An institutional-grade, object-oriented framework for active portfolio management rooted in the Grinold & Kahn Fundamental Law. Unlike standard backtesters, this engine enforces strict Point-in-Time (PIT) logic to eliminate look-ahead bias, utilizing a custom ETL pipeline to merge ragged CRSP and Compustat data. It features a Fundamental Factor Risk Model (Fama-MacBeth), Gram-Schmidt alpha purification, and a convex optimizer that solves for maximum Information Ratio while controlling turnover.

Tech Stack: Python, Pandas, NumPy, Statsmodels, SciPy.

Figure 1: Cumulative Active Return of Financial Constraints Signal (1995–2024) 

A next-generation alpha factor designed to decouple "Financial Distress" from traditional accounting ratios. While standard indices like Whited-Wu rely on backward-looking balance sheet data, this model leverages Large Language Models (Google Vertex AI) to parse the "context" of management sentiment in MD&A disclosures.

The Edge: The resulting signal exhibits a correlation of 0.00 with accounting benchmarks, offering a source of purely orthogonal alpha. In out-of-sample testing (2022–2023), the strategy demonstrated "All-Weather" robustness: acting as a Crisis Hedge during the 2022 inflation bear market (+1.0% vs -4.0% Market) while capturing Idiosyncratic Growth during the 2023 AI bull run (+2.5%).

Methodology: The system decomposes distress into specific drivers ("Debt" vs. "Equity" constraints), proving that holistic LLM scoring outperforms simple keyword extraction. 

Tech Stack: Python, Google Vertex AI (PaLM/Gemini), SEC EDGAR, Statsmodels (Orthogonalization), CVXPY.

A discrete-event simulation engine designed to stress-test hedging strategies under extreme liquidity constraints. Unlike static "Day-1 Greek" models, this framework analyzes path-dependent risks, specifically the "Convexity Trap"—the structural P&L bleeding that occurs when hedging long-dated liabilities with short-dated assets. The engine models endogenous transaction costs (dynamic bid-ask spreads based on volatility regimes), margin loan funding topologies, and the efficacy of "Lazy Hedging" (risk limits) during market crashes.

Tech Stack: Python, NumPy, SciPy, Matplotlib.