        "fig = go.Figure()\n",
        "\n",
        "# Close price line plot\n",
        "fig.add_trace(go.Scatter(x=data.index, y=data['Close'], mode='lines', name='Close Price'))\n",
        "\n",
        "# Buy signals\n",
        "fig.add_trace(go.Scatter(x=trades_df[trades_df['Action'] == 'Buy']['Timestamp'],\n",
        "                         y=trades_df[trades_df['Action'] == 'Buy']['Price'],\n",
        "                         mode='markers+text',\n",
        "                         marker=dict(color='green', symbol='triangle-up', size=10),\n",
        "                         text=['Buy Price: ' + str(price) for price in trades_df[trades_df['Action'] == 'Buy']['Price']],\n",
        "                         name='Buy Signal'))\n",
        "\n",
        "# Sell signals\n",
        "fig.add_trace(go.Scatter(x=trades_df[trades_df['Action'] == 'Sell']['Timestamp'],\n",
        "                         y=trades_df[trades_df['Action'] == 'Sell']['Price'],\n",
        "                         mode='markers+text',\n",
        "                         marker=dict(color='red', symbol='triangle-down', size=10),\n",
        "                         text=['Sell Price: ' + str(price) for price in trades_df[trades_df['Action'] == 'Sell']['Price']],\n",
        "                         name='Sell Signal'))\n",
        "\n",
        "# Display the figure\n",
        "fig.show()\n",
        "\n",
        "# Display the DataFrame with buy and sell information\n",
        "print(\"\\nTrade Details:\")\n",
        "print(trades_df)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "DSFDsqCwn8_8",
        "outputId": "458f118c-d93e-4610-de85-7a541375a698"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n",
            "Initial Capital: 50,000 INR\n",
            "Final Capital: 54251.20 INR\n",
            "Profit/Loss: 4251.20 INR\n"
          ]
        },
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            "\n",
            "Trade Details:\n",
            "   Action  Timestamp        Price  Quantity       Profit  Loss\n",
            "0     Buy 2022-01-27  3650.100098      13.0     0.000000     0\n",
            "1     Buy 2022-02-22  3586.649902      13.0     0.000000     0\n",
            "2     Buy 2022-02-23  3565.300049      14.0     0.000000     0\n",
            "3     Buy 2022-02-24  3402.250000      14.0     0.000000     0\n",
            "4     Buy 2022-02-25  3519.699951      14.0     0.000000     0\n",
            "5     Buy 2022-05-16  3376.649902      14.0     0.000000     0\n",
            "6     Buy 2022-05-19  3270.699951      15.0     0.000000     0\n",
            "7     Buy 2022-05-20  3293.899902      15.0     0.000000     0\n",
            "8     Buy 2022-05-23  3320.949951      15.0     0.000000     0\n",
            "9     Buy 2022-05-24  3288.050049      15.0     0.000000     0\n",
            "10    Buy 2022-05-25  3166.600098      15.0     0.000000     0\n",
            "11    Buy 2022-05-26  3228.600098      15.0     0.000000     0\n",
            "12    Buy 2022-05-27  3262.199951      15.0     0.000000     0\n",
            "13    Buy 2022-06-13  3220.149902      15.0     0.000000     0\n",
            "14    Buy 2022-06-14  3211.250000      15.0     0.000000     0\n",
            "15    Buy 2022-06-15  3206.800049      15.0     0.000000     0\n",
            "16    Buy 2022-06-16  3142.350098      15.0     0.000000     0\n",
            "17    Buy 2022-06-17  3089.250000      16.0     0.000000     0\n",
            "18    Buy 2022-06-20  3113.199951      16.0     0.000000     0\n",
            "19    Buy 2022-07-12  3084.649902      16.0     0.000000     0\n",
            "20    Buy 2022-07-13  3038.550049      16.0     0.000000     0\n",
            "21    Buy 2022-07-14  2998.699951      16.0     0.000000     0\n",
            "22    Buy 2022-07-15  2993.550049      16.0     0.000000     0\n",
            "23   Sell 2022-12-30  3259.250000       0.0  4251.199219     0\n"
          ]
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    {
      "cell_type": "code",
      "source": [
        "import yfinance as yf\n",
        "import pandas as pd\n",
        "from tqdm import tqdm\n",
        "\n",
        "# Function to calculate RSI\n",
        "def calculate_rsi(data, window=6):\n",
        "    delta = data['Close'].diff(1)\n",
        "    gain = delta.where(delta > 0, 0)\n",
        "    loss = -delta.where(delta < 0, 0)\n",
        "\n",
        "    avg_gain = gain.rolling(window=window, min_periods=1).mean()\n",
        "    avg_loss = loss.rolling(window=window, min_periods=1).mean()\n",
        "\n",
        "    rs = avg_gain / avg_loss\n",
        "    rsi = 100 - (100 / (1 + rs))\n",
        "\n",
        "    return rsi\n",
        "\n",
        "# Function to calculate Bollinger Bands\n",
        "def calculate_bollinger_bands(data, window=200, multiplier=2):\n",
        "    basis = data['Close'].rolling(window=window, min_periods=1).mean()\n",
        "    dev = data['Close'].rolling(window=window, min_periods=1).std()\n",
        "\n",
        "    upper_band = basis + multiplier * dev\n",
        "    lower_band = basis - multiplier * dev\n",
        "\n",
        "    return upper_band, lower_band\n",
        "\n",
        "# Function to simulate the trading strategy\n",
        "def simulate_strategy(data, initial_capital=50000):\n",
        "    capital = initial_capital\n",
        "    position = 0\n",
        "    buy_price = 0\n",
        "    sell_price = 0\n",
        "    trades = []\n",
        "\n",
        "    for i in range(1, len(data)):\n",
        "        if data['Buy_Signal'][i]:\n",
        "            position = capital // data['Close'][i]  # Use integer division\n",
        "            buy_price = data['Close'][i]\n",
        "            trades.append(('Buy', data.index[i], buy_price, position))\n",
        "        elif data['Sell_Signal'][i] and position > 0:\n",
        "            sell_price = data['Close'][i]\n",
        "            capital = position * sell_price\n",
        "            position = 0\n",
        "            trades.append(('Sell', data.index[i], sell_price, position))\n",
        "\n",
        "    # Sell remaining position at the end\n",
        "    if position > 0:\n",
        "        sell_price = data['Close'].iloc[-1]\n",
        "        capital = position * sell_price\n",
        "        trades.append(('Sell', data.index[-1], sell_price, position))\n",
        "\n",
        "    return capital, pd.DataFrame(trades, columns=['Action', 'Timestamp', 'Price', 'Quantity'])\n",
        "\n",
