In today’s New York Times magazine section there is a lengthy article entitled "Depression's Upside." Not surprisingly, it heads the most e-mailed list.
Whatever the ultimate scientific merit of the hypothesis, the article points to experiments demonstrating that "negative moods lead to better decisions in complex situations." An experimenter found, for instance, that shoppers could remember more trinkets (in fact, nearly four times as many) that had been placed near the checkout counter on gray, rainy days when Verdi's "Requiem" was playing in the background than they could on sunny days when Gilbert and Sullivan was playing. Lower moods were also associated with higher scores on the IQ test.
Do these findings have any ramifications for traders? Perhaps. Although we can't control the weather, we can control what we listen to during the day. Maybe all that upbeat music is precisely the wrong medicine for success.
Sunday, February 28, 2010
Saturday, February 27, 2010
Lessons from SAC
I highly recommend this piece on Steven A. Cohen from Bloomberg. Lots of trading lessons here, particularly toward the end of the article. For instance, focusing on what you do best, SAC's "down-and-out" clauses, and intuitive tape reading.
Wednesday, February 24, 2010
A brief hiatus
Sane people take vacations. But not me. I simply need a few days to sort out some ideas that are swirling around in my currently overloaded brain. With any luck this time will be productive both for me and for the blog. That's of course on the assumption that the approaching storm doesn't leave me stranded in a cold, dark house.See you soon.
Tuesday, February 23, 2010
Quants video
Quants: The Alchemists of Wall Street is an important documentary, about 50 minutes long. There’s a brief introduction in Dutch; the rest is in English with Dutch subtitles. Some big guns—for instance, Paul Wilmott and Emanuel Derman—are interviewed at length. Anyone with an interest in quantitative finance or high frequency trading should set aside the time to view this documentary.
Images for options traders
Soon enough I’m going to write a normal-length post about David L. Caplan’s book The New Option Secret: Volatility. But today I merely want to share two powerful images from the book that might make it easier for beginning option traders to understand the impact of time on their position. More experienced traders can empathize.
Let me quote Caplan.
“The short premium trader thinks like a troubled adolescent who cannot wait for the time to pass, and move on to the next stage of life. . . . Each day presents the possibility of a major tragedy. The adolescent thinks, ‘When will it be over?’
“The long premium trader worries like an old man who has not yet left his mark on life, but is still trying to. He knows his days are numbered. . . . He says to himself, ‘If I only had more time.’”
Let me quote Caplan.
“The short premium trader thinks like a troubled adolescent who cannot wait for the time to pass, and move on to the next stage of life. . . . Each day presents the possibility of a major tragedy. The adolescent thinks, ‘When will it be over?’
“The long premium trader worries like an old man who has not yet left his mark on life, but is still trying to. He knows his days are numbered. . . . He says to himself, ‘If I only had more time.’”
Monday, February 22, 2010
The case against stop losses
The common wisdom is that one should never enter a trade without having a stop in place, whether it’s a hard stop or a mental one. There are those, however, who find stops a conceptually bad idea. They would undoubtedly side with enlisted members of the U.S. military where a stop-loss order extends the term of their service (and presumably their pain).
First, a simple one-liner: “Stops are basically breakouts in reverse, and by their nature all breakouts give up a major portion of any potential trading profit.” José Silva, MetaStock Tools.
Second, from an article “The Hidden Cost of the Stoploss” by Robert Macrae, which appeared in the AIMA Journal (April 2005). Macrae admits that stop losses make sense for trend traders: “for positively-autocorrelated (or trending) series, . . . when the stop is hit your expectation is that further losses would have followed.” But, in one of the more often-heard critiques, he contends that on mean-reverting trades stop losses “systematically take you out of your best positions.” (Macrae’s paper goes well beyond this basic point; readers interested in his argument should also look at a follow-up paper by Detko, Ma, and Morita, “Re-examining the Hidden Costs of the Stop-Loss.” )
The debate about the efficacy of stop losses is important. Perhaps sometime down the road I’ll go into this debate in more detail, but for now I’m just throwing out a couple of ideas as food for thought. In the meantime don’t abandon your stops based on this brief post. Ideas come cheap, real losses are expensive.
First, a simple one-liner: “Stops are basically breakouts in reverse, and by their nature all breakouts give up a major portion of any potential trading profit.” José Silva, MetaStock Tools.
Second, from an article “The Hidden Cost of the Stoploss” by Robert Macrae, which appeared in the AIMA Journal (April 2005). Macrae admits that stop losses make sense for trend traders: “for positively-autocorrelated (or trending) series, . . . when the stop is hit your expectation is that further losses would have followed.” But, in one of the more often-heard critiques, he contends that on mean-reverting trades stop losses “systematically take you out of your best positions.” (Macrae’s paper goes well beyond this basic point; readers interested in his argument should also look at a follow-up paper by Detko, Ma, and Morita, “Re-examining the Hidden Costs of the Stop-Loss.” )
The debate about the efficacy of stop losses is important. Perhaps sometime down the road I’ll go into this debate in more detail, but for now I’m just throwing out a couple of ideas as food for thought. In the meantime don’t abandon your stops based on this brief post. Ideas come cheap, real losses are expensive.
Saturday, February 20, 2010
Expiration week
Options expiration week, we are told, can be a tricky time to trade. Tom Sosnoff of Think or Swim is not convinced. In a chat entitled "Expiration Again" (2/17/2010) he explains some of the market pressures that occur during expiration week as index arbitrageurs unwind their positions. It's definitely worth a listen.
Friday, February 19, 2010
Addiction to perfection
The article is old (2005), but the message is timeless. Take a look at “Trading Wisdom: Addiction to Perfection” by Janice Dorn, M.D., Ph.D.
Thursday, February 18, 2010
Parnos, Option Profits
In Options Profits: “The Naked Truth” (Traders Press, 2009) Mike Parnos spices up his options teaching with lots, and lots, and lots of humor. Not my kind of humor (I prefer the clever and subtle), but I will defer to others for comedic criticism. In between what might be comic relief if it weren’t so pervasive Parnos takes the novice from options chains to puts and calls and finally to “little known conservative non-directional strategies used by option pros.”
These “little known” strategies include the “meat and potatoes” iron condor as well as a range of other non-directional trades in the standard repertoire. So what does Parnos have to offer the reader who is familiar with non-directional strategies? He devotes the most space to the iron condor and its distant relative that the author dubs the “Siamese condor,” so let me confine my remarks to these two strategies.
Parnos’s approach is not always mainstream, and I am not experienced enough with condors to pass judgment on it. I will simply describe his entry and adjustment techniques. He deals first with entry criteria. Let’s say a person is trading the SPX with strikes at 10-point intervals. Since the iron condor is profitable if all strikes expire worthless, the trader should place the credit spreads as far away from the underlying as possible while still collecting enough premium ($0.50 to $0.70 on a 10-point spread) to make the trade worthwhile. Parnos’s math gets a little fuzzy when describing the probability of the trade’s success using delta as a proxy (I suspect a typo), but the upshot is that the trader wants a delta of between 7 and 12 for the calls and between -7 and -12 for the puts. I modeled out a short-duration iron condor (February expiry) with this profile on the Think or Swim platform using January 29 as the trade date and 1073.87 as the closing price. I would have received a $1,200 credit for a 10-lot iron condor with a 1140/1150 call spread and a 970/960 put spread and a total delta of -13.90. The risk on the trade would be $8,800 and my maximum profit at expiration would be $1,200. (I obviously did a good job simulating Parnos’s trade because my profit and risk are the same as that in his example.) At expiration my break-evens are 969.46 and 1141.41.
