Alpha Trading E-Book

Perry J. Kaufman

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Serie: Wiley Trading Series
• Sprache: Englisch

Seitenzahl: 448

Veröffentlichungsjahr: 2011

Beschreibung

From a leading trading systems developer, how to make profitable trades when there are no obvious trends

How does a trader find alpha when markets make no sense, when price shocks cause diversification to fail, and when it seems impossible to hedge? What strategies should traders, long conditioned to trend trading, deploy? In Alpha Trading: Profitable Strategies That Remove Directional Risk, author Perry Kaufman presents strategies and systems for profitably trading in directionless markets and in those experiencing constant price shocks. The book

• Details how to exploit new highs and lows
• Describes how to hedge primary risk components, find robustness, and craft a diversification program
• Other titles by Kaufman: New Trading Systems and Methods, 4th Edition and A Short Course in Technical Trading, both by Wiley

Given Kaufman's 30 years of experience trading in almost every kind of market, his Alpha Trading will be a welcome addition to the trading literature of professional and serious individual traders for years to come.

Leseprobe

Contents

Cover

Series

Title Page

Copyright

Preface

Chapter 1: Uncertainty

IMPACT ON TRADING

THE INEVITABLE PRICE SHOCKS

WHY SO MUCH ABOUT PRICE SHOCKS?

COMPLEXITY AND CONTAGION RISK

THE UGLY SIDE

TAKING DEFENSIVE ACTION

ACCEPTING PERFORMANCE FOR WHAT IT IS

Chapter 2: The Importance of Price Noise

NOISE EXPLAINED

DIFFERENT MARKETS

A CLOSER LOOK AT EQUITY INDEX MARKETS

IMPORTANCE OF NOISE

DETERMINING THE STRATEGY

CAPITALIZING ON THE TREND OF NOISE

Chapter 3: Pairs Trading: Understanding the Process

THE PROCESS

THE BASICS

TARGET VOLATILITY

HOME BUILDERS

USING ETFS

PORTFOLIO OF HOME BUILDER PAIRS

EXECUTION AND THE PART-TIME TRADER

STOP-LOSSES

TRADING INTRADAY

KEY POINTS TO REMEMBER

Chapter 4: Pairs Trading Using Futures

FUTURES

MECHANICS OF A PAIRS TRADE IN FUTURES

INFLATION SCARES

TRADING ENERGY PAIRS

REVISITING MOMENTUM WITH ENERGY MARKETS

A MINIPORTFOLIO OF NATURAL GAS PAIRS

THE INFLATION PAIRS: CRUDE, THE EURUSD, AND GOLD

EQUITY INDEX PAIRS

LEVERAGING WITH FUTURES

LONDON METALS EXCHANGE PAIRS

VOLATILITY FILTERS

INTEREST RATE FUTURES

SUMMARY

Chapter 5: Risk-Adjusted Spreads

DELL AND HEWLETT-PACKARD

TRADING BOTH LONG-TERM (HEDGED) TRENDS AND SHORT-TERM MEAN REVERSION

GOLD, PLATINUM, AND SILVER

THE PLATINUM/GOLD RATIO

IMPLIED YIELD

THE YIELD CURVE

TREND TRADING OF LONDON METAL EXCHANGE PAIRS

SUMMARY

Chapter 6: Cross-Market Trading and the Stress Indicator

THE CROSSOVER TRADE

THE STRESS INDICATOR

GOLD, COPPER, AND PLATINUM

MINING COMPANIES

AGRIBUSINESS PAIRS

THE MAJOR ENERGY PRODUCERS

PORTFOLIO OF CROSS-MARKET ENERGY PAIRS

OTHER OPPORTUNITIES

SOME FINAL NOTES

Chapter 7: Revisiting Pairs Using the Stress Indicator

FUTURES MARKETS AND THE STRESS INDICATOR

EQUITY INDEX FUTURES

INTEREST RATE FUTURES

THE PORTFOLIO SPREADSHEET

SUMMARY OF PAIRS TRADING

Chapter 8: Traditional Market-Neutral Trading

HOME BUILDERS

TREND OR MEAN REVERSION?

