Algorithmic trading is one of the most popular ways to use computers in the financial markets. Major banks and Wall Street institutions use algorithms to trade anything from traditional assets such as stocks to newer markets like cryptocurrencies. It is definitely advantageous to automate your trading as you do not need to be in front of a terminal for hours a day and letting a bot do your work also erases the emotion that so many traders bring to the marketplace but there’s a little more to it than that. Understanding the tools is only one part of developing automated trading strategies.
Here are some of the best books on algorithmic trading that you can find to learn more on the topic. They all have something different to offer, and all of them are worth a deeper look.
Advances in Financial Machine Learning
by Marcos Lopez de Prado
Advances in Financial Machine Learning addresses some of the most practical aspects of how automated tools can be used in financial markets. Artificial Intelligence (AI) and Machine Learning (ML) operate with large amounts of data, and the author of the book discusses how to best use these data sets in creating trading tools.
Marcos Lopez de Prado talks about both the theories that go into creating successful algorithmic trading tools, and also how to code these ideas into a usable form. While these coding sections may not be a perfect fit for every investor who is interested in automated trading, the theoretical ideas that Prado brings to the table will have a wide appeal.
In addition to being an AI/ML developer, Prado is also a portfolio manager. This range of experience allows him to take the ideas that go into creating a successful trading system, and demonstrate how to code them into a practical tool, and also show how they operate in the real world.
Prof. Frank Fabozzi, of the EDHEC Business School and the Editor of The Journal of Portfolio Management, commented on the book,
"...Dr. López de Prado's book is the first one to characterize what makes standard machine learning tools fail when applied to the field of finance and the first one to provide practical solutions to unique challenges faced by asset managers. Everyone who wants to understand the future of finance should read this book."
To be sure, the application of existing ML tools to finance is bound to be fraught with difficulties. Many of these advanced big data tools were created to understand specific biotic systems, or with purely academic purposes in mind. As a result, many automated tools fail to create profits when deployed to the markets, even if they appear to succeed in a backtested environment.
Prado accomplishes his work by:
- Showing how big data can be best used by AI/ML platforms
- Processing data with ML for optimal results
- Avoiding false positives that can be created when AI/ML is applied to datasets incorrectly
- Addressing the best ways to use supercomputers in financial algorithms
Anyone interested in making the most of ML in algorithmic trading can easily gain from Prado's work. Bridging the gap between established ML methods and financial applications is still a new area of research, and the author of this book has made a meaningful contribution to the field.
Python for Finance: Mastering Data-Driven Finance
by Dr. Yves Hilpisch
Python, the programming language is the hottest thing in the financial software development space. Dr. Yves Hilpisch is widely recognized in the industry as being both an expert in Python, and also in how to use it and other programming environments in the financial markets.
In Python for Finance: Mastering Data-Driven Finance, Dr. Hilpisch dives into how to best develop Python programming skills that can be put to immediate use in the algorithmic trading sector. This isn't his first book on the subject, and he is also a founder, as well as a managing partner, of The Python Quants.
The Python Quants is a group that is focused on developing open source solutions to algorithmic trading challenges in the global financial markets. While the group does have a much wider scope than the Python language, Dr. Hilpisch is clearly an authority on how to set up trading algos in the cutting edge language.
Unlike some devs, Dr. Hilpisch and The Python Quants are centered on both developing algorithmic trading tools and helping people learn how to use them, even if they have few existing programming tools at their disposal.
From a practical perspective, this makes Dr. Hilpisch's views invaluable for people who are just beginning to learn Python or need to see how to better apply their skills to the financial markets. Python has become a very popular development tool for a wide range of applications, but using it in the markets to create profits requires a specialist view.
Python for Finance: Mastering Data-Driven Finance does require that the reader have some background in programming, as the book focuses on how to use the language in real trading environments. If a person needs to get up to speed on how these tools can be used in finance, The Python Quants is worth looking into for more educational resources.
Python is also widely used in the blockchain and crypto development space, which is another reason to understand how these tools can be used in a global financial market that evolving every year.
Dr. Hilpisch has also written and worked on many other books on effective programming for financial markets, including Python for Finance, Derivatives Analytics with Python, as well as Listed Volatility and Variance Derivatives.
Python for Finance: Mastering Data-Driven Finance is a great resource for anyone that wants to learn more about how to use this language in algorithmic trading development, and also for coders who need more insight on how their skills may be useful in the financial markets.