Unlike many, Parnos believes in adjusting iron condors. When one of the short strikes is threatened (when the delta of the short strike to the downside, assuming a downward movement in the underlying, reaches -25), it’s time to adjust. Close out the 970/960 vertical, at a cost of somewhere around $2,200, and sell another bull put spread 40 points beneath the original to take in an additional $600. The trader’s balance sheet now shows a debit of $400 ($600 + $600 - $2,200 + $600). If the market doesn’t turn around but continues to threaten the new lower short strike, the trader can adjust again, rolling down and racking up an additional $1,600 loss, now down a total of $2,200. “The whole idea,” Parnos writes, “is to manage your risk. If you take a loss . . . that can be made back in a month, the four or five profitable months will provide you a very impressive percentage return.” (p. 160)
So what is the Siamese condor? It’s a near-term iron condor with the two spreads joined at the short strike. This is not a trade to adjust; the trader should bail when the underlying reaches the common short strike plus the net credit or the common short strike minus the net credit. Why a Siamese condor instead of an iron condor? It has the potential, Parnos claims, to make more money with lower risk. I’ll have to think that through (unless some kind experienced options trader does it for me). My brain is rebelling against a long work day and is rejecting any new mental challenges.
These “little known” strategies include the “meat and potatoes” iron condor as well as a range of other non-directional trades in the standard repertoire. So what does Parnos have to offer the reader who is familiar with non-directional strategies? He devotes the most space to the iron condor and its distant relative that the author dubs the “Siamese condor,” so let me confine my remarks to these two strategies.
Parnos’s approach is not always mainstream, and I am not experienced enough with condors to pass judgment on it. I will simply describe his entry and adjustment techniques. He deals first with entry criteria. Let’s say a person is trading the SPX with strikes at 10-point intervals. Since the iron condor is profitable if all strikes expire worthless, the trader should place the credit spreads as far away from the underlying as possible while still collecting enough premium ($0.50 to $0.70 on a 10-point spread) to make the trade worthwhile. Parnos’s math gets a little fuzzy when describing the probability of the trade’s success using delta as a proxy (I suspect a typo), but the upshot is that the trader wants a delta of between 7 and 12 for the calls and between -7 and -12 for the puts. I modeled out a short-duration iron condor (February expiry) with this profile on the Think or Swim platform using January 29 as the trade date and 1073.87 as the closing price. I would have received a $1,200 credit for a 10-lot iron condor with a 1140/1150 call spread and a 970/960 put spread and a total delta of -13.90. The risk on the trade would be $8,800 and my maximum profit at expiration would be $1,200. (I obviously did a good job simulating Parnos’s trade because my profit and risk are the same as that in his example.) At expiration my break-evens are 969.46 and 1141.41.
Unlike many, Parnos believes in adjusting iron condors. When one of the short strikes is threatened (when the delta of the short strike to the downside, assuming a downward movement in the underlying, reaches -25), it’s time to adjust. Close out the 970/960 vertical, at a cost of somewhere around $2,200, and sell another bull put spread 40 points beneath the original to take in an additional $600. The trader’s balance sheet now shows a debit of $400 ($600 + $600 - $2,200 + $600). If the market doesn’t turn around but continues to threaten the new lower short strike, the trader can adjust again, rolling down and racking up an additional $1,600 loss, now down a total of $2,200. “The whole idea,” Parnos writes, “is to manage your risk. If you take a loss . . . that can be made back in a month, the four or five profitable months will provide you a very impressive percentage return.” (p. 160)
So what is the Siamese condor? It’s a near-term iron condor with the two spreads joined at the short strike. This is not a trade to adjust; the trader should bail when the underlying reaches the common short strike plus the net credit or the common short strike minus the net credit. Why a Siamese condor instead of an iron condor? It has the potential, Parnos claims, to make more money with lower risk. I’ll have to think that through (unless some kind experienced options trader does it for me). My brain is rebelling against a long work day and is rejecting any new mental challenges.
Wednesday, February 17, 2010
Kanter, Confidence
The other day I linked to a Bloomberg piece by Rosabeth Moss Kanter. I subsequently decided to look into her book Confidence: How Winning Streaks and Losing Streaks Begin and End (Three Rivers Press, 2004, 2006). To be more accurate, I looked at the Google Books preview. Let me share a couple of her insights that may be helpful to traders.
“Failure and success are not episodes, they are trajectories. They are tendencies, directions, pathways. Each decision, each time at bat, each tennis serve, each business quarter, each school year seems like a new event, but the next performance is shaped by what happened last time out. . . . In sports, each game starts at zero in a strictly technical sense. Statistically, each player is no more likely to sink the same number of baskets in the next game as in the last game. . . . But many things are carried over from game to game and shape the mood in the locker room; that mood can then follow players out onto the field.” (p. 9)
“Success means that people or teams or organizations survive long enough to need maintenance and repairs—in other words, reinvestment. . . . Their facilities, tools, and bags of tricks get older, deteriorate, run down. . . . These normal processes put a brake on momentum. The upward trajectory cannot continue; repeating the pattern brings diminishing returns. A winning streak requires renewal and rebuilding.” (p. 73)
“Failure and success are not episodes, they are trajectories. They are tendencies, directions, pathways. Each decision, each time at bat, each tennis serve, each business quarter, each school year seems like a new event, but the next performance is shaped by what happened last time out. . . . In sports, each game starts at zero in a strictly technical sense. Statistically, each player is no more likely to sink the same number of baskets in the next game as in the last game. . . . But many things are carried over from game to game and shape the mood in the locker room; that mood can then follow players out onto the field.” (p. 9)
“Success means that people or teams or organizations survive long enough to need maintenance and repairs—in other words, reinvestment. . . . Their facilities, tools, and bags of tricks get older, deteriorate, run down. . . . These normal processes put a brake on momentum. The upward trajectory cannot continue; repeating the pattern brings diminishing returns. A winning streak requires renewal and rebuilding.” (p. 73)
Tuesday, February 16, 2010
Plight of the Fortune Tellers: two views of probability
It might seem that I’m going off the deep end. First a foray into admittedly phony astrology, and now a book entitled Plight of the Fortune Tellers. But this book is serious stuff. It’s written by Riccardo Rebonato, I assume still global head of market risk and global head of the quantitative research team at RBS, a visiting lecturer at Oxford, and adjunct professor at Imperial College; it’s subtitled Why We Need to Manage Financial Risk Differently; and it was published by Princeton University Press (2007). The fact that the Royal Bank of Scotland subsequently had to be bailed out to the tune of some $74 billion might make you snicker (yes, I guess RBS should have managed its financial risk differently). But I’m not reading this book as a call for reform, despite its subtitle. Rather I’m interested in Rebonato’s analysis of concepts we can all use in managing our trades and our portfolios. I’ll share insights from this book in dribs and drabs over time. Fortune tellers might seem like a sexy subject; risk management, I know, is not.
Today’s topic is probability. Tomes have been written about probability, but here I’m getting down to basics. There are two general ways to look at probability. The first is the frequentist view, best illustrated by coin tosses. The second is the Bayesian (or subjective) view, “often seen as a measure of belief, susceptible of being changed by new evidence.” (p. 19) The frequentist view underlies most current risk management: “We estimate the probabilities, and from these we determine the actions.” Rebonato suggests that the opposite should apply: “We observe the actions, and from these we impute the probabilities.” (p. 18)
The frequentist view requires an event such as the tossing of a coin that can, in principle, be repeated many times under virtually identical conditions. The findings can be precise, down to many decimal points. We can say, for instance, that the chance of a coin coming up heads is 0.5000001, that the probability of a newborn baby being female is 0.52345, or that the probability of a randomly chosen individual in a well-specified population having red hair is 0.1221.
But not all probabilistic statements have these characteristics. If, for instance, you are speculating about the probability of a Democrat being elected president (and are just as willing to wager on the outcome of the election as you would be on the outcome of a coin toss or the sex of the next newborn), you’ve moved outside the world of the repeatable. You have to rely on beliefs about the likelihood of a president being elected if the economy is soft, if we’re involved in war, and so on. You can look back on previous presidential elections for guidance, but they did not take place under identical conditions. Moreover, you would never make such a bizarre statement as that the probability of a Democrat being elected president is 0.520032.
