low. These days, anyonein America can open an online trading account in minutes. Concurrently, given thecompetition in the brokerage industry, trading costs such as commissions havedeclined. With the market open to so many participants, different styles of tradingand investing have emerged. Speak with 100 traders and it’s likely that you’ll hear100 different trading philosophies. Momentum, value, trend following, and pairstrading are a few of the trading methodologies used today. Instead of declaring onestrategy superior to any other, my personal approach as a trader is to test as manystrategies on as much historical data as possible in order to scientifically study themerits of each methodology. Assessing historical performance means:1. Following the scientific method by creating a hypothesis (our tradingmethod)2. Testing the hypothesis (back-test on historical data)3. Drawing conclusions based on our data (evaluating results and implementing a trading program)When we analyze the markets within the context of the scientific method, webecome quantitative traders.The life of a quantitative trader is unique. While the trading process itself issimilar from day to day, the results and outcomes are always unknown. Intrigued by3
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the possibility of new trading theories, quantitative traders research ideas every daythat have never been explored before. Today may very well be the day a trader discovers a new strategy that puts his or her trading over the top.So much about trading has changed in such a short time. With the advancesin technology and the advent of the home computer, there’s been an increase in thenumber of quantitative traders using statistical and numerical methods to determine when to buy and when to sell. While these methods are sometimes complexcomputer programs whose calculations require hours to perform, more often thestrategies are simple rules that can be described on the back of an envelope.New software has allowed traders to test ideas without having to risk a dimeof capital. Before, traders could only speculate if their methods had any historicprecedent of profitability. These days, using the new software, strategies can betested over thousands of markets spanning the globe, giving traders the confidencethat their methods have stood the test of time. The entire process can now beaccomplished in a couple of minutes.Of course, before all this wonderful technology became so readily available,most trading decisions were made by analyzing news and price charts, and being intouch with gut feelings. Some of these so called “discretionary traders” naturallypossess this gut feel of market direction and can trade profitably without the needfor systematic rules, but it’s rare. It requires getting a handle on one’s emotions andbeing able to process information in an unbiased manner, and only a handful ofvery talented discretionary traders have achieved this and been successful.One question that’s long been argued is whether discretionary traders are onthe whole better than their quantitative trading counterparts. The Barclay Group, aresearch group dedicated to the field of hedge funds and managed futures, hasmaintained performance records of various Commodity Trading Advisers basedon their trading style. CTAs are individuals or firms that advise others about buying or selling futures and futures options, with some of the largest CTAs managing over $2 billion. Any CTA whose trading is at least 75 percent discretionary orjudgment-oriented is categorized as a discretionary trader by Barclays, while anyCTA whose trading is at least 95 percent systematic is classified as systematic.From these two categories, Barclays maintains the Barclays Systematic TradersIndex and the Barclays Discretionary Traders Index. Both indices are compiledbased on the monthly profit and loss of the underlying money managers.At any rate, concerning our question about discretionary versus quantitativetraders: Between 1996 and the end of 2001, the average annual return on the systematic (or quantitative) group was 7.12 percent, versus only 0.58 percent for thediscretionary group. What’s more, the systematic index outperformed the discretionary index in five out of the six years in the test period. These statistics suggestthat we may want to focus our trading on the systematic side. Figure 1.1 details theperformance of the systematic traders in relation to discretionary traders from1996 through 2001.
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Barclays Systematic versus Discretionary Traders Index
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Barclays Systematic versus Discretionary Traders Index. As seen above, the Systematic Traders Index hasconsistently outperformed the Discretionary Traders Index.
