Writing
All Articles
Design notes, engineering trade-offs, and lessons from building AI systems and data products.
Rigorous Audit Rules for Quantitative Backtesting
A systematic guide to backtesting integrity: causality enforcement (shift(1)), point-in-time financial data, cost sensitivity stress testing, Walk-forward isolation, and sample-level look-ahead prevention.
Engineering a Trustworthy AI Quantitative Research System
Building a robust quantitative research platform with state machine persistence, data versioning, and auditable execution across collection, factor mining, backtesting, and learning.