Top Binary Options Broker 2020!
Best Choice For Beginners!
Big Sign-Up Bonus!
Free Trading Education!
Free Demo Account!
Only For Experienced Traders!
How to Accept Trading Losses and Their Importance in One’s Self-Development as a Trader
When I post articles discussing a day’s trading results, I have no issue going through and explaining losing trades. One reader actually expressed gratitude that I also discuss the trades that I lose. At first I thought it was a bit strange, given losing trades is simply part of the game and it’s nothing to be ashamed of obviously.
It really does underscore the point that many can treat trading losses as something that they should not be proud of and something that represents a failing in the trading experience. It’s understandable, as losing trades can cause both financial and psychological pain – decrease or wipe out trading accounts while diminishing personal self-confidence and potentially engendering irrational trading decisions. But I do believe it is very important to recognize, think about, and actively study losing trades to determine how and what you can learn from them. Occasionally in my posts, I will ruminate about how a trade that I lost could have possibly been avoided – for example, being more cognizant of increasing momentum and volatility in the market.
I believe that trading is about doing the same thing over and over again – with regards to an effective strategy and money management plan while keeping an even mindset. But you can always improve while keeping this multifaceted routine stability constant in the process.
1. Understand the importance of accepting your losses. It sounds like a very banal and unhelpful thing to say, but everybody will lose trades. In fact, in some forms of trading, you might actually lose the majority of your trades yet still earn a net profit. It depends on the reward-to-risk ratio. This can also apply to some forms of binary options traded on an exchange, where this ratio is not always in the broker’s favor, as it almost always is with the offshore sites.
Trading, fundamentally, is a game of probabilities. Whenever probabilities are involved, there will be essentially zero chance for absolute perfection especially when it comes to predicting the future direction of a financial market. It is actually genuinely quite rare to even have a market scenario in front of you where you have even a 60% chance of winning a trade. This is why I trade relatively infrequently – sometimes several hours between trades and sometimes not at all. I’m looking for moments in the market that I can identify as apt opportunities to take a trade such that the chances of winning are better than the broker’s break-even odds for that asset. For example, if the payout structure on an asset is 85% (and no return for a losing trade), you should have a greater than break-even chance of winning the trade to expect long-term profitability (54.05% in this case), assuming fixed-investment money management.
But probabilities are the focus in trading, as they are in many other heavily probability-based endeavors. It occurs in blackjack with the entire card-counting concept. When you’re in a favorable count, you bet more money per hand expecting that your probability of winning is higher than when you’re in a more negative count. Theoretically, over time, and assuming you count effectively in a robust card-counting system and know basic blackjack strategy, you will reap a profit. In Major League Baseball (MLB), teams play 162 regular season games per year. No team will ever win every single game. In fact, the best clubs only win about 55% of their games. A 60% win rate is exceptional and likely a marker of the fact that you’re one of the top two or three teams in the league. In binary options, your win rate is very important. It doesn’t need to be perfect or even necessarily high, but high enough such that you’re turning a profit.
Failure to properly accept losses on a psychological level will lead to poor trading behaviors. For example, in binary options this could mean using overly aggressive money management to gain back previous losses, trading set-ups that aren’t truly there because you’re frustrated and bitter that things haven’t been going your way, or even failing to pull the trigger altogether because you might be worried that it could lead to yet another trade.
2. You need to develop the right mindset toward viewing trading losses. As I’ve asserted previously, the biggest roadblock people have in trading successfully is themselves. Mastering the mental aspect of trading and all the possible emotions involved is critical otherwise you simply cannot possibly do well long-term. You almost need to be robotic in your actions and temperament to a large extent.
3. Never view a losing trade as a so-called “bad trade” if it genuinely isn’t. If you followed your strategy, entered where you wanted to, stood by your risk limitations, and traded free from bias and emotional input, then you can consider a trade successfully taken but one that simply did not go your way. Even if the most set-up in the world shows up one day, understand that it simply isn’t going to work 100% of the time and it’s not something to get discouraged or emotional over. It’s just the way it goes and should therefore not necessarily be considered a mistake, error, or “bad trade.”
4. Whenever you trade the markets you are making a prediction about where the market will be or how it will act in one way or another. If you fall short on this prediction and end up with a losing trade, it is important to review it after the trading day or ideally some point in the very near future to see if there is anything to be learned from it. They can teach you, for example, how the market acts at a certain time, when these types of set-ups might be okay and when they might not be, how a certain asset behaves, and any mental errors or mistakes you may have committed. It is through losses and failures of sorts in which I have developed into the type of trader that I am now and there is always room for self-improvement.
I understand that it can be painful to look at losing trades, as they don’t represent a good thing on the most superficial level. But reviewing the market scenario in which they were taken, and deriving any type of insight into what occurred can translate into a legitimate learning experience and offer continued growth and positive development as a trader.
Exploiting the edge from historical market patterns
Top Binary Options Broker 2020!
Best Choice For Beginners!
Big Sign-Up Bonus!
Free Trading Education!
Free Demo Account!
Only For Experienced Traders!
Tuesday, December 01, 2009
The Value of Trading as a Performance Activity
Is trading a useful activity? A thoughtful reader writes:
“I am unable to reconcile as to how traders are providing any value to the society by what they are doing. I accept that we may be called providers of liquidity (which I really doubt we are) or guys who determine correct asset price helping bring market efficiency, but it still does not make trading relevant from a social perspective. I sometimes feel that as a trader we are pretty selfish guys concerned with our own well being. When we make profits, we do not regard the losses someone else made and trading seems like a zero sum game to me.”
The real issue here is the assumption that one’s value–and the value of one’s activities–is a function of help to others.
The great scientific discoveries, for the most part, have reflected the very selfish concerns of investigators who become consumed with finding the answers to challenging questions. Doing what they love and following their passion does indeed bring benefit to others: that is the happy synthesis. In starting a business and seeking success, an entrepreneur brings jobs and needed goods and services to the world. In creating a great work of art, a painter absorbs herself in her medium and brings something of beauty to others.
When you make the most of yourself, you become a greater value to the world.
Like all performance disciplines, from sports to games of skill, successful trading requires self-development. In developing ourselves and mastering our own thought processes and behaviors, we have the opportunity to not just become better traders, but to also become better human beings. And, yes, that brings benefit to those with whom we interact: from the role modeling we provide to younger people to the fruits of self-mastery that aid our roles as parents and spouses.
Please review the post on the value of trading , particularly the last paragraphs. Trading is a useful activity to the degree that it pushes us to become more than we are: to enact the best within us, not just in markets but throughout life.
How to Accept Trading Losses and Their Importance in One’s Self-Development as a Trader
Trader Performance Weblog
$ЂҐ $ЂҐ $ЂҐ $ЂҐ $ЂҐ $ЂҐ
Recent, Favorite Entries
December 23 , 200 7
After an initial post to the blog, I am continuing my research on separate measures of buying and selling interest. So far those measures are yielding worthwhile results. One interesting finding is that, until Friday’s rally, selling pressure exceeded buying interest for eight consecutive sessions. What made Friday’s rally unique was that buying interest was actually below average relative to the prior 30-day average. It was selling interest that was significantly below average that helped sustain the rally. In other words, it was more the absence of selling than the presence of significant buying that enabled stocks to move higher through the day.
This raises interesting questions: Are rallies based on excess buying more durable than those supported by subnormal selling? Are the most durable rallies typified by both superior buying interest and subnormal selling? Are declines based on a deficiency of buyers more likely to reverse than those typified by an excess of sellers? This is a most promising area of research. It applies to any variable/indicator and simply requires a conceptual separation of buying events and selling ones over the course of a trading day.
December 16 , 200 7
Markets move thematically, reflecting money flows across national boundaries and asset classes. Once these themes appear, they can persist for a significant time. Inflation themes dominated in the 1970 and 1980s; technology was hot in the 1990s. Now, in the most recent markets, weak real estate, emerging markets, and weak dollar have been at the fore.
My most recent blog posts examine 2007 opportunity as a function of sector, national markets, and investment styles.
Moving in and out of markets can blind one to the bigger themes that shape longer-term opportunity. That is one way in which trading differs from investment.
Research in psychotherapy suggests that people who are more actively involved in change efforts–behaviorally and emotionally–are more likely to make lasting changes. An interesting corollary of this finding is that people who experience moderate levels of distress are more likely to make changes as well. If their distress levels are too high, they become overwhelmed and can’t sustain a change process. If their distress levels are too low, they lack the motivation to sustain change. The moderate distress is a powerful driver for change.
We see some of the same dynamics among those who work on their trading. The intensity of change and learning efforts is correlated with the success of those efforts. People who work on their trading each day and each week, with concrete goals and frequent feedback, are much more likely to make improvements in their trading than people who simply talk with a coach every so often and don’t set specific goals or action steps.
Much of the differences in intensity related to moderate distress. It is rare for traders who are doing well to sustain intense change efforts; more often, they think that they shouldn’t try to fix what isn’t broken. Similarly, traders who are doing very poorly and losing great deals of money are often too distressed to keep a level-headed focus on goals and improvements. It’s the traders who are unhappy with their trading but not overwhelmed that often are ready to make the intense change efforts.
The work of Prochaska and DiClemente suggests that readiness for change is a major contributor to the success of change efforts. Some people are at a “contemplation” stage of change where they are not yet ready to sustain goal-directed action. Many times, this is because consequences have not yet accumulated to the point where the emotional drive for action kicks in. Sadly, problems sometimes have to get worse and distress has to increase before traders will tackle intensive efforts at change.
In training programs in professional psychology, we’re encouraged to see the world through the eyes of others. With that in mind, let’s look at the U.S. stock market (S&P 500 Index) as denominated in a basket of world currencies (U.S. Dollar Index):
We can see that the Dollar Index-Adjusted SPX has greatly lagged its dollar-based equivalent during the recent bull market. Indeed, while the dollar-based SPX marginally exceeded its 2000 levels, the Dollar Index-based SPX has only retraced half the bear market declines of 2000-2003.
More recently, we can see that the dollar-based SPX is less than 10% off its all-time highs. The Dollar Index-based SPX has moved all the way back to its May, 2006 levels.
This illustrates in a small way how U.S. assets become less attractive to overseas investors when the basic unit of those assets–the dollar–loses its value.
As those overseas investors–from China to Middle East sovereign wealth funds–become increasingly important to international markets and money flows, the role of currencies and interest rates will continue as key determinants of value among equities.
The successful investor will be one who can see the world through global lenses
November 1 8, 200 7
My recent blog posts have examined what I call “herding sentiment”, the concentration of volume into either advancing or declining stocks. In the past few months, we have seen very high herding sentiment, as we shift between extreme buying and selling with above average regularity. I am playing with the hypothesis that, with the concentration of capital in the hands of very large institutions–and the increasing participation of those institutions in the markets day to day–we may see a secular increase in herding behavior. The August decline gave us a bit of a taste of what happens to markets when multiple funds (hedge, sovereign wealth, etc.) trade similar strategies.
