easyquant-eqlib 1.0.1

Event-driven quantitative trading framework for China A-share backtesting and research

EasyQuant

Event-driven quantitative backtesting framework for China A-share market.

中文文档 · Docs Site · Tutorials · Doc Center · API Reference · Examples · FAQ

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Features

• Event-driven backtesting — initialize → run_daily → handle_data, compatible with JoinQuant / Zipline programming model
• A-share data — daily OHLCV, minute K-lines, tick data, real-time quotes, fundamentals, money flow via free AKShare API
• Position management — buy/sell by shares, value, or target; automatic lot-size rounding (100 shares), commission calculation
• Risk analysis — Sharpe, Sortino, max drawdown, alpha/beta, Brinson attribution, Fama-French factor analysis
• Portfolio optimization — minimum variance, maximum Sharpe, risk parity
• Paper trading — run strategies live with real-time market data before going live
• PTrade/QMT adapter — one-click export to broker platforms
• Stock selection — periodic rebalancing with factor screening (ST/PB/PE/momentum filters, Top-N, multi-factor scoring)
• Utility library — 30+ technical indicators (MA, MACD, RSI, KDJ, Bollinger, ATR, ADX), statistical tools, position sizing (Kelly, ATR-based, fixed fractional)
• Reports — interactive HTML, chart PNG, Markdown, JSON with 20+ risk/return metrics
• Chainable stock screening — fluent API (query / valuation / get_fundamentals) for fundamental analysis

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Quick Start

pip install easyquant-eqlib   # or: pip install -e .  (from source)
python -c "from eqlib import *; print('eqlib OK')"
python examples/03_run_backtest.py

Open the generated .html report in reports/ in your browser.

Write Your First Strategy

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/ — .png chart, .html interactive report, .md summary, .json data.

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Report Preview

run_strategy generates an interactive HTML report plus PNG, Markdown, and JSON. Below are snapshots from real backtest runs.

Profitable Strategies

MACD Trend + Volume (600536)	Bollinger Mean Reversion (601088)	Support/Resistance (8 stocks)
+103.48% · 16 trades	+57.77% · 8 trades	+119.97% · 171 trades

Losing Strategies (For Learning)

Momentum Portfolio (5 stocks)	Local Data (000768)
−25.69% · 52 trades	−33.28% · 16 trades

HTML Report (Interactive)

How to read reports: header summary → metric cards (Sharpe, max drawdown, alpha) → K-line chart with MA/RSI/MACD/Bollinger Bands → cumulative returns vs benchmark → drawdown curve → daily P&L → trade/position tabs. Full guide: Report & Metrics.

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Project Structure

EasyQuant/
├── eqlib/                 # Core library (backtest engine, data API, analysis)
├── agent/                 # AI optimization utilities
│   ├── optimizer.py       # Rule-based parameter search (reference)
│   ├── audit_log.py       # Structured JSONL + Markdown audit logging
│   └── strategy_template.py  # Parameterized strategy template
├── examples/              # 24 runnable example scripts
├── tutorials/             # Step-by-step learning tutorials
│   └── prerequisites/     # Python, technical analysis, A-share basics
├── doc/                   # User manual, API reference, FAQ
├── docs/                  # GitHub Pages site source (MkDocs Material)
├── tests/                 # Test suite
├── assets/                # Brand assets (logo, icons)
├── web_strategy_studio/   # Web Strategy Studio (FastAPI + React)
│   ├── backend/           # FastAPI backend + Alembic migrations
│   ├── frontend/          # React + Vite + TypeScript frontend
│   ├── Dockerfile         # Multi-stage: frontend-builder → api → nginx
│   ├── docker-compose.yml
│   └── CONTRIBUTING.md    # Studio-specific contributor guide
├── CLAUDE.md              # AI agent configuration & optimization workflow
└── mkdocs.yml             # Documentation site configuration

Examples

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
14–17	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

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Documentation

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 → write strategy → run backtest → 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

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Installation

PyPI (recommended for users)

pip install easyquant-eqlib
python -c "from eqlib import *; print('eqlib OK')"

From source (for contributors / editable install)

git clone https://github.com/AlanFokCo/EasyQuant.git
cd EasyQuant
pip install -e ".[dev]"
python -m pytest tests/

Requirements: Python 3.10+ · macOS / Linux / Windows

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Performance

• Memory-aware data loading — automatic memory limit (default 1 GB) with fallback to compact slicing; identical results, slightly slower
• Fast I/O — in-memory attribute_history reduces 6+ year backtests from ~20 min to ~1 min
• Parallel data loading — multi-threaded preload for faster startup

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Contributing

We welcome contributions! Please read CONTRIBUTING.md for guidelines.

For contributions to the Web Strategy Studio, see web_strategy_studio/CONTRIBUTING.md.

Development Setup

pip install -e ".[dev,docs]"
python -m pytest tests/ -v

Studio — Quick Dev Start

cd web_strategy_studio
npm run install:all          # installs deps + builds symbol manifest
npm run dev:all              # API on :8080, frontend on :5173

Or with Docker:

cd web_strategy_studio
docker compose up --build    # full stack on http://localhost:8080

CI/CD

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

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License

This project is licensed under the MIT License.

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Disclaimer: This project is for educational and research purposes only. It does not constitute investment advice.