The awesome-systematic-trading repository is a curated collection of resources designed to help traders find, develop, and run systematic and quantitative trading strategies. Hosted on GitHub by the paperswithbacktest organization, the repository serves as a comprehensive directory linking to 97 libraries and packages, over 40 documented strategies, 55 books, 23 videos, plus blogs and courses relevant to algorithmic trading. The repository is primarily written in Python and maintains a homepage at paperswithbacktest.com where users can access additional trading strategy implementations.
The repository's main purpose is to aggregate and organize tools and educational materials across multiple categories of systematic trading infrastructure. The libraries section is subdivided into specialized domains including backtesting and live trading frameworks, with further organization into event-driven and vector-based approaches. Beyond core trading platforms, the collection includes packages for cryptocurrencies, trading bots, analytics, technical indicators, metrics computation, optimization, pricing models, risk management, broker APIs, and data sources. The data science section encompasses databases, graph computation, machine learning libraries, time series analysis tools, and visualization packages relevant to quantitative finance.
The strategies section categorizes documented approaches by asset class, covering bonds, commodities, currencies, equities, REITs, and cryptocurrencies. The books collection is organized by topic including beginner guides, biographies, coding resources, cryptocurrency trading, general finance, high-frequency trading, and machine learning applications. The repository includes a Chinese language version of the README, indicating an international audience focus.
The most active contributor tracked by GitGenius is jerheng with one recorded event.
The repository encourages community contributions through GitHub issues and social media sharing, explicitly inviting users to submit suggestions and help expand the curated list. The collection is structured with a detailed table of contents allowing users to navigate between libraries organized by functionality, strategies categorized by asset classes, books grouped by expertise level and topic, and supplementary educational content. Each library entry includes a description, GitHub star count, and implementation language, enabling users to assess popularity and technical implementation details when selecting tools for their trading development needs.
