As a beginner looking to get started with algorithmic trading and quantitative investment strategies, utilizing open source code frameworks on GitHub can be hugely beneficial. These frameworks offer ready-made tools for backtesting, data analysis, strategy building and even live trading. With examples and documentation tailored for beginners, they allow quickly testing and validating trading ideas without needing to build infrastructure from scratch.
Backtrader – Python backtesting framework with detailed tutorials
Backtrader is an event-driven backtesting framework written in Python, designed to be easy to use for beginners. It has detailed documentation and step-by-step tutorials that cover everything from installation, to writing a simple moving average strategy, to optimizing and automating strategies. With community support and many built-in features like analyzers, cerebro optimization and live broker integration, backtrader allows quickly researching and testing investment ideas.
Freqtrade – Open source crypto trading bot with examples
As a popular open source crypto trading bot written in Python, Freqtrade comes packed with strategy examples for beginners to reference. These include sample moving average crossovers, Ichimoku strategies and more. By showcasing coding templates and best practices out of the box, Freqtrade accelerates the learning process for Python programmers looking to program their own automated bitcoin and altcoin game plans.
Lean – C# and Python algorithmic trading engine
For beginners with some C# or Python experience, Lean is an algorithmic trading platform that facilitates rapid strategy prototyping and testing. With integrated brokerages and a huge library of community strategies, beginners can reference implementation examples ranging from simple technical analysis to advanced machine learning techniques. Lean emphasizes ease of use, aiming to minimize coding requirements for quants.
Zipline – Pythonic library with bundles for testing
As a Pythonic algorithmic trading library, Zipline offers beginner-friendly features like bundles that come preloaded with historical data. This allows seamlessly backtesting strategies out of the box across stocks, cryptocurrencies and other product classes. An event-driven architecture optimized for backtesting makes it simple for novice coders to test ideas. Detailed documentation and guides provide references for investors with basic Python under their belt.
Leveraging open source GitHub investment strategy examples and frameworks allows beginners to hit the ground running. With ready-made tools tailored for simplicity and testing, quants can focus efforts on strategy research versus infrastructure buildout during initial phases.