THE ORIGINS OF THIS BOOKAt age 12, my father, a theoretical chemist, brought home a couple of books aboutthe stock market from his office during his annual cleaning. Knowing that I wasinterested in business (even at that age), my father casually presented me with thebooks. That day forever changed my life.I forget the title of the first book, but the second was the investment classic Technical Analysis of Stock Trends. First published in 1948, the RobertEdwards and John Magee book is widely considered the classic text on technical analysis and the original reference for many of today’s trading patterns, suchas triangles, wedges, head and shoulders, and rectangles. At that first reading, Iwas enthralled. Before I knew it, I was reading everything I could find related totechnical analysis.When I was 15 years old, I received an advertisement in the mail for a tradingstrategy designed to trade the overnight moves in the 30-year Treasury bond futuresmarket. The thought of creating a fixed rule strategy to take human discretion outof the trading process fascinated me, and I set out to develop trading systems fortrading the futures markets. Since that time, I have created and tested thousands ofideas. My first published work, when I was 19, was featured in Technical Analysisof Stock and Commodities. Later, I published further research in Futures magazineas well. In 1996, I published a 250-page trading manual entitled A Comparison of
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Popular Trading Systems. It detailed 10 years of performance data of 30 popularsystems tested on 29 futures markets.For years, I was puzzled why books introducing unique ideas and tradingstrategies never confirmed their past performance by presenting a simulation ofresults. I endeavored in my trading manual to shed light on that question. Over 50percent of the strategies I tested lost money—even before taking into accounttransaction costs such as commission and slippage. In addition, the best performing strategies were those most simplistic in nature—neither complex nor esoteric.Although I set out to prove the value of these new strategies, I quickly learned theimportance of independently verifying trading performance.
NEW MARKETS AND METHODS OF TRADINGThe introduction of financial futures in the early 1980s, the proliferation of equities in the American household portfolio, and the deregulation of various industries such as energy marketing has spawned many new products and markets. Withthese new markets have come opportunities.Using quantitative analysis as a way to spot trading opportunities has becomepopular. Such analyses study markets based on historical information like prices,volume, and open interest.System trading is one example of quantitative analysis. It involves tradersautomating buy and sell decisions by building mathematical formulae to modelmarket movement. Among this method’s advantages is that the human element isremoved from trading positions, as discussed above. Even successful traders tendto take profits too early in the trade, giving up a larger profit down the line. Or evenworse, traders hold on to losses that eventually cause their demise. The beauty of amechanical trading system is that no trades are executed unless the trading systemdeems it necessary. This is the key to the success of mechanical trading systems:removing the irrational emotional element.Perhaps we’ve gotten ahead of ourselves. Let’s ask first: What are tradingsystems?A trading system is a set of fixed rules that provide buy and sell signals. A simplistic example would be to buy a market if its price rose above the average of thepast 20 closes and sell if prices fell below its average of the past 20 closes. If the market continually rises, you will be long in that market. The longer the market rises, themore money you make. Very simply, you’re following the trend of the market.Typically, returns using a trend-following approach applied to a diverse set of markets are higher than returns of the S&P 500, with similar or even smaller risk.
THE SCIENTIFIC BENT AND QUANTITATIVE TRADINGYou might ask: With numerous books written about trading systems and methodsavailable and more coming out each month, why read this particular book? I’d answer
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that Quantitative Trading Strategies is unique because I bring quantitative analysisinto the mainstream by presenting concepts in a realistic and logical manner.While most books promote a specific trading method, they often fail to produce historical track records of their ideas or a background of other trading methods. In this book, I will take old and new trading ideas and test them on a wideportfolio of markets. While other books specifically focus on stocks or futures,this book will apply quantitative trading strategies to all markets.We will apply techniques to futures, stocks, and some new markets thatreaders may not be familiar with. In addition, we’ll test the historical performanceof both current popular systems and some new ideas I have formulated over thepast 15 years of trading. These tests will be run on 29 commodities, 34 stocks andstock indices, and 30 relative value markets on the past 12 years of daily pricedata. Historical performance will be examined from multiple angles.Further, I will illustrate how readers can recreate my results and create, test,and evaluate trading systems on their own. In addition, I will outline both the benefits and the limitations of quantitative analysis by analyzing many of the tools Iuse as a trader. And, drawing on personal experience, I’ll also illustrate certainpoints by drawing on anecdotes from my trading career.While it’s important to illustrate the profitability of quantitative tradingmethods, it’s equally important to discuss the method’s limitations. No tradersmake money every day. Very few make money every month. Some strategies thatperformed profitably in the past will break down and become unprofitable in thefuture. Trading with quantitative strategies involves much risk—risk that we hopeto limit by using state of the art techniques to design, test, and trade our tradingmethods.Readers will notice that I continually refer to the process of using fixed rulesto trade markets based on previous price history as quantitative trading, rather thanthe popular term, “technical analysis,” typically used in the industry. The reason forthis distinction has to do with the quality of analysis. I admit to having disdain fortechnical analysts who use charts to explain past price action. An example is todraw trendlines, or lines that connect market tops or bottoms (see Figure 1.2). Thetheory is that the extension of these lines will act as support or as a resistant in themarket’s future moves. You will often hear statements such as the following frommore traditional technical analysts:Statement 1: “The S&P 500 has been selling off due to a break of thesix month trendline at 1100.”The preceding statement provides very little predictive value in the tradingprocess. Attempting to reconcile past market action using technical analysis isnonsensical. Markets decline due to news and information. Poor corporate earnings, worries over corporate accounting practices, excess crop supply, and lack ofend-user demand for products are just a few of the many possible reasons for amarket to decline. When explaining history, we can usually create a clear picture
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S&P 500 Price Graph1300120011001000Break of trendline signals lower prices900800
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S&P 500 Price Graph. Once prices broke a trendline connecting September and February lows, pricesheaded much lower.
