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alpha-zoo

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.

See how to install

Good for

  • Find bundled momentum or reversal factors
  • Inspect a named factor's formula and metadata
  • Compare a factor library across a chosen universe and period
  • Check which factor libraries loaded successfully

Files included (1)

  • SKILL.md3.7 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

Alpha Zoo

Purpose

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.

Tools Available

ToolWhen to use
alpha_zooBrowse 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_benchRun IC / IR on one alpha or a whole zoo over a universe + period. Emits an HTML report.
factor_analysisAd-hoc factor evaluation from a user-supplied factor CSV + return CSV. Use this when the user has their own factor (not in the zoo).

Decision Tree

  • "list all momentum alphas" → alpha_zoo with action=list_alphas, theme=momentum.
  • "show me gtja191_alpha_001" → alpha_zoo with action=get_alpha, alpha_id=gtja191_alpha_001.
  • "bench all of GTJA 191 on CSI 300 from 2020 to 2024" → alpha_bench with zoo=gtja191, universe=csi300, period=2020-2024.
  • "is the registry healthy" → alpha_zoo with action=health — surfaces loaded, failed, and per-error reasons.
  • User uploads my_factor.csv → factor_analysis (zoo tools are for prebuilt alphas only).

Zoo Inventory

ZooDescriptionApprox. count
kakushadze101Formulaic alphas from Kakushadze's 2015 paper. Mix of momentum, reversal, volume, and microstructure.~101
gtja191Guotai Junan 191 alphas — A-share focused cross-sectional factors.~191
qlib158Microsoft Qlib's 158 alpha factors — features tuned for ML pipelines.~158
classicalFama-French 3/5-factor + Carhart momentum.<10

Counts are nominal; check alpha_zoo action=health for the live count currently loaded.

Constraints

  • No per-stock per-date factor values are surfaced to the agent. IC results are aggregate stats (mean / std / IR / positive-ratio); the HTML report shows top-N by IR plus formulas, never the underlying panel.
  • Lookahead is banned in the operator set. 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.
  • Universe loaders may not be wired for every market yet. When 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.
  • Do not expose absolute filesystem paths in agent output. The bench tool writes to ~/.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.

Common Pitfalls

  • Filter mismatch on list_alphas: theme / universe must match the alpha's declared metadata exactly (e.g. equity_cn, not cn or china).
  • Calling alpha_bench with both alpha_id and zoo set — they are mutually exclusive; pick one.
  • Empty registry (loaded=0) means no zoo modules are populated yet; treat it as "zoos pending W3 porting" rather than a bug.

Reference

  • Operator catalogue: docs/alpha-zoo/spec.md
  • Registry contract: src/factors/registry.py (frozen; do not modify)
  • IC / layered NAV math: src/factors/factor_analysis_core.py

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