Building Winning Algorithmic Trading Systems
by Kevin J. Davey
No matter how it is created, an algorithmic trading system has to make money when it hits the markets. In Building Algorithmic Trading Systems: A Trader's Journey From Data Mining to Monte Carlo Simulation to Live Training Kevin Davey shows readers how to take top-tier algorithmic systems and make sure they live up to a trader's needs in reality.
Davey had a background in aerospace engineering and quality assurance before he jumped into the markets. At first, he didn't do much to ensure that his trading systems were backtested, and worked under extensive simulation. The result – wasn't pretty.
According to Davey,
"I remember starting out with a $5,000 account...and within a week or so I had lost about $1,000 trading a moving average crossover system. So, I then decided if my crossover system wasn't working, surely the opposite of that signal would work - a classic 'newbie' mistake. Within another week or two, I had lost another $2,000, or 60% of my bankroll. At that point, I got smart and quit trading for a while."
It was during this time that Davey took a deep dive into how to create winning strategies. He researched algorithmic trading, and also how to test these systems before putting them into use with a live trading account.
The results were dramatic. Davey won a global futures trading competition in 2006 with triple-digit returns and also placed in the top three positions for three years running.
Building Algorithmic Trading Systems: A Trader's Journey From Data Mining to Monte Carlo Simulation to Live Training includes a tremendous amount of background information on algorithmic trading, as well as a proprietary Monte Carlo simulator readers can use to test their algorithms that Davey created.
According to other money managers, these tools are extremely useful. Peter Hagen of Citracado Capital, LLC, wrote,
“Of all the trading books that I’ve read, this book takes the cake. Kevin Davey brings us a realistic perspective in an industry full of dreamers. I suggest that all traders drop what they’re doing and read the incredibly valuable lessons summed up in this book. This book is the quickest path for a new trader to stop dreaming and start succeeding.”
For anyone that wants a practical, no-nonsense book to guide them through creating, testing, and finally deploying trading algorithms into the financial markets, Building Algorithmic Trading Systems: A Trader's Journey From Data Mining to Monte Carlo Simulation to Live Training deserves their attention.
The years of trading success that Davey brings to bear are valuable from a technical perspective, but also from one that shows trading failure can lead to learning, and big profits in the future.
Algorithmic Trading: A Practitioner's Guide
by Jeffrey M Bacidore
While algorithmic trading is becoming very popular, few people have the depth of experience that Dr. Jeffrey M Bacidore has to offer traders and developers. In Algorithmic Trading: A Practitioner's Guide, Dr. Bacidore offers his readers a deep dive into some of the most important aspects of using algorithms in the markets.
The book includes a detailed analysis of a range of tools, including,
-Volume-Weighted Average Price (VWAP)
-Percent of Volume (POV)
-Multi-order algorithms, like Pairs Trading and Portfolio Trading solutions
-Time-Weighted Average Price (TWAP)
-Smart routers – like smart limit, smart market, and dark aggregators
-Variants of the Implementation Shortfall algorithm
-Trading performance measurement
Dr. Bacidore has worked on algorithmic trading programs for companies like Goldman Sachs and ITG (now called Virtu). He was also the Head of Research at the New York Stock Exchange as well as the Head of Research and Consulting in Credit Suisse's Advanced Execution Services (AES) group.
Like any field, there is no replacement for experience working in the real world.
The author has worked to create a guide to algorithmic trading that will apply to established markets, but also new markets, like cryptocurrency. The tools that Dr. Bacidore works on within Algorithmic Trading: A Practitioner's Guide should be of use to anyone interested in the topic, regardless of whether their goals are commercial or academic.
With the increasing use of algorithmic models and trading in the financial markets, the insights that Dr. Bacidore offers in this book could be of use to anyone that wants to better understand how modern markets function, and how they are likely to evolve from here.
Mark Carhart, the Chief Investment Officer and a founding partner of Kepos Capital who is also a former Partner and Co-Chief Investment Officer of the Quantitative Investment Strategies Group at Goldman Sachs Asset Management thinks that,
"...today's algorithmic landscape is highly technical, varied, and intimidating. Jeff brings a unique combination of theoretical knowledge and years of practical experience building and using algos in several different contexts; as a result, he provides an incredibly accessible framework from which to evaluate algo choices.”