If, indeed, we’re seeing new market patterns emerging due to increased concentrations of capital, it will be helpful to keep an eye on herding and how this affects future market performance. I see two kinds of patterns as being particularly promising. First, we have sequences of herding days: multiple buying or selling days in sequence and alternate sequences of extreme buying and selling. What does it mean when an extreme selling day is followed by a similar such day? By an extreme buying day? These are very testable patterns and may become increasingly relevant going forward.
The second promising set of patterns are a bit more ambitious: Intraday patterns of herding. Suppose volume is concentrated into advancing issues during a 30-minute period? During a five minute period? Does this affect near-term market movement? Data regarding concentrations of volume are harder to come by on an intraday basis, but may be quite relevant to the daytrader (and to longer-term market participants seeking improved execution).
Staying ahead of the market with new ways to look at supply and demand is, itself, a component of a trading edge.
November 10 , 200 7
In my recent blog post, I emphasized that Bayesian, discretionary decision making requires an ability to stay focused in the present. The reason for this is that traders must observe and assess new data as they come in, keeping an open mind to whether the data confirm or disconfirm expectations.
So much of difficulty with trading performance occurs because we are not “in the moment”. We are looking back on past losses or looking forward to hopes of gain. We’re worried about our P/L, or we think the market might move without us. All of this is interference with the Bayesian process. Just as a jury member must put biases aside and stay grounded in each new testimony and piece of evidence, we must continually render our market verdicts based on assessments of ongoing data.
This is one reason I find meditation and biofeedback so helpful to trading. These help teach us to quiet our thoughts and remain in the present. Trading discipline, in that sense, begins with a taming of mind.
I’m convinced that one’s market routines during periods when their trading markets are not open have a lot to do with long-term success. Evenings and weekends are ideal times for market research. They provide opportunities to step back and see larger trends and themes in markets and develop ideas for the coming day and week. I consistently find that my preparation during evenings, early mornings, and weekends is correlated with my success the next day and week.
Think of two traders. One follows markets during trading hours; the other does the same but also studies charts and related markets–as well as his/her own trades–before the open and after the close. Day after day, think of how much more exposure to market patterns the second trader has compared with the first.
The really great performers in any field are distinguished by effort and the proper direction of that effort. We see it among Olympic athletes, artists, and scientists. The more mediocre performers simply don’t break a sweat. They put in the normal hours, the normal effort–and they achieve very normal (average) returns.
My work day starts faithfully by 5 AM; many days earlier. By noon I’ve worked as many hours as many people put it during a full work day. My weekend days are exactly the same. By the time a week has ended, I have easily exposed myself to twice as much information, twice as many market patterns, as the average trader. Compound that over time, and it’s not difficult to see how my learning curve can look radically different from someone else’s after a year’s time.
When you love what you’re doing, it’s not really work; it doesn’t feel like effort. So what are people confessing when they don’t make the extraordinary efforts?
Later this weekend, I’ll be reviewing Michael Covel’s book “The Complete Turtle Trader”. It’s an excellent account of the Turtle experiment and what has happened in Turtle-style trading since then.
One topic Covel covers is the personality characteristics of those who have succeeded with trend-following. He makes it clear that these are resilient individuals who can tolerate setbacks (including drawdowns) and who maintain considerable optimism through trading challenges. Interestingly, these are some of the same qualities found among entrepreneurs.
We can differentiate three kinds of traders: one who takes trading as a job, another who treats it as a career, and still another who finds in trading a calling. The career approach presumes a degree of professionalism in one’s approach: preparing each day, searching for opportunities, honing one’s skills. The very successful individuals profiled in Covel’s account also approach trading as a calling. The section of the book on Salem Abraham was particularly illustrative in that regard. He has pursued trading in a dogged way from the very start–and has found unusual success.
Covel emphasizes that the Turtle experiment proves that nurture trumps nature when it comes to trading success. The methods needed to profit can be taught to otherwise ordinary individuals. Still, it’s not clear that everyone has the personality traits needed to follow those methods. It’s difficult to imagine a highly neurotic trader–one prone to anxiety, depression, or deficient self control–being able to sustain the optimism and drive through some of the harrowing drawdowns experienced by the Turtles.
Discipline–the ability to follow rules (including risk management)–is one key to success as we examine the Turtle group (some of whom succeeded much more than others). But there’s another key as well, and it relates to emotional resilience and sustaining the courage of one’s convictions. Successful traders seem to lose and drawdown every bit as much as unsuccessful ones. It’s their ability to control those losses and bounce back from them–to not take them personally–that generates a career and a calling from what otherwise would be their job.
Most discussions of trading focus on “outright” trades: the trading of instruments for directional movement. Such trades are actually relationship trades, but because they are denominated in dollars, we tend to forget that there’s a denominator.
When we trade relationships, we denominate one instrument in terms of another. This is common among currency trades, where one currency is expressed relative to another (Yen/Dollar). When we trade relationships among stocks, sectors, or indexes, we are actually trading a new instrument that represents the relative strength of the numerator relative to the denominator.
We may trade these in pairs fashion, expecting a relative movement of one correlated instrument vs. another to revert back to its norm, or we may trade the relative strength as an outright position in itself.
What is important is that, once we express an instrument as a function of another instrument, we create a new trading vehicle. That vehicle can be charted and studied for recurring patterns, just like any stock or commodity.
For example, if we think the dollar will continue to fall, we might buy shares in a company that has strong international sales and sell shares in a firm in the same sector that is largely domestic. We are thus trading the relative strength of the international firm vs. the domestic one. Note that this relationship does not depend upon the direction of the overall market or sector: as long as the international firm outperforms the domestic one, we will make money.
It is this relative independence from directional market and sector movement that enables such long/short trades to add alpha to a portfolio. This is an approach commonly exploited by hedge funds, but curiously neglected among individual traders and investors.
I recently posted a trilogy of studies that show how relative performance in three areas can illuminate market sentiment:
More important than the specific findings of those posts is the type of reasoning that they embody.
By understanding how various markets are behaving relative to one another, we can gauge the sentiment of traders and investors and acquire an edge that is separate from any edge that we might enjoy as a discretionary trader of intra-market patterns.
Indeed, I find a synergy between explicit, relational reasoning (for big picture ideas) and implicit, discretionary pattern-recognition (for timing and execution).
It’s when the real-time pattern recognition lines up with the bigger-picture edge that we see some of the greatest trading opportunities.
This coming week, I will draw upon Howard Gardner’s recent work on “minds” to illustrate the kind of thinking required by successful trading. Among the categories emphasized by Gardner are the disciplined mind (a mind trained to reason in ways demanded by a field); the synthesizing mind (a mind trained to integrate large amounts of information); and the creating mind (a mind trained to identify new relationships and perceive old topics in new ways).
Gardner emphasizes that these minds develop in a progression: we first gain knowledge and discipline, then learn to synthesize our understandings, and then become able to move beyond these with creative contributions.
A good example would be the training of attorneys. They first study the law and learn legal reasoning through the socratic process. Later, they learn to assemble cases and integrate their knowledge. Later still, they learn creative ways of applying legal precedents to fuzzy cases to best make their arguments.
Such training for attorneys–as for physicians and engineers–takes years of full-time effort. I strongly suspect the same is true for traders, who must learn first about markets and market patterns, then learn to synthesize patterns within and across markets, and then perceive new relationships that promise a probabilistic edge.
One cannot help but read Gardner’s work and come away skeptical of so many of the current efforts that pass for “trader education”. The absence of a true trading curriculum for new traders–one that instills disciplined reasoning, an integration of market information, and practice in uncovering new, promising relationships–is perhaps the greatest barrier to success. But that, as with the law or medicine, would require years of study, application, and mentorship. One can trade for many years and still lack the mind of a trader: the discipline, the ability to synthesize, and the creative capacity to perceive fresh relationships.
Epistemology is the study of knowledge and the process of knowing. Epistemology is important to trading, if for no other reason than to help traders differentiate knowledge from error–and to help traders understand where knowledge about markets comes from and how it can be obtained.
A recent post to the TraderFeed blog got me to thinking about the basic unit of thought for traders. Traders talk about “trading ideas” or “setups”, but these mean different things for different traders.
Let me give an example relevant to the blog post. If I see we’re in a trading range, I try to identify supply/demand within the range via such indicators as NYSE TICK and Market Delta. I also look at how correlated markets are behaving during the rangebound period to see if I can identify a possible lead-lag relationship that would predict a breakout.
Once I have done that, I will place a trade to take advantage of an expected move outside the range. As part of that trade planning, however, I will also have mapped out a stop-and-reverse trade in the event my initial trade is wrong.
In other words, my trade planning includes a contingency for using the initial trade as valuable information if it doesn’t go my way. That trade is a feeler with small size that leaves open a web of alternatives, from adding to the trade to stopping and reversing.
In an important sense, then, the trade idea is really not a single trade, nor is it (as is so often, sadly the case for traders) a mere idea regarding entry. Rather, the unit of thought is defined in terms of market opportunity (break from a range) and can include a number of trades and trading decisions.
On a broader level, the unit of thought for the hedge fund portfolio manager is the “theme”. It might be a weak dollar theme or a theme regarding relative returns in U.S. vs. international equities. That theme will subsume many trades across multiple asset classes.
Many traders fail, I suspect, because their unit of thinking is too small. They see individual entries, not entire trades (entries, stops, position sizes, targets), and they individual trades, not networks of possibilities that are updated with the results of each transaction.
The stop-and-reverse notion is simple, but it’s a leap for traders who simply don’t think that way. They will take a loss, stop trading, or enter an unrelated trade–not unlike a person who jumps from thought to thought without adequately expressing themselves. For them, the unit of thought is the trade. But how might your thinking (and your trading) change if that unit become the opportunity?
What creates elite performance? Is it inborn ability? Developed skill?
One of the most significant conclusions from the research I reviewed for my book on performance was the work of Sandra Scarr, Ph.D.
What she found was that genotypes shape phenotypes. People with inborn characteristics seek out particular kinds of environments, which in turn cultivate those characteristics.
Thus it is that children can be separated by only a small number of IQ points, but end up having very different developmental paths intellectually. The brighter children seek out brighter peers, who in turn stimulate each other’s intellectual growth. The less bright children never seek out such social environments and thus increasingly lag their more gifted peers.
So it is with trading talent. We see young traders who pick up markets just a little faster, who are just a little more able to read market patterns. These traders are more likely to be hired by elite trading firms; they’re also assigned to more stimulating and successful trading environments within those firms. As a result, these slightly more gifted traders receive far better mentorship and undergo exponential growth.
The average trader never experiences stimulating environments and thus progresses at a relatively modest pace.
It’s not talent, and it’s not environment: It’s how talent brings you to superior environments that creates exponential learning curves and elite performance.
