---
product_id: 59141537
title: "Advances in Financial Machine Learning"
price: "587 kr"
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reviews_count: 8
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region: Denmark
---

# Advances in Financial Machine Learning

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Advances in Financial Machine Learning [Lopez de Prado, Marcos] on desertcart.com. *FREE* shipping on qualifying offers. Advances in Financial Machine Learning

Review: Practical, up-to-date, full of nuggets of useful info on building ML trading systems - Well-written, well-researched book that provides new insight in many areas; much better than your run-of-the-mill book that gives a cursory overview of topics like ML and finance. It's obvious from reading this book that the author knows what he's talking about. The book is for people who already know something about both ML and machine trading systems; it's not an introduction. A major theme of the book is the danger of backtest overfitting. Highly recommended! An overview of topics follows, focusing on things I found the most useful. Part 1: Data Analysis (chapters 1-5) Chapter 1 includes a list of reasons that financial ML projects usually fail. It's nice to have the warning at the outset of what traps to avoid. Chapter 2 discusses ways to represent market information; there are surprises here you wouldn't think of on your own, such as "Tick Imbalance Bars", which show up here only because the author has significant experience in HFT (cf. one of his earlier books on market microstructure.) Chapter 3 on Labelling addresses some issues I ran into myself working on dataset preparation. How to do appropriate labelling in computational finance isn't as obvious as in some other ML domains. Chapter 4 continues with appropriate weights for data samples, which help deal with data that violates the IID assumption. I knew the basics of ARIMA processes and the idea of time series stationarity before reading this book, but Chapter 5 introduced me to fractionally differentiated time series, which appears in earlier literature in the 1980s, but which somehow I had missed. This is important in dealing with time series with long memory. Part 2, Modeling (chapters 6-9) Part 2 has a different focus than some readers might expect, talking about modeling in general, but without mentioning specific ML models, such as linear regressions, neural nets, random forests, etc. The book states in its introduction that it is intended to be model-agnostic, and covers issues affecting modeling in general. Chapter 6, for instance, is on ensembles. Chapter 7 deals with cross-validation in financial time series, which is a crucial topic for any model evaluation, and chapter 8 talks about feature importance. Chapter 9 is on hyper-parameter tuning. Part 3: Backtesting (chapters 10-16). Chapter 10 covers bet sizing. Chapter 11-16 discuss backtesting in great detail, including the dangers involved, good statistics to compute, synthetic data use, more on cross-validation, and strategy risk and asset allocation/optimization. Part 4 (chapters 17-19) is called "Useful Financial Features" -- it's about feature engineering, and has a bunch of features that will be new to many readers (there are "entropy features" and "microstructure features", for instance; this isn't just the basic stuff related to returns and volatility. Part 5 (chapters 20-22) seemed the least useful to me, on high-performance computing, since I personally was already aware of this stuff and there is a lot more to say on the topic than these few chapters could cover.
Review: Foundational Resource for SWEs, ML Researchers getting into Investing - If you're coming from a computer science and/or machine learning background, you will learn a lot about how to frame your algorithmic thinking in the domain of finance and will leave you hungry for more hardcore graph theory, parallelization, machine learning (beyond simple random forest ensembles and clustering), advanced algorithms, and gutty details of implementation, which are left for you to explore and enjoy. The purpose of this book is not to explain how to apply Deep Learning to make money, but rather to lay a solid foundation of how to invest in a scientifically rigorous fashion given the modern machine learning toolset and access to PBs of data. In many cases, rather than focussing on the specifics of any given model, Dr. Lopez de Prado focuses on generating and selecting useful features. The book, which is a hybrid of a textbook and a manual, explains using both formal mathematics and empirical evidence why many of the assumptions about Machine Learning applied to the financial world are wrong and follows through with rigorous and practical solutions. For example, one of the most common false assumptions addressed in the book is that of IID samples in financial time series data. Dr. Lopez de Prado manages to pull together ideas from a wide spectrum of academic disciplines including mathematics, econometrics, machine learning, computer science, information theory, and physics to build a strong scientific basis upon which to algorithmically invest. Despite the diversity of subject matter, the book progresses well, building on and reusing early themes and then exploring domain specific topics like market microstructure and quantum computing. Source code to implement many of the methods is provided as a practical toolkit to test out the claims presented. The thorough use of references is particularly helpful as it keeps the content fairly short and to the point. Speed reading not recommended. Using a programming analogy, the mathematical notation is more reminiscent of the explicit verbosity of C++ than that of python (which is used in the book and is meant to be concise). It's not much of a problem but be aware the information content is dense. Something that's mentioned but not explored is how to make use of “alternative datasets”. Given many of the advances in the wider realm of ML have been around data you don’t get from exchanges, it would be nice if some helpful pointers or references for dealing with alternative data were included. That said, it's not the end of the world given the wealth of resources online for analyzing text, image, and video data. Buy this book if you're an experienced programmer getting into Finance or a Financial Professional looking to strengthen your algorithmic understanding. It is densely packed with a wealth of practical methods and breaks down and offers alternatives to faulty investing science.

