7 Best Books on Algorithmic Trading 2024
Unveil the secrets of algorithmic trading with the finest guides of the year. From beginners dipping their toes in the trading waters to seasoned traders seeking fresh insights, there's something for everyone in our handpicked selection of the top seven books on algorithmic trading for 2024!
Whether you're a newbie or a pro, our curated list of the top seven books on algorithmic trading for 2024 is your treasure trove of knowledge, insights, and expert strategies.
Algorithmic trading has a reputation for being a bit of a brain-twister for rookies. It's a broad discipline, with some aspects demanding a fair bit of math and statistics wizardry. So, it can seem a tad intimidating at first glance. But here's the exciting bit — the overarching concepts are actually pretty easy to get your head around, and the nitty-gritty can be mastered progressively, one step at a time.
If you're a self-learner like most of us in this community, here's a treasure trove of reading material that could turn you into an algo-trading ace faster than you think.
We'll kick things off by clarifying what algorithms and algorithmic trading really are, and then plunge straight into your fresh reading roster for 2024. Get ready to turn those pages!
What Is Algorithmic Trading?
Trading algorithms, often referred to as algorithmic trading or algo-trading, utilize a computer program that follows a specific set of instructions, or an algorithm, to carry out trading activities.
👉 The beauty of it? This method can potentially generate profits at a speed and frequency that would leave even the quickest human trader in the dust.
These algorithmic instructions base themselves on timing, price, quantity, and other mathematical models. Not only does algo-trading offer enticing profit opportunities, but it also enhances market liquidity and introduces a systematic approach to trading.
For the crypto market, we've got providers like us — Bitsgap — that have automated widely used trading strategies for you. So you're spared from the nitty-gritty of coding the algorithm by hand. Instead, you can put proven strategies to work without typing a single line of code.
Yet, if you're eager to craft your customized algo, we've got a stellar lineup of books to guide you on that journey. Let's dive in!
Best Trading Books to Read on Algorithms
Best Algorithms Books for Beginners: Dr. Chan’s Trading Algorithms Books and Davey’s Classic
Your initial mission is to build a robust understanding of the basics. It's easier to steer clear of complex mathematical discussions until you've got a good handle on the fundamentals. To help you kickstart your journey, we’ve found a couple of fantastic books, both by Dr. Ernest Chan:
- Quantitative Trading: How to Build Your Own Algorithmic Trading Business: Here, Dr. Chan dishes out a brilliant overview of setting up a 'retail' quantitative trading system, leveraging tools like MatLab. He makes the subject incredibly accessible, leaving you with a sense that "you've got this". While it skims over some details, it's a fantastic intro to the inner workings of algo trading.
- Algorithmic Trading: Winning Strategies and Their Rationale: Dr. Chan hinted at momentum, mean reversion, and high-frequency strategies in his first book and dives deeper here with tons of implementation specifics, though with some complex math (think Kalman Filters or CADF). The code relies heavily on MatLab but can be adapted to C++, Python, or R.
Just a heads-up: both books heavily feature MatLab examples. So, if your MatLab skills are still in the works, you might find some parts challenging.
While 'Algorithmic Trading' is part of their titles, neither book provides a step-by-step guide on hooking up a MatLab model or system to the market for real-time algorithmic trading. But don't fret! A gem of advice from an Amazon reviewer named ETO Trader points to a simple solution: a Google search for "MatLab as an Automated Execution System" will take you to a paper by Dr. Chan that illustrates how to connect MatLab to Interactive Brokers through a third-party MatLab interface.
Lastly, even if 'quant' isn't expressly mentioned in the title, you can bet your bottom dollar that the models are rooted in quant principles. So, if you’re looking for oscillators, Gann, or MACD, these books might not be your perfect match.
Also, consider the classic:
- Building Winning Algorithmic Trading Systems by Kevin Darvey, which can become a priceless gem in your trading reference library, guiding you through the intricate methodology of a real-money day trader. Whether you're evaluating an existing trading system or developing a new one, this book provides an insightful approach on how to test, statistically analyze, and validate your trading ideas. It's less about the technicalities of systems, and much more about transforming a mere idea into a potentially profitable trading system.
