As neural network technologies become more advanced, there is increasing interest in using them for investing and trading strategies. However, quality resources on this topic can be difficult to find, especially free ones. This article provides tips on where to look on platforms like GitHub and Reddit to find free neural network investing strategies, code, and discussion.
Search GitHub for neural network trading algorithm repositories
GitHub hosts millions of open source projects, many of which relate to using neural networks and machine learning for finance and investing. Some key things to look for:
– Repositories specifically focused on neural network trading strategies and algorithms. The readme files and documentation may provide details on the approaches used.
– Notebooks providing walkthroughs and examples of implementing neural network models for tasks like stock price prediction. These show the code in action.
– Libraries and frameworks for building neural network trading systems. These provide reusable components you can build on top of.
– Forking repositories allows you to use others’ code as a starting point for your own experiments. Remember to adhere to license terms.
Use GitHub’s search features to find repositories based on keywords like “neural networks”, “trading”, “finance”, “algorithms”, “quantitative”, etc. Sort by stars/forks to surface popular projects.
Join the algotrading and machine learning subreddits
Reddit hosts vibrant communities discussing quantitative and algorithmic trading strategies. Two useful ones are:
– r/algotrading – General discussion around developing and executing trading strategies and systems. Members share ideas, code, and lessons learned. Lots of interest around machine learning and neural network approaches.
– r/MachineLearning – Broader machine learning subreddit with some discussion around applications in finance. Useful for finding general advice on building and evaluating neural network models.
As a newcomer, read subreddit wikis, top posts, and FAQs to get an overview. Then search historical posts on key topics like “neural networks”, “deep learning”, “trading”, etc. Comment, ask questions, and join the discussion around promising strategies.
Check papers and datasets on ArXiv and Kaggle
In addition to code and discussion forums like GitHub and Reddit, two other useful sources for machine learning and neural network trading research are:
– ArXiv – Massive collection of academic papers including lots of finance and investing focused machine learning research. Can filter by categories like q-fin (quantitative finance), stat, cs, etc.
– Kaggle – Hosts machine learning competitions and datasets. Some challenges provide market data to develop models for tasks like price prediction. Useful for model inspiration and real-world data.
Reviewing papers explains the latest experimental neural network trading approaches being researched. And datasets provide the raw materials to build your own models.
GitHub, Reddit, ArXiv, and Kaggle provide abundant public resources to learn about using neural networks for investing and trading for free. The code repositories, forums, papers, and data available can inspire novel strategies or provide building blocks to implement your own ideas.