A fairly strong case can be made that the frequentist view is a subset or a special case of Bayesian statistics. Rebonato illustrates this with a story about a Martian and a coin toss. You meet the Martian and, “not knowing how to strike up polite conversation, you take a coin out of your pocket.” (p. 50) The Martian has never seen a coin, so he obviously has never taken part in a coin flipping experiment. Your coin is run of the mill, nothing special about it. You toss it four times and each time it comes up heads. Rebonato then asks what you would conclude about the fairness of the coin and what the Martian is likely to conclude. You would probably just soldier on, assuming it was fair, and require odds of 50-50. The rational Martian, however, should not accept these odds; for him heads is much more likely than tails.
Rebonato continues: “You and your Martian friend have observed the same experiment, yet you reach very different conclusions. How can that be? Because, Bayesian statisticians say, we almost never approach an experiment with random outcomes without some prior ideas about the setting and the likelihood of the possible outcomes. New evidence modifies (should modify) our prior beliefs and transforms them into our posterior beliefs. The stronger the prior belief, the more difficult it will be to change it. If we really and truly have no prior beliefs about the situation at hand, as was the case with the Martian when he observed the coin flips, then and only then will we be totally guided by the evidence. This is the situation that is dealt with with infinite care and precision in ‘traditional’ statistics books. Yet it remains a rather special situation.” (p. 52)
Today’s topic is probability. Tomes have been written about probability, but here I’m getting down to basics. There are two general ways to look at probability. The first is the frequentist view, best illustrated by coin tosses. The second is the Bayesian (or subjective) view, “often seen as a measure of belief, susceptible of being changed by new evidence.” (p. 19) The frequentist view underlies most current risk management: “We estimate the probabilities, and from these we determine the actions.” Rebonato suggests that the opposite should apply: “We observe the actions, and from these we impute the probabilities.” (p. 18)
The frequentist view requires an event such as the tossing of a coin that can, in principle, be repeated many times under virtually identical conditions. The findings can be precise, down to many decimal points. We can say, for instance, that the chance of a coin coming up heads is 0.5000001, that the probability of a newborn baby being female is 0.52345, or that the probability of a randomly chosen individual in a well-specified population having red hair is 0.1221.
But not all probabilistic statements have these characteristics. If, for instance, you are speculating about the probability of a Democrat being elected president (and are just as willing to wager on the outcome of the election as you would be on the outcome of a coin toss or the sex of the next newborn), you’ve moved outside the world of the repeatable. You have to rely on beliefs about the likelihood of a president being elected if the economy is soft, if we’re involved in war, and so on. You can look back on previous presidential elections for guidance, but they did not take place under identical conditions. Moreover, you would never make such a bizarre statement as that the probability of a Democrat being elected president is 0.520032.
A fairly strong case can be made that the frequentist view is a subset or a special case of Bayesian statistics. Rebonato illustrates this with a story about a Martian and a coin toss. You meet the Martian and, “not knowing how to strike up polite conversation, you take a coin out of your pocket.” (p. 50) The Martian has never seen a coin, so he obviously has never taken part in a coin flipping experiment. Your coin is run of the mill, nothing special about it. You toss it four times and each time it comes up heads. Rebonato then asks what you would conclude about the fairness of the coin and what the Martian is likely to conclude. You would probably just soldier on, assuming it was fair, and require odds of 50-50. The rational Martian, however, should not accept these odds; for him heads is much more likely than tails.
Rebonato continues: “You and your Martian friend have observed the same experiment, yet you reach very different conclusions. How can that be? Because, Bayesian statisticians say, we almost never approach an experiment with random outcomes without some prior ideas about the setting and the likelihood of the possible outcomes. New evidence modifies (should modify) our prior beliefs and transforms them into our posterior beliefs. The stronger the prior belief, the more difficult it will be to change it. If we really and truly have no prior beliefs about the situation at hand, as was the case with the Martian when he observed the coin flips, then and only then will we be totally guided by the evidence. This is the situation that is dealt with with infinite care and precision in ‘traditional’ statistics books. Yet it remains a rather special situation.” (p. 52)
Saturday, February 13, 2010
Horoscopes for traders and investors
I’m trying something new. The seemingly mandatory disclaimer: I am not an astrologist, I don’t believe in astrology, and I’m not making any actionable predictions. If you act on any information in your horoscope and things go badly, you can’t hold me responsible. On the other hand, if things go well, feel free to think kindly of me. In these posts (weekly if I can come up with enough material, monthly otherwise) I will offer a few insights in horoscope format. I have no idea how many of these posts I’ll do; if the concept is stillborn, I will quietly bury it.Aries March 20-April 20—Think twice before making that fearless trek into the unknown. Do you have the proper training, gear, and escape plan? If not, consider the safety of the paper world. You will not meet a real piranha in a simulated Amazon.
Taurus April 20-May 21—Get your feet out of the mud or you’re liable to get seriously stuck. Have someone throw a couple of planks across the mud. It doesn’t matter what kind of planks you use; the cheapest fir will work just as well as curly maple. By analogy, you don’t have to spring for the most expensive data provider, trading platform, or educational program to get yourself going. Free is sometimes surprisingly good.
Gemini May 21-June 21—Take advantage of your astral ability to go with the flow. You have no idea how great a gift this is. Most people end up in a dance studio for traders following footprints on the floor; you have the potential to be a Fred Astaire. Be cautious, though. Fred Astaire became more famous than his more talented sister because he had “durability.”
Cancer June 21-July 22—You can’t retract that losing trade, but stop brooding over it. If you made a mistake, learn from it; if the market simply didn’t accommodate you, suck it up. That’s part of the trading business. Some inventory you have to move at a loss—that battery-powered hula hoop for couch potato exercisers, for instance, that you thought was such a good deal at $10. Consider yourself lucky to have found a sucker to take it off your hands at $8.
Leo July 22—August 23—If you truly love being center stage, think about becoming a TV commentator or a trading guru. Both fields are crowded, but trading skills are not required. An oversized ego helps.
Virgo August 23-September 23—Continue to pursue your penchant for detail. Trading success comes in large part from perfecting the many details of trade execution, position sizing, risk management, and post-trade analytic metrics. With so much in trading beyond your control, you’ll have a genuine edge over other traders if you perfect what most ignore.
Libra September 23-October 23—Trading is a lonely business; seek companionship elsewhere. Once you opted to be an online trader you exchanged conversations around the water cooler for independence. Cultivate your social ties outside of trading hours and make these relationships count.
Scorpio October 23-November 22—If you are a discretionary trader, your keen sense of intuition should serve you well as long as you’ve done your analytic homework and have honed your intuition through many, many hours of screen time. Discretionary trading is in large measure an art where an ounce of intuition can often trump a pound of pondering.
Sagittarius November 22-December 22—So you’re feeling both lucky and smart? Dirty Harry doesn’t scare you? Well, maybe he should. Overconfidence leads to a sense of invulnerability, to overtrading, to trading in disproportionately large size. Start each trade by asking what could go wrong if it turns out you were both unlucky and made a stupid mistake. Perhaps then you really will be lucky and smart.
Capricorn December 22-January 20—Your disciplined, methodical work will pay off if only your perfectionism doesn’t get in the way. No trader has a perfect track record, not even those who advertise “no losing trades in three months.” Trading is about probabilities, so lighten up. After all, dear Capricorn, we’ve all heard that goats eat tin cans, certainly not the perfect cuisine. Oops, that report falls under the category of farmyard legend.
Aquarius January 20-February 18—Your quirkiness may be endearing, but make sure it isn’t hurting your trading. Don’t confuse Joe’s trading business with the quirky Trader Joe’s.
Pisces February 18-March 20—Take those rose-colored glasses back to the optician but keep your sense of optimism. When you’re in a positive mood your visual cortex takes in more information; negative moods result in tunnel vision. And there’s lots of actionable information that can be gleaned from the periphery.