BASIC MARKET-NEUTRAL CONCEPT

VOLATILITY-ADJUSTING THE POSITION SIZE

ARBING THE DOW: A LARGE-SCALE PROGRAM

THOUGHTS ABOUT MARKET-NEUTRAL TREND FOLLOWING

MARKET NEUTRAL USING FUTURES

MARKET-NEUTRAL COMMENTS

Chapter 9: Other Stat-Arb Methods

TRADE-OFFS

SYSTEM BRIEFS

NEW HIGHS AND NEW LOWS

MERGER ARB

CREATING YOUR OWN INDEX ARBITRAGE

ARBING THE DOW

ARBING THE S&P 500— INDEX ARBITRAGE

About the Companion Web Site

About the Author

Index

Founded in 1807, John Wiley & Sons is the oldest independent publishing company in the United States. With offices in North America, Europe, Australia and Asia, Wiley is globally committed to developing and marketing print and electronic products and services for our customers' professional and personal knowledge and understanding.

The Wiley Trading series features books by traders who have survived the market's ever changing temperament and have prospered—some by reinventing systems, others by getting back to basics. Whether a novice trader, professional or somewhere in-between, these books will provide the advice and strategies needed to prosper today and well into the future.

For a list of available titles, visit our Web site at www.WileyFinance.com.

Copyright © 2011 by Perry Kaufman. All rights reserved.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey. Published simultaneously in Canada.

No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, [phone], fax [phone], or on the Web at www.copyright.com. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, [phone], fax [phone], or online at http://www.wiley.com/go/permissions.

Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives or written sales materials. The advice and strategies contained herein may not be suitable for your situation. You should consult with a professional where appropriate. Neither the publisher nor author shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages.

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Library of Congress Cataloging-in-Publication Data:

Kaufman, Perry J. Alpha trading : profitable strategies that remove directional risk / Perry Kaufman. p. cm. – (Wiley trading series) Includes index. ISBN 978-0-470-52974-4 (cloth); ISBN 978-1-118-00120-2 (ebk); ISBN 978-1-118-00121-9 (ebk); ISBN 978-1-118-00122-6 (ebk) 1. Pairs trading. 2. Futures. I. Title. HG4661.K33 2011 332.64′52–dc22 2010032753

Preface

I wanted to write this book after the collapse of the tech bubble in 2000, but it wasn’t until the subprime disaster of 2008 that I decided to do it. Investors should not be subject to the tremendous losses that the market serves up. And traders do not need to make a commitment to a long or short position all the time. There are other choices, and those choices do not necessitate compromising returns. They do require somewhat more complicated positions, but the reward is that, if the S&P collapses because of a terrorist attack, or program trading by one of the big investments houses runs amok and causes a 10 percent drop in the S&P, you are safe.

We’ve learned greater respect for risk in the past few years. It’s a lesson that we all should have learned much sooner, but any time is a good time to improve your skills. Part of that advancement is to be aware of unconscious risks. When we trade more than one stock, each trade should have equal risk. That gives each trade an equal opportunity to contribute to the final results. If you don’t do that, you are consciously or unconsciously saying that you think the trade with the largest risk is most likely to give you the best return. If that’s the case, you should only make one trade in the best item and forget about diversification.

This book is as much about the process as it is about the results. Its target audience is active traders but not necessarily intraday traders. The intended reader is someone who spends time deciding which stocks or futures markets to buy or sell and doesn’t hold a trade indefinitely when it goes the wrong way. Each step is explained, and there are examples of how the numbers should look. There is also a website that has the basic spreadsheets needed to do all the calculations.

The strategies in this book are well known to be profitable. They are called statistical arbitrage, or stat-arb, and they can be traded by holding positions for a few days, as suggested here, or for milliseconds, as implemented by the big investment houses. To trade, all you need is a spreadsheet to do a few calculations; then enter prices at the end of the day or anytime during the day when you think there is an opportunity. Trades have a high probability of success.

You cannot just believe that something works; you need to prove it to yourself. The black box approach is unacceptable and has proved a disaster to many investors. It’s your money, and you owe it to yourself to understand and verify everything—even what is shown in this book.