## Technical Specifications

| Specification | Value |
|---------------|-------|
| Best Sellers Rank | #40,630 in Books ( See Top 100 in Books ) #6 in Machine Theory (Books) #10 in Business Investments #224 in Investing (Books) |
| Customer Reviews | 4.4 out of 5 stars 677 Reviews |

## Images

![Advances in Financial Machine Learning - Image 1](https://m.media-amazon.com/images/I/71Me6awuErL.jpg)

## Customer Reviews

### ⭐⭐⭐⭐⭐ Practical, up-to-date, full of nuggets of useful info on building ML trading systems
*by E***S on November 3, 2019*

Well-written, well-researched book that provides new insight in many areas; much better than your run-of-the-mill book that gives a cursory overview of topics like ML and finance. It's obvious from reading this book that the author knows what he's talking about. The book is for people who already know something about both ML and machine trading systems; it's not an introduction. A major theme of the book is the danger of backtest overfitting. Highly recommended! An overview of topics follows, focusing on things I found the most useful. Part 1: Data Analysis (chapters 1-5) Chapter 1 includes a list of reasons that financial ML projects usually fail. It's nice to have the warning at the outset of what traps to avoid. Chapter 2 discusses ways to represent market information; there are surprises here you wouldn't think of on your own, such as "Tick Imbalance Bars", which show up here only because the author has significant experience in HFT (cf. one of his earlier books on market microstructure.) Chapter 3 on Labelling addresses some issues I ran into myself working on dataset preparation. How to do appropriate labelling in computational finance isn't as obvious as in some other ML domains. Chapter 4 continues with appropriate weights for data samples, which help deal with data that violates the IID assumption. I knew the basics of ARIMA processes and the idea of time series stationarity before reading this book, but Chapter 5 introduced me to fractionally differentiated time series, which appears in earlier literature in the 1980s, but which somehow I had missed. This is important in dealing with time series with long memory. Part 2, Modeling (chapters 6-9) Part 2 has a different focus than some readers might expect, talking about modeling in general, but without mentioning specific ML models, such as linear regressions, neural nets, random forests, etc. The book states in its introduction that it is intended to be model-agnostic, and covers issues affecting modeling in general. Chapter 6, for instance, is on ensembles. Chapter 7 deals with cross-validation in financial time series, which is a crucial topic for any model evaluation, and chapter 8 talks about feature importance. Chapter 9 is on hyper-parameter tuning. Part 3: Backtesting (chapters 10-16). Chapter 10 covers bet sizing. Chapter 11-16 discuss backtesting in great detail, including the dangers involved, good statistics to compute, synthetic data use, more on cross-validation, and strategy risk and asset allocation/optimization. Part 4 (chapters 17-19) is called "Useful Financial Features" -- it's about feature engineering, and has a bunch of features that will be new to many readers (there are "entropy features" and "microstructure features", for instance; this isn't just the basic stuff related to returns and volatility. Part 5 (chapters 20-22) seemed the least useful to me, on high-performance computing, since I personally was already aware of this stuff and there is a lot more to say on the topic than these few chapters could cover.