The nuanced and deep world of algorithmic trading is where Dr. Bacidore's talents have grown, and this book is being used by both professional traders and for university-level instruction. Anyone that wants to leverage Dr. Bacidore's lifetime of learning and experience may want to dig a little deeper into what may be one of the best books on algorithmic trading on the market.
Learn Algorithmic Trading
by Sebastien Donadio and Sourav Ghosh
Anyone who wants to get into trading can learn from Learn Algorithmic Trading by Sebastien Donadio and Sourav Ghosh. If you are new to the world of trading or don't understand the fundamentals of algorithmic trading, this book is a great place to start. Donadio and Ghosh take a bottom-up approach to algorithmic trading and get the reader ready for using algorithms in the market.
The book begins with an overview of what algorithmic trading is, and how it can help traders to make money in the financial markets. While many AI/ML books take a wider view of the technology, Learn Algorithmic Trading is 100% focused on how algorithms can be used to create profitable trading strategies.
The work continues by opening the door to how technical analysis is used in trading, and how automated trading strategies can use these tools to analyze how markets move. Basic ML tools are introduced, and there are examples of how ML can be used to predict price moments. Donadio and Ghosh then dive into how trading strategies can be built into an algorithm and used for real-world trading.
In some ways algorithmic trading uses existing trading techniques, and with the ability to crunch big data, these tools are used in a way that could only be accomplished with the help of a computer. Like any successful trading system, the authors work with risk management tools so that traders can limit the losses that occur in any trading operation.
Both of the authors have a strong background in trading to draw on. Sebastien Donadiois the CTO at Tradair, where he creates trading tools for use in the markets. He has also worked on high-frequency trading operations, and also developed trading tools for Sun Trading. Sourav Ghosh has a background in high-frequency trading, and he has built numerous tools for the HFT sector.
Opening the door to algorithmic trading can seem daunting, especially if a person plans to create their own tools. With Learn Algorithmic Trading many of the most challenging topics are handled in an easy to understand way, and the authors take the reader from theory into practical development, and then actual market trading.
This book is a wonderful place to start learning about algorithmic trading, as well as how statistical models can be used to make money in the financial markets. The two authors do a great job at making a complex topic easy to grasp so that the reader can enter the algorithmic trading sector with confidence.
A Guide to Creating a Successful Algorithmic Trading Strategy
by Perry J. Kaufman
For anyone that wants to create their own algorithmic trading system, A Guide to Creating a Successful Algorithmic Trading Strategy by Perry J. Kaufman is a must-own book. In his work, Kaufman lays out all the ingredients that allow a developer to find the right trading tools, and build them into a trading strategy that makes consistent profits.
In addition to creating solid algorithmic trading strategies, readers will also gain insight into where algorithmic trading came from, and how it evolved into the toolset that many professional traders use today. There is a lot that goes into creating and testing a successful trading algorithm, and in this book, Kaufman helps his readers to build new strategies from the ground up.
To be sure, there are loads of trading strategies and tools out there, and a successful trader has to know how to identify the good tools and drop things that simply don't create profits. This book is equally valuable to traders that want to buy existing algorithmic trading tools, but don't want to waste money on an expensive mistake that will never make them a dime in the markets.
According to Stanley Dash, CMT, who is VP of Applied Technical Analysis at TradeStation®,
"While many authors obfuscate, Perry Kaufman demystifies. For new and experienced algorithmic traders, this book maintains a steady focus on the critical elements that can drive success. The book itself follows Perry's guidelines: put the probabilities on your side, pay attention to details, and don't overcomplicate the machinery. Thank you, Perry."
Kaufman has been building trading algos since the 1970s and has a great handle on how the market has changed over the years. With his professional insight, almost anyone who is interested in entering the algorithmic trading sector will have a much better idea of what tools and background knowledge they will need once they finish this book.
Algorithmic trading starts with learning more about how statistical models can be used to generate trading strategies, and also how those tools are used in the markets. Kaufman creates a structure to both develop, and evaluate trading strategies, and also gives his readers information on how others have created winning trading tools.
While many algorithmic trading books focus on the technical side of creating the code to operate trading strategies, A Guide to Creating a Successful Algorithmic Trading Strategy begins at a place almost anyone can understand, even if they don't have coding abilities.
This book is a good place to start learning about algorithmic trading, and it can also help people who have the coding skills to create trading algorithms but need more information about how to use trading strategies to create profits.
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