Validate any A-share trading idea in one sentence.
简体中文 | English
You have a trading idea. MaoQuant generates a complete backtest report with equity curves, proving whether your strategy works -- programmatically.
npx skills add Fidingks/Mao-QuantThen tell your AI assistant:
/backtest ema-crossover SH600000
Done. Full report with charts, metrics, and trade log.
| Just ask like this... | What you get |
|---|---|
| "Can I make money on Moutai with a moving average strategy?" | Full backtest with equity curve and report |
| "Is CATL good for short-term trading? Try KDJ" | KDJ strategy backtest, every trade marked on chart |
| "Does buying on MACD golden cross actually work? Test it on Ping An" | MACD backtest with win rate, profit factor |
| "Find me stocks with PE below 15 and high volume" | Full-market scan, filtered stock list |
| "How much drawdown if I trade Bollinger Band bounces?" | Bollinger band backtest, max drawdown highlighted |
Talk to it like you'd talk to a quant-savvy friend. MaoQuant handles the rest.
| Strategy | Logic | Best For |
|---|---|---|
| EMA Crossover | Fast/slow EMA golden cross / death cross | Trending markets |
| RSI | Overbought / oversold reversal | Range-bound markets |
| MACD | DIF / DEA crossover | Medium-term trends |
| KDJ | Stochastic extreme values | Short-term swings |
| Bollinger Bands | Price touching upper/lower bands | Mean reversion |
Dual data engine -- choose what fits:
| Engine | Coverage | Setup |
|---|---|---|
| FaceCat API | A-shares, daily bars | Zero config, works out of the box |
| TDX (TongDaXin) | Full A-share, daily/1min/5min | Requires TDX client with local data |
Built-in data works immediately. No API key needed.
No configuration needed. MaoQuant enforces these automatically:
- T+1: Buy today, earliest sell is tomorrow
- Price Limits: Main board +/-10%, ChiNext/STAR +/-20%, BSE +/-30%
- Lot Sizing: Minimum 100 shares per trade
- Stamp Tax: 0.1% on sell side
- Commission: 0.025% both sides, minimum 5 CNY
MaoQuant follows the AI Skill Manifest specification. The skill system is fully self-describing:
skills/
SKILL.md # Root manifest with capabilities, contracts, environment
backtest/SKILL.md # Backtest skill (user-invocable)
scan/SKILL.md # Screening skill (user-invocable)
data/SKILL.md # Data engine reference
catquant-expert/ # Knowledge base + 6 rule files
catquant/ # Python engine (backtest, indicators, charts, data)
Key design decisions:
- BarSeries container:
get_history()returns aBarSerieswhosereprshows only a summary -- raw K-line data never leaks into AI context - Selftest:
python -m catquant.selftestvalidates the entire environment in 10 seconds - Contracts: T+1, price limits, fees, and lot sizing are enforced by the engine, not by prompts
pip install -r requirements.txt
cp .env.sample .env # Edit FaceCat_URL and TDX_DIR if needed
python -m catquant.selftestOpenClaw, Claude Code, Cursor, Windsurf, Copilot, Cline, OpenCode, Trae, and 40+ AI coding clients.
Built by the FaceCat Quantitative Research Team. Members from: DZH (LongRuan), East Money, Soochow Securities, GF Securities, Donghai Securities, Shanxi Securities, Xiangcai Securities, Huatai Securities, Hengtai Futures, Deutsche Bank.
We offer:
- Full-market data API -- A-share real-time and historical data with proprietary analytics
- Custom strategy development -- Bespoke backtesting solutions for your trading ideas
Contact us: https://www.jjmfc.com
Built by FaceCat Quantitative Research Team