Explore bundled libraries of investment factors, signals used to compare securities, and inspect individual formulas and metadata. Evaluate a factor or an entire library across a chosen universe and period, producing aggregate performance statistics and an HTML report.
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Browse and bench the bundled alpha zoos — prebuilt cross-sectional factor libraries (Kakushadze 101, GTJA 191, Qlib 158, Fama-French / Carhart). Use when the user asks "which alphas exist", wants metadata on a named alpha, or wants to run IC/IR on a whole zoo over a universe.
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When the user asks about prebuilt cross-sectional alphas — Kakushadze 101, GTJA 191, Qlib 158, Fama-French / Carhart — or wants to bench a whole zoo on an investable universe (CSI 300, S&P 500, BTC-USDT, ...), this skill orients you. The zoo is the curated library; the bench is the evaluator.
| Tool | When to use |
|---|---|
alpha_zoo | Browse the library. action=list_alphas to enumerate (filterable by zoo / theme / universe), action=get_alpha for one alpha's metadata, action=health for registry load status. |
alpha_bench | Run IC / IR on one alpha or a whole zoo over a universe + period. Emits an HTML report. |
factor_analysis | Ad-hoc factor evaluation from a user-supplied factor CSV + return CSV. Use this when the user has their own factor (not in the zoo). |
alpha_zoo with action=list_alphas, theme=momentum.alpha_zoo with action=get_alpha, alpha_id=gtja191_alpha_001.alpha_bench with zoo=gtja191, universe=csi300, period=2020-2024.alpha_zoo with action=health — surfaces loaded, failed, and per-error reasons.my_factor.csv → factor_analysis (zoo tools are for prebuilt alphas only).| Zoo | Description | Approx. count |
|---|---|---|
kakushadze101 | Formulaic alphas from Kakushadze's 2015 paper. Mix of momentum, reversal, volume, and microstructure. | ~101 |
gtja191 | Guotai Junan 191 alphas — A-share focused cross-sectional factors. | ~191 |
qlib158 | Microsoft Qlib's 158 alpha factors — features tuned for ML pipelines. | ~158 |
classical | Fama-French 3/5-factor + Carhart momentum. | <10 |
Counts are nominal; check alpha_zoo action=health for the live count currently loaded.
delta(df, d) requires d >= 1; the negative-shift Ref(df, -n) form does not exist. See docs/alpha-zoo/spec.md for the full operator catalogue.alpha_bench returns universe loader for X not yet implemented, that's the W2 scaffold — the universe is recognised but the data pull lands in W4.~/.vibe-trading/reports/ by default; refer to it by that shorthand, not by the resolved absolute path.alpha_zoo is read-only. alpha_bench writes a single HTML file per run — no scratch state elsewhere.list_alphas: theme / universe must match the alpha's declared metadata exactly (e.g. equity_cn, not cn or china).alpha_bench with both alpha_id and zoo set — they are mutually exclusive; pick one.loaded=0) means no zoo modules are populated yet; treat it as "zoos pending W3 porting" rather than a bug.docs/alpha-zoo/spec.mdsrc/factors/registry.py (frozen; do not modify)src/factors/factor_analysis_core.py아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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Compare a Chinese company's US depositary receipts, Hong Kong shares, and mainland shares after adjusting for exchange rates and share ratios. Analyze pricing gaps, valuation differences, and delisting risks to support research into its different listings.
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Retrieve stock prices, futures quotes, exchange rates, and economic indicators with the free Python library AKShare. The skill explains market-specific functions, symbol formats, and data preparation for analysis and testing strategies against historical data.
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Assess whether a Chinese A-share company may receive ST or *ST risk warnings next financial year. Review financial forecasts, dividends, audit opinions, and regulatory penalties, then report risk severity separately from confidence in the prediction.
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Plan portfolio allocations across assets using risk, correlations, return forecasts, and trading costs. Compare five optimization methods, produce settings for config.json, and define rebalancing rules that adjust holdings back toward their target weights.
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Diagnose failed or abnormal backtests, which test trading strategies on historical data. Inspect result files, strategy code, and configuration to identify bugs, make targeted repairs, rerun tests, and verify the updated metrics.
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Use investor psychology to examine trading signals and risk controls, especially in China A-shares. The skill provides sentiment measures, checklists for biased decisions, and frameworks for assessing momentum, which follows trends, and reversal strategies.
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