Cross-Asset Momentum Strategy

Designed and implemented a systematic momentum strategy operating across equity index, fixed income, and commodity futures. The framework combines multiple lookback horizons with volatility-scaled position sizing to capture persistent trends while managing tail risk.

Research Approach

The strategy was developed through a structured research process: hypothesis formulation grounded in behavioral finance literature, followed by rigorous out-of-sample testing with conservative transaction cost assumptions. Signal construction uses a blend of time-series momentum signals across multiple horizons, avoiding overfitting to any single parameter set.

Portfolio Construction

Positions are sized using an inverse-volatility weighting scheme with an overall portfolio risk target. Correlation-aware allocation ensures diversification benefits are realized across asset classes. The framework includes dynamic leverage adjustment based on realized portfolio volatility relative to the target.

Execution & Infrastructure

Built on a modular Python-based backtesting and live-trading infrastructure. The system handles signal generation, position sizing, order management, and reconciliation. Execution is managed through a scheduling layer that sequences trades to minimize market impact.

Risk Management

Integrated stop-loss mechanisms at both the instrument and portfolio level. The strategy includes drawdown-based deleveraging rules and exposure caps per sector. All risk parameters were calibrated using historical stress periods rather than optimized in-sample.