Neural networks have shown promising results in financial forecasting and investment analysis. With the rise of deep learning and increased computing power, various open source neural network projects for investing have emerged on platforms like GitHub. In this article, we summarize some of the best github repositories that demonstrate neural network applications in stock prediction, algorithmic trading, portfolio optimization and risk management.
Using LSTMs for stock price prediction
One popular application is using recurrent neural networks (RNN) like long short-term memory (LSTM) models to predict future stock prices based on historical price data. Repositories like ‘Stock-Prediction-Models’ and ‘deep-trading’ showcase LSTM models that can effectively capture longer-term patterns in time series data.
Algorithmic trading systems based on deep reinforcement learning
Reinforcement learning allows neural networks to optimize trading decisions and maximize cumulative rewards. Libraries like ‘Deep-Trading’ and ‘LazyFA’ implement deep Q-learning networks combined with technical indicators to automate high frequency trading.
Neural networks for portfolio optimization
Projects like ‘opf-dnn’ demonstrate using neural networks for portfolio optimization by predicting asset returns and covariances. This allows constructing optimal portfolios dynamically adjusted to changing market conditions.
Fraud detection in finance using neural networks
Fraud detection is a key application for neural networks in risk analytics. Repositories like ‘creditcardfraud’ implement models using PyTorch and TensorFlow for detecting fraudulent credit card transactions and payments.
In summary, deep neural networks show broad applicability across forecasting, algorithmic trading, portfolio management and risk analytics in quantitative finance. The highlighted GitHub projects offer useful reference for applying these models in real-world investment scenarios.