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x512/correlation_matrix_of_2_periods.ipynb

Created May 7, 2023 00:47
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correlation_matrix_of_2_periods.ipynb
{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"A demonstration of plotting a covariance matrix of two variables.\n",
"\n",
"* An quick example with random data:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Combined Correlation Matrix')"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 500x400 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"# Set style and font\n",
"sns.set_style(\"white\")\n",
"sns.set(font=\"Helvetica Neue\", font_scale=0.78)\n",
"\n",
"# Generate two random correlation matrices\n",
"X = np.random.rand(5, 5)\n",
"Y = np.random.randn(5, 5)\n",
"\n",
"corrX = np.corrcoef(X)\n",
"corrY = np.corrcoef(Y)\n",
"\n",
"# Combine the matrices\n",
"CorrCombined = np.triu(corrX, k=1) + np.tril(corrY, k=-1)\n",
"\n",
"# Add diagonal elements of the original correlation matrices\n",
"np.fill_diagonal(CorrCombined, np.diag(corrX))\n",
"np.fill_diagonal(CorrCombined, np.diag(corrY))\n",
"\n",
"# Plot the combined correlation matrix\n",
"fig, ax = plt.subplots(figsize=(5, 4), constrained_layout=True)\n",
"fig.suptitle(\"Correlation Matrices $X$ and $Y$\")\n",
"\n",
"sns.heatmap(CorrCombined, cmap=\"Blues\", cbar=True, annot=True, center=CorrCombined.mean(), fmt=\".2f\", ax=ax, cbar_kws={\"shrink\": 0.7})\n",
"\n",
"# Set labels\n",
"ax.set_xlabel(\"$X$\")\n",
"ax.set_ylabel(\"$Y$\")\n",
"\n",
"# Set title and subtitle\n",
"ax.set_title(\"Combined Correlation Matrix\", loc=\"center\")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"* Doing this with a time series of returns data. The 7-year and 9-month correlations."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"import yfinance as yf\n",
"from typing import Tuple\n",
"from datetime import datetime, timedelta\n",
"from pypfopt import expected_returns, risk_models\n",
"import numpy as np\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 800x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Define tickers and start/end dates\n",
"startdate = \"2020-04-01\"\n",
"enddate = \"2023-04-30\"\n",
"startdate2 = (datetime.today() - timedelta(days=(252 / 12) * 9)).strftime('%Y-%m-%d')\n",
"symbols = ['XLF', 'XLE', 'XLU', 'XLI', 'VNQ', 'XRT']\n",
"benchmarks = [\"YM=F\"]\n",
"\n",
"tickers = symbols + benchmarks\n",
"\n",
"def get_yf(startdate: str, enddate: str, tickers: list) -> Tuple:\n",
" ohlc = yf.download(tickers, start=startdate, end=enddate, progress=False)\n",
" prices = ohlc[\"Adj Close\"].dropna()\n",
" returns = prices.pct_change().dropna()\n",
" mu = expected_returns.mean_historical_return(prices)\n",
" S = risk_models.CovarianceShrinkage(prices).ledoit_wolf()\n",
" S_corr = risk_models.cov_to_corr(S)\n",
" Scov = risk_models.semicovariance(prices, benchmark=0.00014)\n",
" return prices, returns, mu, S, S_corr, Scov\n",
"\n",
"# Get data for each ticker\n",
"prices, returns, mu, S, S_corr, Scov = get_yf(startdate, enddate, tickers)\n",
"prices2, returns2, mu2, S2, S2_corr, Scov2 = get_yf(startdate2, enddate, tickers)\n",
"\n",
"if len(tickers) != prices.shape[1]:\n",
" raise ValueError(\"Number of tickers does not match number of columns in prices DataFrame\")\n",
"\n",
"# Combine the correlation matrices\n",
"CorrCombined = np.triu(S_corr) + np.tril(S2_corr, k=-1)\n",
"np.fill_diagonal(CorrCombined, np.diag(S_corr))\n",
"np.fill_diagonal(CorrCombined, np.diag(S2_corr))\n",
"\n",
"# Convert the combined correlation matrix to a DataFrame\n",
"df = pd.DataFrame(CorrCombined, columns=tickers, index=tickers)\n",
"\n",
"# Plot the combined correlation matrix\n",
"fig, ax = plt.subplots(figsize=(8, 4.8), constrained_layout=True)\n",
"fig.suptitle(\"Correlation Matrix\")\n",
"\n",
"sns.heatmap(\n",
" df,\n",
" cmap=\"coolwarm_r\",\n",
" cbar=False,\n",
" annot=True,\n",
" annot_kws={\"size\": 7},\n",
" fmt=\".2f\",\n",
" ax=ax,\n",
")\n",