This, I believe, is why the vast majority of traders cannot sustain a living from their work.
If I were a beginning independent trader and knew what I know now after 30 years in the markets, what would I do? A few ideas come to mind:
1) I wouldn’t give up my day job quickly – I realize now more than I did as a newbie that only a fraction of traders make money, only a fraction of those continue to make money over time, and only a fraction of those are sufficiently capitalized to make a living from their trading. As I mentioned in a recent blog post, pursuing trading full time before you’ve developed a track record of success is not a dream; it’s a fantasy.
2) I would undertake considerable simulated trading before trading live – When I think of all the practice hours on the basketball court working on dribbling, rebounding, passing, defending–and when I think of all the practice time in scrimmage getting plays down pat–I realize that game time is just the tip of a large learning iceberg. Trading truly is the only performance domain I know of in which a majority of participants expect to spend less time in practice than in actual live performance. No wonder so many lose money.
3) I would try out many markets and time frames before settling on any one – I’m continually amazed by how traders who are mediocre performers in one market can blossom once they move to a different market. I’m also impressed by the ways traders find time frames that work for their personal needs, capturing the right blend of market involvement and freedom from the screen.
4) I would find just one or two setup patterns and master those – It’s hard to imagine successfully trading multiple patterns and markets before mastering one. By working on a specific setup, a trader can then focus attention on pattern recognition, execution skills, money management, and discipline: the skills that will be of help when it comes time to extend the trading reach. It took many, many months of printing out and studying intraday charts for me to find the few patterns that I now trade.
Most of all, if I were a beginning trader and knew what I know now, I’d realize that trading is no less of a business than opening a store or a doctor’s office. It requires talents, developed skills, and a clear plan for success. It requires adequate capitalization, and it requires a firm ability to limit overhead during the early, lean years. Fantasies are exciting, but there is so much more to performance success in any discipline.
In recent posts, I’ve been using a basket of 40 S&P stocks, evenly divided among eight market sectors and highly weighted within those sectors, to approximate the index as a whole. With the basket, for example, I can track the number of stocks displaying positive, neutral, and negative directional movement: the basis for my Technical Strength Index. I can also track new highs vs. new lows on multiple time frames and can create a custom Advance-Decline line to see how many stocks are participating in moves. The advantage of a basket that contains equal numbers of stocks from the major market sectors is that you can then track the sectors that are gaining and losing strength, a possible consideration in a relative strength sector-based strategy.
Where the basket data shine is in monitoring intraday patterns of strength and weakness. Here is a chart of Thursday and Friday, 8/30/07 – 8/31/07. The chart tracks the number of stocks in the basket making hourly new highs minus lows on a closing five-minute basis. In short-term uptrends, pullbacks will normally terminate in the -20 range; in downtrends, we’ll see bounces up to the +20 region. Note the drying up of new lows in the afternoon of the 31st, leading to the late rally. Also notice the modest strength of that rally, which led to the broad selling late in the day.
The broad market averages, dominated by a relative handful of highly weighted issues, don’t always reflect the strength and weakness of the stock market. By looking under the hood at the stocks comprising the averages, we can see if strength and weakness are limited to a few sectors or are broad phenomena. It’s when we see poor participation in rallies and declines that we most want to look for reversals.
In my March 25th posting, I mentioned the Power Measure as a way to track the market’s trendiness. More recently, I posted about the Power Measure on the TraderFeed blog.
Below is the chart from the blog post. This covers a multi-day period with five minute data. Note that pullbacks in the Power Measure occur at higher price lows, suggesting an intact uptrend over that timeframe.
Below we create a higher magnification view of trendiness by using one-minute data for the trading session of 8/23/07. This time we see a turnaround (reversal) pattern: up to midday, we see Power Measure peaks at successively lower prices, an indication of downtrend. We then get a substantial rally in the Measure and a subsequent dip at a higher price low. That sets the stage for late day firmness in price and Friday’s eventual rally.
As the examples suggest, it’s the relationship between peaks/valleys in the Power Measure and price levels that is most important in tracking trends. Of equal importance is the nesting of peaks and valleys across time frames. It is very helpful to see what the Power Measure is doing at at least one time frame above your own. Many good swing trading ideas can follow from such analyses.
The key is to enter the market in the direction associated with greatest volatility: this puts the market winds at your back, rather than leaving you to fight them.
A trend is defined not only by the price change between point A and B in the market, but also by the amount of time that lapses between A and B. A single bar that moves up or down sharply won’t be defined as a trend. If, however, you break that (say, hourly) bar into 30 two-minute bars, you may well see a short-term trend.
But what makes traders *feel* as though a trend is in place?
One answer I’m playing with is that traders will label market periods as strong or weak when a high proportion of the bars covering the period are either up or down. In other words, a 30-bar period that has 15 up bars and 15 down bars will not feel as bullish as a 30-bar period with 25 up bars and 5 down bars–even if the two periods cover the same price change.
Why is it important that a market *feel* bullish or bearish? That’s because a large proportion of the traders participating in that time frame will have already committed their positions. This would make the market vulnerable to reversal in the near term. A system that caught extremes of psychological bullishness or bearishness could capitalize on the market’s tendency to reverse short-term strength or weakness.
The formal logic of the system would be as follows:
1) An initial alert would be triggered when X% of the past Y bars are either bullish or bearish. The bars would vary in length from 1 minute to 1 day. This would ensure that alerts could be generated for a variety of time frames.
2) Once an alert is generated, the system goes into setup mode. The setup has to consist of a series of bars in which buying (in the case of an up market) or selling (down market) dries up, as measured by volume, division of volume at bid/offer, participation of market sectors, etc.
3) Once there is a setup, the system goes into execution mode and enters a position to retrace at least half of the prior move, with entries set so that there is a 2:1 risk-reward ratio vis a vis initial stops and price targets.
This may appear to be a countertrend system and, in a sense, it is. It is entirely possible, however, that the trades would be in the direction of longer-term trends, even as they fade short-term trending moves. One of my projects is to see if trading this concept in the direction of longer-term trends leads to more favorable outcomes.
While this is not a purely mechanical system, it is potentially highly rule-governed, which would take a fair amount of the guesswork and emotion out of trade decisions. I will update my progress on this project.
I’ve recently read an excellent e-book called “Taking Your IRA to the Next Level” by Dr. Humphrey Lloyd. Dr. Lloyd, a physician by occupation but also an experienced trader, synthesizes a wide range of classic and newer technical analysis methods with recent developments in markets, such as ETFs. I was not surprised to learn that Yale Hirsch of the Stock Trader’s Almanac had reviewed Dr. Lloyd’s book favorably. It’s rare to find a presentation of methods that incorporates innovative work from prior decades.
One of the ideas in the book that caught my attention was the classification of markets in stages through the use of moving averages of differing durations. In an initial stage of upthrust, a market will trade above both its shorter and longer-term averages. As the upmove loses steam, it will trade below the shorter-term average but remain above the longer-term one. Then we’ll get a downphase in which the market drops below both averages before righting itself and moving above the short-term average.
It should not be a problem to quantify returns from various stocks, ETFs, etc. when they are in various stages over a defined lookback period. I will be pursuing this in an upcoming post.
I’m also thinking that one could use market indicators rather than price alone for classifying stages. For example, we could look at when current new highs/lows are above or below moving averages for these.
This could provide a worthwhile conceptual aid for traders, which is how Dr. Lloyd uses it for stock/fund selection for IRAs. More to come–
I recently posted an example of a trade setup to the blog. The chart appears below; click here for a larger version.
I use the term trade idea or trade setup to describe the basic rationale of the trade. In the above example, we have a market that is in a downtrend, that makes a bounce from the lows, and that is expected to revisit those lows given the lack of vigor in the bounce.
I use the term trade execution to refer to the trader’s ability to get the best price for his or her trade for implementing the trade setup.
For example, if I had chased the lows in the 13:42 bar above and sold the market at 1458, I would have had a good trade idea, but poor execution. I would have endured two full points of heat prior to the trade eventually going my way. If my stop was set at, say, 1462, I would have taken 4 points of risk in the trade. Given that my target was the 1456 region (bottom of the short-term trading range), the risk : reward ratio on the trade would not have been favorable.
Suppose, however, I had sold the market when the buyers could not push the market to new highs at 1460 in the 13:45 bar. Now I have minimal drawdown in the trade and only 2 points of risk, with a profit potential of 4 points. That’s much better.
This is a small example, but it illustrates the importance of execution for the short-term trader. When you chase market moves, you frequently turn a good trade setup into an unfavorable risk : reward trade.
My best trades wait for us to put in a likely high or low price, then wait for a counter move to verify a shift in short-term direction/participation, and then enter on the first bounce from that counter movement. In practice, that means I’m identifying a candidate high or low and then selling bounces that cannot make new highs and buying dips that cannot make new lows.
The key to good execution is getting into the trade close enough to the candidate high or low that you have a favorable risk : reward profile to the trade.
If the trade gets away from you, you let it go.
It takes real patience to limit trading to those favorable risk : reward situations. Only when the setup is there and the execution is good do you participate. A good trade idea is only good if it can be executed well.
Going into this past week, my trading had been much better than average. I was running 75% winning trades, and my equity curve was making daily new highs. During the last three days of the week, however, I was at best 50-50 on my trades and net lost 1% from my peak.
It’s not a dire drawdown, and I’ll work at getting it back. But it was the dramatic shift in the *feel* of my trading that got my attention. In a word, I lost the feel. It was as if I were watching and trading a different market–like you had taken me from the Spooz and suddenly had me trading nat gas. Alarmed, I cut my size and cut my frequency of trading, which is why I only lost 1%. I would have gotten crushed had I traded aggressively.
I’ll be posting to the blog re: the change in market volatility and what that means for the short-term trader. There’s no doubt in my mind that the enhanced volatility was a big part of what made the last three days feel different to me.
But I think there’s more to it than mere volatility.
Volume has increased significantly during the last few sessions in the ES futures, and I believe that this represents a shift in market participation. In other words, the market is not only quantitatively different than before (more volatile), but qualitatively different. This is because a different class of trader is active in the market place.
Specifically, I propose that large shifts in volume are primarily a function of the activity of professional traders in the marketplace. There aren’t enough small, retail traders trading size to account for significant increases in equity futures volume. Indeed, my speculation is that it is the high frequency “black box” trading that expands most significantly during times of enhanced market movement. This would explain why volume would be above normal throughout the day during times such as this past week.
To a trader who follows the ES market closely, a market dominated by automated trading simply feels different than one in which the black boxes are quiet. The automated trade will engage in spurts of buying or selling, often pushing the market just beyond a recognized resistance or support level. This pushes other traders to cover their positions or jump aboard, further exaggerating the move. The automated trader, however, has resting orders above or below the market to take quick profits on the move–which leads to rapid retracements.
The net effect of this trade is, at multiple time frames, many false breakout moves. It also leads to sudden rises or declines that often end up going nowhere on balance. This is what traders refer to when they say a market is “choppy”.
