Benchmark cross-sectional alpha factors with IC and IR metrics.
npx skills add https://github.com/hxhyyy/Vibe-Trading --skill alpha-zoo-hxhyyy
Please help me install this Agent Skill. Skill: alpha-zoo Source: https://github.com/hxhyyy/Vibe-Trading/tree/main/agent/src/skills/alpha-zoo Command: npx skills add https://github.com/hxhyyy/Vibe-Trading --skill alpha-zoo-hxhyyy
This skill solves the complexity of managing and evaluating large libraries of cross-sectional alpha factors, allowing traders to quickly identify predictive signals without manual coding.
Use the alpha-zoo skill to run a performance benchmark of the gtja191 library on the csi300 universe for the period 2020 to 2024.
High-intent search queries and answers about installing and using this skill.
To benchmark cross-sectional alpha factors, you run IC and IR analysis on a defined market universe over a specific time period. This evaluates the statistical performance of predictive signals without lookahead bias to identify viable trading indicators.
Available prebuilt quantitative trading factor libraries include Kakushadze 101, GTJA 191, and Qlib 158. You can browse and filter these cross-sectional alpha factor libraries to access predictive signals for immediate performance evaluation.
IC and IR analysis evaluates predictive trading signals by computing statistical metrics against specific market universes. This benchmarking measures the predictive strength and consistency of cross-sectional alpha factors without lookahead bias.
Yes, you can evaluate momentum factors from the GTJA 191 library on the CSI 300 index. You define the market universe and time period, such as 2020 to 2024, to run performance benchmarks on those specific quantitative signals.
No, you do not need to manually code backtesting logic to analyze alpha factors. The system integrates with an internal registry and factor analysis modules to compute IC and IR metrics automatically, streamlining quantitative research workflows.
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