of the market factors that caused rallies and declines. History and hindsight arealways 20/20.While I believe using technical analysis and chart reading to explain pastmarket behavior is foolish, technical analysis can help in predicting future marketmoves. Consider the usefulness of the following statement:Statement 2: “A break of the six month trendline may bring about extrasellers into the market and drive prices lower.”This statement has merit and can be used by traders. Because the market isbreaking below previous support, we are likely to see lower prices in the near term.Therefore, we should sell long positions and establish short positions. Skillfultechnical analysts will make accurate market calls based predominately on priceaction and leave the explanation of historical market moves to the fundamentalanalysts dissecting news and new information.While the second statement above may be useful to traders, we can take theprocess one step further by incorporating historical performance. After all, are wesure that breaks of trendlines are a precursor to lower prices? How often in the pasthas this strategy worked? Consider the following statement, which suggests that wetake action based on a particular price formation—the crossing of a moving average:Statement 3: “Because the market crossed below its 200-day movingaverage, we expect prices will continue their decline.”
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In this case, the technical analyst is predicting lower prices due to priceclosing below its average of the past 200 days. The 200-day moving average isfrequently used in market timing, and the above example is commonly used inpractice. While Statement 3 does involve a forward-looking prediction, we canadd more value to the trading forecast. For example, if we followed the fixedrule-trading strategy of buying when a market rose above its 200-day movingaverage and selling when the market fell below its 200-day moving average,would we beat a buy-and-hold strategy? How much incremental return did aninvestor make by following the 200-day moving average rule over the past 5 or10 years? The crossing of the 200-day moving average is a market prophecy thathas existed for years. But does it stand up to statistics and historical testing?In this book, we will attempt to solve the two problems cited above. First,unlike Statement 1, all of our trading analysis will be geared for future trades—notto explain previous price action. Second, unlike Statement 3, when we suggest usinga trading strategy that generates buy and sell signals, we’ll test that strategy overmany differing markets, each comprising multiple years of data. These results willbe scrutinized to separate promising ideas from those fated to be unprofitable. Afterall, if an idea has not been profitable in the past, why should we use it in the future?
