With the rise of big data and artificial intelligence, data science has become an increasingly important tool for investing and trading. As a beginner looking to apply data science to investing, there are many high-quality open source resources on GitHub that you can leverage for free. In this article, we will introduce some of the best github repositories covering data science algorithms, backtesting frameworks, and end-to-end quantitative trading systems for beginners to get started with data science in investing.
Comprehensive guides to algorithmic trading with Python on GitHub
Two of the most comprehensive guides for learning algorithmic trading with Python on GitHub are ‘algorithmic-trading-with-python’ and ‘quantitative-trading’. The first repository provides a structured curriculum for using pandas, NumPy, and scikit-learn for trading simulation and backtesting. The content covers data preprocessing, feature engineering, machine learning algorithms, backtesting frameworks, and performance evaluation. The second repository contains notebooks and code snippets demonstrating quantitative trading strategies, including momentum, mean reversion, portfolio optimization, and automatic trade execution. Together, these two repositories offer a rich set of materials for developing a solid foundation in algorithmic trading with Python.
Backtesting frameworks for evaluating trading strategies
Effective backtesting is essential for systematically evaluating the performance of quantitative trading strategies. Some popular Python backtesting libraries on GitHub include zipline, backtrader, pybacktest, and quantopian/pyfolio. Zipline and backtrader offer industrial-strength event-driven backtesting frameworks modeled after real-world trading, while pybacktest provides a lightweight vectorized backtesting engine. Quantopian’s pyfolio is tailored for analyzing portfolio risk and performance metrics. By combining a backtester like zipline with pyfolio, one can conduct a comprehensive quantitative analysis to assess and improve trading strategies.
End-to-end quantitative trading systems for hands-on learning
For hands-on learning, GitHub hosts complete toolkits for building end-to-end systematic trading systems. These include enzoampil’s FastQuant and hridoyhr/easytrade projects. FastQuant automates the entire pipeline from strategy research, backtesting, optimization, live trading, to performance analysis. The easytrade repository focuses on dataset preparation,strategy backtesting, broker integration, and auto trading based on technical indicators. Working through the documentation and API of these projects accelerates learning as it exposes developers to real-world practices in deploying algorithmic trading systems.
In summary, GitHub offers plentiful resources including tutorials, frameworks, and full trading toolkits for beginners looking to apply data science to investing. By leveraging these materials, one can rapidly gain practical skills in quantitative trading strategy development, backtesting, and implementation.