signal-validation

To statistically validate a trading signal, you can apply hypothesis testing and confidence intervals to historical trade data to compute p-values and assess profitability before live deployment.

A Wilson confidence interval quantifies win-rate uncertainty by calculating a score interval around your signal's historical trade outcomes, helping you determine if observed profitability is statistically significant or due to chance.

Walk-forward analysis detects overfitting by evaluating trading signals across out-of-sample data, ensuring that the predictive power and performance outcomes hold up beyond the initial historical backtest data.

You set Go/No-Go criteria by predefining thresholds for p-values, sample sizes, and expected value, then using binomial tests and conditional probability assessments to decide if a signal meets production readiness.

No external statistical libraries are required because this signal validation framework implements standard statistical routines natively in Rust, computing p-values and confidence intervals without any dependencies.

Information coefficient measures trading signal predictive power by correlating predicted signal values with actual historical performance outcomes, providing a quantitative assessment of the signal's forecasting ability.