Navigating Markets with Intelligent Exploration
Quantinaut — An open-source, fully autonomous algorithmic trading platform integrating machine learning, quantitative analysis, and feature engineering to deliver adaptive, high-performance strategies in real-time markets.
Welcome to Quantinaut — your gateway to the future of algorithmic trading! This project brings together the power of machine learning and the speed of the Nautilus Trader framework to build intelligent, adaptive trading bots. Whether you're a quant, a developer, or just curious about AI in finance, Quantinaut is designed to help you experiment, learn, and deploy cutting-edge trading strategies.
Quantinaut aims to:
- Empower traders and researchers to build robust, autonomous trading systems.
- Leverage state-of-the-art ML algorithms for both predictive indicators and strategy optimization.
- Enable rapid experimentation and validation in realistic market environments.
Quantinaut separates where different types of machine learning shine in a trading stack:
- Reinforcement Learning (RL): Powers trading strategies, making smart decisions with full market and portfolio context.
- Supervised & Unsupervised Learning: Fuels predictive indicators, forecasting market variables like price direction, volatility, and order flow.
This approach matches the right ML paradigm to the right problem, making your trading system both precise and context-aware.
ML models act as predictive indicators — like supercharged technical signals, trained on historical and live data.
- Purpose: Generate buy/sell probabilities, price targets, volatility forecasts, and more.
- Integration: Plug into strategies (RL or rule-based) for smarter decisions.
- Best for: Indicators, adapting to new data as markets evolve.
Here, ML is the brain of your trading strategy, deciding when to enter, exit, or hold positions with a holistic view.
- Purpose: Optimize trade execution and portfolio management.
- Integration: Combine multiple inputs — indicators, order book data, market regimes, and more.
- Best for: Strategies (batch training) or deep RL, learning optimal policies from simulations or historical data.
- Modular design: Mix and match ML models, indicators, and strategies.
- Fast retraining: Keep your models up-to-date with changing markets.
- Realistic backtesting: Validate ideas in the Nautilus Trader environment.
- Open-source: Build on top of a rich ecosystem of trading and ML libraries.
Ready to dive in? Check out the docs or explore the examples/ folder for sample scripts. To install dependencies, see pyproject.toml or use the provided install scripts in scripts/.
- Nautilus Trader
- Nautilus Talks
- Microsoft Qlib
- FreqTrade
- VNpy
- River (Online ML)
- Mlfin.py
- MLFinLab
- HyperTrade
- PML Trader
- Intelligent Trading Bot
Happy trading! 🐚🤖