Data Pipelines
A trading strategy is only as good as the data flowing through it. I write about building pipelines that ingest market data, compute signals, and feed validation frameworks, all with the reliability guarantees that financial systems demand. These articles cover the engineering patterns I use to ensure data integrity from source to decision.
A Taxonomy of Backtest Lies: Survival Bias, Lookahead Bias, and the Rest
Every backtest is biased. The question is how badly and in which direction. This article catalogs the biases that corrupt backtesting results, from survivorship and lookahead to time-period, …
Stationarity Testing for Strategy Signals: ADF, KPSS, and Why Your Backtest Depends on It
A strategy built on a non-stationary signal is a strategy built on sand. This article covers the statistical tests that detect non-stationarity (ADF, KPSS, Phillips-Perron, Zivot-Andrews), explains …
Property-Based Testing Meets Financial Data: Turning Market Invariants into Executable Specifications
Property-based testing generates random inputs and checks invariants. Financial markets are full of invariants: non-negative spreads, consistent OHLC bars, monotonic timestamps. This article shows how …
Parsing Market Data: A Practical Guide for Quant Developers
Market data comes in many formats: FIX messages, exchange feeds, CSV exports, JSON APIs. Parsing is how you turn raw bytes into validated domain objects. This guide covers parsing fundamentals with …
An Ode To ISO 8601
A ISO 8601 guide to date, time, duration, interval formats and more for working developers.