Bottom line: the market at 10 VIX is both qualitatively and quantitatively different from the market at 24 VIX. Trading patterns are not the same (the assertions of technical analysts to the contrary), and the expectations following given setups are not uniform. This is what statisticians mean when they say that stock market returns are non-stationary: they are not generated by a single, common process. This is because the makeup of the market–its participants–differ as a function of volume and volatility.
This is why good traders can have a feel for the market at one time and seem to entirely lose it at other times. When markets shift volatility significantly, they become different markets and discretionary traders need to immerse themselves in the new patterns to regain their feel. Very high volatility markets may be the hardest to trade of all because they rarely stay at highly elevated levels for enough time to allow traders to gain their feel. For that reason, volatility is as likely to represent risk and danger as opportunity.
In my recent article, I took a look at four dominant learning styles–verbal, auditory, reading/writing, and kinesthetic–and how those might affect how traders process information. What makes one trader a chartist and other a quant? It may well be a function of learning strengths and preferences.
I see two points of intersection for learning styles and trading performance:
1) The Learning Process – Might traders struggle to succeed because the ways in which they learn trading don’t match well with their learning styles? Chart displays won’t be helpful to a trader (such as myself) who doesn’t possess visual strengths. Similarly, trading books won’t be useful to a trader who doesn’t process information well through reading and writing. Traders may seek out seminar events and webinars because they are auditory learners. Still, it is difficult to sustain the learning process through the auditory channel unless you have a trading coach/mentor on site. The learning curve for traders is necessarily extended, as traders must experience and internalize patterns from a variety of markets: trending, consolidating, volatile, slow, etc. When the mode of learning does not fit with a trader’s strengths, however, the curve will be needlessly frustrating. Similarly, if the trader’s desktop does not display information in modalities that are most useful for the trader, decision-making will be hampered. An important implication is that effective trading education (like all effective education) needs to be multimodal. This is not only because students in a group will process information differently; it’s also because information encoded through multiple modalities is more likely to “stick”. Seeing, doing, discussing: this cements learning for students with multiple processing competencies.
2) Trading Psychology – What happens when traders experience high degrees of flight-or-fight fear and frustration? Under emotional duress, they regress to modes of coping that were learned at an earlier phase of development. The trader who learned to deal with conflict through withdrawal as a child may find himself withdrawing from markets following a a loss, regardless of the opportunity that may be present. Similarly, traders under stress may also abandon their mature learning strengths. I recently encountered a situation in which a trade unexpectedly went against me. My own strengths in processing information are reading/writing, followed by kinesthetic. Nonetheless, I found myself furiously glancing through charts to figure out what was happening in the market. I have never been a chart reader and anything of worth I get from a chart is by luck only. Still, charts were the first thing in front of me and, in the heat of the situation, they were the first things I turned to. It was only after steadying myself that I switched over to reading the real-time flow of volume at bid vs. offer to see if the move against me was caused by an influx of large traders. It wasn’t and the trade retraced its adverse move. Had I stuck with my frantic chart review, I’m sure I would have found a reason to bail out of the good trade. How many trading problems occur because, under duress, traders abandon their strengths as information processors and decision makers?
Traders hope that success will come from the right indicators, the right software. That’s like a golfer hoping to win a PGA event by buying the right golf clubs. Success comes from the process of skill acquisition: it is an intensive learning process that adds knowledge and skills to basic talents. Traders are most likely to progress along their learning curves if they figure out how they best learn and adapt their education–and trading styles–to that.
Several readers have recently contacted me to share their performance statistics and ask for advice on making improvements. By keeping those stats, they’ve made an important first step in understanding what they do well, what isn’t working, and how to make the most of their strengths.
One set of statistics that I particularly like is a breakdown of performance as a function of the specific setups being traded. For example, you might categorize your trades as either breakout trades, countertrend (mean reversion) trades, or trend trades. Further categorization would break each of these setups down by time of day, instrument being traded, long/short, and size being traded.
What you are likely to find is:
1) A few setups are providing most of your profits;
2) Setups work best as certain times of the day; other times, there is little or no edge;
3) Setups work best when aligned with larger timeframe trends;
4) Setups work better with some instruments than others.
Once you have a global sense of what is working and what is not, then you can place your trades under a microscope. Look at your entries. Could you have been more patient and gotten meaningfully better prices for many of your trades? Look at your exits and stops. Could you have made more money overall by holding trades longer? Look at your losing trades. Are a few large losers depressing your overall P/L?
In monitoring these metrics, you begin a transition: from thinking of yourself as a trader to thinking of yourself as a portfolio manager. Your various setups are strategies that you trade within your portfolio. Are the returns from these strategies strongly correlated from day to day, week to week? If not, might you become more intentional about allocating portions of your capital to each strategy to diversify your risk?
It is one thing to study markets. Successful traders, I find, also study themselves. Continuous quality improvement (CQI) is a norm at many companies: they assess their products and processes to ensure that they are both effective (achieving desired ends) and efficient (making the most of limited resources in pursuing those ends). Such a CQI mindset is equally applicable to traders.
As I mentioned in my recent blog post, traders sometimes focus on market direction at the exclusion of volatility. How volatile the market is–how much price movement is likely to occur during the day–is quite important to risk management (the placement of stops and sizing of positions) and to the maximization of profits (the setting of profit targets). When traders don’t adjust their trading for enhanced volatility, they are likely to exit good positions too early and set stops too close, resulting in whipsaws. Similarly, when traders don’t adjust their trading for reduced volatility, they fail to take profits when they’re available and see those winners quickly retrace. They may also place stops too far away, making it difficult to recoup losses.
The predictive variable I found to be most related to the current day’s volatility is the prior day’s closing VIX, the option volatility. Going back to 2004 (N = 883 trading days), VIX has varied between 9.89 and 23.81. Here is the high-low range of the current day’s S&P 500 Index (SPY) as a function of the previous day’s closing VIX:
VIX = 12 and under: .78% Average Range (N = 251)
VIX = 12.01 – 14.00: .86% Average Range (N = 285)
VIX = 14.01 – 16.00: 1.03% Average Range (N = 209)
VIX = 16.01 and over: 1.18% Average Range (N = 140)
What we can see is that a high VIX reading yields an average daily trading range that is 50% greater than that seen under a low VIX regime.
We also know that the current day’s volatility is related to the current day’s volume levels. By noting the prior day’s closing VIX and then updating estimates of volatility based on present volume, we can ascertain whether the market is likely to show above average or below average volatility for that particular VIX level. For instance, when the VIX is 12 or below *and* volume is below average for that VIX level (N = 126), the day’s average high-low range is only .64%. With the same VIX level and above average volume (N = 125), the average range rises to .91%.
Knowing the volatility expectable at a given VIX level and then knowing whether or not we’re trading with above or below average volume for the trading day provides us with a superior handle on the day’s likely movement. And that will tell us quite a bit about the opportunity present for daytraders.
I met Trevor Harnett yesterday AM at Starbucks to catch up on developments at Market Delta. Our conversation led to an interesting idea that I’ll be pursuing through the Market Delta program and trying out in my own trading. Barcharts are usually denominated in time units: 1 minute, 5 minutes, etc. Occasionally we see traders create volume based bars (new bars form every time a given number of contracts/shares trade) or volatility based bars (new bars form every time the market moves a given number of ticks). The advantage of the volume and volatility bars is that they adapt to market conditions, creating fewer bars during slow market periods. That is helpful in reducing overtrading at such times.
Suppose, however, that new bars on a chart form as a function of sentiment. Every time a threshold number of contracts trade at the offer vs. bid, a new bar forms. We would thus tend to form more bars during high volume periods and during periods of directional market activity. Rangebound periods, in which volume at bid roughly equals that at the offer, would take longer to form new bars. Moreover, one could draw the bars solely on the basis of the volume of large traders, isolating their directional participation. New bars would form more readily when large participants are active in the marketplace.
My initial sense is that such a way of drawing charts would highlight trendiness or directionality, helping traders see emerging moves more clearly. It’s a novel concept; more to come as I experiment with this.
The market’s inefficiencies, and hence its greatest returns, occur when traders and investors behave in a herdlike manner. That’s when they’re buying or selling indiscriminately, lifting or pummeling all shares. One way of assessing such herdlike behavior is tracking the number of stocks across the major exchanges (NYSE, NASDAQ, ASE) that are making fresh 65-day lows.
Going back to 2004 (N = 814 trading days), there have been 82 occasions in which 65 day lows have exceeded 700. Fifty days later, the S&P 500 Index (SPY) has been up by an impressive average of 3.65% (73 up, 9 down).
Conversely, when we have had fewer than 100 stocks making fresh 65 day lows (N = 52), the next 50 days in SPY have averaged a loss (!) of -.08% (21 up, 31 down).
For all other occasions (N = 679), SPY has averaged a 50-day gain of 1.61% (466 up, 213 down).
By fading the herd, buyers at times of broad selling would have doubled the market’s average return. By going with the herd, they would have lost money on average.
In terms of trading performance, such parameters provide guidelines for possible criteria for lightening up core positions and adding to them, adding to buy-and-hold returns.
FYI, the most recent signal occurred on 6/13, when we registered 760 new 65-day lows. If the precedent of the past 3-1/2 years holds, we should see higher prices into the fall.
In my recent post regarding stock market performance as a function of hour of the day, I found that the first hour of trading has accounted for about 3/4 of all gains in the recent bull market. A finer grained look finds that most of this gain is attributable to overnight action: the movement from the close of the prior day to the open of the next trading session. What this means is that the trend of prices for the daytrader is not necessarily the trend of prices for the longer-term trader. One of the most common mistakes I see very short-term traders make is extrapolating trends from daily charts to their own, intraday trading. In point of fact, from market open to close, there may be no such trend whatsoever.
To remedy this problem in my short-term trading, I like to see trending moves emerge during the course of the day. Four indicators have proven particularly useful in this regard:
1) Tracking Intraday New Highs/Lows – By following a basket of stocks that highly correlate with the S&P 500 Index (and are highly weighted within the SPX), I examine how many issues are making hourly new highs and lows. As my recent post indicates, shifts in these new high/low numbers can alert us to transitions between short-term market trends.
2) Tracking the Trend of the NYSE TICK – I calculate the Adjusted NYSE TICK by adjusting each one-minute TICK reading for the 20-day average value of the TICK. This measure is sensitive to small cap movement as well as the large caps and provides a useful gauge of shifts in sentiment. When we see the NYSE TICK providing readings consistently above or below its 20-day average, this provides a nice alert for a trending market.
3) Tracking the Emerging Moving Average of the NYSE TICK – With this measure, I calculate and update the average NYSE TICK reading for that specific trading day and then see if the readings are sloping upward or downward. The emerging average is sensitive to breakout moves in the TICK, which often signal shifts in short-term trader sentiment. I’m trying to see, over time, whether more traders are hitting bids or lifting offers across the broad universe of NYSE stocks.
