Concept family
Performance, Backtesting & Validation
Return, risk and trade metrics plus the anti-overfitting methodology — the honesty layer.
Validation is where a trading idea meets evidence. This family collects the metrics used to summarize performance, such as win rate and the Sharpe ratio, alongside the methodology that keeps those numbers honest: testing on data the strategy has never seen and checking that results survive small changes in settings.
The recurring theme is that an impressive backtest is easy to produce and easy to misread. A high win rate can coexist with poor risk-reward, a strong Sharpe ratio can mask tail risk, and any metric can be inflated by fitting parameters to history. The entries here explain what each number does and does not tell you, and how to structure tests so a good result is more likely to reflect a real effect than a curve fit.
Start here
Win Rate
Why the percentage of winning trades says little on its own, and how to read it against average win and loss size.
3 indicators
In-sample / Out-of-sample Split
The basic defense against overfitting: tune a strategy on one stretch of history and judge it on another.
1 indicators
Sharpe Ratio
The classic risk-adjusted return measure, and the situations where it flatters or understates a strategy.
1 indicators
Return/risk metrics
Measures that weigh returns against the risk taken to earn them, with the Sharpe ratio as the standard reference point.
Trade metrics
Statistics built from individual trade outcomes, where a number like win rate only becomes meaningful next to average win and loss size.
Validation methodology
Procedures that help separate genuine edges from curve fits, including holding out unseen data and checking stability across nearby parameter values.