algo.py is a robust, Python-first algorithmic trading framework designed for traders, developers, and institutions to build, test, and deploy trading strategies with unparalleled speed and flexibility. Built for modern markets, it bridges the gap between strategy ideation and live execution by combining a lightning-fast backtesting engine, a unified data layer, and broker-agnostic deployment tools.
🔹 Join the official Discord Server: Click here
🎥 View Algo.Py's Tutorials: Watch on YouTube
The framework empowers users to:
- Accelerate strategy development with live data streaming, multi-market/crypto support, and Python-native tools.
- Eliminate friction between backtesting and live trading via one-click workflows.
- Optimize execution with AI-enhanced order management (OMS), fee-saving algorithms, and real-time risk controls (RMS).
- Visualize markets through institutional-grade charts (footprint, DOM, volume bubbles) and live dashboards.
Whether you're automating a simple moving average strategy or building high-frequency arbitrage bots for crypto, algo.py provides the infrastructure, speed, and intelligence to trade confidently across global markets.
- ✨ Features
- 🚀 Installation
- ⚡ Quick Start
- 🛠️ Usage
- 📂 Project Structure
- 📈 Examples
- 🔧 Configuration
- 📝 Roadmap
- 🤝 Contributing
- 📜 License
- 📬 Contact
- One-Click Backtests: Execute complex backtests with a single command.
- Instant Deployment: Seamlessly deploy backtested strategies to live markets with zero code changes.
- Lightning-Fast Engine: Optimized for speed, enabling high-frequency strategy testing in seconds.
- Build sophisticated strategies using Python, with support for live data streaming integration.
- Test and deploy strategies across historical and real-time data streams simultaneously.
- Unified data interface for quick fetching, storing, and retrieving market data.
- Supports tick, candle, and bulk historical data across equities, crypto, and derivatives.
- Markets: Crypto (BTC, ETH, etc.), Indian (NSE, BSE), US (NYSE, NASDAQ), and more.
- Brokers: Integrated with Binance, Zerodha, Interactive Brokers, and custom broker APIs.
- Smart OMS:
- Advanced order types with market order chaser to minimize taker fees.
- AI-Powered OMS: Interact naturally (e.g., "Close 50% of my BTC position") via chat.
- Risk Management (RMS):
- Real-time alerts for portfolio anomalies (e.g., margin breaches, unusual drawdowns).
- Automated position sizing and exposure checks.
- Advanced charting tools:
- Footprint Charts: Visualize order flow and liquidity.
- DOM (Depth of Market): Real-time ladder for limit order analysis.
- Volume Bubbles: Track liquidity hotspots and market sentiment.
- Monitor live trades, P&L, and strategy performance in a unified interface.
To setup Algo.Py, run:
git clone https://github.com/himanshu2406/Algo.Py.git
cd Algo.Py
docker compose up -d📺 Watch the YouTube tutorial for a step-by-step installation guide:
Here’s how you can start Algo.Py Dashboard:
docker exec -it algopy_app bash
streamlit run Dashboard/main_dash.py📌 Planned Features:
- AI Backtesting Agent
- AI Trading journal
- Support for more brokers
- Migration to React / better UI
We welcome contributions! To contribute:
-
Fork the repository.
-
Clone your forked repo:
git clone https://github.com/himanshu2406/Algo.Py.git cd Algo.Py -
Create a new branch:
git checkout -b feature-name
-
Make your changes and commit:
git commit -m "Added feature-name" -
Push changes and open a Pull Request:
git push origin feature-name
AlgoPy is licensed under the AlgoPy Personal Use License.
- ✅ Free for personal & research use.
- ❌ Cannot be used in paid products, SaaS, hedge funds, or financial firms without a commercial license.
- 📝 See the LICENSE file for details.
📧 Email: [email protected]
🐦 Twitter: @yourhandle
📌 LinkedIn: Himanshu Rathore