### ⭐⭐⭐⭐⭐ Foundational Resource for SWEs, ML Researchers getting into Investing
*by A***Y on April 20, 2018*

If you're coming from a computer science and/or machine learning background, you will learn a lot about how to frame your algorithmic thinking in the domain of finance and will leave you hungry for more hardcore graph theory, parallelization, machine learning (beyond simple random forest ensembles and clustering), advanced algorithms, and gutty details of implementation, which are left for you to explore and enjoy. The purpose of this book is not to explain how to apply Deep Learning to make money, but rather to lay a solid foundation of how to invest in a scientifically rigorous fashion given the modern machine learning toolset and access to PBs of data. In many cases, rather than focussing on the specifics of any given model, Dr. Lopez de Prado focuses on generating and selecting useful features. The book, which is a hybrid of a textbook and a manual, explains using both formal mathematics and empirical evidence why many of the assumptions about Machine Learning applied to the financial world are wrong and follows through with rigorous and practical solutions. For example, one of the most common false assumptions addressed in the book is that of IID samples in financial time series data. Dr. Lopez de Prado manages to pull together ideas from a wide spectrum of academic disciplines including mathematics, econometrics, machine learning, computer science, information theory, and physics to build a strong scientific basis upon which to algorithmically invest. Despite the diversity of subject matter, the book progresses well, building on and reusing early themes and then exploring domain specific topics like market microstructure and quantum computing. Source code to implement many of the methods is provided as a practical toolkit to test out the claims presented. The thorough use of references is particularly helpful as it keeps the content fairly short and to the point. Speed reading not recommended. Using a programming analogy, the mathematical notation is more reminiscent of the explicit verbosity of C++ than that of python (which is used in the book and is meant to be concise). It's not much of a problem but be aware the information content is dense. Something that's mentioned but not explored is how to make use of “alternative datasets”. Given many of the advances in the wider realm of ML have been around data you don’t get from exchanges, it would be nice if some helpful pointers or references for dealing with alternative data were included. That said, it's not the end of the world given the wealth of resources online for analyzing text, image, and video data. Buy this book if you're an experienced programmer getting into Finance or a Financial Professional looking to strengthen your algorithmic understanding. It is densely packed with a wealth of practical methods and breaks down and offers alternatives to faulty investing science.

### ⭐⭐⭐⭐⭐ A must read
*by I***N on November 13, 2019*

A fantastic addition to finance literature with must read chapters on backtesting pitfalls, hierarchical risk parity, deflated Sharpe ratios, explosiveness tests, entropy(complexity) estimators, and microstructural features. You can and should read this book even with no knowledge of machine learning. You will find it more useful if you have worked through the first four chapters of Aurelian Geron’s Hands-On Machine Learning. No knowledge of fully connected neural networks, convolutional networks or recurrent networks is required. Dr de Prado works in the realm of systematic quantitative investing. I think of this as trading rather than investing, where you expect to make a large number of decisions, each with a probability of success somewhat over 50%. The comprehensive book Expected Returns by Antti Ilmanen explains how difficult it is to find systematic opportunities that last. I suspect this applies to all quantitative strategies, including ones informed by machine learning, although we all would love to understand exactly how the folks at Renaissance Technologies construct and update their models. I think of investing as making a small number of decisions based on extensive due diligence, where you expect a high probability of success, much greater than 50%. A private equity professional or an old fashioned stock picker like Warren Buffett makes at most a handful of investment decisions a year. If they see a business with legs, one that will sustain growth of 20%+ per year trading a P/E of under 20, that business is unlikely to see much multiple compression and so is likely to yield 15% annual returns over the next 5 years. Or they might anticipate similar returns from a mature business growing at 5% per year- with no margin erosion - and 10% free cash flow yields. These investors usually work best in teams due to the broad scope of their due diligence and also to achieve some diversification. I do believe Dr de Prado’s ideas can be used to help inform some of these decisions but clearly we are dealing in the world of small numbers, even at the largest private equity groups and hedge funds. Nevertheless, any young professional entering these fields would benefit by at least understanding how to work with data scientists who can help improve their decision making. I am inclined to agree with Dr de Prado that there are a lot of charlatans in the investing/trading world and it is not easy to figure out who they are. This, taxes and the wonders of compounding in a low signal to noise world are what makes low cost passive investing the right choice for most people trying to build a nest egg for retirement.

## Frequently Bought Together

- Advances in Financial Machine Learning
- Machine Learning for Algorithmic Trading: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python, 2nd Edition
- Machine Learning for Asset Managers (Elements in Quantitative Finance)

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*Last updated: 2026-03-11*