With the rise of algorithmic and quantitative trading, more investors are looking to develop their skills in this area. Having access to the right books is crucial for building a solid foundation. In this article, we provide essential free ebook recommendations covering beginner to advanced quantitative investing topics. All books contain practical insights into formulating rule-based strategies, analyzing market inefficiencies, leveraging computing power and big data. Key focus areas include statistical arbitrage, machine learning models, backtesting systems, and python programming. Read on for the top free ebooks to advance your quant trading knowledge.
Classic books explaining quant trading fundamentals
Some of the most highly regarded introductory books provide a comprehensive overview of core concepts in quantitative analysis and modeling. These include Ernie Chan’s classic ‘Algorithmic Trading’ which explores practical aspects like mean reversion and momentum strategies. Additionally, the influential ‘Quantitative Trading’ by Xin Guo outlines techniques across stocks, futures and options using MATLAB code examples. For hands-on practice, ‘Quantitative Investing’ by Diana Kudajarova features Jupiter notebooks guiding readers through actual trading strategy implementation. After digesting theoretical groundwork, these free references offer ample models to experiment with when getting started in quant finance.
Cutting edge machine learning algorithms for predictions
With computing power expanding exponentially, sophisticated statistical and machine learning algorithms are being deployed for market forecasts and trade signals. Must-read books in this field include Marcos Lopez de Prado’s ‘Machine Learning for Asset Managers’ which demonstrates concepts like clustering, dimensionality reduction and Keras neural nets for robust strategies. Furthermore, the new book ‘Advances in Financial Machine Learning’ by the same author provides an academic synthesis of the latest techniques based on his pioneering research. For additional machine learning models, check out the free ebook ‘Python for Finance’ covering regression, classification and deep learning for financial analysis.
Backtesting platforms and frameworks for quants
Backtesting trading strategies on historical data is a prerequisite to gauging validity and profitability. The gold standard text on creating rigorous backtesting systems is the recent book ‘Building Reliable Trading Systems’ by Keith Fitschen. Moreover, Dr. Ernest Chan’s newer book ‘Quantitative Trading’ has a supplementary section on selecting optimal tools ranging from paid platforms like QuantConnect to open-source Python libraries like Zipline. For additional comparisons, the free ebook ‘Algorithmic Trading 101’ analyzes a diverse compilation of backtesting packages. Aspiring quants can further build test environments with resources in the Python ecosystem like Pandas, Numpy, Scikit-Learn, PyTorch and TensorFlow.
Supplementary programming languages for trading systems
While Python has become the de facto standard, other languages have specialized applications in quantitative analysis. For alternative open-source options, the book ‘Quantitative Trading with R’ demonstrates quantitative trading workflows using the R statistical language as an alternative to Python. Although less mainstream presently, the Julia language may hold promise for scientific computing applications. The free online book ‘Quantitative Economics with Julia’ by Nobel laureate Thomas Sargent adapts traditional texts to this new language. For lower level languages, the book ‘C++ Design Patterns and Derivatives Pricing’ links financial engineering concepts with high performance at scale.
This selection of essential free ebooks equips quants-in-training with both practical and theoretical knowledge. Budding algorithmic traders can progress from introductory statistical arbitrage to state-of-the-art machine learning techniques. Moreover, these books detail leading open-source libraries for backtesting historical tick data and live trading. By mastering this diverse curriculum, aspiring professionals can participate in quantitative investing at its highest levels.