FX Statistical Arb

We use: 1) Cointegration testing (Engle-Granger methodology) finding historically linked pairs, 2) Half-life analysis measuring mean-reversion speed, 3) Regime-specific correlation matrices, and 4) Liquidity scoring ensuring executable spreads. Pairs like AUD/NZD, EUR/CHF, and CAD/MXN show persistent statistical relationships.

Core models include: 1) Ornstein-Uhlenbeck processes for mean-reversion modeling, 2) Kalman filters dynamically updating hedge ratios, 3) Bollinger Band percentile scoring identifying extreme deviations, and 4) Monte Carlo simulation validating strategy robustness. All models incorporate transaction cost analysis.

Our protocols feature: 1) Volatility-adjusted position sizing, 2) Correlation stop triggers exiting at 2σ divergence, 3) Liquidity contingency plans, and 4) Fundamental catalyst filters pausing strategies before events. Stress testing includes 2015 CHF unpeg and 2020 March crisis scenarios.

We employ: 1) Pair-synchronized order routing, 2) Spread-sensitive limit entry algorithms, 3) Volume-weighted participation strategies, and 4) Cross-venue liquidity aggregation. These achieve 92%+ fill rates with average slippage under 0.3 pips on major pairs.

Our adaptive framework uses: 1) Regime classification networks (RCNs) detecting shifts, 2) Dynamic hedge ratio recalibration, 3) Volatility-scaling of position bands, and 4) Machine learning forecasters predicting relationship persistence. Parameters update hourly without human intervention.

Key metrics: 1) Sharpe ratio consistency (>1.8 target), 2) Profit factor per trade (>1.4), 3) Maximum consecutives losses (