Skill UI

Guides building robust backtesting systems that avoid common pitfalls and deliver realistic performance estimates through event-driven execution, point-in-time data pipelines, cost modeling, and walk-forward validation for trading strategies.

Build features guided by data insights, A/B testing, and continuous measurement using specialized agents for analysis, implementation, and experimentation.

Production-ready dbt patterns covering model organization, testing, documentation, incremental builds, and workflow automation to standardize data transformation pipelines.

Guides quantitative analysts through building financial models, backtesting strategies with realistic assumptions, analyzing risk metrics, optimizing portfolios, and supporting statistical arbitrage using pandas, numpy, and scipy.

Guides hypothesis testing, regression, correlation, and Bayesian analyses with automated assumption checks, diagnostics, power planning, and APA-style reporting so researchers can choose proper tests, interpret effect sizes, and document academic findings.

This expert tool guides users through rigorous statistical analysis, specializing in A/B testing, hypothesis testing, and interpreting experimental results. It helps define sample sizes, executes appropriate tests (Z-test, T-test, Chi-square), and critically distinguishes between 'statistical significance' and 'practical significance' to ensure data-driven decision-making for product and business strategy.