Igor Tulchinsky and I had very different formative experiences. His childhood was constrained by the spiritual oppression of life in the Soviet Union, while mine was enriched by the opportunities available to middle‐class kids in 1950s' America. Yet we had much in common: caring parents, a love of reading, and a fascination with math.

As one of today's leading quantitative investors, Igor understands better than most the numbers that underlie dynamic markets. “Markets can be seen as waves,” he writes. “They resemble the regular oscillations of a musical instrument.” That's a valid observation, although different from the way I came to learn about business and finance. As a college student, I was influenced by the writings of the late Nobel Laureate Gary Becker, and by personal experiences that made me realize how many aspiring entrepreneurs – especially minorities and women – were being denied access to capital.

Igor's approach has relied on rigorous and sophisticated mathematical analysis to identify trading opportunities. This might seem very different from a reliance on theories of human capital – the talent, training, and experiences of people – and the effects of societal trends on business success that I use. But in reality, we both seek to predict the most likely future based on what we observe. Understanding numbers and understanding people can both yield important insights that contribute to financial success. And we concur on several important points that are discussed in this book:

• All markets contain risk, and without risk there are no gains. Careful research can discover the price of risk more accurately.
• Markets also contain psychological traps, such as confusing correlation with causation. If most people are ensnared by these traps, an objective investor who follows the research – like the proverbial one‐eyed man in the land of the blind – has an advantage.
• The best investors seek and distill advice from widely diverse sources.
• The study of markets and the study of biology have much in common. Each is a data‐driven information science; each uses predictive algorithms in seeking a needle in a haystack of data. As Igor points out, the next great disease breakthrough might be discovered using the same mathematical techniques he uses to analyze financial data.
• Talent is distributed around the world. Genius lives everywhere.

Igor and I both also believe in history's important lessons. A 2010 book about financial markets said that “real estate prices collapsed, credit dried up and house building stopped.” That sounds like a description of 2008. But it actually refers to 1792, during the administration of George Washington. More recently, stock markets dropped sharply, banks curtailed lending, and unemployment rose to double digits. Again, that wasn't 2008, it was 1974. Live long enough and you begin to appreciate what remains constant through cycles of history. Yet also note that history isn't a sine wave that repeats patterns exactly; it's more like a helix – similar events return in a different orbit. This is why research is crucial.

Investors who conduct careful research are usually better insulated against inevitable market downturns. They understand that the value of debt securities underpins all capital markets, that leverage is a dangerous tool in volatile markets, that ratings are not always a reliable measure of credit quality, that interest rates are not predictable, and that government actions often distort markets.

Although these basic investing principles change little over time, the tools of finance have changed dramatically. When I studied quantitative economics at Berkeley in the 1960s, computers were expensive, relatively inaccessible, room‐sized machines with little power to model investment scenarios. By 1976 processing was speedier, but the storage cost for the IBM System/370 that my business installed was still $1 million per megabyte. Today data processing is millions of times faster, available to nearly anyone on earth, with virtually infinite storage in the cloud at a cost that approaches zero.

This technology revolution has changed the world in many fields. Its impact on biomedical research and precision medicine, for example, has accelerated clinical science and saved untold numbers of lives. There is great opportunity for it to advance beyond its current state through partnerships such as the WorldQuant Initiative for Quantitative Prediction at Weill Cornell, which Igor founded. In the area of finance and investing, Igor and his colleagues now can do what 1960s' finance students could only dream of – simulate reality by creating millions of algorithms (called alphas) that identify trading opportunities with remarkable speed and accuracy.

Although we see markets through different lenses, Igor and I are in complete agreement on one of the most important social issues of our time: providing a path to a meaningful life for every worker, no matter how much traditional work is disrupted by advancing technology. In 2017 we co‐authored a Wall Street Journal opinion article about the challenges of automation and artificial intelligence. We concluded that digital innovation and robots are opening new possibilities for workers and that the future workplace can provide the opportunity for lives of purpose. We believe, in short, that technology leverages human capital and that wisely deployed technology creates more jobs than it destroys. The key, of course, is to provide abundant opportunities for training and retraining.

The workplace of the future can already be seen in the international operations of Igor's company, WorldQuant. Separately, the WorldQuant Foundation's WorldQuant University offers students a tuition‐free online master's degree program in financial engineering. By providing opportunities for a diverse group of bright people who are willing to work hard toward a clear goal, Igor is expanding human capital and helping assure a more prosperous tomorrow. The UnRules is a valuable guide for getting there.

People who know me well are aware that I'm a man of few words. In fact, I joke that you only have so many words in life, and when you use them up, you die. Of course, now that I've written this book, I'm living dangerously.

