Correlation Heatmap
OpenAlgo Stock Correlation Heatmap Application
Overview
A financial analysis tool that visualizes statistical relationships between India's top 8 Nifty 50 stocks using color-coded correlation heatmaps. Helps traders and portfolio managers identify diversification opportunities and sector clustering patterns.
Prerequisites
OpenAlgo Python SDK: Version 1.0.0.30 or higher
OpenAlgo Self-Hosted: Running on
http://127.0.0.1:5000Valid API Key: Generated from OpenAlgo web interface
Market Data Access: NSE data permissions
What It Does
Data Analysis
Fetches daily price data for 8 major stocks (RELIANCE, TCS, HDFCBANK, INFY, ICICIBANK, HINDUNILVR, SBIN, AXISBANK)
Calculates 20-day rolling correlations between all stock pairs
Generates symmetric correlation matrix with latest market relationships
#!/usr/bin/env python3
"""
Simple Correlation Heatmap - No prints, only visualization
"""
from openalgo import api, ta
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from datetime import datetime, timedelta
import time
def main():
print("🔁 OpenAlgo Python Bot is running.")
# Initialize OpenAlgo client
client = api(
api_key='84f72702d742a7a46bb0dd26187b0ae220d4fff6078a50ffd67c0f1fe0a16a2e',
host='http://127.0.0.1:5000'
)
# Top 8 Nifty 50 stocks
nifty_top8 = [
"RELIANCE", "TCS", "HDFCBANK", "INFY",
"ICICIBANK", "HINDUNILVR", "SBIN", "AXISBANK"
]
# Date range
end_date = datetime.now().strftime("%Y-%m-%d")
start_date = (datetime.now() - timedelta(days=60)).strftime("%Y-%m-%d")
# Fetch data
stock_data = {}
for stock in nifty_top8:
time.sleep(1)
try:
data = client.history(symbol=stock, exchange="NSE", interval="D",
start_date=start_date, end_date=end_date)
data.reset_index(inplace=True)
stock_data[stock] = data[['timestamp', 'close']].rename(columns={'close': stock})
except:
continue
# Merge data
merged_data = None
for stock, data in stock_data.items():
if merged_data is None:
merged_data = data
else:
merged_data = pd.merge(merged_data, data, on='timestamp', how='inner')
# Calculate 20-day rolling correlations
stock_columns = [col for col in merged_data.columns if col != 'timestamp']
correlation_data = {}
for i, stock1 in enumerate(stock_columns):
correlation_data[stock1] = {}
for j, stock2 in enumerate(stock_columns):
if i == j:
correlation_data[stock1][stock2] = 1.0
else:
rolling_corr = ta.correlation(merged_data[stock1], merged_data[stock2], period=20)
latest_corr = rolling_corr.dropna().iloc[-1] if len(rolling_corr.dropna()) > 0 else 0.0
correlation_data[stock1][stock2] = latest_corr
# Create correlation matrix
correlation_matrix = pd.DataFrame(correlation_data)
# Create heatmap
plt.figure(figsize=(12, 10))
sns.heatmap(correlation_matrix, annot=True, cmap='RdYlBu_r', center=0,
square=True, fmt='.3f', linewidths=0.5,
cbar_kws={'label': 'Correlation Coefficient'})
plt.title('Nifty 50 Top 8 Stocks - 20 Day Correlation Matrix',
fontsize=16, fontweight='bold', pad=20)
plt.xticks(rotation=45, ha='right')
plt.yticks(rotation=0)
plt.tight_layout()
plt.show()
return correlation_matrix
if __name__ == "__main__":
corr_matrix = main()Visualization
Creates professional heatmap using color-coded correlation coefficients
Red colors indicate strong positive correlation (stocks move together)
Blue colors show weak/negative correlation (stocks move independently)
Displays exact correlation values on each cell for precise analysis
Key Features
Smart Data Processing
60-day lookback for robust correlation calculation
Rate limiting with 1-second delays between API calls
Error handling continues analysis if individual stocks fail
Data alignment ensures all stocks use identical trading dates
Professional Output
Publication-quality heatmap visualization
Intuitive color scheme for immediate insight recognition
Proper labeling with rotated stock symbols
Statistical annotations showing exact correlation values
Business Applications
Portfolio Management
Diversification Analysis: Identify low-correlation stocks for risk reduction
Sector Clustering: Understand which stocks move together
Risk Assessment: Quantify asset relationships during market events
Trading Insights
Pairs Trading: Find stocks with stable correlation patterns
Hedge Selection: Choose assets with negative correlation for hedging
Sector Rotation: Time market entry based on correlation changes
Interpretation Guide
High Correlation (>0.8)
Banking stocks (HDFC, ICICI, SBI, AXIS) typically cluster together
IT stocks (TCS, INFY) often show strong correlation
Limited diversification benefits
Moderate Correlation (0.4-0.7)
Related business models with some independence
Balanced portfolio exposure opportunities
Sector interconnectedness
Low Correlation (<0.4)
Strong diversification potential
Independent price movements
Risk mitigation opportunities
Technical Approach
Correlation Method
Uses OpenAlgo's
ta.correlation()with 20-day rolling windowCaptures current market conditions vs. historical averages
Updates dynamically with latest price movements
Data Quality
Validates sufficient data points for meaningful correlation
Handles missing data gracefully
Ensures temporal consistency across all assets
Limitations
Linear relationships only: Doesn't capture non-linear correlations
Historical bias: Past patterns may not predict future relationships
Market regime sensitivity: Correlations change during bull/bear markets
NSE trading hours: Analysis limited to exchange operating sessions
Use Cases
For Portfolio Managers: Optimize asset allocation and reduce concentration risk
For Traders: Identify pair trading opportunities and market relationships
For Risk Analysts: Monitor portfolio correlation exposure and stress test scenarios
For Researchers: Study sector dynamics and market structure relationships
This application transforms complex statistical relationships into actionable investment insights through intuitive visual analysis.
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