Note: The image that accompanies this post is the horoscope of Prince Iskandar, grandson of Tamerlane, the Turkman Mongol conqueror. It shows the positions of the heavens at the moment of Iskandar’s birth on April 25, 1384. Credit: Wellcome Library, London.
Friday, February 12, 2010
Sklarew, Techniques of a Professional Commodity Chart Analyst
I enjoy reading books that others might consider outdated. Arthur Sklarew’s Techniques of a Professional Commodity Chart Analyst (Commodity Research Bureau, 1980) is a transitional book, written when “the click-buzz of computers almost drown[ed] out the shouting in the pits in Chicago and New York.” (p. xi) It covers some familiar ground—chart patterns, Elliott wave theory, trend lines, oscillators, and moving averages—all in support of trend trading. But there are twists even here; moreover, Sklarew offers some tools that are not commonplace.
Sklarew suggests that the commodity trader follow both the rule of multiple techniques (that is, the more technical indicators confirm each other the better the chance of an accurate forecast) and the principle of selective techniques. The former applies to chart analysis in general; the latter to automatic trading methods. “In very general terms, the Principle of Selective Techniques states simply that the automatic trading method that appears to work better than other methods in a particular market at a particular time is the one that should be used in that market at that time.” (p. 5)
Although Sklarew writes that “a chartist’s asset lies not so much in his being able to forecast how high or how low a market will go, or when it will get there, as in being able to identify the direction of a trend and to call the turn of a trend when it comes,” (p. 1) he offers a dozen methods for predicting how far a move will go. Here let me summarize one novel measurement technique. I haven’t tested it out, in part because I personally am not comfortable with measured targets. But a lot of people swear by them, so here is one of Sklarew’s contributions.
The rule of seven enables a trader to project one or more objectives in the direction of the new trend based on the initial leg of the trend. The formulas for uptrend and downtrend projections vary slightly. First, the uptrend formula: “Measure the size of the initial up-leg by subtracting the low price from the high; multiply that figure by seven; then divide that product by four to get the distance from the low to the first objective, divide the product by three for the second objective, and by two for the third objective.” In every case add the distance to the low. Or, in its simplified form: “Upside objective #1: High minus low, multiply by 1.75, add to low price. Upside objective #2: High minus low, multiply by 2.33, add to low price. Upside objective #3: High minus low, multiply by 3.50, add to low price.” In a downtrend, Sklarew writes, “the formula is moved back one notch. The three downside objectives are obtained by multiplying the size of the initial down-leg by 7/5, 7/4, and 7/3, or 1.40, 1.75, and 2.33 respectively, and subtracting the result from the high.” (p. 83)
Sklarew fleshes out the rule of seven, applying it to both minor and major trends, to trends of both large and small magnitude. Moreover, he suggests that in dynamic markets a fourth objective must be considered. In an uptrend, it is seven times the initial leg measured from the low of that leg; in a downtrend, it is 3.50 times the first leg projected downward from the high.
Sklarew also sets forth a 17-35 measurement. He admits that it is “difficult to find a logical reason why many sustained commodity futures price moves congest or reverse after covering a distance equal to 17% or 35% of the recent high or low price,” but he says that “this happens so frequently that it must be more than just a coincidence.” (p. 87)
This book offers tips from the former coffee and cocoa trader, mathematical formulas for many indicators, and backtested results from a study designed to find the best moving average for each of 13 commodity futures contracts. It may be thirty years old, but it offers fodder for today’s system testers.
Sklarew suggests that the commodity trader follow both the rule of multiple techniques (that is, the more technical indicators confirm each other the better the chance of an accurate forecast) and the principle of selective techniques. The former applies to chart analysis in general; the latter to automatic trading methods. “In very general terms, the Principle of Selective Techniques states simply that the automatic trading method that appears to work better than other methods in a particular market at a particular time is the one that should be used in that market at that time.” (p. 5)
Although Sklarew writes that “a chartist’s asset lies not so much in his being able to forecast how high or how low a market will go, or when it will get there, as in being able to identify the direction of a trend and to call the turn of a trend when it comes,” (p. 1) he offers a dozen methods for predicting how far a move will go. Here let me summarize one novel measurement technique. I haven’t tested it out, in part because I personally am not comfortable with measured targets. But a lot of people swear by them, so here is one of Sklarew’s contributions.
The rule of seven enables a trader to project one or more objectives in the direction of the new trend based on the initial leg of the trend. The formulas for uptrend and downtrend projections vary slightly. First, the uptrend formula: “Measure the size of the initial up-leg by subtracting the low price from the high; multiply that figure by seven; then divide that product by four to get the distance from the low to the first objective, divide the product by three for the second objective, and by two for the third objective.” In every case add the distance to the low. Or, in its simplified form: “Upside objective #1: High minus low, multiply by 1.75, add to low price. Upside objective #2: High minus low, multiply by 2.33, add to low price. Upside objective #3: High minus low, multiply by 3.50, add to low price.” In a downtrend, Sklarew writes, “the formula is moved back one notch. The three downside objectives are obtained by multiplying the size of the initial down-leg by 7/5, 7/4, and 7/3, or 1.40, 1.75, and 2.33 respectively, and subtracting the result from the high.” (p. 83)
Sklarew fleshes out the rule of seven, applying it to both minor and major trends, to trends of both large and small magnitude. Moreover, he suggests that in dynamic markets a fourth objective must be considered. In an uptrend, it is seven times the initial leg measured from the low of that leg; in a downtrend, it is 3.50 times the first leg projected downward from the high.
Sklarew also sets forth a 17-35 measurement. He admits that it is “difficult to find a logical reason why many sustained commodity futures price moves congest or reverse after covering a distance equal to 17% or 35% of the recent high or low price,” but he says that “this happens so frequently that it must be more than just a coincidence.” (p. 87)
This book offers tips from the former coffee and cocoa trader, mathematical formulas for many indicators, and backtested results from a study designed to find the best moving average for each of 13 commodity futures contracts. It may be thirty years old, but it offers fodder for today’s system testers.
Thursday, February 11, 2010
Noise
More than once on this blog I’ve written about things I know nothing about. At least I know that I don’t know, I vet my sources to be reasonably certain that they do know, though if they don’t I’m in trouble. And, of course, if you rely on my second-hand knowledge you’re also in trouble. Well, here I go again. This time I’m trying to glean some insights from the world of sound synthesis.
Many traders try to devise ways to filter out market noise to produce tradable signals, so I thought that the world of electronic music might offer some clues. When I found a paper that talked about Brownian motion, the random walk, and algorithms, I figured that it was worth summarizing ever so briefly on the off chance that one or more of my readers might have a eureka moment.
Market noise is, of course, not same as noise in a musical setting. Let’s start with a definition of the latter. In its broadest form, “noise is an audio signal that consists of an accumulation of sinewaves of all the possible frequencies in the hearing range and with all possible amplitudes and phase relations.” Noise stands in contrast to sound, which is a sinewave signal with a single frequency. Is it possible to filter noise to produce sounds that should be hidden somewhere in the noise? The answer is no, and not just for technical reasons. “When the frequencies are filtered out correctly the amplitudes still vary wildly, making it virtually impossible to create steady tones.”
But noise signals can be differentiated according to their statistical distribution profile. Although noise has no apparent pitch, “when each possible frequency has an equal chance of occurrence . . . it sounds like a very bright hissing sound” and is what we know as white noise. We can filter white noise to produce a dull red noise, a pleasant pink noise akin to the sound of a distant ocean surf, or a very dark brown noise that is actually derived from Brownian motion. But we can’t get sound.