        "# Function to check if a stock is profitable\n",
        "def analyze_stock(symbol):\n",
        "    initial_capital = 50000\n",
        "    try:\n",
        "        # Download historical stock data\n",
        "        data = yf.download(symbol, period='6mo', interval='5d')\n",
        "\n",
        "        # Calculate RSI and Bollinger Bands\n",
        "        data['RSI'] = calculate_rsi(data, window=6)\n",
        "        data['BB_Upper'], data['BB_Lower'] = calculate_bollinger_bands(data, window=200, multiplier=2)\n",
        "\n",
        "        # Identify buy and sell signals\n",
        "        data['Buy_Signal'] = (data['RSI'] < 50) & (data['Close'] < data['BB_Lower'])\n",
        "        data['Sell_Signal'] = (data['RSI'] > 50) & (data['Close'] > data['BB_Upper'])\n",
        "\n",
        "        # Simulate the trading strategy\n",
        "        final_capital, trades_df = simulate_strategy(data)\n",
        "\n",
        "        # Check if the strategy is profitable\n",
        "        is_profitable = final_capital > 50000\n",
        "\n",
        "        # Calculate buy and sell counts\n",
        "        buy_count = trades_df[trades_df['Action'] == 'Buy'].shape[0]\n",
        "        sell_count = trades_df[trades_df['Action'] == 'Sell'].shape[0]\n",
        "\n",
        "        # Calculate profitability score (you can define your own formula)\n",
        "        profitability_score = final_capital / initial_capital\n",
        "\n",
        "        return {\n",
        "            'Symbol': symbol,\n",
        "            'Profitable': is_profitable,\n",
        "            'Profitability Score': profitability_score,\n",
        "            'Total Buy Count': buy_count,\n",
        "            'Total Sell Count': sell_count\n",
        "        }\n",
        "\n",
        "    except Exception as e:\n",
        "        print(f\"Error processing {symbol}: {e}\")\n",
        "        return None\n",
        "\n",
        "# Get a list of Indian stock symbols (you can extend this list)\n",
        "indian_stocks = [\n",
        "    'TCS.BO', 'RELIANCE.BO', 'INFY.BO', 'HDFCBANK.BO', 'HINDUNILVR.BO', 'WIPRO.BO', 'HDFC.BO', 'KOTAKBANK.BO',\n",
        "    'ITC.BO', 'HCLTECH.BO', 'TECHM.BO', 'BAJAJFINSV.BO', 'BAJFINANCE.BO', 'LT.BO', 'ULTRACEMCO.BO', 'POWERGRID.BO',\n",
        "    'ONGC.BO', 'SHREECEM.BO', 'KINDSTEL.BO', 'BRITANNIA.BO', 'NTPC.BO', 'ICICIBANK.BO', 'DIVISLAB.BO', 'NESTLEIND.BO',\n",
        "    'SBILIFE.BO', 'MARUTI.BO', 'TATASTEEL.BO', 'UPL.BO', 'SBIN.BO', 'DRREDDY.BO', 'HDFCLIFE.BO', 'GRASIM.BO',\n",
        "    'JSWSTEEL.BO', 'INDUSINDBK.BO', 'M&M.BO', 'HEROMOTOCO.BO', 'IOC.BO', 'INFY.BO', 'HCLTECH.BO', 'HINDUNILVR.BO',\n",
        "    'KOTAKBANK.BO', 'HDFCBANK.BO', 'TECHM.BO', 'WIPRO.BO', 'ADANIPORTS.BO', 'NTPC.BO', 'BAJFINANCE.BO', 'AXISBANK.BO',\n",
        "    'TITAN.BO', 'ASIANPAINT.BO', 'LT.BO', 'ONGC.BO', 'BPCL.BO', 'TATAMOTORS.BO', 'SUNPHARMA.BO', 'ULTRACEMCO.BO',\n",
        "    'IOC.BO', 'BAJAJFINSV.BO', 'BAJAJ-AUTO.BO', 'POWERGRID.BO', 'SAIL.BO', 'ADANIGREEN.BO', 'BRITANNIA.BO',\n",
        "    'NESTLEIND.BO', 'RELIANCE.BO', 'CIPLA.BO', 'JSWSTEEL.BO', 'BHARTIARTL.BO', 'HINDALCO.BO', 'VEDL.BO',\n",
        "    'HINDPETRO.BO', 'DRREDDY.BO', 'HDFCLIFE.BO', 'COALINDIA.BO', 'GRASIM.BO', 'TATASTEEL.BO', 'EICHERMOT.BO',\n",
        "    'SBILIFE.BO', 'HDFC.BO', 'MARUTI.BO', 'TATACONSUM.BO', 'SHREECEM.BO', 'DIVISLAB.BO', 'TATAMTRDVR.BO',\n",
        "    'HINDUNILVR.BO', 'HCLTECH.BO', 'TECHM.BO', 'WIPRO.BO', 'BAJAJFINSV.BO', 'BAJFINANCE.BO', 'LT.BO', 'ULTRACEMCO.BO',\n",
        "    'POWERGRID.BO', 'ONGC.BO', 'SHREECEM.BO', 'KINDSTEL.BO', 'BRITANNIA.BO', 'NTPC.BO', 'ICICIBANK.BO', 'DIVISLAB.BO',\n",
        "    'NESTLEIND.BO', 'SBILIFE.BO', 'MARUTI.BO', 'TATASTEEL.BO', 'UPL.BO', 'SBIN.BO', 'DRREDDY.BO', 'HDFCLIFE.BO',\n",
        "    'GRASIM.BO', 'JSWSTEEL.BO', 'INDUSINDBK.BO', 'M&M.BO', 'HEROMOTOCO.BO', 'IOC.BO', 'INFY.BO', 'HCLTECH.BO',\n",
        "    'HINDUNILVR.BO', 'KOTAKBANK.BO', 'HDFCBANK.BO', 'TECHM.BO', 'WIPRO.BO', 'ADANIPORTS.BO', 'NTPC.BO', 'BAJFINANCE.BO',\n",
        "    'AXISBANK.BO', 'TITAN.BO', 'ASIANPAINT.BO', 'LT.BO', 'ONGC.BO', 'BPCL.BO', 'TATAMOTORS.BO', 'SUNPHARMA.BO',\n",
        "    'ULTRACEMCO.BO', 'IOC.BO', 'BAJAJFINSV.BO', 'BAJAJ-AUTO.BO', 'POWERGRID.BO', 'SAIL.BO', 'ADANIGREEN.BO',\n",
        "    'BRITANNIA.BO', 'NESTLEIND.BO', 'RELIANCE.BO', 'CIPLA.BO', 'JSWSTEEL.BO', 'BHARTIARTL.BO', 'HINDALCO.BO', 'VEDL.BO',\n",
        "    'HINDPETRO.BO', 'DRREDDY.BO', 'HDFCLIFE.BO', 'COALINDIA.BO', 'GRASIM.BO', 'TATASTEEL.BO', 'EICHERMOT.BO',\n",
        "    'SBILIFE.BO', 'HDFC.BO', 'MARUTI.BO', 'TATACONSUM.BO', 'SHREECEM.BO', 'DIVISLAB.BO', 'TATAMTRDVR.BO'\n",
        "]\n",
        "\n",
        "# Analyze each stock and create a DataFrame\n",
        "results = [analyze_stock(symbol) for symbol in indian_stocks]\n",
        "\n",
        "# Create a DataFrame from the results\n",
        "results_df = pd.DataFrame(results)\n",
        "\n",
        "# Display the DataFrame\n",
        "print(\"Stock Profitability Analysis:\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rZ8Jgw42s1NW",
        "outputId": "d8567f26-845b-4ed2-9da2-8473735a3558"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "ERROR:yfinance:\n",
            "1 Failed download:\n",
            "ERROR:yfinance:['KINDSTEL.BO']: Exception('%ticker%: No data found, symbol may be delisted')\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
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          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "ERROR:yfinance:\n",
            "1 Failed download:\n",
            "ERROR:yfinance:['KINDSTEL.BO']: Exception('%ticker%: No data found, symbol may be delisted')\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
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            "Stock Profitability Analysis:\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "results_df.query(\"Profitable == True\").sort_values(\"Profitability Score\", ascending=False).drop_duplicates(subset=\"Symbol\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "q-6x4Yyxs1GB",
        "outputId": "12f1d97b-1405-4bcc-c8b6-46a6c2dfa383"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Symbol  Profitable  Profitability Score  Total Buy Count  \\\n",
              "70    HINDPETRO.BO        True             1.234764                1   \n",
              "36          IOC.BO        True             1.137148                1   \n",
              "138  ADANIGREEN.BO        True             1.090400                1   \n",
