portfolio-correlation-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import yfinance as yf\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "portfolio = {\n",
    "  \"portfolio\": [\n",
    "\n",
    "    {\"symbol\": \"NVDA\", \"totalValue\": 1000, \"percentageOfPortfolio\": 12.5},\n",
    "    {\"symbol\": \"AMD\", \"totalValue\": 1000, \"percentageOfPortfolio\": 12.5},\n",
    "    {\"symbol\": \"META\", \"totalValue\": 1000, \"percentageOfPortfolio\": 12.5},\n",
    "    {\"symbol\": \"GOOGL\", \"totalValue\": 1000, \"percentageOfPortfolio\": 12.5},\n",
    "    {\"symbol\": \"TSLA\", \"totalValue\": 1000, \"percentageOfPortfolio\": 12.5},\n",
    "    {\"symbol\": \"AMZN\", \"totalValue\": 1000, \"percentageOfPortfolio\": 12.5},\n",
    "    {\"symbol\": \"MSFT\", \"totalValue\": 1000, \"percentageOfPortfolio\": 12.5},\n",
    "    {\"symbol\": \"FSLY\", \"totalValue\": 1000, \"percentageOfPortfolio\": 12.5}\n",
    "  ]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           NVDA       AMD      META     GOOGL      TSLA      AMZN      MSFT  \\\n",
      "NVDA   1.000000  0.729671  0.405242  0.496233  0.447567  0.493294  0.622393   \n",
      "AMD    0.729671  1.000000  0.438100  0.527038  0.375018  0.542687  0.600063   \n",
      "META   0.405242  0.438100  1.000000  0.561709  0.287239  0.576420  0.540426   \n",
      "GOOGL  0.496233  0.527038  0.561709  1.000000  0.322845  0.638838  0.654918   \n",
      "TSLA   0.447567  0.375018  0.287239  0.322845  1.000000  0.388614  0.339403   \n",
      "AMZN   0.493294  0.542687  0.576420  0.638838  0.388614  1.000000  0.640413   \n",
      "MSFT   0.622393  0.600063  0.540426  0.654918  0.339403  0.640413  1.000000   \n",
      "FSLY   0.396224  0.400352  0.342318  0.377715  0.357521  0.461317  0.360754   \n",
      "\n",
      "           FSLY  \n",
      "NVDA   0.396224  \n",
      "AMD    0.400352  \n",
      "META   0.342318  \n",
      "GOOGL  0.377715  \n",
      "TSLA   0.357521  \n",
      "AMZN   0.461317  \n",
      "MSFT   0.360754  \n",
      "FSLY   1.000000  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Define the period for analysis\n",
    "start_date = \"2022-10-01\"\n",
    "end_date = \"2024-02-22\"\n",
    "\n",
    "# Initialize a DataFrame to store the adjusted close prices of each stock\n",
    "price_data = pd.DataFrame()\n",
    "\n",
    "# Fetch and store the adjusted close prices for each stock in the portfolio\n",
    "for item in portfolio['portfolio']:\n",
    "    symbol = item['symbol']\n",
    "    data = yf.download(symbol, start=start_date, end=end_date)\n",
    "    price_data[symbol] = data['Adj Close']\n",
    "\n",
    "\n",
    "# Calculate daily returns of the stocks\n",
    "daily_returns = price_data.pct_change()\n",
    "\n",
    "# Calculate the correlation matrix of the daily returns\n",
    "correlation_matrix = daily_returns.corr()\n",
    "\n",
    "# Display the correlation matrix\n",
    "print(correlation_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 2448x2304 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Assuming 'correlation_matrix' is your DataFrame from the previous step\n",
    "# Display the heatmap\n",
    "plt.figure(figsize=(34, 32))  # Increase figure size\n",
    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', vmin=-1, vmax=1)\n",
    "plt.title('Portfolio Correlation Matrix')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}