We know that high frequency traders function in the world of noise and presumably act on algorithms that filter cacophonous market noise into actionable “colored” noise. Noise is not, however, the exclusive property of high frequency traders. There is market noise on all time frames. Normally traders are told to stay clear of noise, but perhaps it’s time to embrace noise and simply try to filter it. One standard piece of advice is to drop down to a smaller time frame. What is noise on a 60-minute chart might appear to be a clear trend on a 5-minute chart. But are we deluding ourselves? Can we really filter 60-minute noise in such a way as to produce 5-minute “sound”? If the analogy to electronic sound holds, what we see on the 5-minute chart is still noise. The lengths of 5-minute price bars “vary wildly”; there are no steady tones here. Perhaps what we describe as a trend is really colored noise.
Many traders try to devise ways to filter out market noise to produce tradable signals, so I thought that the world of electronic music might offer some clues. When I found a paper that talked about Brownian motion, the random walk, and algorithms, I figured that it was worth summarizing ever so briefly on the off chance that one or more of my readers might have a eureka moment.
Market noise is, of course, not same as noise in a musical setting. Let’s start with a definition of the latter. In its broadest form, “noise is an audio signal that consists of an accumulation of sinewaves of all the possible frequencies in the hearing range and with all possible amplitudes and phase relations.” Noise stands in contrast to sound, which is a sinewave signal with a single frequency. Is it possible to filter noise to produce sounds that should be hidden somewhere in the noise? The answer is no, and not just for technical reasons. “When the frequencies are filtered out correctly the amplitudes still vary wildly, making it virtually impossible to create steady tones.”
But noise signals can be differentiated according to their statistical distribution profile. Although noise has no apparent pitch, “when each possible frequency has an equal chance of occurrence . . . it sounds like a very bright hissing sound” and is what we know as white noise. We can filter white noise to produce a dull red noise, a pleasant pink noise akin to the sound of a distant ocean surf, or a very dark brown noise that is actually derived from Brownian motion. But we can’t get sound.
We know that high frequency traders function in the world of noise and presumably act on algorithms that filter cacophonous market noise into actionable “colored” noise. Noise is not, however, the exclusive property of high frequency traders. There is market noise on all time frames. Normally traders are told to stay clear of noise, but perhaps it’s time to embrace noise and simply try to filter it. One standard piece of advice is to drop down to a smaller time frame. What is noise on a 60-minute chart might appear to be a clear trend on a 5-minute chart. But are we deluding ourselves? Can we really filter 60-minute noise in such a way as to produce 5-minute “sound”? If the analogy to electronic sound holds, what we see on the 5-minute chart is still noise. The lengths of 5-minute price bars “vary wildly”; there are no steady tones here. Perhaps what we describe as a trend is really colored noise.
Wednesday, February 10, 2010
The gain-loss spread as an intuitive measure of risk
Today’s post relies on a paper written by Javier Estrada for the Fall 2009 issue of the Journal of Applied Corporate Finance, a Morgan Stanley publication. It is entitled “The Gain-Loss Spread: A New and Intuitive Measure of Risk.”
Estrada seeks to replace standard deviation as the benchmark way to quantify risk with a metric that is intuitive and that is based on numbers that investors consider relevant when assessing risk. “This measure is the gain-loss spread (GLS), which takes into account the probability of a loss, the average loss, and the average gain.” Estrada shows that the GLS provides basically the same kind of information as the standard deviation of returns but in a much clearer way. “Furthermore, the evidence shows that: (1) the GLS is more correlated with mean returns than both the standard deviation and beta, thus providing a tighter link between risk and return; and (2) it is better able to discriminate between high-return and low-return portfolios than beta and equal to or better than the standard deviation, and therefore is a useful tool for portfolio selection.”
The GLS is very easy to calculate. You start with annual percentage returns for an index over a specified period of time. Estrada uses the MSCI World Index for the 20-year period 1988 to 2007. During five of those years the index delivered negative returns, so the probability of a loss is 25%. Next calculate the average annual loss, in this case -13.9%. The expected annual loss is the product of these two numbers: 25% * -13.9% = -3.5%. Similar calculations will yield the expected annual gain of the asset, in this case 75% * 18.9% = 14.2%. The gain-loss spread is the difference between the expected gain and the expected loss: 14.2% - (-3.5%) = 17.6%. As risk measurements go, that’s as easy as it gets.
Can such a simple risk measurement be useful? Estrada here relies on statistical tests that are too technical for this post; the interested reader can go to the original paper. The answer (refer back to the second paragraph of this post) is clearly yes. GLS may not satisfy investors who have learned to worry about fat tails, but I consider it wonderful, even close to miraculous, that a model that requires such rudimentary arithmetical skills can compete with those that have been the provenance of statisticians.
Estrada seeks to replace standard deviation as the benchmark way to quantify risk with a metric that is intuitive and that is based on numbers that investors consider relevant when assessing risk. “This measure is the gain-loss spread (GLS), which takes into account the probability of a loss, the average loss, and the average gain.” Estrada shows that the GLS provides basically the same kind of information as the standard deviation of returns but in a much clearer way. “Furthermore, the evidence shows that: (1) the GLS is more correlated with mean returns than both the standard deviation and beta, thus providing a tighter link between risk and return; and (2) it is better able to discriminate between high-return and low-return portfolios than beta and equal to or better than the standard deviation, and therefore is a useful tool for portfolio selection.”
The GLS is very easy to calculate. You start with annual percentage returns for an index over a specified period of time. Estrada uses the MSCI World Index for the 20-year period 1988 to 2007. During five of those years the index delivered negative returns, so the probability of a loss is 25%. Next calculate the average annual loss, in this case -13.9%. The expected annual loss is the product of these two numbers: 25% * -13.9% = -3.5%. Similar calculations will yield the expected annual gain of the asset, in this case 75% * 18.9% = 14.2%. The gain-loss spread is the difference between the expected gain and the expected loss: 14.2% - (-3.5%) = 17.6%. As risk measurements go, that’s as easy as it gets.
Can such a simple risk measurement be useful? Estrada here relies on statistical tests that are too technical for this post; the interested reader can go to the original paper. The answer (refer back to the second paragraph of this post) is clearly yes. GLS may not satisfy investors who have learned to worry about fat tails, but I consider it wonderful, even close to miraculous, that a model that requires such rudimentary arithmetical skills can compete with those that have been the provenance of statisticians.
Tuesday, February 9, 2010
Caplan, Profiting with Futures Options
For those who are impatient with long-winded explanations David L. Caplan provides welcome relief. Profiting with Futures Options (Center for Futures Education/Traders Press, 1994) is a pamphlet under 50 pages long that nonetheless provides a wealth of information.
Caplan is writing for futures traders, or at least those familiar with futures, who could profit from either supplementing or replacing their futures trading with trading options on futures. He outlines cases in which options should be used in place of futures contracts. For example, when a commodity is overvalued and overbought, the trader can sell an out-of-the-money, overvalued call. The options trader is also the only one who can profit in a flat commodity market. Limited-risk options strategies insulate the trader against “limit” days and can be used to prevent being stopped out on a trade that turns around and becomes profitable.
Caplan outlines a series of strategies, all spreads, that provide the trader with a significant advantage. Most of the strategies are familiar, but Caplan sometimes develops these strategies in novel ways. All in all, for anyone who trades futures this pamphlet will open the door to new opportunities for both profit and risk management.
Caplan is writing for futures traders, or at least those familiar with futures, who could profit from either supplementing or replacing their futures trading with trading options on futures. He outlines cases in which options should be used in place of futures contracts. For example, when a commodity is overvalued and overbought, the trader can sell an out-of-the-money, overvalued call. The options trader is also the only one who can profit in a flat commodity market. Limited-risk options strategies insulate the trader against “limit” days and can be used to prevent being stopped out on a trade that turns around and becomes profitable.
Caplan outlines a series of strategies, all spreads, that provide the trader with a significant advantage. Most of the strategies are familiar, but Caplan sometimes develops these strategies in novel ways. All in all, for anyone who trades futures this pamphlet will open the door to new opportunities for both profit and risk management.
