With the rise of big data and machine learning, data science has become an integral part of modern investing. More and more investors are leveraging open source data science strategies on platforms like GitHub to gain an edge. In this article, we will explore the best open source data science strategies for investing available on GitHub, and provide guidance on how individual investors can take advantage of these free resources.
Find Profitable Stock Trading Strategies
GitHub has a thriving community of data scientists and quants who share open source stock trading strategies. These include strategies based on statistical arbitrage, pattern recognition, sentiment analysis and machine learning models. For example, the ‘mlfinlab’ repository implements various financial machine learning models from Marcos Lopez de Prado’s book. Investors can analyze and backtest these strategies on their own stock market data to identify profitable ones. The advantage is that these open source systems have already done the heavy lifting of strategy development.
Implement Automated Trading Systems
With the right coding skills, investors can use GitHub strategies as a starting point to build automated trading systems. For example, the ‘Crypto-Trading-Bot’ repository provides a template cryptocurrency trading bot using Python. Users can tweak the technical indicators and add their own trading rules and risk management logic. The bot can then execute trades automatically based on the defined strategy. Automation eliminates emotional decision making and enforces disciplined execution for any proven data science strategy.
Find Alternative Datasets
Alternative datasets from non-traditional sources can unlock new alpha opportunities. Many researchers share code on GitHub to collect and process these datasets. For instance, the ‘RedditSentimentTrader’ project aggregates sentiment data from Reddit to generate trading signals. The ‘google-trends-markets’ project uses Google Trends data to create profitable mean-reversion strategies. By studying these projects, investors can discover new datasets and derive novel insights from them.
Learn From Strategy Codebases
Even without directly using the strategies, studying their open source codebases can impart valuable knowledge. For example, the ‘Deep-Trading’ repository implements deep learning algorithms for financial prediction tasks. By poring through the code, investors can understand how recurrent neural networks can model market dynamics. The ‘alphalens’ library provides a framework for analyzing alpha factors in a disciplined manner. Investors can learn from its source code optimal techniques for validating strategy performance.
In summary, GitHub offers a wealth of open source data science strategies, codebases and tools that DIY investors can leverage for free. With some technical aptitude, these resources can be translated into profitable automated trading systems and new alpha opportunities. Studying GitHub projects can also impart domain knowledge to become a systematic trader.