4) Volume Transacted at the Market Bid and Offer for the ES Futures – Here we’re focusing on large traders and whether they are dominantly hitting bids or lifting offers in the market. The Market Delta program charts this indicator quite effectively, catching shifts in large trader sentiment. The idea is to ride the coattails of the largest market participants: they create the trending moves.
Price is not always the best indication of market trending. False breakout moves are legion in the stock market due to the influence of a handful of highly weighted issues within an index. By looking underneath the market hood at strength (new highs/lows), sentiment (TICK), and large trader behavior, we can observe trends in the making–and shifts among them.
Last week’s announcement of a project in which I would provide free coaching to a trader for a month brought a significant response. I greatly appreciate the time and effort people spent in volunteering for the project. Note: I have selected the first trader for the project on 6/3 and cannot accept further applications at this time. Those who have responded, however, can be considered for the project at a later time if they’re interested. In this post, I thought I’d provide some feedback about the submissions and raise some issues that might help those who expressed interest. Here are some observations:
1) About 80% of the submissions were from traders relatively early in their development. The remaining fifth were from traders who have an established track record of success. The established traders spent more time talking about their trading methods, risk management, etc; the developing traders spent more time talking about psychological barriers to success. My sense, having read the submissions multiple times, is that many of the traders trying to find their success are too quick to attribute their problems to psychological sources and not sufficiently critical of their trading methods. This was particularly true of traders who described rather plain vanilla technical analysis strategies for trading.
2) A major problem among the traders, experienced as well as new, was that their goals for coaching and self-development were vague. They expressed a general sense of what they wanted to accomplish (“I need to let profits run and cut losses sooner”), but did not seem to have a concrete understanding of how, specifically, they would do that. My sense is that many of the traders who wrote to me are *not* ready to jump into trading psych exercises and techniques for change. Rather, they would benefit from working with a coach to explore their successful and unsuccessful trades and get a more concrete handle on their strengths and the areas needed for improvement. From such specific exploration, the traders could develop positive, measurable goals that would spark their progress.
3) The “discipline” issue is overrated. I’ll write something for TraderFeed on this topic. Suffice it to say for now that many of the traders are working with relatively fixed trading size and trading strategies. Markets, however, are not fixed–especially with regard to volatility. When volatility expands, the traders are taking profits “too quickly” and letting losses get away from them; when volatility contracts, they are letting small gains turn into losses because they’re holding positions for follow-through that never comes. I’m suggesting that the problem is not necessarily one of discipline. It’s a lack of flexibility, and it’s an inability to adjust to shifting conditions of volatility.
4) Some traders expressed goals that struck me as unrealistic. Some traders expressed an interest in full-time trading (e.g., trading for a living), but also reported account sizes that were very small. It was not at all clear to me how a person could support themselves on such a small portfolio without taking major risks that, eventually, would damage both the account and the trader. I think a trader who can average 20% (after expenses) over many years of changing market conditions is a rare, superior trader. Even given that level of skill and success, such a trader could not support a family on a portfolio under $100K. There’s nothing wrong with trading a small account; it’s a great way to get your feet wet and preserve your capital during your learning curve. The problem is when the account is small and the goals are huge. It’s a setup for frustration.
5) Very few traders provided me with metrics. I did not get the sense that the majority of traders who responded to the project have done the hard work of actively reviewing their trades, identifying what they’re doing right, and isolating their mistakes. Had any trader quoted detailed metrics to me, I would have considered that person very seriously. I don’t get the sense that the majority of traders have that Tiger Woods/Lance Armstrong/Nolan Ryan mentality that picks apart every performance to find things to improve–even in the midst of success. Last week a trader I’ve worked with contacted me with an urgent request to meet. He had one of his best weeks that week, making six figures two out of the four trading sessions. He analyzed what had worked for him and wanted to meet with me so that he’d keep the momentum going. That’s what winners do: they’re fanatical about keeping score and getting better. The keeping score part–the detail focus–is huge.
Most trading coaches would not provide the above feedback to traders. They’re too afraid of losing business to raise the hard challenges. A coach that cares about your success, however, will want to know:
1) What specific methods are you using for entries, exits, stops?
2) What evidence do you have for the value of your methods (your trading edge)?
3) How are you sizing positions to take advantage of your edge, but also protect yourself against ruin?
4) How are you specifically, concretely identifying what you’re doing right and wrong and making efforts at improvement?
5) How do you adapt your trading to changes in market trends? Changes in market volatility?
The best way to coach yourself is to clearly identify your edge; keep score religiously; and set concrete goals based upon your identified strengths and weaknesses. I hope to illustrate this process in the Trading Coach Project.
Thanks again for your interest!
With this post I’m announcing a coaching project that, to the best of my knowledge, will be a first on the Web. What I propose is that I collaborate with a trader over the period of a month in a coaching arrangement to help the trader enhance his or her performance. The coaching would be entirely free of charge. The catch would be that regular summaries of the coaching would be posted to the TraderFeed blog so that the coaching can be an observational learning experience for all readers. To protect the trader’s identity, the summaries would not have to name the trader–a pseudonym could be used. The details of the coaching, however, would be shared openly.
If you’re interested in being considered for the project, all I need from you is an email sent to the address below. The subject header should read: Coaching Project. Please no attachments–just a straightforward email.
Your email should include the following information:
1) Background on you: Who you are, what makes you tick, where you’re located, your skills and career background, etc.
2) Background on your trading: market(s) you trade; your trading methods; your success as a trader; your trading history; whether or not you trade full time, your strengths/weaknesses, etc.
3) Your goals: What you hope to accomplish via coaching. Be specific.
4) Your view of the project: Why you think the project would be helpful to you and to other traders who read about you.
Please provide enough detail so that I can get a sense for you and your interest, but don’t feel like you need to write more than a good paragraph for each of the above points.
To begin, I will select only one trader. If the project goes well and proves to be of interest (and helpful) to readers, I’ll be happy to reopen the search. I hope to have gone through the emails and selected a trader for coaching by next weekend.
TERMS AND CONDITIONS: Please note that the coaching will be trading focused. I will not be providing any clinical, psychotherapeutic services for the project; nor will I be diagnosing or treating any emotional disorders. The coaching will be slated for a month’s duration but can be terminated before that at the request of either party. As part of the coaching, the trader may be required to complete homework assignments, including the keeping of a journal and collection of statistics on his/her trading. The coaching may also require phone and email communications both during and outside of market hours, as well as participation in writing the summaries for the blog. In other words, this will be a real commitment of time and effort by the coach and the trader; please don’t apply if you’re not ready for such a commitment.
I’m looking forward to this. Coaching invariably takes place behind closed doors. This will open things up and hopefully make the experience a learning exercise for all of us. We will track the trader’s strengths and weaknesses, work on himself/herself, work on his/her trading, and actual trading progress. My hope is that, by the end of a month, the trader will have some tools to be able to sustain his or her own progress through self-coaching.
Thanks as always for your interest–
Anytime you have one thing traded against something else, you have a market. Normally that something else, for U.S. traders and investors, is the dollar. The reality, however, is that you can trade U.S. large caps vs. small caps, vs. another currency, or vs. any other asset class. Just as we can identify historical patterns in common markets (trading the S&P 500 Index vs. the dollar), we can find patterns that capture the relationship among the markets that interrelate asset classes. The most tradable markets, from that vantage point, are the ones with the patterns that capture the greatest historical edges.
My recent TraderFeed post contains a chart of a market from January, 2004 through May 18, 2007. The market is the relationship between Materials stocks in the S&P 500 Index (XLB) and Technology stocks (XLK). We’re looking at the tendency of investors and traders to put their capital into physical assets vs. intellectual assets.
When the relationship between XLB and XLK is up more than 2% on a 20-day basis, the next 20 days in the relationship averages a gain of only .23% (99 up, 75 down). When the relationship is down more than 2% on a 20-day basis, the next 20 days average a gain of .81% (65 up, 38 down).
This is but a very simple pattern identified with no optimization whatsoever, but it suggests the kinds of trading patterns one can find when you trade markets in the broadest sense.
The opportunity has arisen to significantly expand my work with traders at hedge funds and investment banks. Just as crisis can bring opportunity, sometimes opportunities also yield crises. In my case, the crisis is one of identity: whether I want to devote my primary energies to trading (especially in light of very promising research with the money flow indicators) or whether I wish to focus on my work as a psychologist. As I’ve done consistently through my career, I’ve opted for the latter. What that means concretely is that I’ll be working with a large cohort of traders managing tens and hundreds of millions each–and sometimes much more than that. It’s a unique opportunity to work with the best in the world of finance, and I greatly look forward to the experience.
I’ve entirely discontinued my daytrading as a result and will not be tracking the market nearly so intensively on an intraday basis. Rather, I’m pulling back in my research to identify larger prospective market movements and unusual stock and sector opportunities, as I transition from an index trading perspective to portfolio management. Largely because of the promise of the money flow research, I am convinced that I can obtain larger returns from a diverse and hedged portfolio of stocks held over an intermediate time frame than from intraday trading of stock indices.
The Trading Psychology Weblog will be published on a weekly basis, focusing on the intermediate-term picture. The usual indicators will be a Weblog focus: strength (new highs/lows); momentum (Demand/Supply); sentiment (Adjusted TICK; relative put/call ratios); and money flows (adjusted relative dollar volume flow). I’ll especially stress occasions in which these indicators provide skewed readings that are associated with a historical directional bias. I’ll also use the Weblog to sketch promising market themes among sectors and styles. In short, the Weblog will continue as a kind of trading diary or sketch pad for my thinking, with an emphasis on the markets’ larger picture.
My hope is that the revamped Weblog will be of help to traders, even as it serves as my guide through this sharp and unexpected transition.
I’ve been working on simple market patterns that contain an edge over the past 1-3 years of trading history. There’s no attempt to establish that these patterns are equally effective across all market periods and conditions. Rather, the setups reflect the recent tendencies of the market. They can be thought of as the rules that the market has been playing by. I refer to them as “simple setups”, because they are straightforward conditions that capture a future directional tendency of the market. I’m not looking for complex historical patterns.
The idea is not to trade these simple setups mechanically (although they could be the starting point for system development). Rather, they serve as a heads up to alert readers of a directional leaning to the market based on recent precedent. When intraday conditions then set up in such a way as to confirm this leaning, it is possible to take positions with a solid winning percentage. The key is ensuring that conditions in the current day’s market fit the conditions of the simple setup. For instance, if a simple setup calls for rising prices over the next five days of trading, I’d wait for an intraday decline, look for selling to dry up, and then get on board the subsequent rise. A portion of the position could be left on for the five day period with a trailing stop; another portion could be taken as profits once pivot targets were hit. A break below the lows where selling had dried up would clearly stop one out of the position.
