README.md

April 14, 2026 · View on GitHub

Alpha Skills

Your AI-Powered Senior Quant Researcher.
Discover alpha. Evaluate factors. Monitor decay. Backtest strategies.
All through natural language — in any AI coding assistant.

Quick Start · What Can It Do · Multi-Market · Skills · Contribute

Hiring a quant researcher costs $300K/year. This one is free, open-source, and works 24/7.

Alpha Skills turns any AI coding assistant into a senior quantitative researcher. It discovers factors, evaluates them with institutional-grade methodology (IC/ICIR/quintile/robustness), monitors for alpha decay, and runs multi-factor backtests — all from a single sentence.

招一个量化研究员年薪百万。这个免费、开源、7×24小时工作。

Alpha Skills Demo

What Can It Do

You say one sentence. It does the rest.

You:  "Evaluate the price-volume divergence factor"
AI:   📊 IC Mean=0.066 | ICIR=0.696 | Rating: ⭐ Strong
      Quintile spread monotonic. Best holding period: 20 days.
      Report saved → output/eval_pv_diverge.png

You:  "Mine 50 candidate factors and show me the best ones"
AI:   ⛏️ Scanned 50 candidates → 12 passed IC screen
      Top: PV divergence 20d (ICIR=0.70), Low downside vol (ICIR=0.53)...
      Register to library?

You:  "Backtest using my top 3 factors"
AI:   📈 Sharpe=0.74 | MaxDD=-13.9% | Profit Factor=2.24
      Gate check: ✓ PF>1 ✓ MDD>-25% ✗ Sharpe<1.0

No boilerplate. No notebooks. No 200 lines of pandas. Just results.

Skills Reference

Skill	What It Does	Try Saying
🔍 alpha-discover	Design factors from natural language	"find me a low-volatility factor"
📊 alpha-evaluate	IC / ICIR / quintile / long-short / robustness	"evaluate reversal_5"
⛏️ alpha-mine	Auto-mine factor candidates, IC screen, rank	"mine 50 factors"
📚 alpha-library	Register, list, search, retire factors (SQLite)	"show my factor library"
📈 alpha-backtest	Single & multi-factor portfolio backtest	"backtest with pv_diverge + turnover"
🏥 alpha-monitor	Detect IC decay, crowding, regime shift	"check factor health"
📋 alpha-report	Panoramic, deep-dive, comparison reports	"generate factor report"
📡 alpha-signal	Daily trading signal — target portfolio output	"today's signals" / "生成信号"
🤖 alpha-autopilot	Autonomous loop: mine → evaluate → register → monitor → retire	"run autopilot" / "自动驾驶"

Quick Start

1. Get the skills

git clone https://github.com/VernonOY/alpha-skills.git

2. Load into your AI assistant

Platform	How
Cursor	Copy `skills/alpha-*/SKILL.md` → `.cursorrules`
Windsurf	Copy → `.windsurfrules`
Claude Code	`cp -r skills/alpha-* ~/.claude/skills/`
Any LLM	Paste SKILL.md as system prompt

3. Install Python deps

pip install pandas numpy scipy matplotlib pyarrow
pip install tushare    # A-share
pip install yfinance   # US / HK

4. Talk to it

"evaluate the momentum_20 factor"
"mine volatility factors"
"backtest my top 3 factors, 2022 to 2025"

Works out of the box for three markets. Auto-adapts trading rules per market:

	A-share 🇨🇳	Hong Kong 🇭🇰	US 🇺🇸
Data	Tushare Pro	Yahoo Finance	Yahoo Finance
Price Limit	±10%	None	None
T+N	T+1	T+0	T+0
Cost	0.3%	0.2%	0.1%
Benchmark	CSI 300	HSI	S&P 500
Pool	5000+ stocks	78 HSI constituents	143 S&P 500

Switch markets in one line:

MARKET: US
DATA_MODULE: examples.us_data_yfinance

Bring your own data. Write a 7-function Python adapter for Bloomberg, AkShare, Binance, or any source — see interface spec.

How It Works

┌─────────────────────────────────────────────┐
│           You (natural language)             │
├─────────────────────────────────────────────┤
│          AI Coding Assistant                │
│   (Cursor / Windsurf / Claude Code / ...)   │
├─────────────────────────────────────────────┤
│           Alpha Skills (7 SKILL.md)         │
│  discover · evaluate · mine · library       │
│  backtest · monitor · report                │
├─────────────────────────────────────────────┤
│     Python (pandas/numpy/scipy/matplotlib)  │
│     → factor computation                    │
│     → IC/ICIR/quintile evaluation           │
│     → portfolio backtesting                 │
│     → SQLite factor registry                │
├─────────────────────────────────────────────┤
│  Data: Tushare │ YFinance │ CSV │ Custom    │
└─────────────────────────────────────────────┘

Zero framework dependency. Each skill is a self-contained Markdown file. The AI reads it, writes the Python, runs it. Nothing to install except standard data science packages.

Evaluation Pipeline

Your AI quant researcher doesn't just compute IC. It runs a 4-level institutional-grade evaluation:

Level	What	Speed
L0	Syntax + data validation	instant
L1	Quick IC screen (sampled 200 stocks × 2 years)	<30s
L2	Full: IC series, ICIR, quintile returns, long-short, monotonicity	1-3 min
L3	Robustness: parameter perturbation, rolling window, start-date sensitivity	5-15 min

Plus optional qtype pre-flight — static analysis to catch look-ahead bias before you waste compute on fake alpha.

Factor Mining Engine

`alpha-mine$ \text{systematically} \text{searches} \text{the} \text{factor} \text{expression} \text{space}:

3 \text{mining} \text{strategies}:

\text{Template}-\text{based} — \text{momentum}, \text{mean}-\text{reversion}, \text{volatility}, \text{volume}, \text{composite} \text{templates} \times \text{multiple} \text{window} \text{sizes}
\text{Combinatorial} — \text{chain} \text{operators}: $cs_rank(ts_corr(close, volume, 20))`
Mutation — take a known strong factor, mutate parameters/operators

Pipeline: Generate 50+ candidates → IC quick screen → full evaluate top 10 → LLM judges economic intuition → present ranked results

Overfitting guard: Every surviving factor gets an economic intuition score (Strong / Moderate / Weak). Factors without a clear behavioral story are flagged as potential data mining.

Built-in Factors (25+)

Category	Factors
Price-Volume	momentum · reversal · volatility · pv_diverge · rsi · macd · bollinger · atr_ratio · turnover · abnormal_turnover
Fundamental	roe · roa · gross_margin · net_profit_growth · revenue_growth
Valuation	pe_ttm · pb · ps_ttm · dividend_yield · peg
Composite	quality_score · value_score · growth_momentum

All gate checks and evaluation thresholds are user-configurable:

GATE_SHARPE: 1.0
GATE_MAX_DRAWDOWN: -0.25
GATE_PROFIT_FACTOR: 1.0
EVAL_ICIR_STRONG: 0.5

What's New

v0.3 — Autopilot & Live Signals

alpha-signal: daily trading signal generator — outputs target portfolio from active factors
alpha-autopilot: autonomous research loop — auto-mine, evaluate, register, monitor, retire
Professional knowledge base: 6 expert-level reference documents (2,795 lines)

v0.2 — Automated Factor Mining

alpha-mine: systematically search factor expression space, IC screen, economic intuition scoring
All skills fully self-contained — zero external package dependencies
Optional qtype pre-flight check

v0.1 — Initial Release

7 core skills · A-share/HK/US support · bilingual EN/ZH · multi-platform