              "64     RELIANCE.BO        True             1.053800                4   \n",
              "41     HDFCBANK.BO        True             1.041556                1   \n",
              "21    ICICIBANK.BO        True             1.003320                1   \n",
              "49   ASIANPAINT.BO        True             1.002896                1   \n",
              "\n",
              "     Total Sell Count  \n",
              "70                  1  \n",
              "36                  1  \n",
              "138                 1  \n",
              "64                  1  \n",
              "41                  1  \n",
              "21                  1  \n",
              "49                  1  "
            ],
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              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6bb8964e-5f02-4022-9a57-e24a64917681 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to check if a stock is profitable\n",
        "def analyze_stock(symbol):\n",
        "    initial_capital = 50000\n",
        "    period='3mo'\n",
        "    interval='1d'\n",
        "    try:\n",
        "        # Download historical stock data with daily interval\n",
        "        data = yf.download(symbol, period, interval)\n",
        "\n",
        "        # Calculate RSI and Bollinger Bands\n",
        "        data['RSI'] = calculate_rsi(data, window=6)\n",
        "        data['BB_Upper'], data['BB_Lower'] = calculate_bollinger_bands(data, window=200, multiplier=2)\n",
        "\n",
        "        # Identify buy and sell signals\n",
        "        data['Buy_Signal'] = (data['RSI'] < 50) & (data['Close'] < data['BB_Lower'])\n",
        "        data['Sell_Signal'] = (data['RSI'] > 50) & (data['Close'] > data['BB_Upper'])\n",
        "\n",
        "        # Simulate the trading strategy\n",
        "        final_capital, trades_df = simulate_strategy(data)\n",
        "\n",
        "        # Check if the strategy is profitable\n",
        "        is_profitable = final_capital > 50000\n",
        "\n",
        "        # Calculate buy and sell counts\n",
        "        buy_count = trades_df[trades_df['Action'] == 'Buy'].shape[0]\n",
        "        sell_count = trades_df[trades_df['Action'] == 'Sell'].shape[0]\n",
        "\n",
        "        # Calculate profitability score (you can define your own formula)\n",
        "        profitability_score = final_capital / initial_capital\n",
        "\n",
        "        # Calculate net profit and ROI\n",
        "        net_profit = final_capital - initial_capital\n",
        "        roi = (net_profit / initial_capital) * 100\n",
        "\n",
        "        d = {\n",
        "            'Symbol': symbol,\n",
        "            'Profitable': is_profitable,\n",
        "            'Profitability Score': profitability_score,\n",
        "            'Total Buy Count': buy_count,\n",
        "            'Total Sell Count': sell_count,\n",
        "            'Net Profit': net_profit,\n",
        "            'ROI': roi\n",
        "        }\n",
        "\n",
        "        return d\n",
        "\n",
        "    except Exception as e:\n",
        "        print(f\"Error processing {symbol}: {e}\")\n",
        "        return None\n"
      ],
      "metadata": {
        "id": "o1EePxPF0bzc"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "analyze_stock(\"HINDPETRO.BO\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WCATghVS2J9U",
        "outputId": "fec5b676-40e8-4ed4-8701-3f0d98f36e4a"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Symbol': 'HINDPETRO.BO',\n",
              " 'Profitable': True,\n",
              " 'Profitability Score': 1.142464974975586,\n",
              " 'Total Buy Count': 3,\n",
              " 'Total Sell Count': 1,\n",
              " 'Net Profit': 7123.248748779297,\n",
              " 'ROI': 14.246497497558593}"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import yfinance as yf\n",
        "import pandas as pd\n",
        "import gradio as gr\n",
        "from tqdm import tqdm\n",
        "\n",
        "# Function to calculate RSI\n",
        "def calculate_rsi(data, window=6):\n",
        "    delta = data['Close'].diff(1)\n",
        "    gain = delta.where(delta > 0, 0)\n",
        "    loss = -delta.where(delta < 0, 0)\n",
        "\n",
        "    avg_gain = gain.rolling(window=window, min_periods=1).mean()\n",
        "    avg_loss = loss.rolling(window=window, min_periods=1).mean()\n",
        "\n",
        "    rs = avg_gain / avg_loss\n",
        "    rsi = 100 - (100 / (1 + rs))\n",
        "\n",
        "    return rsi\n",
        "\n",
        "# Function to calculate Bollinger Bands\n",
        "def calculate_bollinger_bands(data, window=200, multiplier=2):\n",
        "    basis = data['Close'].rolling(window=window, min_periods=1).mean()\n",
        "    dev = data['Close'].rolling(window=window, min_periods=1).std()\n",
        "\n",
        "    upper_band = basis + multiplier * dev\n",
        "    lower_band = basis - multiplier * dev\n",
        "\n",
        "    return upper_band, lower_band\n",
        "\n",
        "# Function to simulate the trading strategy\n",
        "def simulate_strategy(data, initial_capital=50000):\n",
        "    capital = initial_capital\n",
        "    position = 0\n",
        "    buy_price = 0\n",
        "    sell_price = 0\n",
        "    trades = []\n",
        "\n",
        "    for i in range(1, len(data)):\n",
        "        if data['Buy_Signal'][i]:\n",
        "            position = capital // data['Close'][i]  # Use integer division\n",
        "            buy_price = data['Close'][i]\n",
        "            trades.append(('Buy', data.index[i], buy_price, position))\n",
        "        elif data['Sell_Signal'][i] and position > 0:\n",
        "            sell_price = data['Close'][i]\n",
        "            capital = position * sell_price\n",
        "            position = 0\n",
        "            trades.append(('Sell', data.index[i], sell_price, position))\n",
        "\n",
        "    # Sell remaining position at the end\n",
        "    if position > 0:\n",
        "        sell_price = data['Close'].iloc[-1]\n",
        "        capital = position * sell_price\n",
        "        trades.append(('Sell', data.index[-1], sell_price, position))\n",
        "\n",
        "    return capital, pd.DataFrame(trades, columns=['Action', 'Timestamp', 'Price', 'Quantity'])\n",
        "\n",
        "# Function to check if a stock is profitable\n",
        "def analyze_stock(symbol, period='3mo', interval='1d'):\n",
