Event-driven quantitative backtesting framework for China A-share market.
δΈζζζ‘£ Β· Docs Site Β· Tutorials Β· Doc Center Β· API Reference Β· Examples Β· FAQ
- Event-driven backtesting β
initializeβrun_dailyβhandle_data, compatible with JoinQuant / Zipline programming model - A-share data β daily OHLCV, minute K-lines, tick data, real-time quotes, fundamentals, money flow via free AKShare API
- Position management β buy/sell by shares, value, or target; automatic lot-size rounding (100 shares), commission calculation
- Risk analysis β Sharpe, Sortino, max drawdown, alpha/beta, Brinson attribution, Fama-French factor analysis
- Portfolio optimization β minimum variance, maximum Sharpe, risk parity
- Paper trading β run strategies live with real-time market data before going live
- PTrade/QMT adapter β one-click export to broker platforms
- Stock selection β periodic rebalancing with factor screening (ST/PB/PE/momentum filters, Top-N, multi-factor scoring)
- Utility library β 30+ technical indicators (MA, MACD, RSI, KDJ, Bollinger, ATR, ADX), statistical tools, position sizing (Kelly, ATR-based, fixed fractional)
- Reports β interactive HTML, chart PNG, Markdown, JSON with 20+ risk/return metrics
- Chainable stock screening β fluent API (
query/valuation/get_fundamentals) for fundamental analysis
pip install easyquant-eqlib # or: pip install -e . (from source)
python -c "from eqlib import *; print('eqlib OK')"
python examples/03_run_backtest.pyOpen the generated .html report in reports/ in your browser.
from eqlib import *
def initialize(context):
g.security = '601390'
set_benchmark('000300.XSHG')
run_daily(market_open, time='every_bar')
def market_open(context):
hist = attribute_history(g.security, 20, '1d', ['close'])
ma20 = hist['close'].mean()
price = hist['close'].iloc[-1]
if price > ma20 * 1.02:
order_value(g.security, context.portfolio.available_cash)
elif price < ma20 * 0.98 and context.portfolio.positions.get(g.security):
order_target(g.security, 0)
result = run_strategy(
initialize,
start_date='2024-01-01',
end_date='2024-12-31',
starting_cash=100000,
securities=['601390'],
)Execution model:
order*APIs queue orders in the current callback; they are filled at the next trading day's open to avoid look-ahead bias.Output: 4 files in
reports/β.pngchart,.htmlinteractive report,.mdsummary,.jsondata.
run_strategy generates an interactive HTML report plus PNG, Markdown, and JSON. Below are snapshots from real backtest runs.
| MACD Trend + Volume (600536) | Bollinger Mean Reversion (601088) | Support/Resistance (8 stocks) |
|---|---|---|
| +103.48% Β· 16 trades | +57.77% Β· 8 trades | +119.97% Β· 171 trades |
 |
 |
 |
| Momentum Portfolio (5 stocks) | Local Data (000768) |
|---|---|
| β25.69% Β· 52 trades | β33.28% Β· 16 trades |
 |
 |
How to read reports: header summary β metric cards (Sharpe, max drawdown, alpha) β K-line chart β cumulative returns vs benchmark β drawdown curve β daily P&L β trade/position tabs. Full guide: Report & Metrics.
EasyQuant/
βββ eqlib/ # Core library (backtest engine, data API, analysis)
βββ agent/ # AI optimization utilities
β βββ optimizer.py # Rule-based parameter search (reference)
β βββ audit_log.py # Structured JSONL + Markdown audit logging
β βββ strategy_template.py # Parameterized strategy template
βββ examples/ # 24 runnable example scripts
βββ tutorials/ # Step-by-step learning tutorials
β βββ prerequisites/ # Python, technical analysis, A-share basics
βββ doc/ # User manual, API reference, FAQ
βββ docs/ # GitHub Pages site source (MkDocs Material)
βββ tests/ # Test suite
βββ assets/ # Brand assets (logo, icons)
βββ web_strategy_studio/ # Web Strategy Studio (FastAPI + React)
β βββ backend/ # FastAPI backend + Alembic migrations
β βββ frontend/ # React + Vite + TypeScript frontend
β βββ Dockerfile # Multi-stage: frontend-builder β api β nginx
β βββ docker-compose.yml
β βββ CONTRIBUTING.md # Studio-specific contributor guide
βββ CLAUDE.md # AI agent configuration & optimization workflow
βββ mkdocs.yml # Documentation site configuration
Full index at examples/Examples.md.
