Yong Zhang, Jiahao Li, Xingyu Yang, and Hong Lin. "Aggregating exponential gradient expert advice for online portfolio selection under transaction costs" Journal of the Operational Research Society, , 2023, 74(8): 1940-1953.
This project addresses the real-world portfolio optimization problem, going beyond classical mean-variance models. Actual portfolio construction involves discrete investment decisions, transaction costs, and monitoring constraints, making the problem a Mixed-Integer Optimization (MIO) challenge that is computationally intractable at scale
Intraday mean‑reversion trading strategy with realistic execution costs, turnover caps, and walk‑forward backtesting.
ML regime detection for adaptive offensive/defensive factor allocation in US equities (1980–2024) — reproducible pipeline & cost-aware backtests.
End-to-End Python implementation of Devanathan et al.'s (2026) ADMM-based distributed optimization for institutional market impact mitigation. Features 3/2-power transaction cost modeling, proximal operator calculus, VAR(1) alpha generation, and 25-year walk-forward validation, via backtesting, across 434 assets.
PyTorch Dueling-DDPG for American put option hedging under transaction costs (0%/3%), benchmarked vs delta hedging. Project: Oct 2024–Oct 2025.
Implementation of the Gârleanu–Pedersen dynamic trading framework with transaction costs, factor-based return prediction, and reinforcement learning (PPO) for adaptive portfolio optimization and backtesting.
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