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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

# PyPI install (recommended for users)
pip install easyquant-eqlib

# Or install from source (for contributors)
git clone https://github.com/AlanFokCo/EasyQuant.git
cd EasyQuant
pip install .

Verify installation and run your first backtest:

python -c "from eqlib import *; print('eqlib OK')"
python examples/03_run_backtest.py  # run in repo directory

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

New to Python? If you prefer a browser-based interface, check out Web Strategy Studio — write strategies, run backtests, and view reports without installing any Python environment. See Web Studio Docs for details.

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
Web Studio	Browser-based strategy development (no Python install needed)
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.