In recent years, neural networks have become a popular machine learning technique for quantitative trading and investment. By leveraging large datasets and computational power, neural networks can model complex nonlinear relationships and uncover predictive signals in financial markets. On code-sharing platforms like GitHub, there are many open-source projects using neural networks for algorithmic trading. In this article, we will summarize some of the key resources on GitHub for applying neural networks to investment strategies and walk through their implementations.
LSTM models are well-suited for predicting time series like stock prices
One of the most common applications of neural networks in algo trading is predicting stock prices and trends. The long short-term memory (LSTM) architecture is well-suited for this time series forecasting task. On GitHub, there are implementations of LSTM models for forecasting stock prices, trading volumes, and price movements based on news articles or social media sentiment. For example, the Stock Prediction by LSTM project trains LSTM models on historical OHLC data to predict future closing prices. The creator experimented with different input sequences, optimization algorithms, and model architectures. There is also code to backtest the strategy by running the live predictions against a test dataset.
Using multi-task models and reinforcement learning can enhance strategies
Beyond vanilla LSTMs, some projects explore more advanced model architectures like multi-task networks. The Multi-task Recurrent Neural Networks project jointly trains models to predict price, volatility, and trading volumes. This multi-task approach led to better performance compared to individual models. Reinforcement learning is also gaining traction for trading algorithms. The Deep Reinforcement Learning for Trading repository implements an agent that makes buy/sell decisions to maximize portfolio returns. It uses LSTM networks as function approximators and deep Q-learning to train the agent. Strategies based on multi-task learning and reinforcement learning are still highly experimental but represent exciting areas for further research.
GitHub hosts various frameworks to streamline research and development
Rather than building models from scratch, leveraging existing libraries and frameworks can accelerate the development and evaluation of neural network trading strategies. Libraries like TensorFlow and Keras provide easy-to-use APIs for designing and training deep learning models. The FinRL project offers a collection of reference implementations and a framework for applying reinforcement learning techniques to trading. For data, the StockNet Dataset brings together price data, fundamentals, and news articles for S&P 500 stocks. Kaggle’s Two Sigma Financial Dataset includes millions of data points across markets, asset classes, and time horizons. By building on these tools and datasets, practitioners can focus more on strategy design rather than infrastructure.
GitHub has become a hub for open-source innovation and collaboration for applying neural networks to finance. From LSTM price forecasting models to multi-task networks and deep reinforcement learning, there are many examples and frameworks to learn from. While neural networks show promise, careful backtesting and validation is still critical before deploying live strategies.