Live Forex Currency Pair Correlation Matrix: Statistical Arbitrage & Hedging Table

The Mathematics of Pearson Correlation in Currency Portfolios

Holding simultaneous long positions on EUR/USD and GBP/USD does not represent diversification—it concentrates your portfolio into a double short-dollar trade. The Pearson correlation coefficient measures linear co-movement between asset returns on a scale from -1.00 (perfect inverse relationship) to +1.00 (perfect direct co-movement):

# Pearson Correlation Formula
r_xy = Covariance(X, Y) / (StdDev(X) * StdDev(Y))

Current Major Institutional Correlation Heat Map (Rolling 90-Day Matrix)

Python Automated Correlation Matrix Engine

import MetaTrader5 as mt5
import pandas as pd
import numpy as np

def compute_forex_correlation(symbols: list, timeframe=mt5.TIMEFRAME_D1, bars: int = 90):
    mt5.initialize()
    close_data = {}
    
    for s in symbols:
        rates = mt5.copy_rates_from_pos(s, timeframe, 0, bars)
        if rates is not None and len(rates) > 0:
            df = pd.DataFrame(rates)
            # Logarithmic returns provide stationarity for statistical rigor
            df['log_ret'] = np.log(df['close'] / df['close'].shift(1))
            close_data[s] = df['log_ret']
            
    mt5.shutdown()
    returns_df = pd.DataFrame(close_data).dropna()
    correlation_matrix = returns_df.corr(method='pearson')
    return correlation_matrix

if __name__ == "__main__":
    pairs = ["EURUSD", "GBPUSD", "USDCHF", "USDJPY", "AUDUSD", "USDCAD"]
    matrix = compute_forex_correlation(pairs)
    print("=== 90-DAY FOREX CORRELATION MATRIX ===")
    print(matrix.round(2))

Risk Rules for Correlated Portfolios

• Correlation > +0.75: Treat two trades as a single exposure. Split your standard risk budget in half (e.g., 0.50% on EURUSD and 0.50% on GBPUSD).
• Correlation < -0.75: Buying EURUSD and buying USDCHF simultaneously cancels directional return while doubling broker commission and swap spread fees.