FactorHub is an open-source modern quantitative factor analysis platform designed specifically for the Chinese A-share market. FactorHub = Factor + Hub A full
FactorHub
FactorHub is an open-source modern quantitative factor analysis platform designed specifically for the Chinese A-share market.
FactorHub = Factor + Hub
A full-stack quantitative investment research system integrating factor management, analysis, mining, portfolio optimization, and strategy backtesting.
Language Options
Core Value Proposition
| Value Pillar |
Description |
| Complete Factor Lifecycle Management |
Full support from factor creation, validation, analysis to deployment |
| Scientific Factor Evaluation System |
Professional indicators including IC/IR analysis, monotonicity test, turnover analysis |
| Intelligent Factor Mining |
Genetic algorithm-based automated factor mining to discover alpha signals |
| AI Research Assistant |
Supports natural-language factor generation, AI interpretation of analysis results, and report drafting |
| Professional Backtesting Engine |
Support for multi-factor combination, strategy comparison, and performance attribution analysis |
Core Features
1. Factor Management
- ✅ Custom Factor Definition - Supports Tongda Xinhua (MyLanguage) syntax and TALib functions
- ✅ Formula Validation - Real-time syntax checking and logical verification
- ✅ Version Control - Factor modification history and version rollback
- ✅ Pre-built Factor Library - Built-in common technical factors (MA, RSI, MACD, Bollinger Bands, etc.)
2. Factor Analysis
- ✅ IC/IR Analysis - Information Coefficient and Information Ratio calculation (supports 1-day, 5-day, 10-day prediction cycles)
- ✅ Factor Exposure Analysis - Analyze stock exposure distribution on factors
- ✅ Factor Effectiveness Testing - Multi-dimensional assessment of factor predictive power
- ✅ Factor Attribution Analysis - Decompose factor contribution to returns
- ✅ Dynamic Monitoring - Factor performance tracking across time series dimensions
3. Factor Mining
- ✅ Genetic Algorithm Mining - DEAP-based evolutionary algorithm for automatic factor search
- ✅ Multi-objective Optimization - Simultaneously optimize IC, IR, monotonicity, and other objectives
- ✅ Factor Generation - Supports basic operators, function calls, and time window operations
- ✅ Parallel Computing - Parallel population evaluation for acceleration
4. Portfolio Analysis
- ✅ Multi-factor Portfolio - Supports equal weight, market cap weighting, IC_IR maximization, etc.
- ✅ Risk Modeling - Factor neutralization processing
- ✅ Optimization Configuration - Factor weight optimization based on historical performance
- ✅ Portfolio Performance - Annual return, Sharpe ratio, maximum drawdown, and other metrics
5. Strategy Backtesting
- ✅ Single Factor Backtesting - Factor quantile-based stock selection backtesting
- ✅ Multi-factor Strategies - Composite factor signal generation
- ✅ Strategy Comparison - Multi-strategy parallel backtesting and comparison analysis
- ✅ Performance Metrics - Complete metric system including returns, risk, and turnover
- ✅ Visualization Charts - Equity curves, drawdowns, factor performance charts, etc.
6. AI Capabilities
- ✅ AI Factor Generation - Turn research ideas written in plain language into candidate factors for further validation
- ✅ AI-assisted Formula Refinement - Help improve factor expressions into clearer, library-ready definitions
- ✅ AI Interpretation of Factor Analysis - Produce structured research commentary based on factor definitions, analysis results, and chart context
- ✅ AI Support for Use-case and Risk Review - Help researchers quickly understand market intuition, limitations, and potential risks behind a factor
- ✅ AI Report Export - Organize AI interpretation output into shareable Markdown research notes
Technical Architecture
Tech Stack
Backend:
- FastAPI 0.135+ - High-performance web framework
- SQLAlchemy 2.0 - ORM database operations
- SQLite - Lightweight data storage
- Pandas 2.0+ / NumPy - Data processing
- TA-Lib - Technical analysis library
- VectorBT 0.25+ - Backtesting engine
- DEAP 1.3+ - Genetic algorithm framework
- XGBoost 2.0+ - Machine learning models
- SHAP 0.42+ - Model interpretation
- akshare 1.12+ - Chinese A-share data source
Frontend:
- React 19 - UI framework
- TypeScript - Type safety
- Ant Design 6 - UI component library
- ECharts 6 - Data visualization
- React Router 7 - Routing management
- Axios - HTTP client
- Vite - Build tool
Project Structure
…
Quick Start
Prerequisites
- Python 3.11+
- Node.js 18+
- pnpm (package manager)
- TA-Lib (technical analysis library)
Installation
# Install pnpm
npm install -g pnpm
# Install Python dependencies (using uv)
uv sync
# Install frontend dependencies
cd frontend/react-antd
pnpm install
One-click Startup
python start_all.py
This script will automatically:
- Check environment prerequisites
- Install dependencies if needed
- Start backend service (http://localhost:8000)
- Start frontend development server (http://localhost:5173)
- Open browser automatically
Manual Startup
Backend
uv run python start_api.py
# API available at http://localhost:8000
# API docs at http://localhost:8000/docs
Frontend
cd frontend/react-antd
pnpm dev
# Frontend available at http://localhost:5173
Docker Deployment
If you prefer to deploy the full application with Docker, use the built-in Docker configuration:
cd docker
docker compose build
docker compose up -d
After startup, access:
Common commands:
# View logs
docker compose logs -f
# Stop services
docker compose down
Notes:
- The Docker image includes the application code and frontend build artifacts required for runtime
- The
data/ directory is mounted for persistent storage of factor data and runtime outputs
- Local AI model configuration, cache files, test logs, and database backups are not baked into the image
License
Dual License
Personal Use:
This project is licensed under the GNU Affero General Public License v3.0 (AGPL-3.0).
You are free to:
- ✅ Use this software for personal learning, research, and non-commercial purposes
- ✅ Modify and improve this software
- ✅ Distribute modified versions (must retain the same license)
- ✅ Reference this project in your own projects
Commercial Use:
⚠️ Important Note: Any commercial use (including but not limited to:
- Integrating this project into commercial products
- Using this project to provide paid services
- Using this project for production quantitative trading
- Using this project internally within companies for investment research)
Requires separate commercial authorization.
Contact for Commercial Authorization
Email: [email protected]
When contacting, please specify:
- Your company/organization name
- Your usage scenario and requirements
- Expected scale of use
- Contact information
We will respond within 3 business days.
Contact
Project Maintainer: FactorHub Team
Email: [email protected]
Feedback Welcome:
- Bug reports
- Feature suggestions
- Technical discussions
- Cooperation inquiries
Last Updated: 2026-03-14