Monday, February 8, 2010
Why winning streaks end
The Super Bowl provided the perfect opportunity for media musings on performance. Here’s a good piece from Bloomberg written by Rosabeth Moss Kanter, a professor at the Harvard Business School and author of Confidence: How Winning Streaks & Losing Streaks Begin & End.
The sustaining power of rituals
Earlier I wrote about interval training for the brain, an idea developed by Loehr and Schwartz in The Power of Full Engagement. Today’s topic also comes from their book.
Rituals, as the authors define them, are routines that are more or less automatic and relatively effortless; we feel worse if we don’t do them. Some examples of rituals are brushing your teeth, taking a shower, kissing your spouse goodbye in the morning, calling your parents on the weekend.
The sustaining power of rituals, Loehr and Schwartz claim, “comes from the fact that they conserve energy.” The authors quote A. N. Whitehead, who wrote in 1911: “We should not cultivate the habit of thinking of what we are doing. The precise opposite is the case. Civilization advances by extending the number of operations which we can perform without thinking about them.” (p. 169)
“Since will and discipline,” the authors continue, “are far more limited and precious resources than most of us realize, they must be called upon very selectively. Because even small acts of self-control use up this limited reservoir, consciously using this energy for one activity means it will be less available for the next one. The sobering truth is that we have the capacity for very few conscious acts of self-control in a day.”
If the authors are correct, discretionary traders face a daily uphill battle that they can win only by ritualizing as much of their routine as possible.
Rituals, as the authors define them, are routines that are more or less automatic and relatively effortless; we feel worse if we don’t do them. Some examples of rituals are brushing your teeth, taking a shower, kissing your spouse goodbye in the morning, calling your parents on the weekend.
The sustaining power of rituals, Loehr and Schwartz claim, “comes from the fact that they conserve energy.” The authors quote A. N. Whitehead, who wrote in 1911: “We should not cultivate the habit of thinking of what we are doing. The precise opposite is the case. Civilization advances by extending the number of operations which we can perform without thinking about them.” (p. 169)
“Since will and discipline,” the authors continue, “are far more limited and precious resources than most of us realize, they must be called upon very selectively. Because even small acts of self-control use up this limited reservoir, consciously using this energy for one activity means it will be less available for the next one. The sobering truth is that we have the capacity for very few conscious acts of self-control in a day.”
If the authors are correct, discretionary traders face a daily uphill battle that they can win only by ritualizing as much of their routine as possible.
Saturday, February 6, 2010
Weekend listening/reading
Ed Thorp is best known to the world at large as the author of Beat the Dealer: A Winning Strategy for the Game of Twenty-One (1966). He then took his skills to Wall Street and became one of the earliest quants. His story is among those told in Scott Patterson’s new book The Quants: How a New Breed of Math Whizzes Conquered Wall Street and Nearly Destroyed It. I haven’t read the book, but Terry Gross interviewed both Thorp and Patterson recently on her NPR program Fresh Air. "The Quants": It Pays to Know Your Wall Street Math runs about half an hour. It’s definitely worth a listen; it doesn’t tax the brain. Also note that on the page I’ve linked to there is an excerpt from the book.
If you’ve never read 18 Trading Champions Share Their Keys to Top Trading Profits (1996) it’s available for free download on Scribd. The book is very short—one page per trader. But it features many of the best-known technical traders, among them George Angell, Walter Bressert, Tom DeMark, Cynthia Kase, George Lane, Linda Bradford Raschke, and Larry Williams.
From Advisor Perspectives comes a little piece by Tom Brakke entitled “Thinking about Doing.” It expands on a statement by Ryan St. Onge, the freestyle aerialist, in a recent New York Times article: “I probably spend 80 percent of my time thinking about it, and 20 percent doing it.”
Finally, speaking of The New York Times, here’s another science section article “Abstract Thoughts? The Body Takes Them Literally” that may not have obvious connections to trading, but then again who knows?
If you’ve never read 18 Trading Champions Share Their Keys to Top Trading Profits (1996) it’s available for free download on Scribd. The book is very short—one page per trader. But it features many of the best-known technical traders, among them George Angell, Walter Bressert, Tom DeMark, Cynthia Kase, George Lane, Linda Bradford Raschke, and Larry Williams.
From Advisor Perspectives comes a little piece by Tom Brakke entitled “Thinking about Doing.” It expands on a statement by Ryan St. Onge, the freestyle aerialist, in a recent New York Times article: “I probably spend 80 percent of my time thinking about it, and 20 percent doing it.”
Finally, speaking of The New York Times, here’s another science section article “Abstract Thoughts? The Body Takes Them Literally” that may not have obvious connections to trading, but then again who knows?
Friday, February 5, 2010
Interval training for the brain
I’m a believer in interval training, not that I have ever seriously trained for anything physical. No marathon on my c.v. At least I incorporate it into my daily exercise routine.
Jim Loehr and Tony Schwartz, in The Power of Full Engagement: Managing Energy, Not Time, Is the Key to High Performance and Personal Renewal (The Free Press, 2003) extend the principle of interval training to mental tasks. Their basic hypothesis is simple: people need to cycle their mental efforts to achieve a balance between expending and recovering energy. Grinding is a death knell.
Loehr and Schwartz are writing primarily for the business community; they offer a “corporate athlete full-engagement training system.” But it’s easy enough to extrapolate their guiding premise to the trading world. The crux of the matter is that “thinking uses up a great deal of energy. The brain represents just 2 percent of the body’s weight, but requires almost 25 percent of its oxygen. The consequences of insufficient mental recovery range from increased mistakes of judgment and execution to lower creativity and a failure to take reasonable account of risks. The key to mental recovery is to give the conscious, thinking mind intermittent rest.” (p. 96)
The intraday trader is particularly vulnerable to the downside effects of not taking mental breaks. Staring at a screen all day, afraid that to turn away for even a few minutes will mean lost riches is in fact, according to the authors, a prescription for diminished performance. If your eyes start to glaze over, are you really at the top of your game? Better to take a break—whatever type of break works best for you.
If you’re the type of person who returns to the screen only to realize that you missed the perfect setup, keep a log of all these “lost” opportunities. I suspect you’ll find one of two things. Either you are overestimating the number of times the market came knocking at your door when you were away. Or you are avoiding good trades by taking breaks just as they are about to set up. If you’re not at your screen you don’t have to be afraid to trade. A log should make the difference clear.
Jim Loehr and Tony Schwartz, in The Power of Full Engagement: Managing Energy, Not Time, Is the Key to High Performance and Personal Renewal (The Free Press, 2003) extend the principle of interval training to mental tasks. Their basic hypothesis is simple: people need to cycle their mental efforts to achieve a balance between expending and recovering energy. Grinding is a death knell.
Loehr and Schwartz are writing primarily for the business community; they offer a “corporate athlete full-engagement training system.” But it’s easy enough to extrapolate their guiding premise to the trading world. The crux of the matter is that “thinking uses up a great deal of energy. The brain represents just 2 percent of the body’s weight, but requires almost 25 percent of its oxygen. The consequences of insufficient mental recovery range from increased mistakes of judgment and execution to lower creativity and a failure to take reasonable account of risks. The key to mental recovery is to give the conscious, thinking mind intermittent rest.” (p. 96)
The intraday trader is particularly vulnerable to the downside effects of not taking mental breaks. Staring at a screen all day, afraid that to turn away for even a few minutes will mean lost riches is in fact, according to the authors, a prescription for diminished performance. If your eyes start to glaze over, are you really at the top of your game? Better to take a break—whatever type of break works best for you.
If you’re the type of person who returns to the screen only to realize that you missed the perfect setup, keep a log of all these “lost” opportunities. I suspect you’ll find one of two things. Either you are overestimating the number of times the market came knocking at your door when you were away. Or you are avoiding good trades by taking breaks just as they are about to set up. If you’re not at your screen you don’t have to be afraid to trade. A log should make the difference clear.