It is one thing to be given a strategy and another to use it successfully. Once you have verified and paper-traded the strategies, you have a better chance of being successful because you have become part of the development process.

The development process is an exciting exploration. It begins with a sound premise and moves down various paths that may or may not turn out to be useful. But at the same time, it teaches valuable lessons. You understand why one idea works and another doesn’t. You understand the robust and the fragile parts of the strategies. At some time in the future, you may be called upon to change the strategy because the market has changed—volatility has dropped to a level that limits opportunity or risen to a point of unacceptable risk. Markets that used to move together no longer do so, or as in the fourth quarter of 2008, markets moved together for no apparent reason.

Without having gone through the process, you do not have the knowledge to make these changes or the confidence that they will work. This book will present important strategies that should be part of any trader’s portfolio. It will develop and explain the features that are incorporated, as well as choices that were not taken. But it is the sound premise of these ideas that is the underlying reason for its success. At the end, I hope you have learned a lot and that you trade successfully.

Perry Kaufman January 2011

Chapter 2

The Importance of Price Noise

Market noise is an important but elusive component of price movement. It is the up-and-down, erratic price movement that goes nowhere and often causes you to be stopped out of a trade only to see prices reverse back in your direction. Traders have no trouble recognizing noise. Most price shocks are an extreme case of noise, when the large move is followed by an equally sharp reversal the next day. A price shock is not noise if prices continue in the direction of the shock. That is most likely a structural change. The elusive part is:

How do you tell the difference between a structural change and a price shock?When does a price move indicate a trend change, and when is it just noise?How do you take advantage of price noise?

These questions don't have simple answers, but what we learn from exploring them is still very valuable and will explain some of the important choices that are made in the following chapters.

NOISE EXPLAINED

The way we think about price noise is a day with very high volatility but a close nearly unchanged, or a day when prices closed sharply lower, then reversed nearly the entire move the next day. We also associate noise with the way prices react to our trading method. We get stopped out of a long position when prices break a key level, but that turns out to be the low of the move, and we're out at the worst price of the day.

In general, noise is a disorderly move. It doesn't need to be volatile, just erratic and unpredictable. Econometricians say that when you remove the trend, the seasonal pattern, and the cycle, the three main components of price movement, what you have left is noise. That's interesting but not very useful. We don't want to remove those three elements because the combination of everything causes price moves that can generate profits. Instead, we'll think of noise in the same way we approach the walk of a drunken sailor (no offense to sailors—it could be anyone).

If a sailor were to walk from point A to point B in a straight line, we can say that his route has no noise. If he meanders slightly off that straight path, we can see that as a small amount of noise; however, if he staggers first to the right, then sharply to the left, then backward and forward by different amounts, but ultimately heading slightly toward his goal, we would say there was a large amount of noise.

FIGURE 2.1 Noise is calculated as the net move (from A to B) divided by the sum of the individual moves (1 through 7). All values are taken as positive numbers.

Once you understand the picture, seen in Figure 2.1, the concept should become clear: the straighter the path, the less noise; the more erratic the path, the more noise. This pattern can be expressed as a value we call the efficiency ratio. First, we measure the net distance gained from point A to point B, always taken as a positive number. Then we measure the actual path taken by the sailor in his journey from point A to point B. Those values are also always positive, regardless of whether he is stumbling forward or staggering backward. The efficiency of his walk is given by the ratio

Referring now to the efficiency ratio (ER), if the sailor walked in a straight line, the ratio would be 1 because the numerator and denominator would be the same. As the sailor wobbles more, the denominator gets larger. If he wandered back and forth for a really, really long time, the denominator would get very big, and the ratio would move toward zero. Therefore, a walk with no noise will have the ratio of 1.0, and a completely directionless walk would be zero. This can be shown mathematically as

where t represents today, P is the price, and n is the total number of days used in the calculation. As with many financial calculations, this is done over a fixed, relatively short period. By calculating the ratio each day based on rolling time periods, we get a history of the price noise. When we average those individual ratios over a long period of time, we get a profile of the amount of noise in a specific stock, index, or commodities market.