"\n",
"# Set labels and title\n",
"ax.set_xlabel(\"3y. Correlation\")\n",
"ax.xaxis.set_label_position('top')\n",
"ax.set_ylabel(\"9m. Correlation\", loc=\"center\")\n",
"ax.set_xticklabels(tickers, rotation=45, fontsize=7)\n",
"ax.set_yticklabels(tickers, rotation=0, fontsize=7)\n",
"actual_start_date = returns.index.min()\n",
"ax.set_title(f\"{actual_start_date} to {enddate}\", loc=\"center\", fontsize=7)\n",
"\n",
"# Convert CorrCombined to a pandas DataFrame\n",
"CorrCombined_df = pd.DataFrame(CorrCombined, columns=tickers, index=tickers)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" VNQ XLE XLF XLI XLK XLU XRT \n",
"VNQ 1.000000 0.440269 0.700741 0.758094 0.627172 0.708587 0.575150 \\\n",
"XLE 0.440269 1.000000 0.647095 0.623724 0.313960 0.317091 0.386752 \n",
"\n",
" YM=F \n",
"VNQ 0.755072 \n",
"XLE 0.590074 \n",
" VNQ XLE XLF XLI XLK XLU XRT \n",
"XRT 0.671315 0.385620 0.650558 0.707275 0.704265 0.406226 1.000000 \\\n",
"YM=F 0.680240 0.543143 0.745109 0.789924 0.742346 0.560883 0.651509 \n",
"\n",
" YM=F \n",
"XRT 0.651509 \n",
"YM=F 1.000000 \n"
]
}
],
"source": [
"print(S_corr.head(2))\n",
"#print(CorrCombined_df)\n",
"print(S2_corr.tail(2))"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>AUDUSD=X</th>\n",
" <th>EURUSD=X</th>\n",
" <th>GBPUSD=X</th>\n",
" <th>NOKUSD=X</th>\n",
" <th>SGDUSD=X</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2020-04-01</th>\n",
" <td>0.613700</td>\n",
" <td>1.102657</td>\n",
" <td>1.240849</td>\n",
" <td>0.096062</td>\n",
" <td>0.703383</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-04-02</th>\n",
" <td>0.609140</td>\n",
" <td>1.095362</td>\n",
" <td>1.238237</td>\n",
" <td>0.095654</td>\n",
" <td>0.697156</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-04-03</th>\n",
" <td>0.606322</td>\n",
" <td>1.084740</td>\n",
" <td>1.239495</td>\n",
" <td>0.096338</td>\n",
" <td>0.699115</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-04-06</th>\n",
" <td>0.599470</td>\n",
" <td>1.080696</td>\n",
" <td>1.222016</td>\n",
" <td>0.094619</td>\n",
" <td>0.694830</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2020-04-07</th>\n",
" <td>0.611760</td>\n",
" <td>1.080380</td>\n",
" <td>1.224050</td>\n",
" <td>0.096108</td>\n",
" <td>0.698812</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2023-04-25</th>\n",
" <td>0.670381</td>\n",
" <td>1.105950</td>\n",
" <td>1.250000</td>\n",
" <td>0.095033</td>\n",
" <td>0.750582</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2023-04-26</th>\n",
" <td>0.663640</td>\n",
" <td>1.097839</td>\n",
" <td>1.241480</td>\n",
" <td>0.093761</td>\n",
" <td>0.747479</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2023-04-27</th>\n",
" <td>0.660838</td>\n",
" <td>1.104728</td>\n",
" <td>1.247147</td>\n",
" <td>0.094120</td>\n",
" <td>0.749372</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2023-04-28</th>\n",
" <td>0.663040</td>\n",
" <td>1.103205</td>\n",
" <td>1.249750</td>\n",
" <td>0.094269</td>\n",
" <td>0.749092</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2023-05-01</th>\n",
" <td>0.661240</td>\n",
" <td>1.101091</td>\n",
" <td>1.255761</td>\n",
" <td>0.093698</td>\n",
" <td>0.749288</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>804 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" AUDUSD=X EURUSD=X GBPUSD=X NOKUSD=X SGDUSD=X\n",
"Date \n",
"2020-04-01 0.613700 1.102657 1.240849 0.096062 0.703383\n",
"2020-04-02 0.609140 1.095362 1.238237 0.095654 0.697156\n",
"2020-04-03 0.606322 1.084740 1.239495 0.096338 0.699115\n",
"2020-04-06 0.599470 1.080696 1.222016 0.094619 0.694830\n",
"2020-04-07 0.611760 1.080380 1.224050 0.096108 0.698812\n",
"... ... ... ... ... ...\n",
"2023-04-25 0.670381 1.105950 1.250000 0.095033 0.750582\n",
"2023-04-26 0.663640 1.097839 1.241480 0.093761 0.747479\n",
"2023-04-27 0.660838 1.104728 1.247147 0.094120 0.749372\n",
"2023-04-28 0.663040 1.103205 1.249750 0.094269 0.749092\n",
"2023-05-01 0.661240 1.101091 1.255761 0.093698 0.749288\n",
"\n",
