Financial Engineering with Machine Learning and Python: Or How to Build Your Own Quantitative Hedge Fund

by Irene Aldridge

Apply machine learning and AI to real financial challenges, from data analysis to strategy automation, and gain practical skills to thrive in investment, risk management, and quantitative finance roles

Key FeaturesBased on the author’s Cornell MFE course and proven classroom successCutting-edge material in both finance and machine learningIncludes exclusive access to a website with additional learning resourcesBook DescriptionIn a world where machine learning and AI are becoming increasingly prevalent, it is crucial not to be left behind. This book goes beyond the typical machine learning and Python books by incorporating in-depth finance content to provide readers with a unique understanding of how these technologies intersect in the financial realm.

Starting with a review of the basics of financial and econometric analyses and coding, readers will quickly and intuitively progress to more sophisticated techniques. The book equips readers with the necessary knowledge to successfully apply machine learning and AI to various finance-related problems. Instead of solely focusing on the intricacies of machine learning algorithms, this book emphasizes the strategic use of machine learning and python as enablers for solving real-world finance problems.

By the end of the book, readers will not only have a solid grasp of different types of advanced AI mechanisms, but they will also possess the ability to compare various machine learning techniques and select the most appropriate one for the specific problem at hand. Bridging the gap between theory and practice, readers will be able to build their own efficient and effective machine learning models to tackle the challenges and complexities of the finance industry.

What you will learnLearn the fundamentals of financial analysis with PythonUnderstand different types of advanced machine learning and AI mechanisms and how they fit into today's quantitative research frameworksSelect the best machine learning techniques for the financial problem at handAnalyze financial data and structure sound forecastsBuild backtests and reports required by investorsDevelop in-house AI models that automate the processesWho this book is forThis book is ideal for aspiring quants, financial engineers, data scientists, and investment professionals looking to apply machine learning and AI in real-world finance. It also serves quantitative finance students and fintech practitioners aiming to build their own data-driven trading strategies or hedge funds. A basic understanding of Python, probability, and linear algebra is helpful but not required

Table of ContentsMachine Learning and Modern Financial LandscapePrices and ReturnsInvestment Performance MeasuresData CleaningRisk-Return Tradeoffs and Efficient FrontierModel Performance, Linear Regression and Factor ModelsLinear Regression, Statistical Arbitrage and Market-Neutral StrategiesPenalized Regressions and Portfolio OptimizationK-Nearest Neighbors and Support Vector Machines Bayesian Learning Decision Trees Random Forests Semi-supervised LearningNeural Networks Transformers Unsupervised LearningExplaining returnsAdvanced Portfolio Strategie

Kindle Edition

Expected publication September 9, 2026