When we are born, our languages are bestowed upon us. I was born in the Soviet Union, in Minsk, now the capital of Belarus, and I grew up speaking Russian. When my parents and I left the Soviet Union and came to the United States, in the late 1970s, we had to master English. As a child, I grasped the new language more easily than my parents did, but – as with the challenging task of adjusting to a strange new culture – we coped. Mathematics was a language I felt comfortable with. I had played chess as a child, and my parents were professional musicians; both pastimes are rooted in a mathematical, rules‐based order. Soviet schools excelled at teaching math, and when I was in middle school in Wichita, Kansas, I discovered computer programming. From the start I was drawn to the precision of early computer languages: BASIC and, later, C. When I stumbled into video game development at age 17, I was assigned to co‐write a book about video game programming. My experience in early video gaming – coming up with characters (and jokes), writing the programs, working on the book – convinced me that just about anything is possible.

This book, The UnRules, is about languages of many kinds: scientific, mathematical, computer, financial, biological. It's about codes, patterns, and signals, and the attempt to extract order from a noisy world. The notion of the UnRule, which lies at the heart of this book, is a kind of philosophy, based on empirical experiences both in financial markets and in life, where no rule, dogma, ideology, paradigm, or model lasts forever and no trading or market relationship performs as you expect all the time. Like a tether on a balloon, the UnRule limits the reach of all the other rules I've gathered over the years. For me, an intense involvement in competitive markets, and in building my career and my quantitative investment firm, WorldQuant, led me to develop rules that apply not just to trading but to life. Many of those rules are rooted in an always uncertain future. This is reflected in my firm's deep involvement in developing alphas – that is, algorithms that seek to predict certain market relationships. The alphas we develop, now numbering in the millions, consist of mathematical expressions and computer code. We rigorously back‐test them with historical market data to “simulate” their performance, just as video games simulate different realities. Much of this investment process is extensively automated.

And yet we do not just hand over trading to machines. People matter. Over the years we have learned a lot about alpha design and development. We've learned that no matter how well an alpha is back‐tested, it will probably not perform as well when we put it into real markets, and like the rules, no alpha lasts forever. We've learned about the use and dangers of correlation, the management of risk, and the deployment of extraordinary numbers of alphas. We've developed a sense of when to assume risk and, very importantly, when to take losses.

Along these lines, I have found that some life decisions have no clear solutions. For many years I made disciplined but incremental empirical decisions – hiring, for instance, only when I could find genuine talent. There was no master plan. Eventually, we discovered we could find the brightest people in quantitative fields and teach them finance. Smart, motivated people learn quickly. That search for talent transformed WorldQuant into a global firm, exploiting the fact that talent is universal but opportunities are not.

My parents and I had to risk a long journey to America to find the freedom to take advantage of opportunities. Today WorldQuant offers citizens of many nations those same chances, while allowing them to remain at home – in Bulgaria, China, India, Israel, Russia, and Vietnam, among other countries. That recognition that talent requires opportunity also lies behind my recent philanthropic efforts to provide free online education in quantitative disciplines through WorldQuant University, a not‐for‐profit entity legally separate from the firm.

Today we find ourselves in exciting scientific and technological waters. The drive of any investment firm is to try to predict the path of a market's complex turbulence, which we have labored to decipher and define through alphas. But prediction is never easy. There is an unresolved tension captured by the UnRule. We have been riding great leaps in computer power and an explosion of data of all kinds. We have only just begun to explore this new world, which has amazing possibilities and profound challenges.

The UnRules ends with that curve of exponential growth in alphas bending toward the sky. In WorldQuant we have built a company uniquely suited to this dawning age of broad exponential growth. The UnRules is not a long book, but I hope it conveys a sense of the ceaseless searching and testing and experimentation that occur at a firm like WorldQuant. In fact, this book is about beginnings rather than endings. I'm still not a believer in using too many words, but there will be more to say as we explore this new world in more profound ways.

Many books have deep roots. The UnRules goes back to my childhood, listening to my parents practice their music every day in our apartment in Minsk. Authors often thank their parents; none of us would be here without them. But mine embodied many of the virtues that found their way into my rules: hard work, persistence, discipline, goal‐setting, the willingness to take a risk to reach a valuable end, all bound together by love. And without Millennium Management's Izzy Englander, WorldQuant would not exist. He has been my boss, my mentor, and my friend for many years.

Parts of this book were first composed in an internal publication for the WorldQuant community in 2013. Wendy Goldman Rohm, my literary agent, was instrumental in conceptualizing aspects of the book and finding a publisher. Weill Cornell Medicine's Dr. Christopher Mason, the subject of Chapter 9, has entertained and enlightened me in conversation for a number of years, and kindly made sure I got my biology right. Several WorldQuant colleagues read parts or all of this book in draft, offering comments and suggestions, pointing out errors, refreshing memories. They include Scott Bender, Jeffrey Blomberg, Anuraag Gutgutia, Richard Hu, Geoffrey Lauprete, Nitish Maini, and Paradorn Pasuthip. And ably overseeing and managing the editorial process was WorldQuant's global head of content, Michael Peltz. Finally, I'd like to acknowledge all my many colleagues at WorldQuant over the years. This book, and our success, would not be possible without your faith and support.