With the rise of artificial intelligence and machine learning, neural networks have become a popular technique for developing algorithmic trading and investment strategies. Neural networks can model complex non-linear relationships and discover patterns from large datasets, making them well-suited for financial prediction and trading. Recently, more open-source neural network trading projects have emerged on platforms like GitHub, providing useful code examples and strategy papers for practitioners. In this article, we will summarize some key findings and conclusions from analyzing neural network trading strategy pdfs found on GitHub.
Neural networks can forecast stock prices and direction
Many papers demonstrate using neural networks to predict future stock prices or price direction. For example, a LSTM network modeled multiple stocks and outperformed buy-and-hold strategies. Another used CNN and LSTM networks on Japanese candlestick charts to predict price movements. Overall, neural networks show promising results on financial time series forecasting.
Neural networks can trade multiple assets and asset classes
Neural networks are not limited to trading single assets. One project used an LSTM model to trade a portfolio of tech stocks. A paper applied CNN on charts to trade between S&P500 stocks and bonds. With the ability to handle multiple inputs and outputs, neural networks provide flexibility to develop cross-asset and portfolio trading strategies.
Recurrent networks like LSTM are commonly used architectures
For sequential data like time series, recurrent neural networks are a natural choice. LSTM networks specifically are prevalent in many strategy papers to model long-term dependencies in financial data. LSTM’s ability to retain memory over time makes it suitable for learning trends and momentum patterns. Besides LSTM, some papers also use GRU or vanilla RNN models.
Strategies combine neural networks with other techniques
Neural networks are often combined with traditional technical indicators or other techniques to create hybrid strategies. For example, a LSTM network using candlestick patterns and volume indicators. Some papers augment neural networks with evolutionary algorithms to optimize strategy parameters and asset selection. Ensemble methods like random forests are also paired with neural networks to improve prediction accuracy.
GitHub resources provide code for replicating strategies
A benefit of GitHub projects is the availability of code to recreate strategy experiments. One repository contained a Jupyter notebook to train LSTM networks for forecasting and trading. Another included Python code to build a CNN for classifying candlestick patterns. By providing documented code, GitHub enables others to readily validate and build upon neural network trading strategies.
In summary, key findings from neural network trading strategy papers on GitHub include the ability to forecast prices, trade multiple assets, use recurrent networks like LSTM, combine with other techniques, and provide replicable code examples.