THE PIONEERS OF QUANTITATIVE TRADINGQuantitative trading dates back to the turn of the 20th century. W. D. Gann,Richard Donchian, Welles Wilder, and Thomas DeMark are among its well-knownpioneers.William D. GannIn the early 1900s, Gann made his name as a young stock and commodity broker.A legendary trader, Gann put his ideas and his credibility on the line in an interview with the Ticker and Investment Digest magazine in 1909 (Kahn, 1980). Themagazine published a four page interview in which Gann recounted his tradingrecord. His forecasts were incredibly accurate. During October 1909, according tothe interview, Gann made 286 trades in various stocks, 264 of which were profitable and only 22 resulting in losses.Although Gann subsequently wrote a number of books, none truly describehis methods. From what has been published, it appears that his techniques ran thegamut from creating new price charts based on movement independent of time tomore complex numerology methods, including squares of price and time. Gann’sHow to Trade in Commodities is one of my all-time favorite classics.Richard DonchianBorn in 1905, Robert Donchian established the first futures fund in 1949 (Jobman,1980). The fund struggled for the first 20 years, as Donchian traded commodity
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markets with a discretionary technical trading strategy. Having started in the trading business during the deflationary 1930s, his outlook was continually biasedtoward the bearish side. This bias hurt the fund’s performance during many of thecommodity rallies of the 1950s and 1960s. It was not until Donchian quantifiedhis trading approach in the 1970s that steady profits resulted.Despite never writing books on the subject of trading, Donchian utilizedtechniques that are extremely popular and the basis of many of today’s strategies.Among his contributions to the industry are the dual moving average crossoverstrategy, as well as the channel breakout strategy. I included these two strategies tocontrast more recent systems in my Comparison of Popular Trading Systems.Much to my surprise, these two systems were among the best-performing systemstested. We will explore Donchian’s work in more detail later in the book.Welles WilderNew Concepts in Technical Trading by Welles Wilder, published in 1978, was one ofthe first books that attempted to take discretion out of the trader’s hands and replacetrading decisions with mathematical trading methodologies. Wilder introduced theRelative Strength Index, an oscillator that is standard in nearly every software package today, the Parabolic Stop and Reverse system, and seven other methods. Hisstrictly quantitative methods make him a pioneer in the field of quantitative trading.Thomas DeMarkAfter writing a trading advisory service in the early 1980s, Thomas DeMark wentto work for Tudor Investment Corporation, one of the most prestigious CommodityTrading Advisers in the world. Paul Tudor Jones was so impressed with DeMarkthat the two opened a subsidiary, Tudor Systems Corporation, for the sole purposeof developing and trading DeMark’s ideas.Keeping the bulk of his trading techniques to himself throughout his trading career, DeMark, who has been called the “ultimate indicator and systemsguy” (Burke, 1993), decided to give the rest of the world a glimpse of his methods when he published The New Science of Technical Trading in 1994. A sequel,New Market Timing Techniques, followed in 1997. If any readers have not readthese two books, I strongly suggest you do so. Testing and evaluating the ideasin these two books alone might take years for any one person. Among DeMark’scontributions are his Sequential indicator (a countertrend exhaustion technique),DeMarker and REI (new takes on oscillators), as well as numerous other systematic trading strategies.
THE RECENT EXPLOSION OF QUANTITATIVE TRADINGThe proliferation of modern quantitative trading began with a handful of futurestraders in the 1970s. Armed with IBM mainframes and punch cards, these traders
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began to test simplistic strategies on historical market data. The high leverage andlow transaction costs made futures markets a perfect match for this new breed oftrader.Nowadays, quantitative trading is completely accepted and practiced bymany large professional commodity money managers. CTAs such as John Henryand Jerry Parker of Chesapeake Capital manage over a billion dollars each usingtrading systems to place bets on markets spanning the globe. A recent surveyfound that over 75 percent of CTAs use trading systems. In fact, Jack Schwager,author of the critically praised books Market Wizards and New Market Wizards, hasmanaged institutions’ funds using trading systems applied to commodity markets.It was not until the mid-1970s that two changes in the market made quantitative trading feasible for equities: the end of regulated commissions, and theintroduction of the Designated Order Turnaround system (DOT).Until 1975, the New York Stock Exchange fixed the minimum commissionof stock trading. According to Robert Schwartz, a finance professor at the ZicklinSchool of Business, rates for typical large institutional orders during the era offixed commissions was about 0.57 percent of principal. If I traded 50,000 sharesof a $50 stock, this would amount to $0.29 per share in commission costs. Theseextraodinarily high costs hindered quantitative traders from entering the equitymarkets.Although lower transaction costs after the elimination of the fixed commission structure pushed stocks closer to the realm of quantitative traders, it was thecreation of the DOT in 1976 that truly opened the equity markets. Prior to theDOT, all orders were required to be delivered to the specialist on the NYSE via afloor broker—both a timely and costly procedure. With the introduction of theDOT—and subsequent upgrades such as the SuperDOT—orders of virtually anysize may now be delivered electronically and virtually instantaneously to the floorof the NYSE.