Thursday, February 4, 2010
Picerno, Dynamic Asset Allocation
There is no thornier problem for the investor than portfolio design and management. James Picerno, a financial journalist, tackles this problem in Dynamic Asset Allocation: Modern Portfolio Theory Updated for the Smart Investor (Bloomberg Press, 2010). He surveys the background and evolution of modern portfolio theory, analyzes the efficient market hypothesis, studies alternative rebalancing methodologies, and assesses tactical asset allocation. As he says more than once, there is no silver bullet, but Picerno offers the investor some guidelines.
For many investors who have learned the lessons of diversification asset allocation involves nothing more than combining three or four asset classes (choosing from, for instance, stocks, bonds, commodities, real estate, and cash) in ratios determined by the investor’s age or risk tolerance and rebalancing once every year or so. There’s nothing wrong with this strategy; it imposes discipline on the investor, tends to smooth out the ride, and can be tax efficient.
Picerno, however, suggests that the investor need not be purely reactive but can also look ahead using tactical asset allocation. As anybody who has tried this knows, it’s hard to peer into the future. But there are tools at the disposal of the “smart investor,” among them dividend yield, P/E ratios, price momentum, volatility, and correlation.
Is tactical asset allocation a viable portfolio management strategy for everyone? Picerno answers with an unequivocal “no.” He views TAA as essentially a mean-reverting, buy low sell high strategy; the investors most likely to embrace TAA are those who focus on the long term and assume that “prospective return varies inversely with recent market trends.” (p. 181) As Robert Arnott wrote, “tactical asset allocation potentially enhances long-term returns without increasing portfolio risk, but at a cost of lower comfort, hence lower utility, for many investors.” TAA is for those who can buy when there’s blood in the streets and sell when everyone else is drunk with investing success.
Personally I think Picerno frames TAA too narrowly; it is not the sole property of the value investor. For instance, anyone with a tax-free account such as a Roth IRA who pays low commissions can easily pursue a range of strategies, some of which might involve holding periods of days or weeks, not months or years. Yes, it requires more active management, but it doesn’t mean giving up one’s day job. Sometimes it even beats buy and hold!
In this book Picerno offers a little something for everyone. He summarizes an array of financial literature without becoming technical or mathematical, he outlines ways to tweak common portfolio management strategies, he points the way toward the future of asset allocation, and throughout he respects the personality and risk profile of the individual investor—hence the need for customized solutions.
For many investors who have learned the lessons of diversification asset allocation involves nothing more than combining three or four asset classes (choosing from, for instance, stocks, bonds, commodities, real estate, and cash) in ratios determined by the investor’s age or risk tolerance and rebalancing once every year or so. There’s nothing wrong with this strategy; it imposes discipline on the investor, tends to smooth out the ride, and can be tax efficient.
Picerno, however, suggests that the investor need not be purely reactive but can also look ahead using tactical asset allocation. As anybody who has tried this knows, it’s hard to peer into the future. But there are tools at the disposal of the “smart investor,” among them dividend yield, P/E ratios, price momentum, volatility, and correlation.
Is tactical asset allocation a viable portfolio management strategy for everyone? Picerno answers with an unequivocal “no.” He views TAA as essentially a mean-reverting, buy low sell high strategy; the investors most likely to embrace TAA are those who focus on the long term and assume that “prospective return varies inversely with recent market trends.” (p. 181) As Robert Arnott wrote, “tactical asset allocation potentially enhances long-term returns without increasing portfolio risk, but at a cost of lower comfort, hence lower utility, for many investors.” TAA is for those who can buy when there’s blood in the streets and sell when everyone else is drunk with investing success.
Personally I think Picerno frames TAA too narrowly; it is not the sole property of the value investor. For instance, anyone with a tax-free account such as a Roth IRA who pays low commissions can easily pursue a range of strategies, some of which might involve holding periods of days or weeks, not months or years. Yes, it requires more active management, but it doesn’t mean giving up one’s day job. Sometimes it even beats buy and hold!
In this book Picerno offers a little something for everyone. He summarizes an array of financial literature without becoming technical or mathematical, he outlines ways to tweak common portfolio management strategies, he points the way toward the future of asset allocation, and throughout he respects the personality and risk profile of the individual investor—hence the need for customized solutions.
Wednesday, February 3, 2010
Kroll, The Professional Commodity Trader
Earlier I wrote about Stanley Kroll on Futures Trading Strategy (1988). In 1974, the year before his first “retirement” at the age of 40, he invited the readers of The Professional Commodity Trader (reprinted in 1995 by Traders Press) to follow him as he traded between July 1971 and January 1974, during which time for the 39 accounts that he managed he turned $664,379 into $2,985,138. He funded his own account in July 1971 with $18,000; eighteen months later it had appreciated to $130,000. Apparently before he “retired,” he was sitting on a $1 million account. What was the secret of his success?
Kroll was a discretionary trend trader in the tradition of Jesse Livermore. He had simple entry and exit rules. To initiate a position he would trade in the direction of the major trend, against the minor trend. “For example, if the major trend is clearly up, trade the market from the long side, or not at all, buying when: a. the minor trend has turned down, and b. prices are ‘digging’ into support, and c. the market has made a 35-50 percent retracement of the previous up leg.” To close out a long position at a profit, liquidate one-third at a logical price objective into overhead resistance, another third at a long-term price objective into major resistance, and trail stop the remaining third. There are three approaches to closing out a position at a loss. First, enter an arbitrary “money” stop-loss such as 40-50% of the requisite margin; second, enter a chart stop-loss “to close out the position when the major trend reverses against your position—not when the minor trend reverses (that’s just the point where you should be initiating the position, not closing it out).” Finally, “maintain the position until you are convinced that you are wrong (the major trend has reversed against you) and then close out on the first technical correction.” (pp. 27-28) He admits that the last alternative can be potentially lethal; the technical correction may not come in a timely fashion.
Kroll offers some advice to the would-be futures trader. He urges the wannabe to play only for the major moves—not for scalps. As he writes, “Riding a winning commodity position is a lot like riding a bucking bronco. Once you manage to get aboard, you know what you have to do—hang on and stay hung on; not get bumped or knocked off till the end of the ride. And you know that if you can just manage to stay in the saddle, you’re a winner. Sounds simple? Well, that’s the essence of successful trading.” (p. 44)
Put another way, when ahead, “play for the big score and don’t settle for a minor profit.” On the other hand, when a trade isn’t working out, “spend your constructive effort in calculating how to close out the losing position with a minimum loss or perhaps a modest profit—and if such an opportunity is offered, take it.” Contrary to a lot of the literature, he also advocates striving for a high winning percentage. The problem with accepting a small fraction of winning trades is that “the winningest accounts . . . still manage to chalk up some mighty big losses—it seems just about impossible to always keep losses small, no matter how hard you try.” (p. 153)
I’ve extracted some words of wisdom from Kroll’s book, but what makes the book so enjoyable is that Kroll takes the reader through actual trades, some winners and others losers, and shows the courage it took to ride the bronco and the acute pain he felt when he was bucked off. It’s a book that you read in one sitting, fully engrossed.
Kroll was a discretionary trend trader in the tradition of Jesse Livermore. He had simple entry and exit rules. To initiate a position he would trade in the direction of the major trend, against the minor trend. “For example, if the major trend is clearly up, trade the market from the long side, or not at all, buying when: a. the minor trend has turned down, and b. prices are ‘digging’ into support, and c. the market has made a 35-50 percent retracement of the previous up leg.” To close out a long position at a profit, liquidate one-third at a logical price objective into overhead resistance, another third at a long-term price objective into major resistance, and trail stop the remaining third. There are three approaches to closing out a position at a loss. First, enter an arbitrary “money” stop-loss such as 40-50% of the requisite margin; second, enter a chart stop-loss “to close out the position when the major trend reverses against your position—not when the minor trend reverses (that’s just the point where you should be initiating the position, not closing it out).” Finally, “maintain the position until you are convinced that you are wrong (the major trend has reversed against you) and then close out on the first technical correction.” (pp. 27-28) He admits that the last alternative can be potentially lethal; the technical correction may not come in a timely fashion.
