🚀 Open Alpha

The Enterprise-Grade Quantitative Research Platform

From Hypothesis to Alpha — Build, Backtest, and Deploy Institutional-Quality Strategies.

Features • Quick Start • Documentation • Contributing

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Open Alpha is a high-performance, open-source quantitative research platform designed for serious algorithmic traders and researchers. Built on a robust FastAPI backend and a modern React frontend, it bridges the gap between ad-hoc analysis scripts and institutional-grade factor libraries. Whether you are mining new alpha signals, analyzing factor decay, or constructing multi-factor portfolios, Open Alpha provides the rigorously tested tools you need.

✨ Why Open Alpha?

• ⚡ Velocity: Go from idea to statistically valid factor in seconds, not hours.
• 🛡️ Robustness: Industry-standard metrics (IC, ICIR, Turnover) prevent overfitting and false discoveries.
• 🧠 Intelligence: Advanced expression engine supports complex time-series logic (ts_rank, ts_corr, winsorize).
• 🔌 Extensibility: Clean architecture allows easy integration of custom data sources and execution engines.

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💎 Features

🧠 Strategy Builder (New!)

• Multi-Factor Combination: Combine alpha signals with adjustable weights.
• Smart Normalization: Automatic Z-score standardization ensures apples-to-apples factor combination.
• Strategy Persistence: Save and load your winning strategies; build a library of proven alphas.

🧪 Factor Laboratory

• Expression Engine: Powerful Python-like syntax for factor construction (e.g., rank(close) / ts_delay(open, 5)).
• Institutional Metrics:
  • Alpha Quality: IC Mean, ICIR (Information Ratio), t-statistic.
  • Stability: Factor Autocorrelation, turnover analysis.
  • Risk: Sharpe, Sortino, Max Drawdown.
• Deep Visualization: Interactive charts for Cumulative IC, Equity Curves, and Layered Quantile Returns.

📊 Data Explorer

• Professional Charting: TradingView-style interactive charts (Lightweight Charts) with multi-timeframe support.
• Instant Analysis: Browse terabytes of market data with zero latency.

🏆 Factor Ranking

• Leaderboard: Automatically score and rank factors based on a proprietary quality model (0-100).
• Validity Checks: Automatic detection of "fake alpha" via monotonicity and statistical significance tests.

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🚀 Quick Start

Prerequisites

• Python 3.9+
• Node.js 18+
• npm or yarn

Installation

# Clone the repository
git clone https://github.com/yourusername/open-alpha.git
cd open-alpha

# Setup backend
pip install -r backend/requirements.txt

# Setup frontend
cd frontend
npm install
cd ..

# Copy environment template
cp .env.example .env

Running

Option 1: Separate terminals

# Terminal 1 - Backend
python -m backend.main

# Terminal 2 - Frontend
cd frontend && npm run dev

Option 2: Windows batch script

start_platform.bat

Access

• 🌐 Frontend: http://localhost:5173
• 🔌 API: http://localhost:8000
• 📚 API Docs: http://localhost:8000/docs

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📖 Documentation

Factor Expression Syntax

# Basic price data
close, open, high, low, volume

# Time-series functions
ts_delay(x, n)      # Lag n periods
ts_mean(x, n)       # Rolling mean
ts_std(x, n)        # Rolling std
ts_max(x, n)        # Rolling max
ts_min(x, n)        # Rolling min
ts_rank(x, n)       # Rolling percentile rank
ts_corr(x, y, n)    # Rolling correlation

# Preprocessing
winsorize(x)        # Remove outliers
standardize(x)      # Z-score normalization

Example Factors

Factor	Expression	Description
Momentum	close / ts_delay(close, 20) - 1	20-day price momentum
Volatility	ts_std(close / ts_delay(close, 1) - 1, 20)	20-day return volatility
Mean Reversion	(close - ts_mean(close, 20)) / ts_mean(close, 20)	Deviation from MA
Volume-Price	ts_corr(close, volume, 20)	Price-volume correlation

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📊 Backtest Metrics

Metric	Description
IC Mean	Average Information Coefficient
ICIR	IC Information Ratio (IC/IC_std)
t-statistic	Statistical significance of IC
Sharpe Ratio	Risk-adjusted return
Sortino Ratio	Downside risk-adjusted return
Win Rate	Percentage of positive returns
Max Drawdown	Maximum peak-to-trough decline

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🏗️ Project Structure

open-alpha/
├── backend/
│   ├── main.py           # FastAPI application
│   ├── api/routes.py     # API endpoints
│   ├── core/
│   │   ├── backtester.py     # Backtest engine
│   │   ├── factor_engine.py  # Factor evaluation
│   │   ├── data_loader.py    # Data loading
│   │   └── config.py         # Configuration
│   └── reports/          # Saved reports
│
├── frontend/
│   ├── src/
│   │   ├── pages/
│   │   │   ├── DataExplorer.jsx    # Data visualization
│   │   │   ├── FactorLab.jsx       # Factor research
│   │   │   └── BacktestReport.jsx  # Report ranking
│   │   ├── components/     # Reusable components
│   │   └── services/       # API service layer
│   └── ...
│
└── data/                 # Market data (feather format)

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🔧 Configuration

Environment Variables

Variable	Description	Default
DATA_DIR	Path to market data	./data
REPORTS_DIR	Path to saved reports	./backend/reports
LOG_LEVEL	Logging level	INFO
VITE_API_BASE_URL	Backend API URL	http://localhost:8000/api

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🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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🙏 Acknowledgments

• FastAPI - Modern Python web framework
• React - Frontend library
• TradingView Lightweight Charts - Professional charting
• Recharts - React charting library
• Monaco Editor - Code editor

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Made with ❤️ for Quantitative Research

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