With the rise of AI and machine learning, neural networks are becoming an increasingly popular technique for developing algorithmic trading strategies. However, getting started with neural network investing can be challenging for beginners without coding experience or financial background. This article provides an overview of key free resources on GitHub to help beginners learn neural network investing basics, including open-source libraries, tutorial repositories, and sample trading strategies.
Popular Python Libraries for Neural Network Trading Strategies
Some top Python libraries for implementing neural network trading strategies available free on GitHub include TensorFlow Quant, FinRL, Backtrader, and Zipline. These provide tools for market data handling, strategy backtesting, order execution, visualization, and more out-of-the-box for beginners. Hands-on tutorials using these libraries demonstrate step-by-step workflow to develop profitable algo strategies.
Sample Algorithmic Trading Strategies with Source Code
In addition to libraries, GitHub hosts many open-source repositories sharing full sample neural network trading algorithms with documented source code. These include CNN, LSTM, reinforcement learning models for stocks, forex, crypto, and more markets. Studying real-world examples helps beginners understand how to structure strategies, preprocess data, select models, tune hyperparameters and refine performance.
Step-by-Step Tutorials and Guides for Self-Learning
YouTube tutorials, GitHub readmes, blog posts, and published papers provide comprehensive guides for neural network investing fundamentals. These cover explanations of concepts, mathematical derivations, coding tutorials, results analysis, and tips for common issues faced. Leveraging such free educational materials can significantly accelerate the DIY learning curve.
In summary, key free resources on GitHub like reusable libraries, trading strategy examples, and tutorials can provide beginners all components needed to start experimenting hands-on with neural network investing. Learning from community shared open-source projects reduces time spent reinventing wheels.