Strategy Validation
Strategy validation is the discipline of proving a strategy works before you risk capital on it. I write about the complete validation funnel: walk-forward optimization, bias auditing, robustness checks, and the statistical tests that separate genuine edge from noise. A strategy that cannot survive this gauntlet has no business running in production.
Metamorphic Relations for Backtests: Testing the Engine, Not the Strategy
Backtests have a testing problem: you often do not know the correct output. Metamorphic relations sidestep that by asking whether the output changes correctly when the input is transformed. The full …
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. The full taxonomy piece now lives at Referential Labs, along with the rest of my tooling-adjacent quant writing. This page …
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 are themselves easy …
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 …
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 …