However, it's crucial to keep in mind that Darvey is not a professional coder. As a result, his programming code mirrors some typical pitfalls of beginner coders, such as the frequent use of cryptic abbreviations, making it somewhat challenging to read and debug. The section on Monte Carlo is more about utilizing the downloadable tool, rather than creating your own. That being said, the book's straightforward language makes it a straightforward and enjoyable read.
👉 All three books require a sound understanding of technical analysis. However, if you're doubting your experience in TA, don't worry! Consider starting your journey with "The Art and Science of Technical Analysis." Once you've absorbed its knowledge, you'll be fully equipped to explore the depths of the other three books.
Algorithm in Finance Books: Advanced
Dr. Yves J. Hilpisch's book’s primary focus is on surveying the tools and packages available in the scientific stack — numpy, pandas, scipy, scikit-learn, and more. This aspect makes the book an invaluable reference resource. However, bear in mind that some portions of the book might be slightly outdated, though these can be easily circumvented.
The book remains rooted in Python basics and its packages. So, before plunging into other Python-related books we recommend, consider giving this book a read. Be aware that the actual financial explanations are minimal. Consequently, if you lack a sturdy understanding of certain financial concepts, some of the author's examples may be challenging to grasp. Therefore, the book caters best to those with a substantial foundation in finance, including stochastic calculus, but only a basic knowledge of Python.
To begin with, Stefan Jansen, the author of 'Machine Learning for Algo Trading' and the founder and CEO of Applied AI, presumes that the reader has a firm grasp on a multitude of financial concepts and definitions. Therefore, it's essential that you have a robust understanding of financial, investment, and programming concepts before diving into this book. Proficiency in Python is a must, as Jansen's code, crafted with an expert hand, can be complex and sometimes lacks extensive explanation.
However, if you're already a seasoned professional in these areas and are aiming to deepen your knowledge of ML, as well as learn how to design and back-test automated trading strategies for real-world markets using tools like pandas, TA-Lib, scikit-learn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline, backtrader, Alphalens, and pyfolio, then this book is your golden ticket.
One reviewer did point out a potential drawback: the book leans heavily on resources and libraries from Quantopian, which unfortunately shut down in November 2020. If future revisions shift to using, say, QuantConnect's LEAN APIs, the book could regain its acclaimed status. Nevertheless, Jansen is reachable via GitHub for any questions, so he might have solutions to navigate around this predicament.
All in all, it's a hefty tome (800 pages) that provides a comprehensive guide on working with market, fundamental, and alternative data sources such as tick data, minute and daily bars, financial news, or satellite images to generate tradeable signals.
Armed with a foundation in statistics, time series analysis, machine learning, portfolio management, and Python? Then Advanced in Financial Machine Learning is a treasure trove waiting for you to explore! If not, we suggest you first cultivate a solid understanding of these fundamental topics before venturing into this advanced text.
For the seasoned and knowledgeable, this book offers a wealth of insights into intricate subjects such as hierarchical risk parity, explosiveness tests, entropy estimators, deflated Sharpe ratios, and most importantly, the traps of backtesting, which author Marcos López de Prado delves into with remarkable depth.
It's important to note that the book boldly asserts its model-agnostic approach right from the get-go. Instead, it sets its sights on the broad landscape of modeling, adroitly sidestepping individual models like linear regressions or random forests.
👉 And guess what? If you find yourself scratching your head or your curiosity piqued, you can shoot your questions directly to Dr. de Prado. Now that's what we call a platinum-level customer service! Just bear in mind, the author's specialization is in the domain of systematic quantitative investing, which leans more towards trading than your everyday investing.
Statistically Sound Indicators is a must-have for every committed trader. While presented in a semi-introductory style, it brims with invaluable insights, potentially saving you years of trial-and-error in indicator code.
