Neural networks have shown promising capabilities in financial market prediction and investing. As a beginner looking to apply neural networks for investing, there are many free online resources and communities to help you get started. In this article, we will provide an overview of neural network investing, including basic concepts, free tools for building models, and active communities on Reddit and Github to learn from others.
Understand basic concepts of neural networks for trading
As a beginner in neural network investing, firstly you need to understand some basic concepts. A neural network is an artificial intelligence model inspired by biological neural networks. It can find complex patterns in data which are difficult to discover manually. In finance, neural networks excel at processing market data like prices, volumes, news etc. and uncovering signals and insights for trading. Before diving into development, learn about neural network architectures, training techniques, and challenges specific to financial data.
Use free online apps to build basic models
There are many user-friendly apps today which allow you to easily build neural network models without coding experience. For instance, BigML provides a visual interface to develop models on your data. Google Teachable Machine is great for quick prototyping as well. Start with basic data like stock prices to predict future direction. Experiment with parameters and data representations to improve accuracy. This will build intuition before taking on more advanced projects.
Leverage Reddit and Github as learning resources
Active Reddit communities like r/algotrading, r/quant and r/machinelearning provide ample resources for neural network trading. You can find code samples, research papers, tutorials and discussions to learn from. Similarly, Github hosts great repositories like TensorTrade and FinRL which contain reusable components and notebooks. Learn by reading code and notebooks published by experts, and reach out over forums when facing roadblocks.
Keep iterating models with more data and complexity
Start simple but plan to increment scope over time. Add more data sources like fundamentals, alternative data etc. to capture new signals. Architect model ensembles to improve robustness. Shift from predicting only prices to other targets like volatility, liquidity etc. Implement reinforcement learning for autonomous trading. There is no limit to complexity one can build up to.
In summary, neural network investing as a beginner begins with grasping basic concepts, leveraging free online apps for initial modelling, and relying on Reddit and Github communities for continued learning. By incrementally increasing data, targets and techniques, one can develop quite sophisticated models over time.