correlation usdx, usdvnd and vnindex

corrusdx_uv_vnindex.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  3 of 3 completed\n",
      "/var/folders/zh/g_gf_j_s13l4g_rmzlh9125w0000gn/T/ipykernel_14810/560431944.py:17: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Either fill in any non-leading NA values prior to calling pct_change or specify 'fill_method=None' to not fill NA values.\n",
      "  returns = data.pct_change().dropna()\n",
      "[*********************100%***********************]  3 of 3 completed\n",
      "/var/folders/zh/g_gf_j_s13l4g_rmzlh9125w0000gn/T/ipykernel_14810/560431944.py:17: FutureWarning: The default fill_method='pad' in DataFrame.pct_change is deprecated and will be removed in a future version. Either fill in any non-leading NA values prior to calling pct_change or specify 'fill_method=None' to not fill NA values.\n",
      "  returns = data.pct_change().dropna()\n",
      "[*********************100%***********************]  3 of 3 completed"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation Matrix: interval  1d\n",
      "             USDX   USD_VND   VNINDEX\n",
      "USDX     1.000000 -0.008557 -0.001278\n",
      "USD_VND -0.008557  1.000000 -0.003327\n",
      "VNINDEX -0.001278 -0.003327  1.000000\n",
      "Correlation Matrix: interval  1wk\n",
      "             USDX   USD_VND   VNINDEX\n",
      "USDX     1.000000 -0.000555 -0.000372\n",
      "USD_VND -0.000555  1.000000 -0.025753\n",
      "VNINDEX -0.000372 -0.025753  1.000000\n",
      "Correlation Matrix: interval  1mo\n",
      "             USDX   USD_VND   VNINDEX\n",
      "USDX     1.000000 -0.230952 -0.275351\n",
      "USD_VND -0.230952  1.000000  0.247131\n",
      "VNINDEX -0.275351  0.247131  1.000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "import yfinance as yf\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "intervals = [\"1d\", \"1wk\",\"1mo\"]\n",
    "\n",
    "for interval in intervals:\n",
    "    # Define the tickers\n",
    "    tickers = {'USDX': 'DX-Y.NYB', 'USD_VND': 'VND=X', \"VNINDEX\": \"0P0000HY8X.VN\"}\n",
    "\n",
    "    # Fetch data from Yahoo Finance\n",
    "    data = yf.download(list(tickers.values()), start='2020-01-01', end='2023-01-01', interval=interval)['Adj Close']\n",
    "    data.columns = list(tickers.keys())\n",
    "\n",
    "    # Calculate daily returns\n",
    "    returns = data.pct_change().dropna()\n",
    "\n",
    "    # Calculate the correlation matrix of returns\n",
    "    correlation_matrix = returns.corr()\n",
    "\n",
    "    # Print the correlation matrix\n",
    "    print(\"Correlation Matrix: interval \", interval)\n",
    "    print(correlation_matrix)\n",
    "\n"
   ]
  }
 ],
 "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": "3.13.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}