100 Books for Algorithmic Trading Professionals – Part 4: AI and Machine Learning in Trading

Introduction

Machine learning has reshaped financial markets by introducing models that can process vast datasets, identify non-obvious patterns, and optimize trading decisions with speed and precision. What started as statistical arbitrage and rule-based strategies has evolved into a space where deep learning, reinforcement learning, and advanced probabilistic methods define competitive edge. Traders, asset managers, and quants rely on AI not just for predictions but for structuring portfolios, managing risk, and automating execution.

The books in this section explore different facets of machine learning in trading. Some focus on foundational techniques, such as regression models and neural networks, while others dive into specialized areas, including high-frequency trading, statistical arbitrage, and market simulation. Each provides insights into structuring data pipelines, refining predictive models, and managing execution in a trading environment shaped by AI.

Advances in Financial Machine Learning

Author: Marcos Lopez de Prado

Published: 2018

Marcos Lopez de Prado presents practical tools for applying machine learning to finance, focusing on structuring data, conducting research, and reducing errors in predictions. The book explains techniques for testing financial models, using algorithms to solve complex problems, and integrating these methods into trading and investment workflows.

With examples grounded in real-world challenges, it provides readers with the knowledge needed to design effective strategies using scientifically sound solutions and modern computational approaches.

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Machine Learning for Asset Managers

Author: Marcos Lopez de Prado

Published: 2020

The book introduces machine learning tools for asset managers, focusing on developing theory-driven investment strategies rather than relying solely on backtesting. It highlights techniques like out-of-sample predictability, computational modeling to avoid unrealistic assumptions, and managing complex, high-dimensional interactions.

By blending classical statistical methods with advanced machine learning, it provides practical insights into optimizing strategies and uncovering economic and financial patterns without overfitting models.

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Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

Author: Aurélien Géron

Published: 2019

Aurélien Géron provides a hands-on guide to implementing machine learning systems using Scikit-Learn, Keras, and TensorFlow. The book covers essential techniques, from linear regression to deep neural networks, and includes practical exercises to apply these concepts in real-world scenarios.

Readers will learn to build and train models, explore neural network architectures like convolutional and recurrent networks, and scale deep learning systems effectively. It serves as a comprehensive introduction for those with basic programming knowledge.

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Machine Learning for Algorithmic Trading

Author: Stefan Jansen

Published: 2020

Stefan Jansen provides a comprehensive guide to applying machine learning techniques in algorithmic trading. The book covers the entire workflow, from feature engineering and predictive modeling to backtesting strategies using tools like pandas, scikit-learn, and TensorFlow. It demonstrates how to leverage alternative data, such as financial news and satellite images, to generate tradable signals.

Readers will learn advanced methods like NLP, reinforcement learning, and deep learning to optimize trading models and assess performance using tools like Alphalens and SHAP values. This resource is ideal for developing systematic trading strategies using machine learning.

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Artificial Intelligence in Financial Markets

Author: Christian L. Dunis, Peter W. Middleton

Published: 2017

This book explores the applications of artificial intelligence and neural networks in financial forecasting, risk management, and portfolio optimization. It covers a variety of assets, including derivatives and equity instruments, while focusing on methods like time series analysis, market modeling, and pattern recognition.

Organized into four sections, it addresses macroeconomic trends, corporate finance, and credit analysis. Practical insights are provided for portfolio management and optimizing asset allocation, making it a valuable resource for finance professionals and researchers.

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High-Performance Algorithmic Trading Using AI

Author: Melick R. Baranasooriya

Published: 2024

This guide provides a comprehensive framework for developing AI-powered algorithmic trading strategies, covering essential topics such as data preprocessing, feature engineering, and backtesting. It explains how to use Python libraries and tools for financial modeling, platform integration, and the deployment of AI models. Each chapter focuses on actionable skills, from extracting insights from vast datasets to optimizing algorithms for real-world trading scenarios.

With detailed case studies and advanced techniques like deep learning, reinforcement learning, and risk management strategies, the book equips readers to implement effective trading systems. It also introduces emerging technologies, such as quantum computing and blockchain, highlighting their potential applications in finance. Ideal for financial professionals and analysts, this resource bridges theoretical knowledge with hands-on practice.

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Advanced Deep Learning Techniques in Algorithmic Day Trading With CUDA

Author: Jamie Flux

Published: 2024

Jamie Flux explores advanced deep learning applications for algorithmic trading, leveraging CUDA for enhanced computational performance. Key topics include transformer-based time series forecasting to model temporal dependencies, graph neural networks for analyzing inter-stock relationships, and reinforcement learning to improve trading strategies under dynamic conditions.

The book also covers variational autoencoders for anomaly detection, neural differential equations for modeling continuous-time financial systems, and meta-learning techniques for adapting strategies to market changes. Each chapter combines mathematical formulations, practical implementations, and interdisciplinary methods, making it a valuable resource for quantitative analysts and researchers.

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Statistical Arbitrage and Mean Reversion Strategies

Author: Jamie Flux

Published: 2024

Focusing on quantitative trading, this book dives into statistical arbitrage and mean reversion strategies using time series analysis, cointegration theory, and autoregressive models. Tools like the Kalman filter, Bollinger Bands, and Z-Scores are thoroughly explained to help construct effective trading strategies.

It also covers machine learning techniques for feature detection, risk management methods such as VaR and CVaR, and practical approaches to backtesting and model optimization. Complete with real-world examples and Python code, it’s a valuable guide for traders and analysts looking to enhance their quantitative skills.

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Deep Learning in Hilbert Spaces

Author: Jamie Flux

Published: 2024

Jamie Flux introduces Hilbert spaces as a powerful framework for advancing algorithmic trading. The book explores their use in financial modeling, focusing on handling high-dimensional data and capturing nonlinear relationships through techniques like kernel methods and reproducing kernel Hilbert spaces (RKHS).

It extends deep learning architectures into Hilbert spaces, demonstrating their application in functional data processing and high-frequency trading. With detailed explanations of mathematical principles and practical implementations, this resource opens new possibilities for modeling and predicting complex market behaviors.

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Machine Learning for Financial Risk Management with Python

Author: Abdullah Karasan

Published: 2022

Abdullah Karasan presents Python-based machine learning and deep learning techniques for assessing and modeling financial risk. The book explores volatility modeling, market risk measures like VaR and ES, credit risk analysis using clustering and Bayesian methods, and fraud detection.

It also covers the use of Gaussian mixture models, Copula models for liquidity risk, and techniques to predict stock price crashes. By combining theoretical explanations with practical implementations, this guide is ideal for professionals looking to replace traditional risk models with advanced ML-based approaches.

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Modern Time Series Forecasting with Python

Author: Manu Joseph

Published: 2022

Time series forecasting techniques are explored in detail, from traditional ARIMA models to advanced approaches like N-BEATS and Autoformer. The book includes methods for data processing, feature engineering, and visualization, offering tools for building scalable and accurate forecasting systems.

Practical exercises cover ensembling, stacking, and cross-validation strategies, allowing readers to apply machine learning and deep learning techniques effectively. This guide provides a solid foundation for analysts and developers working on real-world time series data.

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Conclusion

Applying AI to trading is not just about predicting prices—it’s about structuring data pipelines, refining models, and ensuring that strategies hold up under real-world conditions. The books in this section illustrate the evolution of machine learning in financial markets, covering everything from basic regression techniques to AI-powered risk assessment and anomaly detection.

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