These simple setups are more numerous than traders might think. Rennie Yang’s Market Tells newsletter does a particularly good job of identifying these; see also Jason Goepfert’s SentimenTrader service. In Sunday’s Webinar, I will offer a couple of setup examples for discussion. It’s a way of combining historical analysis and odds with discretionary entries and exits. When multiple simple setups point in the same direction, particularly good trades are often signaled.
The daytrader closes out positions by the close of trading each day; the swing trader typically holds over a several-day time period. Between swing trading and long-term investment is a broad territory I refer to as active investment. An active investor, for example, may overhaul a portfolio several times during a year and rebalance positions even more frequently. Individual positions may be held for a long time frame, but generally there is an effort to take money off the table when price targets are hit, and there is an effort to put money to work when valuations look attractive.
What I’m finding is that the indicators that are helpful in monitoring strength and weakness on an intraday basis do not necessarily capture the patterns that would benefit the active investor. (Just as the indicators used by a scalper, such as DOM, don’t capture the patterns that benefit a swing trader). As a result, short-term trading may catch pieces of moves during the day, but leaves much on the table by missing the moves of the active investor–especially since much of those moves occur between the market close and the next day’s open.
The active investor doesn’t so much invest in single stocks as broad themes. An example of a theme might be “weak dollar, strong international companies that rely on exports” or “strong oil, strong alternative energy stocks”. These themes may be derived from an analysis of news events, economic statistics, and the like. I’m finding, however, that some of the themes can be identified in a bottom-up manner by tracking the money flows into various market sectors and industries. Before a trend becomes noticed by the mass media–and even before it becomes evident on a chart–it can be identified by distinctive shifts in dollar volume flows.
By investing in a portfolio of themes that combine long and short equity exposure, it is possible to not only outperform index benchmarks, but to do so with reduced risk. It is also possible to add a source of performance (alpha) that is independent of one’s intraday trading. This is a direction I’m finding increasingly promising.
I recently posted on the topic of creating an “emerging average” of the NYSE TICK to track shifts in sentiment within the trading day. The emerging average is different from a moving average in that the starting point for the average is fixed as the first data point of the day. The emerging average at 9:00 AM CT, then, would be the average of all TICK values from the 8:30 AM open to 9:00 AM. The emerging average at 9:05 AM would be the average of TICK values from 8:30 AM to 9:05 AM. As a result, you’re always looking to see if the current TICK values are greater than or less than the average for the day up to that point.
Here is the emerging average of the TICK vs. ES futures for Friday, April 20th. Notice how, in early trade, the TICK was above the average level of 300 for the prior 20 days. In spite of this, price couldn’t make fresh highs on the high TICK values, and the emerging average TICK began to wane. Conversely, you can see how the ES moved higher as the slope of the emerging average for the TICK turned positive. I’m finding a number of tradable patterns from this indicator, especially as it relates to that 20-day average TICK value.
My recent TraderFeed post described how I use tax time to review each trade from the previous year and evaluate the strengths and weaknesses of my performance. Here are some of the elements of a trading report card that I focus upon besides overall profit/loss (P/L). These are metrics that help me understand the patterns of my trading:
1) Number of winning vs. losing trades – For my style of trading, I should have a clear plurality of winners vs. losers. This would not necessarily be the case for a different kind of trader, such as a trend follower.
2) Largest number of consecutive winners and losers – I like to look at what was happening in markets during lengthy streaks. This can help me identify markets I tend to trade best and worst.
3) Average size of winning vs. losing trades – Having a few large losers was a pattern that I needed to change, as this can make a trader unprofitable despite having more winners than losers. A trend follower especially needs to have larger winners than losers. This measure helps me see how well I’ve utilized stop-losses.
4) Largest period of drawdown (and longest period of drawdown) during the year – This would be the greatest drop from an equity curve peak to a low point. It’s one way of measuring risk. Being profitable with small drawdowns means that risk-adjusted returns were probably good. Being profitable with huge drawdowns is a warning flag. Returns may not be superior on a risk-adjusted basis.
5) How well I traded after one or more up days and after one or more down days – This provides some insight into psychological factors (greed and fear) and how they might affect performance.
6) P/L broken down by long and short trades – I like to see if I had more success trading from one side vs. another and whether this might be due to my trading style vs. market conditions.
7) P/L broken down by trade size – I tend to put on larger size when I have more confidence in a trade. Breaking the P/L down by trade size tells me if my confidence was warranted.
Out of these metrics, I like to formulate goals for the coming year. The goals aren’t P/L goals. Rather, they are process goals. I usually like to have one positive goal–something I did well that I want to continue–and one remedial goal: something I could do better next year.
In 2006, my risk management was excellent. My winning trades were larger than my losers and I had more winners than losers. I did not have any large losers all year. That’s what I want to continue. My remedial goal is to up my size and to be more consistent in leaving a small piece of a trade on when there’s the opportunity to hit a further profit target. I’m finding that I continue to do much better with short-term trades than those held overnight. My review confirmed this in spades. As a result, I want to take full advantage of what I’m doing well before branching out.
Next year’s review will track my progress on those goals.
The chart below shows the upside breakout from April 5th. A valid breakout will show greatly enhanced volume, indicating that large traders are repricing value. We see not only a large volume increase on the breakout move in the chart below (white is ES futures; red is NYSE TICK), but also very strong NYSE TICK levels, suggesting broad buying among stocks. If a breakout is for real, we should not retrace the initial upthrust; hence I’m content to buy at the market and place my stop at the price that represents the most recent low in the NYSE TICK (labeled in yellow). I will then hold until my price target, usually defined by the pivot-based levels defined each day in the Weblog. We handily exceeded the 1450.75 R1 target, encouraging us to leave at least a piece of the position on for a test of R2 (1453.25). If we do indeed have an uptrend in the making, we should see waning volume on selling following the breakout, as large traders continue to lean to the long side. That’s exactly what happens, as noted with the yellow arrow. Indeed, the next upmove hit our R2 target.
Here’s a larger view of the chart. The key to trading the breakout is making the identification as early as possible when the breakout is occurring and then having the patience to stick with your stop. Note that, following the second upmove (not shown here), the stop can be raised to the price corresponding to the prior TICK low around 12:45 PM (about 1451). No sense giving up all profits should a large reversal materialize.
Here’s a different way of thinking about how I trade that I’ve been pondering over the last couple of days:
I’ve mentioned before that my research finds that about 85% of all trading days are *not* inside days. That is, in general–particularly when we’re trading average or above average volume–we will either take out the previous day’s high or low.
Knowing that, one can then use an array of market indicators–NYSE TICK, relative volume, Market Delta, Advances/Declines, etc.–to handicap the odds of which extreme we’ll take out. Those same indicators, with a particular nod to relative volume (which correlates with volatility) will also enable us to handicap the odds of hitting pivot-based targets (the R1, R2, S1, and S2 levels tracked in the Weblog).
But it turns out that this dynamic occurs across time frames. About 80% of all 30 minute bars are not inside bars. About 80% of all 5 minute bars are also not inside bars.
What that means is that whenever you close a bar off the high or low, the odds are good that you’ll take out one of the prior bar’s extremes. Moreover, you can use the data from the prior bar to calculate pivot-based trade targets. And you can use indicator data relevant to that time frame (from order flow from the depth of market screen to 10-second TIKI readings to 1 and 5 minute TICK readings to intraday new highs/lows) and trend information from larger time frames to handicap the odds of hitting those targets.
Indeed, any price move of any duration can be conceptualized as a single bar and a trade idea can be formed by trading the following bar for a move beyond the initial bar’s extremes. In that context, any trade might be thought of as a “breakout” trade. Once you condense time into a single bar and think about the action in the next bar (and the bars preceding), it opens the door to a different way about thinking about the duration of trades, definition of profit targets, and placement of stops. I’ll illustrate in future posts.
I’ve heard a great deal lately from traders who feel that their performance is lacking because they’re not taking as much out of good trade ideas as they should. Indeed, I experienced some of the same problem on Friday, as I had a fine short sale idea in the morning when it became clear that we were moving back into the day’s range. I had my initial target at the previous day’s average price, waited patiently to reach the target, and took 3-1/2 ES points profit. I didn’t continue to press the trade, however, as my bias (my belief that we’d have a range bound day) prevented me from continuing to sell bounces in the TICK. I ended the day with a respectable profit, but my caution kept me from a banner day.
Over time, such caution kills. During my recent CNBC appearance, one of the panel members–Ari Kiev–made an astute observation. He pointed out that 3% of all trades account for the lion’s share of a trader’s profits. I believe my own percentage is higher than that, but the same principle holds: it’s the big winners that contribute most to the bottom line. Caution cuts those big winners short.
The very successful traders I’ve known are very aggressive. When they’re right, they press their advantage. They add to good positions or keep re-entering in the direction of their idea as long as nothing is proving them wrong. “No one ever went broke taking a profit” is not how the best traders operate. What Dr. Kiev was saying was get out of losing ideas quickly, but really milk the winners. A good trade is valid until proven wrong. Just a few more big winners make a big performance difference by the end of a year. Risk management is not just cutting losers short; it’s also ensuring that the average size of your winners handily outstrips that of the losers.
The sector analyses for segments of the S&P 500 market are now up on TraderFeed, as well as an analysis for the S&P 500 Index overall. By aggregating the sector data on Adjusted Relative Dollar Volume Flows, I’m able to get a sense for whether large traders are predominantly transacting at the market bid vs. offer as a short-term measure of sentiment. This is helpful, because it’s based on what traders and investors are actually doing–not upon their stated beliefs about market direction.
The next step in the Flow research is to track sentiment for the broad market by tracking 40 stocks that are highly weighted in the ES universe. These are evenly divided among the following sectors: financial, energy, technology, industrial, consumer discretionary, consumer staples, health care, and materials. The measure I’ll be using is quite simple: If a stock shows a daily Dollar Volume Flow reading that is above its 200 day moving average of Dollar Volume Flow, it will count +1 for the index. If the stock shows a Flow reading below its 200 day moving average, it will count -1. I will sum the scores for the 40 stocks and this will be the day’s Flow Index for the S&P 500 Index.
We will then see if the Flow Index helps us predict future price changes in the ES market based upon historical patterns. This will provide an acid test of the value of the data. More to come!
The next step in the Relative Dollar Volume Flow research is to take the most important stocks across various sectors and track their trading on upticks vs. downticks by weighting the trades by size. This would tell us something about sector sentiment. Of particular interest would be sectors that are holding up well during market downmoves and those that are displaying waning buying interest during market rises. My leaning is to create the sectors out of the S&P 500 Index universe (e.g., sector Spyders), so that, taken together, the stocks could provide a direct measure of size hitting bids/lifting offers in the S&P market. This line of research continues to look promising; eventually it could be replicated among NASDAQ and small/mid cap stocks. The key is creating a metric that enables you to compare one stock to another (and one sector to another) on an equal basis, either by expressing net dollar volume flow as a function of total volume or by tracking dollar volume flow as a function of a prior moving average. More to come–
February 26 , 200 7
Well, stoked on far too much caffeine and music from Children of Bodom, Fler, and my all-time favorite video of Barney the Dinosaur, I’ve been researching a new (for me, at least!) market indicator.
