In recent years, neural networks have emerged as a powerful tool for algorithmic trading and quantitative investing. With the rise of deep learning and increased computing power, neural networks allow investors to model financial markets in new ways and discover complex patterns from vast amounts of data. By leveraging neural networks, investors can develop sophisticated trading algorithms that aim to outperform the market. However, building profitable algorithmic trading strategies with neural networks requires significant expertise. This is where open source neural network libraries and frameworks on GitHub come into play, lowering the barriers for investors looking to tap into these advanced techniques. Specifically, GitHub hosts many free and easy-to-use neural network libraries for Python and other languages. These libraries provide pre-built components for constructing, training and validating neural network models. By building on top of these open source tools, investors can accelerate the development of neural network-based trading strategies and optimize their algorithmic investing. Ultimately, neural networks represent the cutting edge of algorithmic trading technology. And with the help of GitHub’s open source ecosystem, this powerful tool is now more accessible than ever for enterprising investors.
TensorFlow and Keras – Leading Deep Learning Frameworks
Two of the most popular neural network libraries on GitHub are TensorFlow and Keras. Developed by Google, TensorFlow is an end-to-end platform for machine learning and deep learning. It includes comprehensive tools for building, training and deploying neural network models. Keras is a high-level API that runs on top of TensorFlow and helps simplify the process of designing and training neural networks. Together, TensorFlow and Keras provide a robust framework for developing neural network trading algorithms. Investors can leverage built-in layers, activations, optimizers and other components to quickly iterate on strategy ideas. Pre-trained models like LSTM and CNN allow for modeling sequential data like stock prices. And tools like TensorFlow Serving make it easy to productionize models. With over 100,000 stars each on GitHub, TensorFlow and Keras have strong community support and a wealth of tutorials and guides for getting started.
PyTorch – Flexible Python Library for Neural Networks
PyTorch is another leading open source library for building neural networks, offering an alternative to TensorFlow. It is designed for flexibility and modularization, allowing investors to customize and tweak neural network architectures. Key features like dynamic computation graphs and Pythonic coding make PyTorch highly intuitive to use. And built-in support for techniques like auto differentiation eliminates much of the heavy lifting involved in training networks. For algorithmic trading, PyTorch Modules provide a simple abstraction for encapsulating trading strategies as neural networks. The library also includes useful tools like TorchText for processing textual data and TorchServe for model deployment. With over 65,000 stars on GitHub, PyTorch benefits from an active developer ecosystem. Many online courses and tutorials are available for learning the PyTorch API and applying it to finance use cases.
Catalyst – Framework for Deep Learning in Finance
While TensorFlow, Keras and PyTorch provide general deep learning capabilities, Catalyst is a framework tailored specifically for algorithmic trading and quantitative finance. Designed by a hedge fund, it includes common building blocks for developing neural network-based trading strategies. Out-of-the-box support for time series data, backtesting, hyperparameter optimization and metrics like Sharpe ratio accelerate strategy development. The Model API provides abstractions for encapsulating trading logic into deep learning models. And tools like Catalyst Alpha help orchestrate the entire workflow from data processing to model training to live trading. With over 1,500 stars on GitHub, Catalyst provides a robust starting point for applying deep learning to finance. The active community enables knowledge sharing on best practices and lessons learned in putting neural network models into production.
In summary, GitHub hosts a vibrant ecosystem of open source neural network libraries for investing and algorithmic trading. TensorFlow, Keras, PyTorch and Catalyst represent leading frameworks for leveraging deep learning. By building on these tools, investors and quants can develop sophisticated neural network-based trading strategies and take their algorithmic investing to the next level.