Generate, screen, and evaluate quantitative factors using predefined templates.
npx skills add https://github.com/VernonOY/alpha-skills --skill alpha-mine
Please help me install this Agent Skill. Skill: alpha-mine Source: https://github.com/VernonOY/alpha-skills/tree/main/skills/alpha-mine Command: npx skills add https://github.com/VernonOY/alpha-skills --skill alpha-mine
Automate the end-to-end process of generating, screening, and evaluating quantitative factors, reducing manual trial-and-error and accelerating research cycles.
Mine 50 candidate factors and present the top 10 results with IC/ICIR metrics.
High-intent search queries and answers about installing and using this skill.
Automated factor mining generates, screens, and evaluates candidate factors using momentum, mean reversion, volatility, and volume templates to produce backtest-ready results. It reduces manual trial-and-error by systematically calculating IC and ICIR metrics across structured templates.
IC and ICIR evaluation quantifies the predictive strength and consistency of candidate factors. The automated mining process calculates these metrics to identify robust factor candidates, ensuring you filter out weak signals before committing to full backtesting.
Yes, you can generate momentum and mean reversion factors using predefined templates. The automated mining process also supports volatility and volume templates, ensuring structured factor creation while flagging potential data-mining risks for safety.
You can register discovered factors to a factor library to monitor health and support multi-market use cases. The LLM-friendly workflow integrates factor registration directly after evaluation, enabling continuous tracking of IC and ICIR performance.
Using predefined templates for factor generation limits custom factor design to momentum, mean reversion, volatility, and volume patterns. While this ensures safety by preventing overfitting, it flags potential data-mining risks and restricts completely novel factor structures.
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