| # | File | Description |
|---|---|---|
| 01 | 01_fetch_data.py |
Data API: history, CSV, local loading, market scan |
| 02 | 02_write_strategy.py |
Strategy templates (MA crossover, RSI, multi-stock) |
| 03 | 03_run_backtest.py |
End-to-end backtest with reports |
| 04 | 04_stock_screener.py |
Real-time stock screening |
| 05 | 05_paper_trade.py |
Paper trading with live quotes |
| 06 | 06_advanced_api.py |
Scheduling, portfolio optimization, attribution |
| 07 | 07_market_data.py |
Financials, industry, index, minute, tick data |
| 08 | 08_lifecycle_callbacks.py |
Lifecycle callbacks & stock pool management |
| 09 | 09_attribution_analysis.py |
Brinson + Fama-French factor analysis |
| 10 | 10_index_concept.py |
Index & concept board strategies |
| 11 | 11_utils_library.py |
Full utility library demonstration |
| 12 | 12_portfolio_backtest.py |
Multi-stock portfolio backtest |
| 13 | 13_ptrade_export.py |
Export to PTrade/QMT platform |
| 14β17 | Strategies | Bollinger, MACD+Volume, Multi-Factor, Grid Trading |
| 18 | 18_strategy_comparison.py |
Side-by-side strategy comparison |
| 19 | 19_local_data_backtest.py |
Local data mode (download once, offline backtest) |
| 20 | 20_sr_strategy/ |
Support & Resistance portfolio (production case) |
| 21 | 21_combined_strategy/ |
All-Weather Alpha comprehensive strategy |
| 22 | 22_stock_selection_strategy.py |
Periodic stock selection with factor screening |
| 23 | 23_small_cap_query_example.py |
Small-cap screening with chainable query API |
| 24 | 24_quick_report_test.py |
Quick report format validation |
| Resource | Description |
|---|---|
| Docs Site | Full documentation site with search, dark theme, navigation |
| Doc Center | Entry point: user guide, API index, FAQ, report metrics |
| User Guide | Install β write strategy β run backtest β read reports |
| API Reference | All public APIs with parameters and examples |
| Utils Reference | Technical indicators, statistics, position sizing |
| Tutorials | Zero to production, with real strategy cases |
| Report & Metrics Guide | Field-by-field report walkthrough |
| FAQ | Installation, data, performance, troubleshooting |
pip install easyquant-eqlib
python -c "from eqlib import *; print('eqlib OK')"git clone https://github.com/AlanFokCo/EasyQuant.git
cd EasyQuant
pip install -e ".[dev]"
python -m pytest tests/Requirements: Python 3.10+ Β· macOS / Linux / Windows
- Memory-aware data loading β automatic memory limit (default 1 GB) with fallback to compact slicing; identical results, slightly slower
- Fast I/O β in-memory
attribute_historyreduces 6+ year backtests from ~20 min to ~1 min - Parallel data loading β multi-threaded preload for faster startup
We welcome contributions! Please read CONTRIBUTING.md for guidelines.
For contributions to the Web Strategy Studio, see web_strategy_studio/CONTRIBUTING.md.
pip install -e ".[dev,docs]"
python -m pytest tests/ -vcd web_strategy_studio
npm run install:all # installs deps + builds symbol manifest
npm run dev:all # API on :8080, frontend on :5173Or with Docker:
cd web_strategy_studio
docker compose up --build # full stack on http://localhost:8080| Pipeline | Trigger | Description |
|---|---|---|
| Tests | Push / PR to main |
Runs eqlib test suite on Python 3.10, 3.11, 3.12 |
| Studio Tests | Push / PR to main (studio paths) |
Backend pytest + ruff/black + frontend typecheck/ESLint/vitest |
| Deploy Docs | Push to main (docs paths) |
Builds and deploys MkDocs site to GitHub Pages |
This project is licensed under the MIT License.
Disclaimer: This project is for educational and research purposes only. It does not constitute investment advice.