Note that the value ERt can be zero (or near zero) if the denominator is extremely large or if the numerator is zero, which can happen if the starting and ending prices are the same. The ratio has no sign, so that we don't know if the prices have gone up or down over the calculation period.

Because the calculation is greatly dependent on the starting and ending values, some mathematicians consider it unstable. However, averaging the values over some period of price history minimizes that problem.

Volatility Neutral

The ratio also removes the volatility from the calculation. For example, if prices moved up an equal amount, say $0.50, every day for 10 days, then the total gain would be $5.00 and the ratio would be 1.0. On the other hand, if it moved up $0.10 each day, the gain would be $1.00, and the ratio would still be 1.0.

That is an important concept because it means that the efficiency ratio may not be sufficient for trading. An arbitrage or relative value strategy that profits from trading against the current move usually needs sufficient volatility to clear a profit. This ratio doesn't have that information, although a separate calculation could easily solve that problem.

Fractal Efficiency

The efficiency ratio was developed and used by this author in the late 1970s. It wasn't until Benoit Mandelbrot wrote The Fractal Geometry of Nature (W. H. Freeman, 1982) that the concept of fractals became clear.1 Up to then, fractals were not thought of as having common properties, but Mandelbrot used them to measure the roughness of the coastline, distinguishing this from classic, orderly, Euclidian geometry. Realists, such as Mandelbrot, believe that straight lines exist only in theory.

It's not clear where the formula or naming of fractal efficiency first appeared, but it's a good description of the efficiency ratio. The concept of a fractal applies to all dimensions, and it appears that the ratio is equally descriptive when used for daily, weekly, and monthly price data as well as 5-minute and hourly data. Noise is noise.

In this book, we'll use the term efficiency ratio because it best describes the way prices move from one point to another.

Calculation Example

Using Excel, the efficiency ratio is calculated in a few easy steps, shown in Table 2.1. Beginning with the S&P cash index, SPX, in August 1998, the date and closing prices are placed into columns B and C. The calculation period will be 10 days, appearing in cell D1. This technique will be used with the Excel feature offset, which will allow us to change the calculation period, and the results will automatically be recalculated.

In column D, beginning in row 12, calculate the absolute value of the 10-day price difference. The Excel formula is abs(C13-OFFSET(C13,-$D$2,0)). This value will be the numerator of the ratio.

TABLE 2.1 Example of calculating the efficiency ratio using SPX.

In column E, beginning in row 3, calculate the 1-day absolute differences, abs(C4-C3).Column F, beginning in row 13, will be the 10-day sum of the values in column E, sum(E13:OFFSET(E13,-$D$2+1,0)). This will be the denominator.Column G is the efficiency ratio, D13/F13.

DIFFERENT MARKETS

Before trying to figure out how to use the ratio, we can discover interesting information from calculating the ratio over a wide range of markets. An 8-day calculation period was chosen because it will show the greatest differences between the data. The efficiency ratio over only 8 days should bounce around quite a bit, offering the opportunity for each market to show its patterns. If the efficient ratio is robust, then using a longer calculation period should simply scale the results. Remember that a ratio value of 1.0 can happen only if prices move in the same direction for every day of the calculation period, so using a value of 40, or even 20, is unnecessarily large and simply compresses the ratio values.

Figure 2.2 shows 44 of the most liquid futures markets, including equity index, fixed income, currency, energy, metals, and agricultural commodities. The ratios span a range of only about 0.11 ratio points, but for a test of nearly five years, the differences are significant. For convenience, the actual ratios are shown from lowest to highest, in four columns, in Table 2.2.

FIGURE 2.2 Average noise level from 2005 through 2009 for selected markets.

TABLE 2.2 Efficiency ratios for 44 markets.

Both Figure 2.2 and Table 2.2 paint an interesting picture. The lowest 10 ratios, in the first set of columns in Table 2.2, are all equity index markets. The only missing indices are the Deutscher Aktienindex (DAX), which appears at the top of the last set, and the Korea Composite Stock Price Index (Kospi), in the middle of the third set. That means the index markets, as a whole, are the noisiest of all markets traded. Is that a surprise? Even during the remarkable period from mid-2008 through the first quarter of 2009, when index markets seemed to go straight down, the ratio says that it was noisy—or at least relatively noisier than other markets.