My goal has been to find a measure that tracks the activity of large institutional traders in individual stocks (as opposed to the index futures).
The indicator looks at each trade in the stock and determines whether it has occurred on an uptick or downtick. The price at which the trade was executed is multiplied by the number of shares transacted to give the dollar volume of the trade. If the trade occurred on an uptick, the dollar volume of that trade is added to the cumulative total for the day. If the trade occurred on a downtick, the dollar volume of the trade is subtracted from the cumulative total. At the end of the day, the session’s cumulative total is its dollar volume flow for the day.
The *relative* dollar volume flow divides this daily dollar volume flow by the day’s total volume in shares. What you’re measuring, therefore, is the proportion of the day’s volume attributable to buying (positive flow) vs. selling (negative flow).
If you follow the logic of the indicator, you can see that it is very sensitive to large trades. When institutions transact a large block of stock on an uptick or downtick, this will affect the cumulative dollar volume flow far more than small trades. As a result, the day’s dollar volume flow is a proxy measure for the buying and selling activity of large traders.
Where the relative volume flow becomes important is in comparing the dollar volume flows for one stock vs. another. Ten million dollars flowing into Exxon-Mobil stock, for instance, is far less significant than ten million dollars flowing into a microcap issue. By measuring dollar volume flow as a function of daily volume, we have a metric that enables us to see, in relative terms, which stocks are attracting more buying vs. more selling.
Note that this measure can also be used to track dollar volume (and relative dollar volume) flowing in and out of sector and index ETFs. I will be posting an ETF analysis of the Dow Jones Industrial Average based on relative dollar volume to TraderFeed very shortly.
I have also constructed relative dollar volume flows for each of the Dow 30 stocks and used the summed values to arrive at an understanding of how much money is flowing in and out of the Dow on an absolute (and relative) basis. What I can say at this point with certainty is that the summed dollar volume flows for the individual Dow 30 stocks contain useful information not obtainable by simply looking at dollar volume flow in the Dow ETF.
The very significant application of the research is in the construction of long-short portfolios, in which you assess the relative dollar volume flows for, say, Dow stocks over the past X days. You then buy those with the most bullish configurations and sell those with the most bearish outlooks, balancing the holdings so that the entire portfolio is hedged with respect to directional moves in the Dow.
The important thing in terms of performance is to always be seeking new sources of edge. I don’t trade individual equities, and I don’t trade intermediate-term time frames. That is precisely why I decided to tackle this project. If you don’t develop new strategies over time, you’re in danger of becoming outdated as markets change. And the best time to be developing new methods is when you’re ahead of the curve, not when you’re in a hole and feeling pressured to bring in the family’s next mortgage payment.
February 19 , 200 7
I typically use volume to track the presence of large traders in the stock index futures markets. This is because large traders control the stock indices; they account for a small percentage of all trades during an average day, but a large proportion of total volume. For that reason, volume is highly correlated with price volatility. In a recent post, I found that emini volume in the Euro FX futures also correlates quite highly with volatility. That led me to conjecture that the emini Euro FX volume might be a “tell” for large, institutional volume in the much larger cash currency market.
It’s interesting to see how volume is distributed in the emini Euro FX market. I took Friday, February 16th’s morning market as an example. We had 12,648 emini Euro FX trades that morning and a total contract volume of 80,728. Fully one-third (N = 4390) of those trades were one lots. About half of the trades were either one or two lots (N = 6015), accounting for 7640 contracts, or less than 10% of the total.
On the other hand, only 818 of the trades–about 1%–were 20 contracts or larger. These accounted for 30,644 contracts, over a third of the total volume. Trades of 10 contracts or higher accounted for only 2081 trades–about 2-1/2% of the total–but accounted for 48,149 contracts. That’s about 60% of the volume.
What we find in the emini Euro FX futures, it seems, is very similar to what we find in the stock index futures: a small proportion of trades from large traders account for a large amount of the total volume and for much of the market’s movement. Knowing how the large traders are trading–whether they’re participating in market rises or declines; entering or fading breakouts–is crucial to understanding the action in the Euro currency market.
My strong impression is that this same dynamic applies to trading in individual stocks as well. It’s the large traders that move the markets, and ferreting out their behavior provides a meaningful edge for the active trader.
February 11 , 200 7
Today I posted a proposal for using the TraderFeed blog to share the best practices of traders: methods that have helped them master markets–and themselves. The idea is to create a repository of what works: a collection of ways of looking at markets and trading that can sustain continuing learning and development. Hospitals commonly track the best practices of physicians to help make health care more efficient and effective; manufacturing firms identify best practices in the search for quality improvement and cost control. Best practices can be considered as a kind of evolutionary process, in which further growth and development by emphasizing what works.
One of the great obstacles to developing such a framework for the trading world is our set of beliefs. What we believe defines our limits and, all too often, narrows our field of possibilities. In particular, three beliefs run contrary to the best practices paradigm:
1) The belief that expertise resides outside of us – It is all too easy to divide the world into gurus and followers. Placing the locus of expertise outside of us keeps us dependent on others, many of whom are pretenders to guru status. Each of us has experience, and each of us has learned. In sharing our expertise across a wide network, we magnify learning, absorbing lessons from others that would take years of experience on our own.
2) The belief that ideas will lose their power if shared – We often hear the fear that, if we share an idea, then everyone will use the idea and it will no longer provide an edge. But once you’re connected to a network of creative individuals, your edge is not dependent on any single idea. Your edge comes from the ability to continually generate new and better ideas. It only makes sense to hoard ideas if you begin with the premise of scarcity. That premise keeps us isolated from learning from others.
3) The belief that we should not imitate others – Yes, it’s true that, in the long run, we must develop our own, individual trading styles. The same, however, is true of artists and scientists: they must find their own niche, but usually begin their learning by absorbing lessons from others with more experience. Our style will be an amalgamation of what we pick up from others; the more input we get, the richer our synthesis can be. There is no contradiction between depending on others for ideas and developing one’s independence. In learning from others, we acquire the building blocks that we will assemble into our own unique structures.
Imagine if you had a group of just 100 hard-working, dedicated traders who decided to view themselves as teachers, not just as students. If each of those traders shared just one valuable trading lesson, the entire group would have two excellent pieces of learning every week for a year. All it takes is a shift of belief, a willingness to look inside and find your own strengths and the experience you have to share. In that group of 100 dedicated traders, sharing one’s ideas doesn’t mean losing an edge: it means you’ll have the opportunity to acquire 99 more.
I’ve noticed for a while that the energy stocks among the S&P 500 issues have a weaker correlation with the other issues than do the stocks from other sectors. This is because the energy stocks react not only to broad movements in the equity indices, but also to commodity energy prices (oil, natural gas, etc.). This had me wondering if the relative performance of energy stocks during short-term moves in the S&P 500 Index (SPY) might have some forecasting value.
Since 2004 (N = 757 trading days), we’ve had 464 occasions in which SPY has been up over the prior 10 sessions. When SPY has been up over the past 10 days and the energy stocks (XLE) have been relatively strong (N = 232), the next ten days in SPY average a loss of -.34% (111 up, 121 down). Conversely, when SPY has been up over the past 10 days and the energy stocks have been relatively weak (N = 232), the next ten days in SPY average a gain of .58% (162 up, 70 down). That’s quite a disparity in performance.
Now let’s look at the 293 occasions since 2004 in which SPY has been down over the prior 10 sessions. When SPY has been down over a ten-day period and the energy stocks (XLE) have been relatively strong (N = 147), the next ten days in SPY average a loss of -.01% (74 up, 73 down). On the other hand, when SPY has been down over a ten-day period and XLE has been relatively weak (N = 146), the next ten days in SPY have averaged a gain of 1.32% (114 up, 32 down). Once again, we see a huge disparity in performance.
In sum, it appears that when we have two-week strength in both the S&P 500 large caps and among the S&P energy issues, returns are noticeably subnormal over the next two weeks. When both the large caps and the energy components have been weak over a two-week period, returns are notably superior. I have examined the tech and financial sector components of the S&P 500 Index for similar dynamics, but have not replicated these results. There appears to be a unique relationship between energy and non-energy large caps that is worth considering when anticipating results over the next two weeks.
In recent posts, I outlined how I trade, setting price targets and handicapping the odds of reaching those based upon measures of sentiment and volume. In this post, I’d like to pull together some of those ideas and lay out the steps I take in developing and executing trade ideas.
The first step comes prior to the market open and involves research to identify a directional edge to the next day’s trade. My recent article on market reversals and implementation of that research in early morning trade the next day is a nice example of that. Many times, the research examines the momentum, participation, and sentiment of the recent market and identifies the odds of hitting key price levels, such as the prior day’s high, low, or average trading price. If I have strong odds on a trade, I will be open to using my maximum size if intraday setups align with my research. If I don’t have strong odds, my maximum permissible size is automatically cut in half and I may not trade at all that day. The idea is to align position sizing and opportunity.
Immediately prior to the market open–for at least 90 minutes prior–I am watching to see how overseas markets are trading and I am watching to see how economic reports impact the index futures. Many of my initial ideas about trading ranges and breakouts from those ranges come from noting the overnight range and the action of the European bourses. I’m also watching how fixed income, oil, and the dollar are trading to see if global/macro forces may be at work. My goal is to identify lead-lag relationships that might be at work in the recent markets, such as stocks following bonds or the DAX leading the ES. Noting a breakout in a leading market may lead me to entertain trade ideas in my (lagging) market, especially if that move is in the direction of my prior research.
Once the market opens, I continue the search for lead-lag relationships. This time, however, I’m scanning for leading market sectors, such as small caps, semiconductors, or value stocks. If I see a leading sector breaking out of a range, I will not trade my market in the opposite direction. I can’t tell you how many bad trades that single principle has kept me out of.
Also early in market action, I’m watching the NYSE TICK, the volume at bid vs. offer in ES, and five-minute volume in ES to gauge if there is a directional bias to the market (more activity at bid vs. offer) and if there is heavy participation in the market (above average volume). From these readings, I formulate my ideas as to whether or not we’re likely to be in a range bound market, a trending market, a volatile market, a slow market, etc.
My first trade almost always occurs in the first 30 minutes of the trading session. Very often it’s a trade with a very concrete near-term price target, such as a breakout from the overnight range, a test of the prior day’s average trading price, etc. On average, I’ll hold such a trade for 20 minutes, and I generally have a tight stop on the trade. For instance, if we bounce off the overnight high and start to see some selling, I’ll be quick to join the selling, with a stop just above that overnight high. Once again, my size on such initial trades will be modest unless the move I’m expecting is strongly supported by my research. If the latter, I put on max size right away.