Kroll offers some advice to the would-be futures trader. He urges the wannabe to play only for the major moves—not for scalps. As he writes, “Riding a winning commodity position is a lot like riding a bucking bronco. Once you manage to get aboard, you know what you have to do—hang on and stay hung on; not get bumped or knocked off till the end of the ride. And you know that if you can just manage to stay in the saddle, you’re a winner. Sounds simple? Well, that’s the essence of successful trading.” (p. 44)
Put another way, when ahead, “play for the big score and don’t settle for a minor profit.” On the other hand, when a trade isn’t working out, “spend your constructive effort in calculating how to close out the losing position with a minimum loss or perhaps a modest profit—and if such an opportunity is offered, take it.” Contrary to a lot of the literature, he also advocates striving for a high winning percentage. The problem with accepting a small fraction of winning trades is that “the winningest accounts . . . still manage to chalk up some mighty big losses—it seems just about impossible to always keep losses small, no matter how hard you try.” (p. 153)
I’ve extracted some words of wisdom from Kroll’s book, but what makes the book so enjoyable is that Kroll takes the reader through actual trades, some winners and others losers, and shows the courage it took to ride the bronco and the acute pain he felt when he was bucked off. It’s a book that you read in one sitting, fully engrossed.
Tuesday, February 2, 2010
“I trade, therefore I am”—I hope not!
As part of my casting time (think fishing, not Hollywood) I looked at Edward Toppel’s little e-book Zen in the Markets: Confessions of a Samurai Trader. It’s not especially enlightening for anyone who has read the basic trading literature. He rails against chartists, saying that since history doesn’t repeat itself we must focus on the present and “listen to the voice of the market.” He advocates an egoless view of the market, “a view that is free of personality needs.” But what stopped me in my tracks was the so-called Samurai trader’s philosophy: “I trade, therefore I am.” (p. 12)
Here’s a man who claims that we have to say goodbye to Aristotle. Aristotle and Aristotelian logic, Toppel announces, belong “in universities and not in markets.” (p. 20) Yet he lifts and twists the famous pronouncement of Descartes, not exactly known as a Zen philosopher, to draw some kind of relationship between trading and being.
I am certain that Toppel is not making a fundamental epistemological point, so we need not retrace Descartes’ argument. He might simply be saying, “I trade, therefore I am vitalized.” As long as it’s not a prescription for overtrading, I don’t have a problem with the Cartesian knock-off. But he might also be saying something very dangerous: trading defines my very being, if I’m not trading I lose my sense of self. (And please don’t tell me I should go back to Logic 101; I’m describing what I suspect is a definition, not an “if . . . then” statement or a truncated syllogism.)
Here’s my point. Trading cannot define who we are. If it did, we could not be egoless traders (if that is even a worthy goal). More important, we would be impossible to live with; I can’t see how we could even live with ourselves. Trading is a business. It’s more than a job, much less than a life. I am, and am glad to be a trader, a reader, and a lot of other things that are peripheral to this blog. (Though, since I believe in working around the edges, I’m sure that sooner or later you’ll be introduced to Delta the geriatric basset hound and will hear about the progress of new varieties in the vegetable garden.) Trading is only part of a whole, however passionate a person may be about it; it cannot consume us to the point of taking over our lives, our very being.
Here’s a man who claims that we have to say goodbye to Aristotle. Aristotle and Aristotelian logic, Toppel announces, belong “in universities and not in markets.” (p. 20) Yet he lifts and twists the famous pronouncement of Descartes, not exactly known as a Zen philosopher, to draw some kind of relationship between trading and being.
I am certain that Toppel is not making a fundamental epistemological point, so we need not retrace Descartes’ argument. He might simply be saying, “I trade, therefore I am vitalized.” As long as it’s not a prescription for overtrading, I don’t have a problem with the Cartesian knock-off. But he might also be saying something very dangerous: trading defines my very being, if I’m not trading I lose my sense of self. (And please don’t tell me I should go back to Logic 101; I’m describing what I suspect is a definition, not an “if . . . then” statement or a truncated syllogism.)
Here’s my point. Trading cannot define who we are. If it did, we could not be egoless traders (if that is even a worthy goal). More important, we would be impossible to live with; I can’t see how we could even live with ourselves. Trading is a business. It’s more than a job, much less than a life. I am, and am glad to be a trader, a reader, and a lot of other things that are peripheral to this blog. (Though, since I believe in working around the edges, I’m sure that sooner or later you’ll be introduced to Delta the geriatric basset hound and will hear about the progress of new varieties in the vegetable garden.) Trading is only part of a whole, however passionate a person may be about it; it cannot consume us to the point of taking over our lives, our very being.
Monday, February 1, 2010
Passarelli, Trading Option Greeks
I think we profit enormously from looking at alternative approaches to a problem. Take long division, for instance. I learned how to do long division in an American school. Then I met someone who had learned arithmetic in a Hungarian school and did long division entirely differently. Or look at the technique at Math Mojo.
Options, more than most trading instruments, require the trader to be mentally flexible, to be able to assess a scenario from multiple perspectives, to know how to accomplish a single goal in a multiplicity of ways. For starters, option trades can be described in terms of a set of theoretical “moving parts,” the Greeks. The Greeks don’t cause a change in the price of the option, that’s the job of supply and demand. Rather, they are alternative ways to understand option pricing and changes in pricing. The Greeks also offer an elegant set of tools to help manage both trade and portfolio risk.
Dan Passarelli covers familiar ground in Trading Option Greeks: How Time, Volatility, and Other Pricing Factors Drive Profit (Bloomberg Press, 2008) but often with a fresh slant. (How could I not smile at a chapter entitled “Greek Philosophy”?) His prose is sometimes vivid: “While choosing closer strikes can lead to higher premiums [in an iron condor], the range can be so constricting that it asphyxiates the possibility of profit.” (p. 191) Moreover, Passarelli is a strategic thinker who urges a careful weighing of the data: “Trading is both cerebral and statistical in nature. It’s about gaining a statistically better chance of success by making rational decisions.” (p. 223)
Passarelli’s book is thorough, covering the basics of option Greeks in the first half (spanning about 150 pages) of the book and then moving on to spreads and volatility trading. It’s both a good text and a worthy reference book. It will have a place in my library.
Options, more than most trading instruments, require the trader to be mentally flexible, to be able to assess a scenario from multiple perspectives, to know how to accomplish a single goal in a multiplicity of ways. For starters, option trades can be described in terms of a set of theoretical “moving parts,” the Greeks. The Greeks don’t cause a change in the price of the option, that’s the job of supply and demand. Rather, they are alternative ways to understand option pricing and changes in pricing. The Greeks also offer an elegant set of tools to help manage both trade and portfolio risk.
Dan Passarelli covers familiar ground in Trading Option Greeks: How Time, Volatility, and Other Pricing Factors Drive Profit (Bloomberg Press, 2008) but often with a fresh slant. (How could I not smile at a chapter entitled “Greek Philosophy”?) His prose is sometimes vivid: “While choosing closer strikes can lead to higher premiums [in an iron condor], the range can be so constricting that it asphyxiates the possibility of profit.” (p. 191) Moreover, Passarelli is a strategic thinker who urges a careful weighing of the data: “Trading is both cerebral and statistical in nature. It’s about gaining a statistically better chance of success by making rational decisions.” (p. 223)
Passarelli’s book is thorough, covering the basics of option Greeks in the first half (spanning about 150 pages) of the book and then moving on to spreads and volatility trading. It’s both a good text and a worthy reference book. It will have a place in my library.
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