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。
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.
| 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" |
git clone https://github.com/VernonOY/alpha-skills.git| 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 |
pip install pandas numpy scipy matplotlib pyarrow
pip install tushare # A-share
pip install yfinance # US / HK"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_yfinanceBring your own data. Write a 7-function Python adapter for Bloomberg, AkShare, Binance, or any source — see interface spec.
┌─────────────────────────────────────────────┐
│ 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.
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.
alpha-mine systematically searches the factor expression space:
3 mining strategies:
- Template-based — momentum, mean-reversion, volatility, volume, composite templates × multiple window sizes
- Combinatorial — chain 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.
| 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: 0.5v0.3 — Autopilot & Live Signals
alpha-signal: daily trading signal generator — outputs target portfolio from active factorsalpha-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
- 9 skills (discover / evaluate / mine / library / backtest / monitor / report / signal / autopilot)
- Daily signal generation (target portfolio output)
- Autonomous research loop (mine → evaluate → register → monitor → retire)
- Professional knowledge base (6 expert-level documents, 2,795 lines)
- A-share, HK, and US market support
- Market-aware trading rules
- Automated factor mining (template + combinatorial + mutation)
- Custom data source support
- Multi-platform (Cursor, Windsurf, Claude Code, ChatGPT, local models)
- qtype integration for static code checks
- Portfolio construction (factor → tradeable portfolio)
- Market regime detection & factor-regime mapping
- Factor crowding detection
- Web UI dashboard
Apache 2.0
See CONTRIBUTING.md — add skills, data adapters, or improve methodology.
Stop writing boilerplate. Start finding alpha.
Built by quants who got tired of copy-pasting the same IC calculation for the 500th time.