Blog
Writing on systematic trading
Precise, opinionated writing on backtesting methodology, statistical validity, and the craft of building durable systematic strategies.
Methodology17 May 2026
Why most backtests overstate edge — and what to do about it
The predictable reasons that backtested performance doesn't translate to live trading, and the statistical tools that help separate real edge from noise.
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Practice17 May 2026
A checklist for evaluating whether a backtest result is worth trusting
Six criteria — from result hash to parameter sensitivity — for distinguishing credible backtests from sophisticated noise.
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Methodology17 May 2026
What Backtesting Actually Measures and What It Does Not
A backtest is a precise measurement of one historical path, not an estimate of future returns, and the distinction governs every downstream decision.
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Practice17 May 2026
The Difference Between Gross and Net Returns
A precise breakdown of the cost stack separating paper backtest performance from realized account returns in systematic strategies.
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Methodology17 May 2026
Why You Need More Data Than You Think
Backtest row counts mislead; the statistical sample size that governs strategy validation is far smaller than most researchers assume.
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Practice17 May 2026
Reading an Equity Curve: What Smooth Actually Means
Equity-curve smoothness is at least five distinct properties; conflating them is how overfit systems pass review and fail in production.
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Practice17 May 2026
How Transaction Costs Silently Destroy Strategy Edge
A precise look at how spreads, impact, and turnover compound against backtested alpha and why most cost models understate the damage.
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Practice17 May 2026
Position Sizing Is Not Optional — It Changes Everything
Why position sizing determines whether a positive-expectancy system survives, and the three-layer sizing stack worth implementing.
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Methodology17 May 2026
The Multiple Testing Problem, Explained Without Statistics
Why testing many strategies guarantees you will find a fake winner, and what the inflation of false discoveries actually looks like in practice.
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Practice17 May 2026
Walk-Forward Analysis in Practice: A Worked Example
A concrete implementation of walk-forward analysis on a momentum strategy, with diagnostics that matter more than the aggregate Sharpe.
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Methodology17 May 2026
Why Optimising for Sharpe Ratio Produces Fragile Strategies
Selecting strategies on Sharpe ratio systematically favours overfit, negatively-skewed configurations whose apparent quality is an artefact of the metric.
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Practice17 May 2026
How to Build a Parameter Sensitivity Heatmap
A practical method for projecting strategy performance across two parameters to distinguish robust edges from overfit point estimates.
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Methodology17 May 2026
In-Sample Performance Is Not Evidence of Edge
Backtests fit on the data used to design them measure optimization artifacts, not strategy edge, and require multiplicity-adjusted out-of-sample validation.
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Methodology17 May 2026
DSR in Practice: How to Count Your Trials Honestly
A practical guide to applying the Deflated Sharpe Ratio by counting trials honestly, including correlated trials and pre-registered budgets.
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Methodology17 May 2026
CPCV vs Walk-Forward: When to Use Each
A practical comparison of combinatorial purged cross-validation and walk-forward analysis, with guidance on sequencing them in a research workflow.
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Statistics17 May 2026
The Mathematics of Overfitting: Degrees of Freedom Explained
A formal accounting of how parameters, trials, and implicit choices consume statistical power and inflate backtested performance metrics.
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Methodology17 May 2026
Regime-Conditional Strategy Evaluation
Pooled performance metrics hide the conditional distributions that matter; regime-conditional evaluation separates strategy quality from historical regime-mix luck.
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Statistics17 May 2026
Why Mean-Reversion Sharpe Ratios Are Almost Always Overstated
Negative autocorrelation and bid-ask bounce systematically inflate the naive Sharpe ratio of mean-reversion strategies, often by a factor of two.
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Practice17 May 2026
Kelly Criterion in Practice: Why Everyone Uses Fractional Kelly
Full Kelly is mathematically optimal under perfect information; fractional Kelly is what survives the gap between backtested edge and forward reality.
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Practice17 May 2026
Building a Strategy That Survives the Deflated Sharpe Test
A practical workflow for designing systematic strategies whose backtested Sharpe ratios survive correction for selection bias and higher moments.
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