Linear_Regresssion.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Linear Regression "
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Linear regression is a fundamental statistical technique used for modeling and analyzing the relationship between a dependent variable (often referred to as the target or outcome) and one or more independent variables (predictors or features). It assumes a linear relationship, meaning that changes in the predictor variables are associated with proportional changes in the dependent variable. The goal of linear regression is to find the best-fitting line (the linear equation) that minimizes the difference between the predicted values and the actual data points, typically done by minimizing the sum of squared differences (residuals). This line can be used to make predictions or understand the strength and direction of the relationships between variables."
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import yfinance as yf\n",
        "from scipy import stats\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# yahoo finance is used to fetch data \n",
        "import yfinance as yf\n",
        "yf.pdr_override()"
      ],
      "outputs": [],
      "execution_count": 1,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2023-10-05T01:35:49.877Z",
          "iopub.execute_input": "2023-10-05T01:35:49.882Z",
          "shell.execute_reply": "2023-10-05T01:35:50.755Z",
          "iopub.status.idle": "2023-10-05T01:35:50.761Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# input\n",
        "# Technology Companies\n",
        "symbols = ['AMD','NVDA'] \n",
        "market = ['^GSPC']\n",
        "start = '2018-01-01'\n",
        "end = '2023-08-01'"
      ],
      "outputs": [],
      "execution_count": 2,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2023-10-05T01:35:50.765Z",
          "iopub.execute_input": "2023-10-05T01:35:50.768Z",
          "iopub.status.idle": "2023-10-05T01:35:50.775Z",
          "shell.execute_reply": "2023-10-05T01:35:50.790Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = yf.download(symbols, start, end)\n",
        "benchmark = yf.download(market, start, end)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  2 of 2 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n"
          ]
        }
      ],
      "execution_count": 3,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2023-10-05T01:35:50.780Z",
          "iopub.execute_input": "2023-10-05T01:35:50.783Z",
          "iopub.status.idle": "2023-10-05T01:35:51.431Z",
          "shell.execute_reply": "2023-10-05T01:35:51.447Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Resample the data to monthly frequency\n",
        "df_monthly = df['Adj Close'].resample('M').ffill()\n",
        "benchmark_monthly = benchmark['Adj Close'].resample('M').ffill()\n"
      ],
      "outputs": [],
      "execution_count": 4,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2023-10-05T01:35:51.435Z",
          "iopub.execute_input": "2023-10-05T01:35:51.440Z",
          "shell.execute_reply": "2023-10-05T01:35:51.450Z",
          "iopub.status.idle": "2023-10-05T01:35:51.456Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to perform linear regression and calculate metrics\n",
        "def perform_linear_regression(symbol_data, benchmark_data, symbol_name):\n",
        "    slope, intercept, r_value, p_value, std_err = stats.linregress(benchmark_data, symbol_data)\n",
        "    regression_line = intercept + slope * benchmark_data\n",
        "\n",
        "    # Plot the linear regression\n",
        "    plt.figure(figsize=(10, 6))\n",
        "    plt.scatter(benchmark_data, symbol_data, label=f'{symbol_name} vs. {market[0]}')\n",
        "    plt.plot(benchmark_data, regression_line, color='red', label='Linear Regression Line')\n",
        "    plt.title(f'Linear Regression for {symbol_name} vs. {market[0]}')\n",
        "    plt.xlabel(f'{market[0]}')\n",
        "    plt.ylabel(symbol_name)\n",
        "    plt.legend()\n",
        "    plt.grid(True)\n",
        "    plt.show()\n",
        "\n",
        "    # Calculate metrics\n",
        "    residuals = symbol_data - regression_line\n",
        "    mae = np.mean(np.abs(residuals))\n",
        "    mse = np.mean(residuals ** 2)\n",
        "    rmse = np.sqrt(mse)\n",
        "    r_squared = r_value ** 2\n",
        "\n",
        "    # Calculate adjusted R-squared\n",