Of the nine equity index markets leading the pack, the Russell and the Standard & Poor's (S&P) are the noisiest. Other studies have shown that volume and noise are related. That is not to say that they are at all the same, but the observation is that newly trading markets and emerging markets with lower volume and often dominated by a few large companies tend to have lower noise. Trading in new markets with low volume tends to be dominated by commercials. These are businesses and institutions using the markets to hedge or those with an arrangement with the exchange to provide liquidity. Commercial positions tend to be single large orders, and many commercials have the same view of the market. The result is that prices move in the same direction more often than is normal, the result of fewer and larger trades. We'll discuss why this is important a little later. Meanwhile, note that the closest we come to an emerging market is the Kospi, which is in the middle of the sorted sequence of ratios. The DAX is not emerging but has much lower volume and much higher volatility than the other index markets, which provides a reason for it to have a high ratio.

On the other side of the scale, the market with the highest ratio and the least noise is the Eurodollar interest rate, and it's much higher than all the other markets. We could say, in general, that interest rates are the group with the lowest noise, with five of them in the last set of columns and almost all of them in the second half. Had we included other 3-month rates, such as the short sterling, Euribor, Euroswiss, or Euroyen, they would have all fallen at the end. The other groups of markets—currencies, metals, agriculturals, and energy—are mostly scattered in the middle two columns. Of the foreign exchange (FX) markets, the EURUSD has the most trend, and the AUDUSD the most noise.

A CLOSER LOOK AT EQUITY INDEX MARKETS

Because the concept of noise is important, we will look at a larger set of equity index markets, using a 40-day calculation period. We noted in the previous section that increasing the calculation period would scale down the ratio values but do little to change the order of ranking. Figure 2.3 shows the results of calculating the efficiency ratio for 24 world equity index markets. The ratio on the left ranges from 0.14 to 0.24 instead of 0.30 to 0.41. The markets with the highest noise are on the right, beginning with London's FTSE and the Paris CAC, then the U.S. markets: the S&P 100 (OEX), Dow Transports, S&P, Dow Industrials, Russell, and NASDAQ. These are a combination of actively traded markets, as in the case of the U.S., or moderately traded markets not dominated by commercial use. On the far left are the emerging markets: India's Sensex, China, Brazil's Bovespa, and Russia's Micex indices. These markets tend to make large, sustained moves in one direction.

FIGURE 2.3 The efficiency ratio calculated for 24 world equity index markets, using a 40-day period.

IMPORTANCE OF NOISE

The two pieces of information we should come away with from the noise study are that index markets are very noisy and that interest rates have a low level of noise. What can we do with that?

When we look at various trading strategies that hold outright long and short positions, the primary choices are trend following, taking a position in the direction of the current price move, and mean reversion, trading against the current move in expectation of prices correcting back the mean (average price). If prices move very smoothly, going from one price level to another with only small retracements, then a trend approach should work well. On the other hand, if prices keep changing direction and rarely sustain a continuous move in one direction, then mean reversion should be the preferred approach. This is exactly what the level of noise tells us.

According to the ratios in Table 2.2, we should be using a trend strategy to trade the Eurodollars and all other interest rates but a mean reverting method to trade the equity index markets, with the exception of the DAX. We don't know exactly what threshold value of noise tells us to change from mean reversion to trending.

This premise can be easily proved by calculating the profitability of a typical trend strategy for equity index and interest rate markets. At the same time, we can find the noise level that distinguishes a trend candidate from a mean reversion one. We'll use a 10-day calculation period, which is close to the 8-day period used for the noise calculation. The rules for entering and exiting are:

Buy (close out shorts and enter a long) when the 10-day moving average turns up.Sell (close out longs and enter a short) when the 10-day moving average turns down.