The book excels in explaining how to stabilize leading indicators for enhanced statistical reliability, promising more profitable buy and sell positions. He also offers techniques for discerning whether fluctuating results are due to your system or market stagnation, potential fixes for the former, explores the challenges of combining multiple markets into systems and introduces The Janus Theory, a strategy Masters personally uses.
However, be prepared to roll up your sleeves; Masters doesn't hand out the final code. You'll need to interpret his advice, conduct rigorous testing, and have advanced C++ programming skills to fully benefit from his expertise.
👉 If you’re keen on exploring more of Masters’s oeuvre, he has plenty of published books, all available on his dedicated page on Amazon. Take a leisurely browse and discover what resonates with you the most.
Sure, you can venture into the exciting endeavor of crafting a trading algorithm from scratch. If you're game for this grand voyage, the algorithmic trading books we've highlighted will be your trusty roadmap. This journey does demand a sturdy grasp of mathematics, finance, and yes, programming skills — even in multiple languages. If you're a tech whiz with a couple of Master's degrees to your name, we're confident you'll excel.
But what if you don't fit that description? Well, that's where we come in. Bitsgap can be your ultimate (and possibly the only one you'll ever need) crypto companion, empowering you to automate your trading strategies without writing a single line of code. We've got that covered for you.
So, why not leap into this opportunity and consider subscribing for a week-long trial today?
Can You Recommend the Best Book on Day Trading?
Yes, surely we can! If you’re just looking for the best trading books on day trading then consider Day Trading QuickStart Guide by Troy Noonan. This gem has already turned hundreds of readers into successful day traders using the precise knowledge encapsulated within its pages. Crafted to assist beginners in deciding if day trading is their cup of tea, the book is neatly divided into four enlightening sections. The first section zooms in on personal suitability. The second part is a masterclass in fundamentals, covering everything from the workings of markets, types of trades, to the art of understanding charts. Moving on, the third section equips you with the skills to understand and analyze information, illustrated with practical examples to seize opportunities and sidestep pitfalls. The final section brings all this information to life, offering advice on how to tailor strategies to align with individual needs. With its friendly, clear, and direct tone, the book is a treasure trove of knowledge, yet remains incredibly approachable.
What Are Best Trading Algorithms?
The "best" trading algorithms are a vibrant mix, changing dramatically based on strategy, goals, and market conditions. These are some commonly used trading algorithms: mean reversion, momentum based algorithms, statistical arbitrage, pair trading, sentimate analysis algorithms, and high frequency trading.Remember, the "best" algorithm is like a bespoke suit—it should fit your trading goals, risk tolerance, and investment horizon.
What Are Investment Algorithms?
Investment algorithms are your pre-defined set of instructions that can execute trades at a remarkable speed and frequency. Harnessing the power of mathematical models and the keen oversight of human intelligence, these algorithms make split-second decisions about buying or selling a dazzling array of financial assets, including crypto.
What Is Intraday Algorithmic Trading?
Intraday algorithmic trading involves using automated programs to open and close trading positions within the same day. By rapidly entering and exiting positions, intraday algorithms aim to accumulate small gains that add up over many trades in a short period of time.
How to Learn Algo Trading?
Mastering algorithmic trading requires learning across multiple disciplines. Start by studying finance to comprehend markets. Then learn a programming language like Python to code trading strategies. Understanding algorithms, data structures, and financial modeling is also essential. Don't worry, you've got allies: Trusty companions like Khan Academy and Coursera are here to guide you, offering both free and paid courses on these brain-tingling topics.
Next, it's time to put theory into practice! Sharpen your strategies on paper trading platforms without risking a penny. Platforms like Interactive Brokers and Alpaca are like your training grounds, providing paper trading accounts where you can test your algorithms with play money. Then, join online communities to gain insights from experienced traders. And finally, backtest strategies on historical data before going live.
Once you've proven your mettle in rehearsals, you're ready for the grand stage. Begin with trading small amounts, but remember, the learning never stops. Markets are like shape-shifters, constantly evolving, and your mastery must evolve with them.