        "    initial_capital = 50000\n",
        "    try:\n",
        "        # Download historical stock data with specified period and interval\n",
        "        data = yf.download(symbol, period=period, interval=interval)\n",
        "\n",
        "        # Calculate RSI and Bollinger Bands\n",
        "        data['RSI'] = calculate_rsi(data, window=6)\n",
        "        data['BB_Upper'], data['BB_Lower'] = calculate_bollinger_bands(data, window=200, multiplier=2)\n",
        "\n",
        "        # Identify buy and sell signals\n",
        "        data['Buy_Signal'] = (data['RSI'] < 50) & (data['Close'] < data['BB_Lower'])\n",
        "        data['Sell_Signal'] = (data['RSI'] > 50) & (data['Close'] > data['BB_Upper'])\n",
        "\n",
        "        # Simulate the trading strategy\n",
        "        final_capital, trades_df = simulate_strategy(data)\n",
        "\n",
        "        # Check if the strategy is profitable\n",
        "        is_profitable = final_capital > 50000\n",
        "\n",
        "        # Calculate buy and sell counts\n",
        "        buy_count = trades_df[trades_df['Action'] == 'Buy'].shape[0]\n",
        "        sell_count = trades_df[trades_df['Action'] == 'Sell'].shape[0]\n",
        "\n",
        "        # Calculate profitability score (you can define your own formula)\n",
        "        profitability_score = final_capital / initial_capital\n",
        "\n",
        "        # Calculate net profit and ROI\n",
        "        net_profit = final_capital - initial_capital\n",
        "        roi = (net_profit / initial_capital) * 100\n",
        "\n",
        "        d = {\n",
        "            'Symbol': symbol,\n",
        "            'Profitable': is_profitable,\n",
        "            'Profitability Score': profitability_score,\n",
        "            'Total Buy Count': buy_count,\n",
        "            'Total Sell Count': sell_count,\n",
        "            'Net Profit': net_profit,\n",
        "            'ROI': roi\n",
        "        }\n",
        "\n",
        "        return d\n",
        "\n",
        "    except Exception as e:\n",
        "        print(f\"Error processing {symbol}: {e}\")\n",
        "        return None\n",
        "\n",
        "# Get a list of Indian stock symbols (you can extend this list)\n",
        "indian_stocks = [\n",
        "    'TCS.BO', 'RELIANCE.BO', 'INFY.BO', 'HDFCBANK.BO', 'HINDUNILVR.BO', 'WIPRO.BO', 'HDFC.BO', 'KOTAKBANK.BO',\n",
        "    'ITC.BO', 'HCLTECH.BO', 'TECHM.BO', 'BAJAJFINSV.BO', 'BAJFINANCE.BO', 'LT.BO', 'ULTRACEMCO.BO', 'POWERGRID.BO',\n",
        "    'ONGC.BO', 'SHREECEM.BO', 'KINDSTEL.BO', 'BRITANNIA.BO', 'NTPC.BO', 'ICICIBANK.BO', 'DIVISLAB.BO', 'NESTLEIND.BO',\n",
        "    'SBILIFE.BO', 'MARUTI.BO', 'TATASTEEL.BO', 'UPL.BO', 'SBIN.BO', 'DRREDDY.BO', 'HDFCLIFE.BO', 'GRASIM.BO',\n",
        "    'JSWSTEEL.BO', 'INDUSINDBK.BO', 'M&M.BO', 'HEROMOTOCO.BO', 'IOC.BO', 'INFY.BO', 'HCLTECH.BO', 'HINDUNILVR.BO',\n",
        "    'KOTAKBANK.BO', 'HDFCBANK.BO', 'TECHM.BO', 'WIPRO.BO', 'ADANIPORTS.BO', 'NTPC.BO', 'BAJFINANCE.BO', 'AXISBANK.BO',\n",
        "    'TITAN.BO', 'ASIANPAINT.BO', 'LT.BO', 'ONGC.BO', 'BPCL.BO', 'TATAMOTORS.BO', 'SUNPHARMA.BO', 'ULTRACEMCO.BO',\n",
        "    'IOC.BO', 'BAJAJFINSV.BO', 'BAJAJ-AUTO.BO', 'POWERGRID.BO', 'SAIL.BO', 'ADANIGREEN.BO', 'BRITANNIA.BO',\n",
        "    'NESTLEIND.BO', 'RELIANCE.BO', 'CIPLA.BO', 'JSWSTEEL.BO', 'BHARTIARTL.BO', 'HINDALCO.BO', 'VEDL.BO',\n",
        "    'HINDPETRO.BO', 'DRREDDY.BO', 'HDFCLIFE.BO', 'COALINDIA.BO', 'GRASIM.BO', 'TATASTEEL.BO', 'EICHERMOT.BO',\n",
        "    'SBILIFE.BO', 'HDFC.BO', 'MARUTI.BO', 'TATACONSUM.BO', 'SHREECEM.BO', 'DIVIS']\n",
        "\n",
        "# Create a Gradio interface\n",
        "iface = gr.Interface(\n",
        "    analyze_stock,\n",
        "    inputs=[\n",
        "        gr.Dropdown(indian_stocks, label=\"Select Stock\"),\n",
        "        gr.Textbox(label=\"Enter Period ('1d', '1mo', '3mo', etc.)\"),\n",
        "        gr.Textbox(label=\"Enter Interval ('1d', '1h', '15m', etc.)\")\n",
        "    ],\n",
        "    outputs=\"text\"\n",
        ")\n",
        "\n",
        "# Launch the Gradio interface\n",
        "iface.launch()\n"
      ],
      "metadata": {
        "id": "A3qdQWl139Ni"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import yfinance as yf\n",
        "import pandas as pd\n",
        "import gradio as gr\n",
        "import plotly.graph_objects as go\n",
        "from tqdm import tqdm\n",
        "\n",
        "# Function to calculate RSI\n",
        "def calculate_rsi(data, window=6):\n",
        "    delta = data['Close'].diff(1)\n",
        "    gain = delta.where(delta > 0, 0)\n",
        "    loss = -delta.where(delta < 0, 0)\n",
        "\n",
        "    avg_gain = gain.rolling(window=window, min_periods=1).mean()\n",
        "    avg_loss = loss.rolling(window=window, min_periods=1).mean()\n",
        "\n",
        "    rs = avg_gain / avg_loss\n",
        "    rsi = 100 - (100 / (1 + rs))\n",
        "\n",
        "    return rsi\n",
        "\n",
        "# Function to calculate Bollinger Bands\n",
        "def calculate_bollinger_bands(data, window=200, multiplier=2):\n",
        "    basis = data['Close'].rolling(window=window, min_periods=1).mean()\n",
        "    dev = data['Close'].rolling(window=window, min_periods=1).std()\n",
        "\n",
        "    upper_band = basis + multiplier * dev\n",
        "    lower_band = basis - multiplier * dev\n",
        "\n",
        "    return upper_band, lower_band\n",
        "\n",
        "# Function to simulate the trading strategy\n",
        "def simulate_strategy(data, initial_capital=50000):\n",
        "    capital = initial_capital\n",
        "    position = 0\n",
        "    buy_price = 0\n",
        "    sell_price = 0\n",
        "    trades = []\n",
        "\n",
        "    for i in range(1, len(data)):\n",
        "        if data['Buy_Signal'][i]:\n",
        "            position = capital // data['Close'][i]  # Use integer division\n",
        "            buy_price = data['Close'][i]\n",
        "            trades.append(('Buy', data.index[i], buy_price, position))\n",
        "        elif data['Sell_Signal'][i] and position > 0:\n",
        "            sell_price = data['Close'][i]\n",
        "            capital = position * sell_price\n",
        "            position = 0\n",
        "            trades.append(('Sell', data.index[i], sell_price, position))\n",
        "\n",
        "    # Sell remaining position at the end\n",