No commissions or slippage was used, so the lower volatility or frequency of trades did not influence the results. For this test, we are interested only in the pure trend results compared with the corresponding noise. For technical traders, we point out that using the direction of the moving average, rather than a price penetration, for determining the change of trend works best for longer-term trends. It reduces the number of false penetrations and greatly reduces the costs. For very short-term trends, when you want the first sign of a change, using the price penetration is more responsive even though there are more false signals.

The markets used were heavily weighted with equity indices, but there was a broad sample of others.

Index markets: Russell, S&P, Dow Jones Industrial Average (DJIA), DAX, Kospi, and Tokyo Stock Price Index (Topix).Interest rates: U.S. 30-year bonds, Eurobund, Eurodollars.Currencies: Euro, Japanese yen, Swiss franc.Energy: Crude oil.Metals: Gold.Agricultural: Wheat, soybeans.

The test was run using TradeStation, and the same test interval, 2005 to May 2009, was used for all markets. The results available were the net profit or loss and the profit factor, the gross profits divided by the gross losses. When the profit factor is above 1.0, the market is net profitable. The profit factor is not as useful as the Sharpe ratio or information ratio (the more commonly used ratio, without the risk-free rate of return), but it does give some measurement of risk and reward, and is better than using only net profits for comparing results.

The pattern to see in Figure 2.4, which plots the profit factor against the efficiency ratio, is that there is a general tendency for the profit factor to move in the same way as the ratio, shown along the bottom axis. Granted, there is a large blob in the middle of the chart, reflecting the variance of results when the ratio is in the middle of the range. Remember that moving average profits can vary based on other price patterns, so that noise will not be a perfect description of expected results. However, the extremes conform to our expectations. The lower left part of the chart shows that the worst performance is associated with the lowest ratio, and the upper right shows that the best trend performance has the highest ratios. The lower left markets are the Russell and S&P, and the upper right are soybeans and Eurodollar interest rates.

FIGURE 2.4 Dispersion of profit factors for 10-day moving average test.

Isolating the equity index markets gives a clearer example of this relationship. If we plot the ratio against the profit factor, we see fewer points but a clear pattern from the lower left to the upper right (see Figure 2.5). The three lower points are the Russell, S&P, and DJIA, showing losses, while the upper points are the DAX, Kospi, and Topix, all profitable. As mentioned earlier, the Kospi and Topix might benefit from a combination of lower liquidity and/or larger commercial trading.

FIGURE 2.5 Dispersion of profit factors for 10-day moving average, index markets only.

DETERMINING THE STRATEGY

What was the purpose of all this analysis? We can see from the index markets that high ratios are profitable with a trend-following approach. We can infer that the low ratios will be profitable using a mean-reverting strategy. At least we can say that a trend-following strategy should not be used with those markets showing the greatest amount of noise, the lowest ratios.

Naturally, there are times when even the noisiest market, the Russell, will produce a profit with a moving average of some calculation period. But a few profits do not make a successful trading program, and the underlying premise is very important. Our premise is that the Russell should not be traded with a trend system but most likely will be successful with a mean-reverting one.

The Time Frame Helps

The calculation period for the trend or mean-reverting method can greatly help the performance of the strategy. This is based on the observation that longer moving averages, or lower-frequency data (weekly instead of daily, daily instead of hourly), implicitly smooths the data. Chartists can recognize the trend in prices more easily when they see a weekly chart rather than an hourly one.

Based on this premise, a trend system is more likely to be successful if the calculation period is longer, for example, 40, 60, or 80 days, and a mean-reverting system is best when using calculation periods from 3 to 8 days. This could be why macrotrend strategies use calculation periods from 40 to 200 days, with most of them closer to 80 days. Mean reverting is often applied to intraday moves.

We conclude that noise appears to be a greater part of price movement when seen up close, and the trend is more visible when viewed from afar. When choosing a strategy, the nature of the market (the underlying noise) and the calculation time frame are both critical.

Volatility Is Still Important

It was mentioned earlier that volatility is removed from the noise calculation. But volatility is a critical ingredient for mean-reverting or any short-term trading. Without volatility, the individual trades may not exceed the cost of doing business, commissions, and slippage. This will be an important issue in the chapters that follow.

Robustness of the Noise Measurement