With the rapid development of artificial intelligence technology, machine learning has become an important tool for investment research and quantitative trading. Among them, open source machine learning frameworks on github provide a convenient channel for investors and traders to quickly get started with applying ML in finance. In this article, we will recommend 5 best free machine learning frameworks for investing research on github, and summarize their key features, advantages and sample use cases in quantitative finance field.
Zipline – A popular Python-based backtesting framework integrated with ML
Zipline is one of the most popular open-source backtesting frameworks written in Python. It is designed for trading algorithm research and development. Zipline comes integrated with machine learning libraries like sklearn, keras and pytorch, making it easy to utilize ML models for algorithmic trading strategies. Quant researchers can take advantage of Zipline’s vectorized backtesting engine and Pandas data structures to efficiently test strategies on historical data.
Tensorflow – Google’s production-ready ML platform with finance solutions
Tensorflow by Google is the most widely used deep learning framework in both industry and academia. It enables building, training and productionizing ML models with great performance and scalability. The TF-Quant-Finance toolkit offers many reference models for quantitative finance tasks, such as option pricing, portfolio optimization, time series forecasting. With abundant documentation and community support, Tensorflow significantly lowers the barrier for applying deep learning in investment practice.
PyTorch – A popular dynamic neural network framework for finance
PyTorch is Facebook’s open source ML library based on the torch framework. It is gaining popularity in recent years due to its dynamic computational graphs and strong GPU acceleration support. For quantitative analysts, PyTorch provides great flexibility for designing and optimizing complex neural network models. With modules like torchfinance, PyTorch is also an ideal choice for tasks like time series prediction, risk modeling and Algorithmic trading in finance.
FinRL – Deep reinforcement learning for automated trading strategies
FinRL is a Python framework for applying cutting-edge deep reinforcement learning algorithms to quantitative finance problems. It aims to facilitate DRL research and development for algorithmic trading. FinRL incorporates high-performance computation frameworks like TensorFlow and PyTorch, provides reference implementations for stock trading and cryptocurrency trading strategies based on DRL models like DDPG, PPO and A2C. For engineers and researchers, FinRL effectively lowers the barriers to utilize deep reinforcement learning for automated trading system design.
Stable Baselines3 – Fast prototyping of RL-based trading agents
Stable Baselines3 is a set of improved reference implementations for deep reinforcement learning algorithms based on TensorFlow 2. It enables fast prototyping, hyperparameter tuning and comparison for a variety of DRL agents. The ultra fast distributed training capability makes Stable Baselines3 a perfect fit for researchers to build and optimize sophisticated trading strategies with large-scale market data. The framework also provides seamless integration with backtesting environments like Zipline and backtrader in Python.
In summary, open-source machine learning frameworks like Zipline, TensorFlow, PyTorch, FinRL and Stable Baselines3 provide great convenience for investors and traders to leverage ML technologies for quantitative analysis and algorithmic trading strategy research. With large amounts of sample code and active community support, these github projects significantly lower the barriers for applying advanced ML in practical financial applications.