        "    n = len(benchmark_data)\n",
        "    k = 2  # Number of predictors (including the intercept)\n",
        "    adj_r_squared = 1 - (1 - r_squared) * (n - 1) / (n - k - 1)\n",
        "\n",
        "    return {\n",
        "        'Symbol': symbol_name,\n",
        "        'MAE': mae,\n",
        "        'MSE': mse,\n",
        "        'RMSE': rmse,\n",
        "        'R-squared': r_squared,\n",
        "        'Adjusted R-squared': adj_r_squared\n",
        "    }\n",
        "\n",
        "# Initialize a list to store the results\n",
        "results = []\n",
        "\n",
        "# Perform linear regression and calculate metrics for each symbol\n",
        "for symbol in symbols:\n",
        "    result = perform_linear_regression(df_monthly[symbol], benchmark_monthly, symbol)\n",
        "    results.append(result)\n",
        "\n",
        "# Create a DataFrame from the results\n",
        "results_df = pd.DataFrame(results)\n",
        "\n",
        "# Print the DataFrame\n",
        "print(results_df)"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 720x432 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 720x432 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Symbol        MAE          MSE       RMSE  R-squared  Adjusted R-squared\n",
            "0    AMD  10.261576   170.497061  13.057452   0.876901            0.873054\n",
            "1   NVDA  33.009675  2302.528439  47.984669   0.764164            0.756795\n"
          ]
        }
      ],
      "execution_count": 5,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2023-10-05T01:35:51.460Z",
          "iopub.execute_input": "2023-10-05T01:35:51.462Z",
          "iopub.status.idle": "2023-10-05T01:35:51.725Z",
          "shell.execute_reply": "2023-10-05T01:35:51.752Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Calculation prediction for each symbol and print DataFrame of actual and prediction\n",
        "for symbol in symbols:\n",
        "    slope, intercept, _, _, _ = stats.linregress(benchmark_monthly, df_monthly[symbol])\n",
        "    predictions = intercept + slope * benchmark_monthly\n",
        "    prediction_df = pd.DataFrame({'Actual': df_monthly[symbol], 'Prediction': predictions})\n",
        "    prediction_df['Difference'] = prediction_df['Actual'] - prediction_df['Prediction']\n",
        "    print(f'Predictions for {symbol}:\\n', prediction_df)\n",
        "\n"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predictions for AMD:\n",
            "                 Actual  Prediction  Difference\n",
            "Date                                          \n",
            "2018-01-31   13.740000   28.782381  -15.042381\n",
            "2018-02-28   12.110000   23.103001  -10.993001\n",
            "2018-03-31   10.050000   19.335339   -9.285339\n",
            "2018-04-30   10.880000   19.706112   -8.826112\n",
            "2018-05-31   13.730000   22.660958   -8.930959\n",
            "...                ...         ...         ...\n",
            "2023-03-31   98.010002   95.165764    2.844238\n",
            "2023-04-30   89.370003   98.272946   -8.902943\n",
            "2023-05-31  118.209999   98.807427   19.402573\n",
            "2023-06-30  113.910004  112.778653    1.131351\n",
            "2023-07-31  114.400002  119.934946   -5.534944\n",
            "\n",
            "[67 rows x 3 columns]\n",
            "Predictions for NVDA:\n",
            "                 Actual  Prediction  Difference\n",
            "Date                                          \n",
            "2018-01-31   60.808376   47.909657   12.898720\n",
            "2018-02-28   59.905487   33.833430   26.072057\n",
            "2018-03-31   57.328564   24.495358   32.833205\n",
            "2018-04-30   55.672504   25.414310   30.258194\n",
            "2018-05-31   62.466568   32.737837   29.728731\n",
            "...                ...         ...         ...\n",
            "2023-03-31  277.718353  212.439504   65.278849\n",
            "2023-04-30  277.438416  220.140592   57.297824\n",
            "2023-05-31  378.269684  221.465291  156.804393\n",
            "2023-06-30  422.985138  256.092687  166.892451\n",
            "2023-07-31  467.251526  273.829411  193.422115\n",
            "\n",
            "[67 rows x 3 columns]\n"
          ]
        }
      ],
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