Quantitative trading strategies harnessing the power of quantitative techniques to create a winning trading program:
Summary:
In most circumstances, trading a portfolio of markets produces better reward-to-risk characteristics than trading any single market on its own.
We examine the benefits of trading a diversified portfolio of markets, strategies, and parameters in our trading accounts.
Intrigued by the possibility of new trading theories, quantitative traders research ideas every day that 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.
One question that's long been argued is whether discretionary traders are on the whole better than their quantitative trading counterparts.
Any CTA whose trading is at least 75 percent discretionary or judgment-oriented is categorized as a discretionary trader
by Barclays, while any CTA whose trading is at least 95 percent systematic is classified as systematic.
From these two categories, Barclays maintains the Barclays Systematic Traders Index and the Barclays Discretionary Traders Index.
Figure 1.1 details the performance of the systematic traders in relation to discretionary traders from 1996 through 2001..
Using quantitative analysis as a way to spot trading opportunities has become popular.
System trading is one example of quantitative analysis.
It involves traders automating buy and sell decisions by building mathematical formulae to model market movement.
Even successful traders tend to take profits too early in the trade, giving up a larger profit down the line.
The beauty of a mechanical trading system is that no trades are executed unless the trading system deems it necessary.
Let's ask first: What are trading systems? A trading system is a set of fixed rules that provide buy and sell signals.
You might ask: With numerous books written about trading systems and methods available and more coming out each month, why read this particular book? I'd answer that Quantitative Trading Strategies is unique because I bring quantitative analysis into 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.
Trading with quantitative strategies involves much risk-risk that we hope to limit by using state of the art techniques to design, test, and trade our trading methods.
Readers will notice that I continually refer to the process of using fixed rules to trade markets based on previous price history as quantitative trading, rather than the popular term, "Technical analysis," typically used in the industry.
If we followed the fixedrule- trading strategy of buying when a market rose above its 200-day moving average 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 an investor make by following the 200-day moving average rule over the past 5 or 10 years? The crossing of the 200-day moving average is a market prophecy that has existed for years.
Second, unlike Statement 3, when we suggest using a trading strategy that generates buy and sell signals, we'll test that strategy over many differing markets, each comprising multiple years of data.
Pioneers:
William D. Gann In 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.
The magazine published a four page interview in which Gann recounted his trading record.
During October 1909, according to the 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 describe his methods.
Gann's How to Trade in Commodities is one of my all-time favorite classics.
Richard Donchian Born in 1905, Robert Donchian established the first futures fund in 1949.
The fund struggled for the first 20 years, as Donchian traded commodity markets with a discretionary technical trading strategy.
Having started in the trading business during the deflationary 1930s, his outlook was continually biased toward the bearish side.
Despite never writing books on the subject of trading, Donchian utilized techniques that are extremely popular and the basis of many of today's strategies.
I included these two strategies to contrast more recent systems in my Comparison of Popular Trading Systems.
Welles Wilder New Concepts in Technical Trading by Welles Wilder, published in 1978, was one of the first books that attempted to take discretion out of the trader's hands and replace trading decisions with mathematical trading methodologies.
Wilder introduced the Relative Strength Index, an oscillator that is standard in nearly every software package today, the Parabolic Stop and Reverse system, and seven other methods.
His strictly quantitative methods make him a pioneer in the field of quantitative trading.
Thomas DeMark After writing a trading advisory service in the early 1980s, Thomas DeMark went to work for Tudor Investment Corporation, one of the most prestigious Commodity Trading Advisers in the world.
Paul Tudor Jones was so impressed with DeMark that the two opened a subsidiary, Tudor Systems Corporation, for the sole purpose of 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 systems guy", decided to give the rest of the world a glimpse of his methods when he published The New Science of Technical Trading in 1994.
If any readers have not read these two books, I strongly suggest you do so.
Testing and evaluating the ideas in these two books alone might take years for any one person.
Among DeMark's contributions are his Sequential indicator, DeMarker and REI, as well as numerous other systematic trading strategies.
The proliferation of modern quantitative trading began with a handful of futures traders in the 1970s.
Armed with IBM mainframes and punch cards, these traders began to test simplistic strategies on historical market data.
CTAs such as John Henry and Jerry Parker of Chesapeake Capital manage over a billion dollars each using trading systems to place bets on markets spanning the globe.
Jack Schwager, author of the critically praised books Market Wizards and New Market Wizards, has managed institutions' funds using trading systems applied to commodity markets.
These extraodinarily high costs hindered quantitative traders from entering the equity markets.
Although lower transaction costs after the elimination of the fixed commission structure pushed stocks closer to the realm of quantitative traders, it was the creation of the DOT in 1976 that truly opened the equity markets.
Some managers trade only futures, while others trade a multitude of investment products, including foreign and domestic stocks, convertible bonds, warrants, foreign exchange, and fixed income instruments.
A legend in the quantitative trading arena, Trout began conducting research for a noted futures trader at the age of 17.