        "    if position > 0:\n",
        "        sell_price = data['Close'].iloc[-1]\n",
        "        capital = position * sell_price\n",
        "        trades.append(('Sell', data.index[-1], sell_price, position))\n",
        "\n",
        "    return capital, pd.DataFrame(trades, columns=['Action', 'Timestamp', 'Price', 'Quantity'])\n",
        "\n",
        "# Function to check if a stock is profitable\n",
        "def analyze_stock(symbol, period='3mo', interval='1d'):\n",
        "    initial_capital = 50000\n",
        "    try:\n",
        "        # Download historical stock data with specified period and interval\n",
        "        data = yf.download(symbol, period=period, interval=interval)\n",
        "\n",
        "        # Calculate RSI and Bollinger Bands\n",
        "        data['RSI'] = calculate_rsi(data, window=6)\n",
        "        data['BB_Upper'], data['BB_Lower'] = calculate_bollinger_bands(data, window=200, multiplier=2)\n",
        "\n",
        "        # Identify buy and sell signals\n",
        "        data['Buy_Signal'] = (data['RSI'] < 50) & (data['Close'] < data['BB_Lower'])\n",
        "        data['Sell_Signal'] = (data['RSI'] > 50) & (data['Close'] > data['BB_Upper'])\n",
        "\n",
        "        # Simulate the trading strategy\n",
        "        final_capital, trades_df = simulate_strategy(data)\n",
        "\n",
        "        # Check if the strategy is profitable\n",
        "        is_profitable = final_capital > 50000\n",
        "\n",
        "        # Calculate buy and sell counts\n",
        "        buy_count = trades_df[trades_df['Action'] == 'Buy'].shape[0]\n",
        "        sell_count = trades_df[trades_df['Action'] == 'Sell'].shape[0]\n",
        "\n",
        "        # Calculate profitability score (you can define your own formula)\n",
        "        profitability_score = final_capital / initial_capital\n",
        "\n",
        "        # Calculate net profit and ROI\n",
        "        net_profit = final_capital - initial_capital\n",
        "        roi = (net_profit / initial_capital) * 100\n",
        "\n",
        "        # Create a scatter plot with buy and sell markers\n",
        "        fig = go.Figure()\n",
        "        fig.add_trace(go.Scatter(x=data.index, y=data['Close'], mode='lines', name='Closing Price'))\n",
        "\n",
        "        # Add Buy markers\n",
        "        fig.add_trace(go.Scatter(\n",
        "            x=trades_df[trades_df['Action'] == 'Buy']['Timestamp'],\n",
        "            y=trades_df[trades_df['Action'] == 'Buy']['Price'],\n",
        "            mode='markers',\n",
        "            name='Buy Signal',\n",
        "            marker=dict(color='green', size=10),\n",
        "            text='Buy'\n",
        "        ))\n",
        "\n",
        "        # Add Sell markers\n",
        "        fig.add_trace(go.Scatter(\n",
        "            x=trades_df[trades_df['Action'] == 'Sell']['Timestamp'],\n",
        "            y=trades_df[trades_df['Action'] == 'Sell']['Price'],\n",
        "            mode='markers',\n",
        "            name='Sell Signal',\n",
        "            marker=dict(color='red', size=10),\n",
        "            text='Sell'\n",
        "        ))\n",
        "\n",
        "        # Show the plot\n",
        "        fig.show()\n",
        "\n",
        "        return {\n",
        "            'Symbol': symbol,\n",
        "            'Profitable': is_profitable,\n",
        "            'Profitability Score': profitability_score,\n",
        "            'Total Buy Count': buy_count,\n",
        "            'Total Sell Count': sell_count,\n",
        "            'Net Profit': net_profit,\n",
        "            'ROI': roi\n",
        "        }\n",
        "\n",
        "    except Exception as e:\n",
        "        print(f\"Error processing {symbol}: {e}\")\n",
        "        return None\n",
        "\n",
        "# Get a list of Indian stock symbols (you can extend this list)\n",
        "indian_stocks = ['TCS.BO', 'RELIANCE.BO']\n",
        "\n",
        "# Create a Gradio interface\n",
        "iface = gr.Interface(\n",
        "    analyze_stock,\n",
        "    inputs=[\n",
        "        gr.Dropdown(indian_stocks, label=\"Select Stock\"),\n",
        "        gr.Textbox(label=\"Enter Period ('1d', '1mo', '3mo', etc.)\"),\n",
        "        gr.Textbox(label=\"Enter Interval ('1d', '1h', '15m', etc.)\")\n",
        "    ],\n",
        "    outputs=\"text\"\n",
        ")\n",
        "\n",
        "# Launch the Gradio interface\n",
        "iface.launch(debug=True)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "XMDXWOkl2NwP",
        "outputId": "1ee084da-f2f7-4d0b-e973-0b32867fb5bd"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Setting queue=True in a Colab notebook requires sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
            "\n",
            "Colab notebook detected. This cell will run indefinitely so that you can see errors and logs. To turn off, set debug=False in launch().\n",
            "Running on public URL: https://cec53aabf283310f53.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<div><iframe src=\"https://cec53aabf283310f53.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
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            "Keyboard interruption in main thread... closing server.\n",
            "Killing tunnel 127.0.0.1:7868 <> https://cec53aabf283310f53.gradio.live\n"
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      "cell_type": "code",
      "source": [
        "import yfinance as yf\n",
        "import pandas as pd\n",
        "import gradio as gr\n",
        "import plotly.graph_objects as go\n",
        "from tqdm import tqdm\n",
        "\n",
        "# Function to calculate RSI\n",
        "def calculate_rsi(data, window=6):\n",
        "    delta = data['Close'].diff(1)\n",
        "    gain = delta.where(delta > 0, 0)\n",
        "    loss = -delta.where(delta < 0, 0)\n",
        "\n",
        "    avg_gain = gain.rolling(window=window, min_periods=1).mean()\n",
        "    avg_loss = loss.rolling(window=window, min_periods=1).mean()\n",
        "\n",
        "    rs = avg_gain / avg_loss\n",
        "    rsi = 100 - (100 / (1 + rs))\n",
        "\n",
        "    return rsi\n",
        "\n",
        "# Function to calculate Bollinger Bands\n",
        "def calculate_bollinger_bands(data, window=200, multiplier=2):\n",
        "    basis = data['Close'].rolling(window=window, min_periods=1).mean()\n",
        "    dev = data['Close'].rolling(window=window, min_periods=1).std()\n",
