Neural networks have shown great promise for algorithmic trading and quantitative investing. By processing market data and discovering complex patterns, neural nets can make predictions and execute trades automatically. On GitHub, there are many open-source libraries that implement neural network strategies for backtesting and live trading. Some key frameworks like TensorTrade and QSTrader use TensorFlow and Keras to build deep learning models. They offer full backtesting capabilities to validate strategies before real-money deployment. In addition, repositories like NeuralAgent contain production-grade neural trading agents ready to connect with brokerages. Besides frameworks, there are also standalone neural net strategies for areas like price forecasting, risk management, and portfolio optimization. With proper training data and hyperparameters, these strategies can adapt to any market and create positive expectancy over long periods.
TensorTrade and QSTrader allow quick strategy prototyping with neural networks
TensorTrade and QSTrader are Python-based backtesting frameworks that simplify building neural trading strategies. They have modular designs so developers can easily swap different model architectures like LSTMs and CNNs. By integrating with data sources like Yahoo Finance, these libraries enable quick prototyping on historical market data. Once a profitable strategy is found through backtesting, it can be connected to live trading via brokerage APIs. The availability of standardized modeling workflows is a huge benefit compared to building machine learning infrastructure from scratch.
Repositories like NeuralAgent offer production-ready trading agents
While TensorTrade and QSTrader are more research-focused, NeuralAgent provides an industrial-strength neural trading implementation. It contains an event-driven engine that handles real-time data feeds, position management, order routing and more. The agents themselves are modular TensorFlow graphs that take in market data and trigger trading actions. NeuralAgent comes with sample agents using models like LSTM, showcasing common strategies like trend following across stocks and forex. As NeuralAgent handles all the infrastructure, developers can concentrate on strategy research and tweak model hyperparameters for better performance.
There are also focused neural net strategies for specific use cases
In addition to full-featured frameworks, GitHub has a range of standalone strategies implementing neural models. Examples include LSTM for price prediction, CNN and graph networks for pattern detection, reinforcement learning for position sizing and risk management. These narrowly scoped strategies solve specific issues in the trading pipeline without requiring complex infrastructure. They provide good starting points for further customization. By mixing and matching different specialized strategies, one could build a comprehensive high-frequency trading system with neural automation of data processing, signal generation, trade execution and risk control.
GitHub contains extensive resources on integrating neural networks into algorithmic trading, from flexible frameworks for strategy building to plug-and-play modules for solving specific problems. By standing on the shoulders of open-source work, quants can accelerate research and create reliable trading systems with machine learning.