Quant & Markets
Quantitative research, market data, backtesting, and trading-system engineering
Technical Blog
Practical notes on automation, deployment, and developer workflows
Right now the site is grounded in three solid tracks: Docker and server deployment, Git workflow habits, and Markdown-driven publishing pipelines. AI automation content will expand on top of that, so the homepage now pushes topic hubs and flagship posts first instead of acting like a plain feed.
Recent Updates
Can You Trust a Win Rate When One Stock Keeps Appearing?
Ten synthetic stock picks yield 60%, 25%, and 83.3% win rates. Reproduce the denominators with Python and SQL, and check duplicate signals and T+5 settlement.
Why Two 80-Point Stock Signals Aren't Comparable: Simpson's Paradox and Calibration
Why identical raw scores cannot be pooled across stock screeners: a reproducible Simpson's paradox toy dataset, Wilson bounds, time leakage, and probability calibration.
52-Week Highs and Volume: Which Stocks Can a Screener Rule Out?
A reproducible six-stock example and a real filter-versus-sort mistake show how price position, volume, and RVOL narrow a research watchlist without predicting returns.
How to Archive Quant Research Data: PostgreSQL PITR, R2, and Reproducible Backtests
When does quant research need R2 beyond PostgreSQL backups? A practical analysis of TOAST, MVCC, manifests, fencing, integrity checks, and point-in-time data versions.
How I Validate Quant Factors with Bucket Spread and Rank IC
A factor that works at 5D may fail at 1D or 3D. This public ETF reproduction tests Bucket Spread, Rank IC, multiple horizons, sampling sensitivity, and factor redundancy.
How to Reweight a Multi-Factor Model Safely: Regimes, Shadow Models, and Weight Drift
A factor passing validation does not mean its latest-window estimate should immediately become production weight. This reproducible regime-switching experiment compares Immediate, Delta Cap, and Shadow-first policies across false promotions, turnover, and adaptation lag.