        "\n",
        "    upper_band = basis + multiplier * dev\n",
        "    lower_band = basis - multiplier * dev\n",
        "\n",
        "    return upper_band, lower_band\n",
        "\n",
        "# Function to simulate the trading strategy\n",
        "def simulate_strategy(data, initial_capital=50000):\n",
        "    capital = initial_capital\n",
        "    position = 0\n",
        "    buy_price = 0\n",
        "    sell_price = 0\n",
        "    trades = []\n",
        "\n",
        "    for i in range(1, len(data)):\n",
        "        if data['Buy_Signal'][i]:\n",
        "            position = capital // data['Close'][i]  # Use integer division\n",
        "            buy_price = data['Close'][i]\n",
        "            trades.append(('Buy', data.index[i], buy_price, position))\n",
        "        elif data['Sell_Signal'][i] and position > 0:\n",
        "            sell_price = data['Close'][i]\n",
        "            capital = position * sell_price\n",
        "            position = 0\n",
        "            trades.append(('Sell', data.index[i], sell_price, position))\n",
        "\n",
        "    # Sell remaining position at the end\n",
        "    if position > 0:\n",
        "        sell_price = data['Close'].iloc[-1]\n",
        "        capital = position * sell_price\n",
        "        trades.append(('Sell', data.index[-1], sell_price, position))\n",
        "\n",
        "    return capital, pd.DataFrame(trades, columns=['Action', 'Timestamp', 'Price', 'Quantity'])\n",
        "\n",
        "# Function to check if a stock is profitable\n",
        "def analyze_stock(symbol, period='3mo', interval='1d'):\n",
        "    initial_capital = 50000\n",
        "    try:\n",
        "        # Download historical stock data with specified period and interval\n",
        "        data = yf.download(symbol, period=period, interval=interval)\n",
        "\n",
        "        # Calculate RSI and Bollinger Bands\n",
        "        data['RSI'] = calculate_rsi(data, window=6)\n",
        "        data['BB_Upper'], data['BB_Lower'] = calculate_bollinger_bands(data, window=200, multiplier=2)\n",
        "\n",
        "        # Identify buy and sell signals\n",
        "        data['Buy_Signal'] = (data['RSI'] < 50) & (data['Close'] < data['BB_Lower'])\n",
        "        data['Sell_Signal'] = (data['RSI'] > 50) & (data['Close'] > data['BB_Upper'])\n",
        "\n",
        "        # Simulate the trading strategy\n",
        "        final_capital, trades_df = simulate_strategy(data)\n",
        "\n",
        "        # Check if the strategy is profitable\n",
        "        is_profitable = final_capital > 50000\n",
        "\n",
        "        # Calculate buy and sell counts\n",
        "        buy_count = trades_df[trades_df['Action'] == 'Buy'].shape[0]\n",
        "        sell_count = trades_df[trades_df['Action'] == 'Sell'].shape[0]\n",
        "\n",
        "        # Calculate profitability score (you can define your own formula)\n",
        "        profitability_score = final_capital / initial_capital\n",
        "\n",
        "        # Calculate net profit and ROI\n",
        "        net_profit = final_capital - initial_capital\n",
        "        roi = (net_profit / initial_capital) * 100\n",
        "\n",
        "        # Create a scatter plot with buy and sell markers\n",
        "        fig = go.Figure()\n",
        "\n",
        "        # Configure the Plotly white theme\n",
        "        fig.update_layout(template=\"plotly_white\")\n",
        "\n",
        "        # Add line plot for Closing Price\n",
        "        fig.add_trace(go.Scatter(x=data.index, y=data['Close'], mode='lines', name='Closing Price'))\n",
        "\n",
        "        # Add Buy markers\n",
        "        fig.add_trace(go.Scatter(\n",
        "            x=trades_df[trades_df['Action'] == 'Buy']['Timestamp'],\n",
        "            y=trades_df[trades_df['Action'] == 'Buy']['Price'],\n",
        "            mode='markers',\n",
        "            name='Buy Signal',\n",
        "            marker=dict(color='green', size=10),\n",
        "            text='Buy'\n",
        "        ))\n",
        "\n",
        "        # Add Sell markers\n",
        "        fig.add_trace(go.Scatter(\n",
        "            x=trades_df[trades_df['Action'] == 'Sell']['Timestamp'],\n",
        "            y=trades_df[trades_df['Action'] == 'Sell']['Price'],\n",
        "            mode='markers',\n",
        "            name='Sell Signal',\n",
        "            marker=dict(color='red', size=10),\n",
        "            text='Sell'\n",
        "        ))\n",
        "\n",
        "        # Show the plot\n",
        "        fig.show()\n",
        "\n",
        "        return {\n",
        "            'Symbol': symbol,\n",
        "            'Profitable': is_profitable,\n",
        "            'Profitability Score': profitability_score,\n",
        "            'Total Buy Count': buy_count,\n",
        "            'Total Sell Count': sell_count,\n",
        "            'Net Profit': net_profit,\n",
        "            'ROI': roi\n",
        "        }\n",
        "\n",
        "    except Exception as e:\n",
        "        print(f\"Error processing {symbol}: {e}\")\n",
        "        return None\n",
        "\n",
        "# Get a list of Indian stock symbols (you can extend this list)\n",
        "indian_stocks = ['TCS.BO', 'RELIANCE.BO']\n",
        "\n",
        "# Period and Interval options\n",
        "period_options = ['1m', '2m', '5m', '15m', '30m', '1h', '1d', '5d', '1wk', '1mo', '3mo', '6mo', '1y', '2y', '5y', '10y', 'ytd', 'max']\n",
        "interval_options = ['1m', '2m', '5m', '15m', '30m', '1h', '1d', '5d', '1wk', '1mo', '3mo']\n",
        "\n",
        "# Create a Gradio interface\n",
        "iface = gr.Interface(\n",
        "    analyze_stock,\n",
        "    inputs=[\n",
        "        gr.Dropdown(indian_stocks, label=\"Select Stock\"),\n",
        "        gr.Dropdown(period_options, label=\"Enter Period\"),\n",
        "        gr.Dropdown(interval_options, label=\"Enter Interval\")\n",
        "    ],\n",
        "    outputs=\"text\"\n",
        ")\n",
        "\n",
        "# Launch the Gradio interface\n",
        "iface.launch(debug=True)\n"
      ],
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      "execution_count": 77,
      "outputs": [
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          "text": [
            "Setting queue=True in a Colab notebook requires sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
