Neural network investing strategies have become increasingly popular in recent years for algorithmic and quantitative trading. By utilizing deep learning and AI, these strategies can analyze large amounts of market data to detect patterns and generate automated trading signals. Some key benefits include adapting to changing market conditions, identifying complex nonlinear relationships, and reducing overfitting. In this article, we will summarize some key findings and conclusions from analyzing the top open-source neural network trading strategies on GitHub.
Neural networks can outperform traditional strategies and benchmarks
Several studies have shown neural network investing strategies consistently outperforming buy and hold benchmarks. One example is a long short-term memory (LSTM) network used to trade S&P500 stocks. Over a 10 year backtest, it achieved over 300% cumulative returns compared to 180% for the index.
Combining neural networks with traditional signals works best
While neural networks alone can generate alpha, combining them with other factors like moving averages, volatility indicators etc. tends to boost strategy performance even further. This provides more robust signals by merging computational pattern recognition with human-based domain knowledge.
Hyperparameter tuning is critical for real-world usage
A common mistake is applying neural networks with default parameters to trading. Extensive tuning of layers, nodes, learning rate etc. based on walk-forward testing and appropriate train-validation splits is needed for optimal live results. Top GitHub implementations focus heavily on tuning.
Market regime adaption remains challenging
A weakness of neural networks is performance degrades significantly from regime changes in volatility or other major shifts. Advanced techniques for continual model retraining, ensemble approaches, and adaption to regime parameters can alleviate but not eliminate this issue.
In summary, neural networks present exciting potential to achieve market-beating returns from algorithmic trading systems. If trained rigorously using substantial data and tuned for evolving conditions, they can form the backbone of highly scalable automated investment strategies.