In 1986 he moved upstairs and started a Commodity Trading Adviser in an effort to concentrate on position trading.
Until he retired in 2002, 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 of models for actual trading.
According to New Market Wizards by Jack Schwager, Trout's trading is approximately half systematic and half discretionary, with an emphasis on minimizing transaction costs.
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Popularly recognized as the man who bought the Boston Red Sox in 2002, in the trading arena John Henry is known as the founder of John W. Henry & Company in 1982.
An owner of farmland, Henry began trading agricultural markets in the 1970s as a means to hedge the prices of his crops.
During a summer trip to Norway in 1980, his trading methodology was shaped while reading the works of W. D. Gann and other trend followers.
Shortly afterward, he developed a quantitatively based system to trade futures, the bulk of which remains largely unchanged today.
After wildly successful periods in the late 1980s and early 1990s, JWH underwent an overhaul of their trading methodology.
John Henry summarizes his trading philosophy in four points: long-term trend identification, disciplined investment process, risk management, and global diversification.
Equity markets were becoming volatile, and Griffin was managing over $250,000 of Florida domiciled partnerships.
Prior to the Crash of 1987, Griffin, whose trading focused on quantitative methods, was short the market.
As the stock slid, Griffin was surprised when the options sold at a price less than their apparent value.
He spent hours at the Harvard Business School Library, researching the popular Black-Scholes option pricing model, and stumbled on what would become his bread and butter: trading and arbitraging convertible bonds.
The fund, still open today, initially traded convertible bonds and warrants from the United States and Japan.
Over the past decade, Citadel Investment Group has entered virtually every business associated with finance, including
risk arbitrage, distressed high yield bonds, government bond arbitrage, statistical arbitrage of equities, and private placements.
If I mentioned the name Renaissance Technology Corporation on Wall Street, the typical reply might be, "No thanks. I got creamed in technology stocks." Renaissance Technology, run by prize-winning mathematician Jim Simons, has everything to do with technology but nothing to do with losses.
If you have not heard of Simons or his firm, you are not alone.
After receiving his undergraduate degree from the Massachusetts Institute of Technology and a Ph.D. from the University of California at Berkeley, Jim Simons taught mathematics at MIT and Harvard.
Successfully investing in companies run by his friends, Simons left academia and created Renaissance Capital in 1978.
urrounding 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.
They are less likely to accept an apparent winning strategy that might be a mere statistical fluke.
Though quantitative traders are certainly curious about how they will make money applying quantitative analysis to the markets, the more encompassing question is why they can make money in the markets.
Why should there be any profits to trading? Most traders have studied the efficient markets hypothesis, or EMH, which
states that current prices reflect not only information contained in past prices, but also all information available publicly.
In such efficient markets, some investors and 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 efficient markets hypothesis remained popular during the 1960s and 1970s, as a number of simplistic studies added
credence to the theory that no effort of quantitative trading could succeed over the long run.
These results suggested that the market is not efficient and that active investors could indeed outperform the market.
Once we systematically identify the appearance of these patterns, we can explore the profitability of trading signals that
these patterns generate.
Curtis Arnold's PPS Trading System was one of the first works to systematically define trading patterns and test trading rule validity when these patterns occurred.
Whereas interpreting charts was very much an art, Arnold defined each pattern and systematically tested trading rules to buy and sell based on when these patterns occurred.
Thomas Bulowski has taken this research even further with his books Encyclopedia of Chart Patterns and Trading Classic Chart Patterns.
The occurrence of each of the 10 patterns on actual stock data should roughly match the occurrence of patterns on the
randomly generated data.
In addition to the increased frequency of some patterns, returns following certain patterns' presence were also
significant-specifically, declines following the head and shoulders top, and rallies that followed the head and shoulders bottom.
Even if a handful of investors and traders act irrationally by buying and selling based on irrelevant information, the market should still be priced efficiently.
Market efficiency runs into trouble when the actions of irrational investors do not cancel out.
Consider the situation where irrational investors all pile on and buy the market at the same time or they all run for the exits at the same time.
If all the irrational investors buy or sell together, they can overwhelm the rational investors and cause market inefficiencies.
Now the 20 irrational investors, all momentum players, begin to buy the stock due to its performance, driving the stock up to $135. They buy from rational people who are willing to sell their stock above their perceived fair value.
When irrational investors move together, market irrationality can exist and take hold for quite some time, eventually leading to bubbles, panics, and crashes.
He found a significant tendency for investors to sell winning stocks too early and hold losing stocks too long.
Over his test period, investors sold approximately 50 percent more of paper profits on winning trades than they sold of paper losses in losing trades.
Based on the data, Odean concluded that winning stocks were sold quicker and more frequently than losing stocks.
Winning stocks that were sold continued to rise, while losing stocks that were held continued to fall in value.
In the year following sales, stocks sold with gains by individual investors outperformed the market by an average 2.35 percent.