            "\n",
            "Colab notebook detected. This cell will run indefinitely so that you can see errors and logs. To turn off, set debug=False in launch().\n",
            "Running on public URL: https://c005fd7ad2edf68408.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
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          "text": [
            "Keyboard interruption in main thread... closing server.\n",
            "Killing tunnel 127.0.0.1:7868 <> https://c005fd7ad2edf68408.gradio.live\n"
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    {
      "cell_type": "code",
      "source": [
        "pip install -U kaleido"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UGI6mLnkCGfE",
        "outputId": "6585136e-78e5-4b58-c5b0-9ab9345dc1d9"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting kaleido\n",
            "  Downloading kaleido-0.2.1-py2.py3-none-manylinux1_x86_64.whl (79.9 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m79.9/79.9 MB\u001b[0m \u001b[31m8.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: kaleido\n",
            "Successfully installed kaleido-0.2.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import yfinance as yf\n",
        "import pandas as pd\n",
        "import gradio as gr\n",
        "import plotly.graph_objects as go\n",
        "from tqdm import tqdm\n",
        "import tempfile\n",
        "import os\n",
        "\n",
        "# Function to calculate RSI\n",
        "def calculate_rsi(data, window=6):\n",
        "    delta = data['Close'].diff(1)\n",
        "    gain = delta.where(delta > 0, 0)\n",
        "    loss = -delta.where(delta < 0, 0)\n",
        "\n",
        "    avg_gain = gain.rolling(window=window, min_periods=1).mean()\n",
        "    avg_loss = loss.rolling(window=window, min_periods=1).mean()\n",
        "\n",
        "    rs = avg_gain / avg_loss\n",
        "    rsi = 100 - (100 / (1 + rs))\n",
        "\n",
        "    return rsi\n",
        "\n",
        "# Function to calculate Bollinger Bands\n",
        "def calculate_bollinger_bands(data, window=200, multiplier=2):\n",
        "    basis = data['Close'].rolling(window=window, min_periods=1).mean()\n",
        "    dev = data['Close'].rolling(window=window, min_periods=1).std()\n",
        "\n",
        "    upper_band = basis + multiplier * dev\n",
        "    lower_band = basis - multiplier * dev\n",
        "\n",
        "    return upper_band, lower_band\n",
        "\n",
        "# Function to simulate the trading strategy\n",
        "def simulate_strategy(data, initial_capital=50000):\n",
        "    capital = initial_capital\n",
        "    position = 0\n",
        "    buy_price = 0\n",
        "    sell_price = 0\n",
        "    trades = []\n",
        "\n",
        "    for i in range(1, len(data)):\n",
        "        if data['Buy_Signal'][i]:\n",
        "            position = capital // data['Close'][i]  # Use integer division\n",
        "            buy_price = data['Close'][i]\n",
        "            trades.append(('Buy', data.index[i], buy_price, position))\n",
        "        elif data['Sell_Signal'][i] and position > 0:\n",
        "            sell_price = data['Close'][i]\n",
        "            capital = position * sell_price\n",
        "            position = 0\n",
        "            trades.append(('Sell', data.index[i], sell_price, position))\n",
        "\n",
        "    # Sell remaining position at the end\n",
        "    if position > 0:\n",
        "        sell_price = data['Close'].iloc[-1]\n",
        "        capital = position * sell_price\n",
        "        trades.append(('Sell', data.index[-1], sell_price, position))\n",
        "\n",
        "    return capital, pd.DataFrame(trades, columns=['Action', 'Timestamp', 'Price', 'Quantity'])\n",
        "\n",
        "# Function to check if a stock is profitable\n",
        "def analyze_stock(symbol, period='3mo', interval='1d'):\n",
        "    initial_capital = 50000\n",
        "    try:\n",
        "        # Download historical stock data with specified period and interval\n",
        "        data = yf.download(symbol, period=period, interval=interval)\n",
        "\n",
        "        # Calculate RSI and Bollinger Bands\n",
        "        data['RSI'] = calculate_rsi(data, window=6)\n",
        "        data['BB_Upper'], data['BB_Lower'] = calculate_bollinger_bands(data, window=200, multiplier=2)\n",
        "\n",
        "        # Identify buy and sell signals\n",
        "        data['Buy_Signal'] = (data['RSI'] < 50) & (data['Close'] < data['BB_Lower'])\n",
        "        data['Sell_Signal'] = (data['RSI'] > 50) & (data['Close'] > data['BB_Upper'])\n",
        "\n",
        "        # Simulate the trading strategy\n",
        "        final_capital, trades_df = simulate_strategy(data)\n",
        "\n",
        "        # Check if the strategy is profitable\n",
        "        is_profitable = final_capital > 50000\n",
        "\n",
        "        # Calculate buy and sell counts\n",
        "        buy_count = trades_df[trades_df['Action'] == 'Buy'].shape[0]\n",
        "        sell_count = trades_df[trades_df['Action'] == 'Sell'].shape[0]\n",
        "\n",
        "        # Calculate profitability score (you can define your own formula)\n",
        "        profitability_score = final_capital / initial_capital\n",
        "\n",
        "        # Calculate net profit and ROI\n",
        "        net_profit = final_capital - initial_capital\n",
        "        roi = (net_profit / initial_capital) * 100\n",
        "\n",
        "        # Create a scatter plot with buy and sell markers\n",
        "        fig = go.Figure()\n",
        "\n",
        "        # Configure the Plotly white theme\n",
        "        fig.update_layout(template=\"plotly_white\")\n",
        "\n",
        "        # Add line plot for Closing Price\n",
        "        fig.add_trace(go.Scatter(x=data.index, y=data['Close'], mode='lines', name='Closing Price'))\n",
        "\n",
        "        # Add Buy markers\n",
        "        fig.add_trace(go.Scatter(\n",
        "            x=trades_df[trades_df['Action'] == 'Buy']['Timestamp'],\n",
        "            y=trades_df[trades_df['Action'] == 'Buy']['Price'],\n",
        "            mode='markers',\n",
        "            name='Buy Signal',\n",
        "            marker=dict(color='green', size=10),\n",
        "            text='Buy'\n",
        "        ))\n",
        "\n",
        "        # Add Sell markers\n",
        "        fig.add_trace(go.Scatter(\n",
