7.1 of this book.
Despite our luck with the longevity of some of the strategies I described, most arbitrage opportunities eventually fade away—the notorious alpha decay that professionals like to lament. Alpha decay can be due to competition—too many people trading the same strategy, but equally often it is due to regime shift caused by market structure or macroeconomic changes. Adapt and evolve your strategies, or watch them die (Lo, 2019). The market is not stationary; why should your strategies be? The most agonizing decision a quantitative trader needs to make is to decide when to abandon a strategy during a prolonged drawdown, despite repeated efforts to evolve it. It is ultimately a discretionary decision—you have to judge based on your market knowledge whether there is a fundamental reason your strategy stopped working. To gain this market knowledge, you have to constantly absorb public knowledge disseminated on social media. That is the reason I set aside an hour each day to go through my Twitter feed (@chanep). I have highlighted some of the Twitterers I follow in Chapter 2. More so than providing specific strategy examples, I hope my books will also improve your market intuition in making these discretionary decisions.
One major addition to this edition is the inclusion of Python and R codes to every example. Even though MATLAB is still my favorite backtesting language, there is no reason to exclude the other two most popular languages. Other things that I added and changed in the 2nd edition:
Chapter 1: A bit more about fully automated trading and marketing your strategies to investors. Also, a scare episode during Covid-19.
Chapter 2: Updated the educational and trading resources for budding quant traders, including the new URL for my own blog. Also, a good word for Millennium Partners’ founder (not that he needs it).
Chapter 3: Extensive changes on MATLAB code that remove a major bug, and new commentary and codes for Python and R. Description of some new quant trading platforms. One item of particular interest: I discuss a mathematically rigorous way to decide how much backtest data and how long a paper trading period is needed. Another mathematical technique was referenced that determines how data snooping will affect your live Sharpe ratio.
Chapter 4: Much has changed in the world of brokers and infrastructure providers for algorithmic traders since the first edition. Even the name of the US regulator for brokers has changed. You will find them all updated.
Chapter 5: It is now much easier than before to build a fully automated trading system. The new ways are described in this chapter.
Chapter 6: New insights on the Kelly formula and its practical impact. Python and R codes for demonstrating capital allocation using the Kelly formula are added. Also included is a discussion on why loss aversion is not a behavioral bias, which is opposite to what I previously believed. It stems from a profound mathematical insight that threatens to upend the economics profession.
Chapter 7: This chapter is extensively updated. I describe a novel machine learning technique we invented called Conditional Parameter Optimization that can be used to optimize the trading parameters of a strategy based on market regimes. Also added are new high-performance MATLAB/Python/R codes on using PCA, new Python/R codes on checking for stationarity and cointegration, and some surprising out-of-sample results on seasonal trading strategies. I also clarified the difference between time-series and cross-sectional factors.
Chapter 8: Conclusions remain largely the same. Yes, a retail trader can beat the professionals. But a retail trader can also hire a professional to help generate alpha and diversify.
REFERENCES
1 Chan, Ernest. 2020. “What Is the Probability of Your Profit?” PredictNow.ai. https://www.predictnow.ai/blog/what-is-the-probability-of-profit-of-your-next-trade-introducing-predictnow-ai/
2 Gershgorn. 2017. “The data that transformed AI research—and possibly the world.” Qz. https://qz.com/1034972/the-data-that-changed-the-direction-of-ai-research-and-possibly-the-world/
3 Lo, Andrew. 2019. Adaptive Markets: Financial Evolution at the Speed of Thought. Princeton University Press.
4 López de Prado, Marcos. 2018. Advances in Financial Machine Learning. Wiley.
Preface
By some estimates, quantitative or algorithmic trading now accounts for over 80 percent of the equity trading volume (Economist, 2019). There are, of course, innumerable books on the advanced mathematics and strategies utilized by institutional traders in this arena. However, can an independent, retail trader benefit from these algorithms? Can an individual with limited resources and computing power backtest and execute strategies over thousands of stocks, and come to challenge the powerful industry participants at their own game?
I will show you how this can, in fact, be achieved.
WHO IS THIS BOOK FOR?
I wrote this book with two types of readers in mind:
1 Aspiring independent (“retail”) traders who are looking to start a quantitative trading business.
2 Students of finance or other technical disciplines (at the undergraduate or MBA level) who aspire to become quantitative traders and portfolio managers at major institutions.
Can these two very different groups of readers benefit from the same set of knowledge and skills? Is there anything common between managing a $100 million portfolio and managing a $100,000 portfolio? My contention is that it is much more logical and sensible for someone to become a profitable $100,000 trader before becoming a profitable $100 million trader. This can be shown to be true on many fronts.
Many legendary quantitative hedge fund managers such as Dr. Edward Thorp of the former Princeton-Newport Partners (Poundstone, 2005) and Dr. Jim Simons of Renaissance Technologies Corp. (Lux, 2000) started their careers trading their own money. They did not begin as portfolio managers for investment banks and hedge funds before starting their own fund management business. Of course, there are also plenty of counterexamples, but clearly this is a possible route to riches as well as intellectual accomplishment, and for someone with an entrepreneurial bent, a preferred route.
Even if your goal is to become an institutional trader, it is still worthwhile to start your own trading business as a first step. Physicists and mathematicians are now swarming Wall Street. Few people on the Street are impressed by a mere PhD from a prestigious university anymore. What is the surest way to get through the door of the top banks and funds? To show that you have a systematic way to profits—in other words, a track record. Quite apart from serving as a stepping stone to a lucrative career in big institutions,
