Alpha Zoo

OrganizationPopular

HKUDS

alpha-zoo

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.

Overview

Publisher	HKUDS
Repository	`Vibe-Trading`
Skill name	`alpha-zoo`
Stars	33.6K
Forks	5.5K
Bundled files	Instructions only
License	MIT
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by HKUDS on GitHub. Read the source before you install it.

Installation

Install the Alpha Zoo AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/HKUDS/Vibe-Trading.git /tmp/Vibe-Trading
mkdir -p .claude/skills
cp -r /tmp/Vibe-Trading/agent/src/skills/alpha-zoo .claude/skills/alpha-zoo

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Alpha Zoo in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Alpha Zoo on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Alpha Zoo is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

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).

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

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.

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

Frequently asked questions

What does the Alpha Zoo AI skill do?

Why use Alpha Zoo on TypingMind?

Because you install it once and use it with any model. Alpha Zoo is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Alpha Zoo in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/HKUDS/Vibe-Trading/tree/main/agent/src/skills/alpha-zoo. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Alpha Zoo?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Alpha Zoo?

As many as you like. As long as a model supports skills, you can use Alpha Zoo with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Alpha Zoo AI skill free?

Yes. It is published on GitHub by HKUDS under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Alpha Zoo

Purpose

Tools Available

Decision Tree

Zoo Inventory

Constraints

Common Pitfalls

Reference

Frequently asked questions

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