Awesome Systematic Trading: Quantitative Finance Resources
paperswithbacktest/awesome-systematic-trading
Awesome Systematic Trading is a curated list of systematic-trading and quantitative-finance resources โ tools, strategies, and educational material for algorithmic trading. Reach for it if you want a broad map of what's available across algo trading; skip it if you want in-depth annotated reviews or step-by-step tutorials for specific tools.
What is the Awesome Systematic Trading List?
Awesome Systematic Trading is a curated GitHub repository compiling an extensive list of resources for individuals looking to find, develop, and execute systematic (quantitative) trading strategies. It functions as a directory, categorizing various tools, academic papers, books, and other educational content essential for anyone delving into algorithmic trading.
The Challenge of Finding Trading Resources
Navigating the fragmented and rapidly evolving world of systematic trading and quantitative finance can be daunting, with valuable tools, academic research, and educational content scattered across numerous platforms. Developers and researchers often struggle to find a centralized, organized starting point for discovering relevant algotrading libraries list, trading strategies collection, and learning materials.
What Resources Are Included?
- โA collection of 97 libraries and packages for research and live trading, including backtesters, trading bots, indicators, and pricers.
- โA list of 40+ systematic trading strategies, previously detailed by institutionals and academics, with some now hosted externally.
- โA selection of 55 books covering systematic trading for various skill levels, from beginners to professionals.
- โA compilation of 23 videos and interviews offering insights into quantitative trading.
- โSections dedicated to blogs and courses to further learning in algorithmic trading.
- โCategorized resources covering analytics (technical analysis, metrics, portfolio optimization, pricing, risk management), broker APIs, and financial data sources.
- โInclusion of data science tools (e.g., TensorFlow, PyTorch, Pandas), databases for financial time series, and graph computation frameworks.
- โSpecific sections for machine learning in finance, including platforms like QLib and FinRL, and time series analysis libraries.
Why Use This Curated List?
- โProvides a broad, categorized overview of systematic trading resources, saving significant research time.
- โIncludes a substantial number of Python-based libraries and frameworks, catering to a popular language in quant finance.
- โCovers a wide spectrum of topics from backtesting frameworks and data sources to machine learning finance and risk management.
- โReferences academic papers and books, offering both practical tools and theoretical foundations for quantitative trading.
- โThe list is actively maintained, inviting community contributions through issues for suggestions.
Potential Drawbacks and Considerations
- โณMany listed resources lack detailed descriptions beyond a single sentence, requiring users to click through to each individual repository or paper for more context.
- โณThe README notes that some listed 'trading bots' may be old and not maintained, implying potential issues with currency and reliability for certain categories.
- โณThe primary list of 40+ strategies has been moved off GitHub to an external site, reducing the self-contained nature of the repository for this key resource type.
- โณThe list itself does not provide explicit licensing information, which might be a concern for some users looking to reuse or build upon the curated content.
- โณWhile comprehensive, it's a directory, not a tutorial; users must still independently learn how to use each listed tool.
Who Benefits Most from This List?
This systematic trading resources list is for aspiring quantitative analysts, experienced algorithmic traders seeking new tools, academic researchers in finance, and developers looking to build out their quant trading curriculum. It serves as an excellent starting point for anyone exploring the diverse landscape of quantitative finance learning and algotrading.
Frequently Asked Questions
Awesome Systematic Trading includes libraries and packages for research and live trading, systematic trading strategies, books, videos, interviews, blogs, and courses. It covers areas like backtesting frameworks, data sources, analytics, and machine learning finance.
Awesome Systematic Trading lists 97 libraries and packages, over 40 strategies (now hosted externally), and 55 books. Additionally, it includes 23 videos and interviews, along with various blogs and courses.
Yes, you can contribute to Awesome Systematic Trading by submitting an issue with your suggestions for new resources or improvements. Sharing it on Twitter also helps support the project.
Yes, Awesome Systematic Trading offers a Chinese version of the README. You can access it by clicking the link provided at the top of the main README file.
The strategies section of Awesome Systematic Trading indicates that the detailed strategies are now hosted on paperswithbacktest.com. The GitHub README provides links to a few examples, some of which are implemented via QuantConnect.
While the repository itself is primarily Python-focused, the listed libraries in Awesome Systematic Trading include resources made with Python, Go, Rust, C++, C#, Javascript, and PHP, reflecting the diverse tools used in algorithmic trading.
Alternatives
Best use cases
- โขDiscovering new libraries and frameworks for building or enhancing algorithmic trading systems.
- โขResearching academic and institutional trading strategies for inspiration or implementation.
- โขFinding educational materials, including books and courses, to learn quantitative finance.
- โขIdentifying tools for financial data collection, analysis, and visualization.
- โขExploring machine learning applications and portfolio optimization techniques in finance.
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