Clearly, the more profitable course of action suggested by the study is to buy winning stocks and sell losing ones.As a result, investors and traders take winners very quickly and hold on to losers.
To demonstrate, give people the following choice: Game 1 75 percent chance of making $1000 25 percent chance of making $0
or 100 percent chance of making $750 We can calculate the expected value of each game by summing the product of each outcome's probability by its payout: Expected payout of risky choice = 75% $1000 25% $0 = $750 While the expected
value of both options is the same in Game 1, individuals tend to be very risk averse with gains.
Most people will take the certain $750 rather than take the risk for a higher payout.
Now consider Game 2, which presents the exact same choice, only among losses: Game 2 75 percent chance of losing $1000 25 percent chance of losing $0 or 100 percent chance of losing $750 Expected payout of risky choice = 75% -$1000 25% $0
= -$750 In Game 2, most people will choose to risk the chance to come out even and take the first option.
Basically, the tendencies revealed in both games show that individuals are risk averse with their winnings and risk seeking with their losses.
From practical experience, I can add that I've often caught myself holding on to losing trades while thinking, "If I can only get out even on this trade," or taking it so far as to calculate breakeven points in hopes of avoiding the disappointment of closing out a losing trade.
The second theory explaining why investors sell winners and hold losers is that investors buy and sell stocks as if they expect mean reversion in prices.
Conversely, when rates are high, they are met by demand from investors looking to lock in the abnormally high interest rates.
If investors believe that stock prices move in a similar path, they will be willing to sell stocks that rally and hold stocks that decline-believing that each will eventually return to its more normal level.
Consider stock XYZ, as seen in Figure 1.14.
A similar example is stock ABC in Figure 1.15.
When ABC breaks above $50 per share after also trading between $45 and $50, we might believe that the stock should be sold on the basis that it too will reenter its previously established $45 to $50 range again.
When prices rise above recent ranges, prospect theory dictates that traders with long positions will exit trades while traders losing money on short positions will hold their trades.
As the market rises above the old trading range, mean reversion thinking will suggest that the market has overextended
itself on the rally and will eventually return to its previous trading range.
Those who fight the trend will lose, while traders who trade with the breakout will feast on the natural human tendencies of those market participants unable to take trading losses.
Smart traders ignore the human bias of both prospect theory and mean reversion and establish positions in the direction of the breakout, while other traders with losing positions hold on.
The pain of these losing trades is eventually realized, and losses are very often closed near a market extreme.
As we will see, using quantitative trading strategies will mitigate these human tendencies and generate trading strategies
based on optimal historical performance-not psychological tendencies.
These zero coupon bonds, unlike a traditional bond that pays a coupon semiannually, are debt obligations that pay no coupons.
Smart bond traders were often able to sell the coupon strips at a price higher than the original bond, profiting the
difference in buy and sell values without any risk.
Futures Contracts In the mid 1980s, Salomon Brothers' Bond Arbitrage Group bought government bonds in the cash market while shorting the 30-year bond future.
The 30-year Treasury bond future, traded on the Chicago Board of Trade, calls for delivery of U.S. Treasury bonds that are not callable for at least 15 years.
The daily mismatch between bond prices and the conversion factors leads to one bond becoming cheaper to deliver than any other bonds.
In the early 1980s, bond futures would sometimes trade expensive compared to the underlying .
Even more recently, sophisticated stock option traders could buy options on individual stocks and sell stock index options to create a profitable risk-free payoff based on the diffusion of individual stock returns.
Sophisticated options traders often trade the difference in volatility between individual stock options and index options.
Fair pricing between the stock and index options is based on the correlation of stocks to other stocks in the index.
The constant theme throughout these stories is that traders with the best quantitative analysis are able to capitalize on market inefficiencies when they appear.
If markets were perfectly efficient, traders would stop research and no longer continue to look for profitable trading strategies or undervalued companies.
With the reappearance of these inefficiencies, traders would slowly begin to exploit them again, bringing the market back to efficiency.
If there are profits in quantitative trading, should we bother studying fundamental analysis as well? I believe there is money to be made on the fundamental side.
As hedge funds have become more popular and their numbers and assets have grown, better independent fundamental analysis is being performed on companies' balance sheets and earnings streams.
While there's no substitute for solid fundamental analysis, I believe that using quantitative trading strategies can detect factors that affect stock prices more quickly than waiting for earnings announcements and company conference calls.
While there are good fundamental analysts performing very rigorous work, when analyzing companies' earnings potential, even the best analysts are subject to the market's underreaction and overreaction.
The problem with fundamental analysis is that the underlying fundamentals of companies change very slowly, making it difficult to capitalize on volatile price swings in the equity market, which are typically caused by investor sentiment and perception.
There are very talented and bright fundamental analysts, but to take advantage of the short-term swings in the markets-
whether it be stocks, futures, or other markets-we will focus on the quantitative side and use past market prices to generate trading signals.
