Inter_IIT_2023_selection_task.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyM80kUUg27NmZHGbyU8YEzq",
      "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/DC769/My-UG-Projects/blob/main/Inter_IIT_2023_selection_task.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "25bDzctf_a9S",
        "outputId": "bd624fb5-6715-412b-eb8d-fcc8c7c69fe5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (1.5.3)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (1.23.5)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (3.7.1)\n",
            "Requirement already satisfied: seaborn in /usr/local/lib/python3.10/dist-packages (0.12.2)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2023.3.post1)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.1.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (4.43.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.4.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (23.2)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (3.1.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas) (1.16.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install pandas numpy matplotlib seaborn\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "\n",
        "aapl_data = pd.read_csv('/content/AAPLstockdata5y.csv')\n",
        "msft_data = pd.read_csv('/content/MSFTstockdata5y.csv')\n",
        "\n"
      ],
      "metadata": {
        "id": "PqTq4Gq7_1Af"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def pairs_trading_strategy(aapl_data, msft_data, z_score_threshold=2.0):\n",
        "\n",
        "    spread = aapl_data['Close'] - msft_data['Close']\n",
        "\n",
        "\n",
        "    z_score = (spread - np.mean(spread)) / np.std(spread)\n",
        "\n",
        "\n",
        "    aapl_position = 0\n",
        "    msft_position = 0\n",
        "    pnl = []\n",
        "\n",
        "    for i in range(1, len(aapl_data)):\n",
        "        if z_score[i] > z_score_threshold:\n",
        "            aapl_position -= 1\n",
        "            msft_position += aapl_data['Close'][i] / msft_data['Close'][i]\n",
        "        elif z_score[i] < -z_score_threshold:\n",
        "            aapl_position += 1\n",
        "            msft_position -= aapl_data['Close'][i] / msft_data['Close'][i]\n",
        "        else:\n",
        "            aapl_position = 0\n",
        "            msft_position = 0\n",
        "\n",
        "\n",
        "        daily_pnl = (aapl_position * aapl_data['Close'][i]) + (msft_position * msft_data['Close'][i])\n",
        "        pnl.append(daily_pnl)\n",
        "\n",
        "    return pnl"
      ],
      "metadata": {
        "id": "zoA1WX2GBz6K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pnl = pairs_trading_strategy(aapl_data, msft_data)\n",
        "\n",
        "\n",
        "winning_trades = sum(1 for x in pnl if x > 0)\n",
        "losing_trades = sum(1 for x in pnl if x < 0)\n",
        "average_win = sum(x for x in pnl if x > 0) / winning_trades if winning_trades > 0 else 0\n",
        "average_loss = sum(x for x in pnl if x < 0) / losing_trades if losing_trades > 0 else 0\n",
        "\n",
        "cumulative_pnl = np.cumsum(pnl)\n",
        "drawdown = np.maximum.accumulate(cumulative_pnl) - cumulative_pnl\n",
        "\n",
        "\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.subplot(2, 1, 1)\n",
        "plt.plot(cumulative_pnl)\n",
        "plt.title('Cumulative PnL')\n",
        "plt.grid()\n",
        "\n",
        "plt.subplot(2, 1, 2)\n",
        "plt.fill_between(range(len(drawdown)), drawdown)\n",
        "plt.title('Drawdown')\n",
        "plt.grid()\n",
        "\n",
        "\n",
        "print(f\"Win Rate: {winning_trades / (winning_trades + losing_trades):.2%}\")\n",
        "print(f\"Loss Rate: {losing_trades / (winning_trades + losing_trades):.2%}\")\n",
        "print(f\"Average Win Amount: {average_win:.2f}\")\n",
        "print(f\"Average Loss Amount: {average_loss:.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 616
        },
        "id": "mgPKGVZaB-Hp",
        "outputId": "a7ba0e62-fc60-45d5-b7b7-6d4a9d4d13cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Win Rate: 48.39%\n",
            "Loss Rate: 51.61%\n",
            "Average Win Amount: 122.27\n",
            "Average Loss Amount: -14.60\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}