With the rise of artificial intelligence and machine learning, using neural networks for stock market prediction and algorithmic trading has become increasingly popular. In this article, we will introduce 4 popular open source GitHub projects that utilize neural networks and other advanced techniques to develop profitable investing strategies.
Qlib – An end-to-end quantitative investment platform from Microsoft Research Asia
Qlib is an AI-oriented quantitative investment platform developed by Microsoft Research Asia to realize the potential of AI technologies in quantitative investing. It aims to empower research and create value. Qlib supports tasks like strategy backtesting, model evaluation, data management, portfolio optimization and order execution. It contains sample strategies using machine learning models like LSTM, attention mechanism, graph neural networks etc.
Deep Quant – Using deep learning models like CNN, LSTM and reinforcement learning for algorithmic trading
Deep Quant is a project that focuses specifically on using deep learning techniques like CNN, LSTM and reinforcement learning for the stock market. It contains implementations of multiple research papers that utilize deep learning for tasks like price prediction, portfolio optimization, trade execution etc. The project is well documented and serves as a good starting point for researchers.
StockNet – Applying computer vision and NLP techniques to stock prediction
StockNet explores some unique applications of computer vision and NLP in finance, like using image recognition on financial charts to detect patterns and using sentiment analysis of news text to predict price movements. It serves as a good example of how the latest innovations in AI can be applied to quantitative trading.
Alphaml – Automated machine learning for creating stock predictive models
Alphaml provides tools to automate parts of the machine learning model building process for stock prediction, like automated feature engineering, model selection, hyperparameter tuning etc. This enables quants to quickly iterate over many models and find profitable signals without tedious manual effort. It directly integrates with other libraries like Zipline, Alphalens for further backtesting and analysis.
In summary, these open source GitHub projects showcase different innovative applications of neural networks and other AI techniques for profitable algorithmic trading. They provide great learning resources and starting points for quants and researchers looking to explore AI-based investing strategies.