Books on algorithmic trading#


Here is a list of books related to quantitative finance and algorithmic trading, with their descriptions and links,

Machine Learning for Algorithmic Trading#

A book by Stefan Jansen alongside the ZipLine reloaded and community forum.

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Python For Finance: Algorithmic Trading#

This Python for Finance tutorial introduces you to algorithmic trading, and much more.

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Master AI-Driven Algorithmic Trading#

This is an intense online training program about Python techniques for algorithmic trading. By signing up to this program you get access to 150+ hours of live/recorded instruction, 1,200+ pages PDF as well as 5,000+ lines of Python code and 60+ Jupyter Notebooks (read the 16 week study plan). Master AI-Driven Algorithmic Trading, get started today.

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Python for Data Analysis#

Get the definitive handbook for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.9 and pandas 1.2, the third edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, and Jupyter in the process.

Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub.

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Practical Guide to Applied Conformal Prediction: Learn and apply the best uncertainty frameworks to your industry applications#

mbark on an insightful journey with ‘Practical Guide to Applied Conformal Prediction in Python’, a comprehensive resource that equips you with the latest techniques to quantify uncertainty in machine learning and computer vision models effectively.

This book covers a wide array of real-world applications, including Conformal Prediction for forecasting, computer vision, and NLP, as well as advanced examples for handling imbalanced data and multi-class classification problems. These practical case studies will enable you to apply your newfound knowledge to various industry scenarios.

Designed for data scientists, analysts, machine learning engineers, and industry professionals, this book caters to different skill levels - making it an ideal resource for both beginners and experienced practitioners. Delve into the latest Conformal Prediction techniques and elevate your machine learning expertise.

If you’re eager to manage uncertainty in industry applications using Python, ‘Practical Guide to Applied Conformal Prediction in Python’ is the ultimate guide for you. Order your copy today and propel your career to new heights!

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Further reading lists#

Here are more books on quantitative finance and algorithmic trading topics by the author.

General Finance Textbooks#

  • Options, Futures and Other Derivatives, John Hull

  • The Concepts and Practice of Mathematical Finance, Mark Joshi

  • Paul Wilmott on Quantitative Finance, Paul Wilmott

Option Pricing Theory and Stochastic Calculus#

  • Financial Calculus: An Introduction to Derivative Pricing, Martin Baxter and Andrew Rennie

  • Arbitrage Theory in Continuous Time, Tomas Björk

  • Stochastic Calculus for Finance I: The Binomial Asset Pricing Model, Steven Shreve

  • Stochastic Calculus for Finance II: Continuous-Time Models, Steven Shreve

  • Martingale Methods in Financial Modelling, Marek Musiela and Marek Rutkowski

  • Mathematical Methods for Financial Markets, Monique Jeanblanc, Marc Yor, and Marc Chesney

  • Financial Modelling With Jump Processes, Rama Cont and Peter Tankov

  • Option Volatility and Pricing, Sheldon Natenberg

Quantitative Risk Management#

  • Risk Management and Financial Institutions, by John C. Hull

  • Quantitative Risk Management: Concepts, Techniques, and Tools” by Alexander J. McNeil, Rüdiger Frey, and Paul Embrechts

  • Market Risk Analysis, Volume I: Quantitative Methods in Finance” by Carol Alexander

  • The Concepts and Practice of Mathematical Finance” by Mark S. Joshi

Asset Pricing#

  • Asset Pricing (Revised Edition), Cochrane, John H. Princeton University Press, 2009.

  • Financial Decisions and Markets: A Course in Asset Pricing, Campbell, John Y. Princeton University Press, 2017.

  • Asset pricing and portfolio choice theory, Back, Kerry. Oxford University Press, 2010.

  • Damodaran on Valuation, Damodaran, Aswath, Wiley Finance, 2006

  • Dynamic Asset Pricing Theory (Third Edition), Duffie, Darrell. Princeton University Press, 2001.

Machine Learning#

  • Machine Learning: A Probabilistic Perspective, Kevin P Murphy

  • Advances in Financial Machine Learning, Marcos Lopez de Prado