Rule Significance Testing

This page covers programmatic usage of Rule Significance Testing via Jesse's research module, making it easy to run the test in Python scripts and Jupyter Notebooks.

TIP

For a full conceptual introduction — including the null hypothesis, how bootstrapping works, and when to use this feature — see the main Rule Significance Testing documentation.

Available functions

• rule_significance_test — runs the full two-phase analysis and returns the observed mean return, the distribution of simulated returns, and the p-value.
• plot_significance_test — renders a histogram of the simulation results with the observed mean marked, so you can visualise where your rule stands relative to the null distribution.

TIP

plot_significance_test requires matplotlib. Install it with pip install matplotlib if you do not already have it.

Next steps

• Usage Example — complete runnable scripts for strategies with and without a genuine edge