        "            x=trades_df[trades_df['Action'] == 'Sell']['Timestamp'],\n",
        "            y=trades_df[trades_df['Action'] == 'Sell']['Price'],\n",
        "            mode='markers',\n",
        "            name='Sell Signal',\n",
        "            marker=dict(color='red', size=10),\n",
        "            text='Sell'\n",
        "        ))\n",
        "\n",
        "        # Save the figure as an image\n",
        "        temp_file_path = os.path.join(tempfile.gettempdir(), 'plot.png')\n",
        "        fig.write_image(temp_file_path)\n",
        "\n",
        "        # Display the image in the Gradio interface\n",
        "        image_element = gr.Image(temp_file_path)\n",
        "        image_element.style(max_width=\"100%\")\n",
        "\n",
        "        return {\n",
        "            'Symbol': symbol,\n",
        "            'Profitable': is_profitable,\n",
        "            'Profitability Score': profitability_score,\n",
        "            'Total Buy Count': buy_count,\n",
        "            'Total Sell Count': sell_count,\n",
        "            'Net Profit': net_profit,\n",
        "            'ROI': roi,\n",
        "            'Plot': image_element\n",
        "        }\n",
        "\n",
        "    except Exception as e:\n",
        "        print(f\"Error processing {symbol}: {e}\")\n",
        "        return None\n",
        "\n",
        "# Get a list of Indian stock symbols (you can extend this list)\n",
        "indian_stocks = ['TCS.BO', 'RELIANCE.BO']\n",
        "\n",
        "# Period and Interval options\n",
        "period_options = ['1m', '2m', '5m', '15m', '30m', '1h', '1d', '5d', '1wk', '1mo', '3mo', '6mo', '1y', '2y', '5y', '10y', 'ytd', 'max']\n",
        "interval_options = ['1m', '2m', '5m', '15m', '30m', '1h', '1d', '5d', '1wk', '1mo', '3mo']\n",
        "\n",
        "# Create a Gradio interface\n",
        "iface = gr.Interface(\n",
        "    analyze_stock,\n",
        "    inputs=[\n",
        "        gr.Dropdown(indian_stocks, label=\"Select Stock\"),\n",
        "        gr.Dropdown(period_options, label=\"Enter Period\"),\n",
        "        gr.Dropdown(interval_options, label=\"Enter Interval\")\n",
        "    ],\n",
        "    outputs=[\"text\", \"text\", \"text\", \"text\", \"text\", \"text\", \"text\", \"text\", \"text\", \"image\"]\n",
        ")\n",
        "\n",
        "# Launch the Gradio interface\n",
        "iface.launch(debug=True)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "B9Kvb29lAeKu",
        "outputId": "65e00a28-f918-4fed-f443-516f3e8e22a7"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Setting queue=True in a Colab notebook requires sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
            "\n",
            "Colab notebook detected. This cell will run indefinitely so that you can see errors and logs. To turn off, set debug=False in launch().\n",
            "Running on public URL: https://8c24b026511a10cd63.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<div><iframe src=\"https://8c24b026511a10cd63.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n",
            "Error processing RELIANCE.BO: \n",
            "Image export using the \"kaleido\" engine requires the kaleido package,\n",
            "which can be installed using pip:\n",
            "    $ pip install -U kaleido\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/gradio/queueing.py\", line 456, in call_prediction\n",
            "    output = await route_utils.call_process_api(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/gradio/route_utils.py\", line 232, in call_process_api\n",
            "    output = await app.get_blocks().process_api(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/gradio/blocks.py\", line 1531, in process_api\n",
            "    data = self.postprocess_data(fn_index, result[\"prediction\"], state)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/gradio/blocks.py\", line 1362, in postprocess_data\n",
            "    self.validate_outputs(fn_index, predictions)  # type: ignore\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/gradio/blocks.py\", line 1336, in validate_outputs\n",
            "    raise ValueError(\n",
            "ValueError: An event handler (analyze_stock) didn't receive enough output values (needed: 10, received: 1).\n",
            "Wanted outputs:\n",
            "    [textbox, textbox, textbox, textbox, textbox, textbox, textbox, textbox, textbox, image]\n",
            "Received outputs:\n",
            "    [None]\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/gradio/queueing.py\", line 456, in call_prediction\n",
            "    output = await route_utils.call_process_api(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/gradio/route_utils.py\", line 232, in call_process_api\n",
            "    output = await app.get_blocks().process_api(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/gradio/blocks.py\", line 1531, in process_api\n",
            "    data = self.postprocess_data(fn_index, result[\"prediction\"], state)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/gradio/blocks.py\", line 1362, in postprocess_data\n",
            "    self.validate_outputs(fn_index, predictions)  # type: ignore\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/gradio/blocks.py\", line 1336, in validate_outputs\n",
            "    raise ValueError(\n",
            "ValueError: An event handler (analyze_stock) didn't receive enough output values (needed: 10, received: 1).\n",
            "Wanted outputs:\n",
            "    [textbox, textbox, textbox, textbox, textbox, textbox, textbox, textbox, textbox, image]\n",
            "Received outputs:\n",
            "    [None]\n",
            "\n",
            "The above exception was the direct cause of the following exception:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/gradio/queueing.py\", line 501, in process_events\n",
            "    response = await self.call_prediction(awake_events, batch)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/gradio/queueing.py\", line 465, in call_prediction\n",
            "    raise Exception(str(error) if show_error else None) from error\n",
            "Exception: None\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Keyboard interruption in main thread... closing server.\n",
            "Killing tunnel 127.0.0.1:7868 <> https://8c24b026511a10cd63.gradio.live\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": []
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vjJDp9P9B22X"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}