TODAY’S QUANTITATIVE TRADERSThere are a number of modern quantitative traders with very successful long-termtrack records. Some managers trade only futures, while others trade a multitude ofinvestment products, including foreign and domestic stocks, convertible bonds,warrants, foreign exchange, and fixed income instruments. Monroe Trout, JohnHenry, Ken Griffin, and Jim Simons are among the best money managers in theworld. Their focus is almost entirely quantitative in nature.Monroe TroutA legend in the quantitative trading arena, Trout began conducting research for anoted futures trader at the age of 17. After graduating from Harvard, he went towork for another well-known trader, Victor Neiderhoffer (Schwager, 1992).
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Working on the floor of the New York Futures Exchange, Trout mostly scalpedmarkets to make a living. In 1986 he moved upstairs and started a CommodityTrading Adviser in an effort to concentrate on position trading. Until he retired in2002, his Trout Trading Management Company produced some of the highest riskadjusted returns in the industry.Over the years, Trout and his staff have tested and implemented thousands ofmodels for actual trading. According to New Market Wizards by Jack Schwager,Trout’s trading is approximately half systematic and half discretionary, with anemphasis on minimizing transaction costs.John HenryPopularly recognized as the man who bought the Boston Red Sox in 2002, in thetrading arena John Henry is known as the founder of John W. Henry & Company(JWH) in 1982. An owner of farmland, Henry began trading agricultural marketsin the 1970s as a means to hedge the prices of his crops. During a summer trip toNorway in 1980, his trading methodology was shaped while reading the works ofW. D. Gann and other trend followers. Shortly afterward, he developed a quantitatively based system to trade futures, the bulk of which remains largelyunchanged today.After wildly successful periods in the late 1980s and early 1990s, JWHunderwent an overhaul of their trading methodology. While the signals generatedfrom the system were largely kept intact, new risk management policies were instituted to improve risk-adjusted returns. Since the overhaul, JWH has continued itsrun of success. John Henry summarizes his trading philosophy in four points:long-term trend identification, disciplined investment process, risk management,and global diversification.We do not try to predict trends. Instead we participate in trends that wehave identified. While confirmation of a trend’s existence is soughtthrough a variety of statistical measures, no one can know a trend’sbeginning or end until it becomes a matter of record.—John W. Henry & Company marketing brochure
JWH’s flagship Financial and Metal’s fund has annualized average returns of30 percent since its inception in October 1984. The firm currently manages over$1 billion, much of which has been placed from retail customers through publicfutures pools.Ken GriffinNot your typical Ivy League student, as a sophomore at Harvard University in1987, Ken Griffin petitioned for permission to install a satellite to receive realtime stock prices in his dorm room. Equity markets were becoming volatile, andGriffin was managing over $250,000 of Florida domiciled partnerships.
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Prior to the Crash of 1987, Griffin, whose trading focused on quantitativemethods, was short the market. He’d read a negative article in Forbes magazine onthe business prospects of Home Shopping Network and shorted the stock by purchasing put options. As the stock slid, Griffin was surprised when the options soldat a price less than their apparent value. After learning that the difference was dueto the market maker’s “take,” he attempted to gain a better understanding of derivative instruments. He spent hours at the Harvard Business School Library, researching the popular Black-Scholes option pricing model, and stumbled on what wouldbecome his bread and butter: trading and arbitraging convertible bonds.After graduating, Griffin opened Wellington Partners with $18 million in capital. The fund, still open today, initially traded convertible bonds and warrants fromthe United States and Japan. Over the past decade, Citadel Investment Group(Griffin’s umbrella organization) has entered virtually every business associatedwith finance, including risk arbitrage, distressed high yield bonds, government bondarbitrage, statistical arbitrage of equities, and private placements. In each case,Citadel is supporting its trading in these new markets with advanced technology andanalytical methods usually seen in only the most quantitative of products. Their goalis to quantify all trading decisions by replacing the human element of decision making with proven statistical techniques. Citadel currently manages over $6 billion.Jim SimonsIf I mentioned the name Renaissance Technology Corporation on Wall Street, thetypical reply might be, “No thanks. I got creamed in technology