A correlation matrix is a quantitative tool used in finance, statistics, and other fields to measure and visualize the relationships between multiple variables. In the context of stock trading and investing, a correlation matrix is typically used to analyze the relationships between the price movements of different stocks over a specific time period. The correlation coefficient, which ranges from -1 to 1, is used to quantify the strength and direction of the relationship between two stocks.
To create a correlation matrix table using Seaborn for the given stocks, you need to follow these steps:
- Install required libraries
- Import the libraries
- Download stock data using yfinance
- Calculate the 20-day correlation
- Plot the correlation matrix using Seaborn
Install the Libraries using PIP command
pip install yfinance seaborn pandas numpy matplotlibHere is a python code that will download the stock data for the given tickers(‘HDFCBANK.NS’, ‘ICICIBANK.NS’, ‘RELIANCE.NS’, ‘MARUTI.NS’, ‘SBIN.NS’, ‘SUNPHARMA.NS’, ‘TATASTEEL.NS’, ‘INFY.NS’, ‘TCS.NS’, ‘AXISBANK.NS’) and calculate the 20-day correlation, and plot the correlation matrix using Seaborn. Note that you may need to adjust the start_date and end_date variables to your desired timeframe.
# Import required libraries
import yfinance as yf
import pandas as pd
import matplotlib.pyplot as plt
# Define the list of stocks
stocks = ['HDFCBANK.NS', 'ICICIBANK.NS', 'RELIANCE.NS', 'MARUTI.NS', 'SBIN.NS', 'SUNPHARMA.NS', 'TATASTEEL.NS', 'INFY.NS', 'TCS.NS', 'AXISBANK.NS']
# Download stock data using yfinance
data = yf.download(stocks, start='2022-01-01', end='2023-04-06')['Adj Close']
# Compute Percentage Change
rets = data.pct_change()
# Compute 20-day rolling correlation
corr_20_day = rets.rolling(window=20).corr()
# Get the latest correlation matrix
corr_matrix = corr_20_day.iloc[-len(stocks):]
# Plot Correlation Matrix using Matplotlib
plt.figure(figsize=(10, 10))
plt.imshow(corr_matrix, cmap='RdYlGn', interpolation='none', aspect='auto')
plt.colorbar()
plt.xticks(range(len(corr_matrix)), corr_matrix.columns, rotation='vertical')
plt.yticks(range(len(corr_matrix)), corr_matrix.columns)
plt.suptitle('20-Day Rolling Stock Correlations Heat Map', fontsize=15, fontweight='bold')
plt.show()
Plotting Correlation Matric in a Table Format – Code Snippet
# Import required libraries
import yfinance as yf
import pandas as pd
# Define the list of stocks
stocks = ['HDFCBANK.NS', 'ICICIBANK.NS', 'RELIANCE.NS', 'MARUTI.NS', 'SBIN.NS', 'SUNPHARMA.NS', 'TATASTEEL.NS', 'INFY.NS', 'TCS.NS', 'AXISBANK.NS']
# Download stock data using yfinance
data = yf.download(stocks, start='2022-01-01', end='2023-04-06')['Adj Close']
# Compute Percentage Change
rets = data.pct_change()
# Compute 20-day rolling correlation
corr_20_day = rets.rolling(window=20).corr()
# Get the latest correlation matrix
corr_matrix = corr_20_day.iloc[-len(stocks):]
# Display the correlation matrix
print(corr_matrix)
Python Output
AXISBANK.NS HDFCBANK.NS ICICIBANK.NS INFY.NS \
Date
2023-04-06 AXISBANK.NS 1.000000 0.569418 0.669686 0.323919
HDFCBANK.NS 0.569418 1.000000 0.553462 0.505160
ICICIBANK.NS 0.669686 0.553462 1.000000 0.532197
INFY.NS 0.323919 0.505160 0.532197 1.000000
MARUTI.NS 0.089447 -0.117898 0.088857 -0.074389
RELIANCE.NS 0.556131 0.561288 0.795301 0.507474
SBIN.NS 0.664366 0.655983 0.542818 0.509560
SUNPHARMA.NS -0.267849 0.164081 -0.291801 -0.069568
TATASTEEL.NS 0.216665 0.164084 0.177518 0.379145
TCS.NS 0.186048 0.415205 0.475583 0.739076
MARUTI.NS RELIANCE.NS SBIN.NS SUNPHARMA.NS \
Date
2023-04-06 AXISBANK.NS 0.089447 0.556131 0.664366 -0.267849
HDFCBANK.NS -0.117898 0.561288 0.655983 0.164081
ICICIBANK.NS 0.088857 0.795301 0.542818 -0.291801
INFY.NS -0.074389 0.507474 0.509560 -0.069568
MARUTI.NS 1.000000 0.263537 0.337897 0.184349
RELIANCE.NS 0.263537 1.000000 0.644776 -0.058505
SBIN.NS 0.337897 0.644776 1.000000 0.113177
SUNPHARMA.NS 0.184349 -0.058505 0.113177 1.000000
TATASTEEL.NS 0.080105 0.168075 0.198621 -0.330021
...
SBIN.NS 0.198621 0.469686
SUNPHARMA.NS -0.330021 0.135291
TATASTEEL.NS 1.000000 0.252214
TCS.NS 0.252214 1.000000 How Correlation Matrix is Useful for Traders/Investors
Diversification: Traders can use the correlation matrix to identify stocks that are weakly correlated or negatively correlated, thereby helping them to diversify their portfolio. By investing in stocks that do not move in the same direction, traders can potentially reduce the overall risk of their portfolio.
Risk management: Correlation analysis can help traders identify and manage risk by highlighting stocks that tend to move together. By understanding these relationships, traders can adjust their positions and hedge against potential losses.
Trading strategies: Traders can use the correlation matrix to develop and validate trading strategies. For example, pairs trading involves taking long and short positions in two highly correlated stocks, expecting that the spread between their prices will converge or diverge. By identifying such pairs, traders can potentially profit from temporary price discrepancies.
Market sentiment analysis: The correlation matrix can also provide insights into market sentiment. For instance, if most stocks in a sector or market are highly correlated, it may indicate that they are all being influenced by a common factor, such as broad market trends, economic conditions, or geopolitical events. This information can help traders make informed decisions about their investments.
