Quantitative Finance
Quantitative finance is my core domain, shaped by over two decades building systems at firms like BNP Paribas and Citadel. I write about the statistical methods, validation techniques, and engineering discipline that separate robust strategies from curve-fitted illusions. These articles are for quant developers who want rigor and for software engineers curious about what makes this field tick.
Premarket Dashboard: Design Specification
A programmatic premarket overview dashboard that combines market profile, volume profile, cash index context, expected moves, gamma exposure, and economic calendar into a tabbed single-page …
Metamorphic Relations for Backtests: Testing the Engine, Not the Strategy
You often don’t know the “correct” output of a backtest. But you know relationships that must hold when you transform the inputs. Increase fees, performance should drop. Scale …
Monte Carlo Permutation Tests for Strategy Significance: Is Your Alpha Real or Random?
Shuffle your signal, re-run the backtest 10,000 times, see if your strategy beats random. Permutation tests provide a distribution-free way to assess strategy significance. This article covers the …
Bootstrap Methods for Strategy Robustness: Resampling When You Can't Get More Data
You have one history of market data. Your strategy was designed on that history. How do you estimate performance on data you haven’t seen? Bootstrap resampling generates synthetic histories that …
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, …
Walk-Forward Optimization: Anchored vs. Rolling Windows and When Each Fails
Walk-forward validation is the backbone of out-of-sample testing for trading strategies. But the choice of window type, window length, and step size introduces meta-parameters that can themselves be …
Autocorrelation and What It Means for Your Backtest P&L
Autocorrelated returns inflate Sharpe ratios, invalidate standard significance tests, and make backtests look better than reality. This article explains why strategy returns are almost always …
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 …
From Hypothesis to Production: A Quant's Productivity Toolkit
Productivity in quantitative work isn’t about doing things faster. It’s about knowing when to stop. A walkthrough of the tools and stages I use to take a trading strategy hypothesis from …