"[804 rows x 5 columns]"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Define tickers and start/end dates\n",
"startdate = \"2020-04-01\"\n",
"enddate = \"2023-05-02\"\n",
"startdate2 = (datetime.today() - timedelta(days=(252 / 12) * 9)).strftime('%Y-%m-%d')\n",
"symbols = [\"SGDUSD=X\", \"NOKUSD=X\", \"AUDUSD=X\", \"GBPUSD=X\", \"EURUSD=X\"]\n",
"tickers = symbols\n",
"\n",
"def get_yf(startdate: str, enddate: str, tickers: list) -> Tuple:\n",
" ohlc = yf.download(tickers, start=startdate, end=enddate, progress=False)\n",
" prices = ohlc[\"Adj Close\"].dropna()\n",
" returns = prices.pct_change().dropna()\n",
" mu = expected_returns.mean_historical_return(prices)\n",
" S = risk_models.CovarianceShrinkage(prices).ledoit_wolf()\n",
" S_corr = risk_models.cov_to_corr(S)\n",
" Scov = risk_models.semicovariance(prices, benchmark=0.00014)\n",
" return prices, returns, mu, S, S_corr, Scov\n",
"\n",
"# Get data for each ticker\n",
"prices, returns, mu, S, S_corr, Scov = get_yf(startdate, enddate, tickers)\n",
"prices2, returns2, mu2, S2, S2_corr, Scov2 = get_yf(startdate2, enddate, tickers)\n",
"prices "
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 800x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Combine the correlation matrices\n",
"CorrCombined = np.triu(S_corr) + np.tril(S2_corr, k=-1)\n",
"np.fill_diagonal(CorrCombined, np.diag(S_corr))\n",
"np.fill_diagonal(CorrCombined, np.diag(S2_corr))\n",
"\n",
"# Convert the combined correlation matrix to a DataFrame\n",
"df = pd.DataFrame(CorrCombined, columns=tickers, index=tickers)\n",
"\n",
"# Plot the combined correlation matrix\n",
"fig, ax = plt.subplots(figsize=(8, 4.8), constrained_layout=True)\n",
"fig.suptitle(\"Correlation Matrix\")\n",
"\n",
"sns.heatmap(\n",
" df,\n",
" cmap=\"coolwarm_r\",\n",
" cbar=False,\n",
" annot=True,\n",
" annot_kws={\"size\": 7},\n",
" fmt=\".2f\",\n",
" ax=ax,\n",
")\n",
"\n",
"# Set labels and title\n",
"ax.set_xlabel(\"3y. Correlation\")\n",
"ax.xaxis.set_label_position('top')\n",
"ax.set_ylabel(\"9m. Correlation\", loc=\"center\")\n",
"ax.set_xticklabels(tickers, rotation=45, fontsize=7)\n",
"ax.set_yticklabels(tickers, rotation=0, fontsize=7)\n",
"actual_start_date = returns.index.min()\n",
"ax.set_title(f\"{actual_start_date} to {enddate}\", loc=\"center\", fontsize=7)\n",
"\n",
"# Convert CorrCombined to a pandas DataFrame\n",
"CorrCombined_df = pd.DataFrame(CorrCombined, columns=tickers, index=tickers)"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 400x400 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Combine the correlation matrices\n",
"CorrCombined = np.triu(S_corr) + np.tril(S2_corr, k=-1)\n",
"np.fill_diagonal(CorrCombined, np.diag(S_corr))\n",
"np.fill_diagonal(CorrCombined, np.diag(S2_corr))\n",
"\n",
"# Convert the combined correlation matrix to a DataFrame\n",
"df = pd.DataFrame(CorrCombined, columns=tickers, index=tickers)\n",
"\n",
"# Plot the combined correlation matrix\n",
"fig, ax = plt.subplots(figsize=(4, 4), constrained_layout=True)\n",
"fig.suptitle(\"Correlation Matrix\")\n",
"\n",
"sns.heatmap(\n",
" df,\n",
" cmap=\"coolwarm_r\",\n",
" cbar=False,\n",
" annot=True,\n",
" annot_kws={\"size\": 7},\n",
" fmt=\".2f\",\n",
" ax=ax,\n",
")\n",
"\n",
"# Set labels and title\n",
"ax.set_xlabel(\"3y. Correlation\")\n",
"ax.xaxis.set_label_position('top')\n",
"ax.set_ylabel(\"9m. Correlation\", loc=\"center\")\n",
"ax.set_xticklabels(tickers, rotation=45, fontsize=7)\n",
"ax.set_yticklabels(tickers, rotation=0, fontsize=7)\n",
"actual_start_date = returns.index.min()\n",
"ax.set_title(f\"{actual_start_date} to {enddate}\", loc=\"center\", fontsize=7)\n",
"\n",
"# Convert CorrCombined to a pandas DataFrame\n",
"CorrCombined_df = pd.DataFrame(CorrCombined, columns=tickers, index=tickers)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Woah, 2 colour palettes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "3.11.2"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
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