stocks.”Renaissance Technology, run by prize-winning mathematician Jim Simons, haseverything to do with technology but nothing to do with losses. If you have notheard of Simons or his firm, you are not alone. Keeping a low profile, Renaissancehas posted some of the best returns in the industry since its flagship Medallionfund was introduced in 1988.After receiving his undergraduate degree from the Massachusetts Institute ofTechnology and a Ph.D. from the University of California at Berkeley, Jim Simonstaught mathematics at MIT and Harvard. Successfully investing in companies runby his friends, Simons left academia and created Renaissance Capital in 1978. Inthe ensuing 24 years the firm has aimed to find small market anomalies and inefficiencies that can be exploited using technical trading methods. Surrounding himself with over 50 Ph.D.’s, and resembling an academic think tank more than a cutting edge trading firm, Simons’s operation manages over $4 billion.The advantage scientists bring into the game is not their mathematical orcomputational skills than their ability to think scientifically. They are lesslikely to accept an apparent winning strategy that might be a mere statistical fluke.—Jim Simons, founder of Renaissance Technology
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WHY QUANTITATIVE TRADING IS SUCCESSFULThough quantitative traders are certainly curious about how they will make moneyapplying quantitative analysis to the markets, the more encompassing question iswhy they can make money in the markets. After all, why should there be any profits to trading?Most traders have studied the efficient markets hypothesis, or EMH, whichstates that current prices reflect not only information contained in past prices, butalso all information available publicly. In such efficient markets, some investorsand traders will outperform and some will underperform, but all resulting performance will be due to luck rather than skill.The roots of the efficient markets hypothesis date back to the year 1900,when French doctoral student Louis Bachelier suggested that the market’s movements follow Brownian motion. (The term is attributed to Robert Brown, anEnglish botanist who in 1827 discovered that pollen grains dispersed in water werecontinually in motion but in a random, nonpredictable manner.) Brownian motionis essentially another term for random motion, synonymous with the populardrunkard’s walk example. If a drunk man begins walking down the middle of aroad, his lack of balance will cause him to veer either left or right. The directionof each step is random—almost like flipping a coin. At the end of our friendlydrunkard’s walk, he could be anywhere—from far left to far right. Perhaps he evenwandered both ways but ended in the middle of the road. The point is, the motionof the walk is completely unpredictable. The random motion of the drunk man isoften used to explain the rise and fall of market prices: completely random andunpredictable (alcohol not necessary).The term Brownian motion was largely unused until 1905, when a young scientist named Albert Einstein succeeded in analyzing the quantitative significance ofBrownian motion. Despite the connection to Einstein’s and others’ work in the naturalsciences, Bachelier’s paper, “Theorie de la Speculation,” went largely unnoticed forhalf a century. In the 1950s the study of finance began to rise in popularity as equities became a larger part of Americans’ investing behavior and academic research wasperformed in an attempt to detect the possible cyclical nature in stock prices.As the number of unsuccessful studies increased, the theory that marketswere efficient became widely accepted and the EMH gained significant credibility.The efficient markets hypothesis remained popular during the 1960s and 1970s, asa number of simplistic studies added credence to the theory that no effort of quantitative trading could succeed over the long run. But as computing power increasedand allowed for more detailed analysis in the 1980s, some holes in the theory ofperfect market efficiency were uncovered. Indeed, the idea of perfectly efficientmarkets has now been questioned.In the Spring 1985 edition of the Journal of Portfolio Management, BarrRosenberg, Kenneth Reid, and Ronald Lanstein produced a study that shed
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doubt on the value of the EMH. The three studied monthly returns of the 1400largest stocks from 1973 to 1984. Each month, long and short portfolios werecreated using the 1400 stocks available. Employing advanced regression techniques, a long portfolio was created using stocks that had underperformed theprevious month, and a short portfolio was created using stocks that outperformed the previous month. The long and short portfolios were optimized so thatboth had equal exposure to quantifiable factors such as riskiness, average market capitalization, growth versus value tilts, and industry exposure. Thus, returnsof one portfolio versus another could not be explained due to