As I mentioned in my recent research, the vast majority of market days and weeks take out either the prior day’s/week’s high or low. My core trade is a trade to test and break that prior high or low, using my volume and sentiment data to handicap the odds of hitting that point. Once we break the price target on an extreme NYSE TICK reading, I get out and take my profit. Very often that ends my trading day. It is rare that I trade beyond mid-morning, simply because I have other pursuits to attend to. I average one or two trades per morning; very rarely do I make large sums or lose large sums. The idea is to be consistent in my approach and make steady profits, gradually ramping up my size as I progress.
At this juncture, I do put on occasional swing trades, but that’s a somewhat different process that I’ll cover another time. The important thing about my daily trading is not so much the specific methods I use, but rather the structure I impose. Each trade risks a very small portion of my total capital, and each trade has to be justified by my data. I limit the number of trades I make each day and I adjust my position size for the opportunity at hand. The trading is not mechanical–I make many discretionary judgments each day–but it is rule-governed. Every trade has a rationale, a price target, and a stop. My risk:reward on each trade has to be at least 2:1, which means I sell after I spot a candidate high and buy after I spot a candidate low. I don’t try to predict tops and bottoms.
It’s not the most exciting way to trade, but I suspect that’s why it’s worked well for me. I don’t lose much money when I’m down and I do tend to get strings of winners eventually. None of the winners is so large that it gets me overconfident or euphoric, and none of the losers amounts to more than a temporary frustration. When I trade, it very much feels like going to work and doing a solid, workmanlike job without a lot of drama and then going home. Because I only trade part of the day, I feel very much like a baseball relief pitcher. I come in, put in my innings, and return to the dugout with the anticipation of being ready to pitch the next day. Having seen so many traders blow up when they trade size that is much too large for their portfolios, I find comfort in my prosaic, blue-collar approach. It’s a good example of how we find trading methods that fit our core talents and skills (for me it’s research and rapid pattern recognition) and our personality needs (part-time, risk-prudent).
My previous post dealt with structuring trading by using daily pivot points as profit targets and trade exits. Today I posted a variation on this idea: using weekly pivots for swing trading purposes. This enables traders to track the market a couple of times per day, instead of continuously.
The reality is that precious few individuals can sustain a good livelihood from their trading, just as few golfers can make a living on the pro tour and few artists can sustain themselves from their craft. That doesn’t stop people from enjoying golf, and it doesn’t prevent those with artistic talent from engaging in creative efforts. Similarly, many traders don’t trade for a living, but enjoy the challenge and supplemental income that trading can bring. With the aging of the baby-boomer generation and the increasing recognition of the need to keep mentally active, such avocational trading will enjoy continued growth.
Interestingly, however, the industry has little interest in promoting trading as a hobby. It is far sexier to hold out the lure of trading for a living, and–of course–brokerage firms and data vendors benefit far more from active traders than occasional ones. As a result, there are few “how-to” guides for the part-time, avocational trader.
Over the next couple of weeks, I’ll be elaborating a trading framework based on weekly pivots and may even extend it to longer time frames. The longer the time frame, the greater the potential for capturing large market moves and the more it frees up the trader for other life pursuits. Of course, the longer the time frame, the larger the potential losses, requiring careful position sizing and risk control. I’ll be addressing all of these in coming posts.
It’s interesting that traders tend to place more emphasis on entries than exits and profit targets. As a result, it is often quite difficult for traders to let profits run on good trades. They have entered effectively, but don’t have a clear sense for the trade’s potential.
My recent posts have looked at the previous day’s high and low prices and pivot point-based levels of support and resistance as potential trade targets. There is nothing magical about these price points; I have no belief that some mysterious underlying order in the universe is guiding prices to those levels. Rather, my strategy is to monitor supply and demand as they manifest themselves in real time and continuously update my estimates of the probabilities of reaching those prices. My primary ways of monitoring supply and demand are via the NYSE TICK (a comparison of the current day’s TICK levels relative to average readings over the prior 20 days) and the distribution of ES volume at the market bid vs. offer. Both of these measures tap the very short-term sentiment of the marketplace: whether buyers are more aggressive (lifting offers) or sellers (hitting bids).
Let’s say that we’ve closed above the average trading price from the previous day and open above the lows from the overnight ES session. We see large traders lifting offers early in the AM. A reasonable trade idea might be to wait for a pullback in the NYSE TICK and see if we can hold above those overnight lows. If selling dries up above that level, we might buy the ES in anticipation of taking out the previous day’s high. A logical stop would be those overnight lows. If buying looks strong during the trade, we might not exit at the prior day’s highs, waiting instead for a move to R1. (This post explains the calculation of R1, R2, S1, and S2). Alternatively, we could take something off the table at the prior day’s highs and let a piece ride for a move to R1. Should the buying pressure in the TICK and volume at the offer persist, we could also let a piece of the position ride for a move to R2. Once we’ve hit our first profit target, moving our stop to breakeven would make good sense as well.
What this accomplishes is a structuring of trading: a way of thinking about when to enter, where to exit, where to take profits, and where to place stops. You don’t have to trade mechanically to be structured in your approach. The advantage of a structured approach is that, with repetition, it can become automatic. It is easiest to stick with your plans if the plans are concrete, familiar, and topmost of the mind. A careful entry is valuable, but it is not a planned trade.
The other advantage of structuring trading with price levels is that it can be combined with almost any method of entry and used with any trading instrument that has daily high-low-close data available. One promising application is with individual stocks and ETFs. Because many individual issues and sectors are highly correlated with the S&P 500 Index, handicapping the odds of reaching pivot levels in ES may also provide clues as to hitting comparable levels for those stocks and sectors. Moreover, it may be possible to use tools such as Market Delta to assess volume at the bid vs. offer for the individual equities and NeoTicker to track TICK measures that are specific to individual stocks and sectors. This could provide unique information that would enable us to handicap the odds of reaching price targets in stocks and ETFs. I will be looking into this as one of my projects for 2007.
The proliferation of ETFs are providing a growing opportunity for equities traders to trade and benefit from intermarket relationships. The idea is to improve performance by not only seeing how patterns in a market are associated with future directional movements of that market, but to also see how patterns in related markets affect one’s primary trading market. Indeed, the identification of several robust intermarket relationships could enable a trader to trade a portfolio of instruments and patterns, greatly diversifying risk.
Here’s a simple example of one such intermarket relationship that could diversify trading. I decided to look at 2006 data for the Euro currency ETF (FXE) and the European stock market ETF (VGK). Specifically, I wanted to see how five day moves in the currency have been related to subsequent five-day moves in the European equities.
When the Euro currency has been up by more than 1% over a five-day period (N = 56), the next five days in the European stocks have averaged a gain of only .03% (34 up, 22 down). However, when the Euro currency has been down by more than 1% over a five-day period (N = 49), the next five days in the European stocks have averaged a gain of 1.34% (40 up, 9 down).
Clearly, these are data covering only the past year of trading, but they are suggestive. When the Euro is strong, that could hurt European exports and might be taken negatively by equities traders. Conversely, when the Euro is weak, that may be viewed as positive for the European economies and shares.
With ETFs now covering the broad commodities market, gold, oil, stock sectors and investment styles, international equities markets, currencies, and bonds, there are many more intermarket relationships available for equities traders to participate in than ever before. This is a most promising area of broadening out one’s trading.
Here is the link to the Peak Performance Trading seminar sponsored by the Chicago Mercantile Exchange. It was a well-received program, with yours truly, Doug Hirschhorn, and Denise Shull as participants.
I would encourage serious traders to set in stone their performance goals for the New Year. Those might include P/L targets, but more importantly should include two components:
1) What you did well during 2006 that you want to continue (and extend) in 2007;
2) What you did not do well during 2006 that you either want to improve or eliminate for 2007.
Then spell out, concretely, what you will be doing–each day and each week–to work on those goals.
During 2006, I successfully broadened my trading to include a greater variety of indices, not just my bread-and-butter ES trading. That was especially true for trading the Russell alongside the ES. During 2007, my goal is to continue this broadening with an array of ETFs from the style cube (see below) in addition to the ES, ER2 trade.
My greatest shortcoming during 2006 was my tendency to take profits early. I need to do a better job of identifying when there is a longer-term pattern setting up and keep a piece of my position on to benefit from this move. Toward that end, I am ramping up my size during the first quarter of 2007, eventually quadrupling my position size. This will enable me to trade a maximum position size of 4 units and keep one unit on for longer-term moves when my patterns show an edge.
Each day I will be assessing the market in the Weblog and determining: 1) whether there is an edge over a multi-day time frame; and 2) which index or ETF is most likely to benefit from the expected directional move.
Given that I have traded the ES for years with an average holding time of 20 minutes, these are major changes. I expect some bumpiness along the path of progress, which will provide the trading shrink with an opportunity to work on himself!
December 23 , 200 6
The style box is a convention that describes trading strategies based upon sectors and segments of the market that might outperform the broad averages. The two axes of the style box are growth/value and large/small cap. Below is a common rendition:
In my current research, I’m adding a third dimension, which is International vs. Domestic markets. Ideally, we would have 27 different flavors of ETFs to represent every cell in the 3X3X3 matrix, but we’re not quite at that point yet. My 12/24/06 TraderFeed post will deal with this topic.
This makes three big research projects for 2007:
1) Sector trading strategies to optimize returns along the Style Cube;
2) Sentiment measure development from options data and ETF relative performance;
3) Refinement of indicators (and indicator-based historical pattern identification) by analyzing significant high/low readings separately from “normal” readings.
Many of my upcoming blog posts will deal with these themes.
Have a great holiday season!
December 15 , 200 6
One of the things I’ve noticed about successful traders is that they’re always on the hunt for an edge. The one-trick ponies don’t last in markets that are ever-changing. The traders who approach their work as a true career make it a priority to stay one step ahead of those market changes.
One way that I’m developing trading ideas is by looking at the widening universe of ETFs coming to the market. My key questions when I see a new instrument are: 1) How is this related to my market? and 2) Who is trading this instrument?
With respect to the first, I’m looking for historical patterns in which one instrument might have a predictive relationship with respect to my market. For example, I’m currently researching EFA, the EAFE ETF that covers most of the major equity markets outside the U.S. Do moves in EFA lead moves in the U.S. equity markets? When I hear of a trading instrument such as EFA, that’s one of the first questions that comes to mind. Well over three-quarters of the time, there will be no reliable relationship to bank on. It’s those few that do pan out, however, that can lead to excellent opportunities.
The second question arises because some instruments are dominated more by small, inexperienced traders than by seasoned professionals. Sometimes, the trading patterns in these instruments can form the basis for a fade. For instance, in the Weblog entry for Dec. 16th, I show how it can be worthwhile to fade the equity put-call ratio when the market is at a 20-day high. I’ve learned over time, with analysis, that it is worthwhile fading equity option traders when they’re leaning strongly one way or another.
I also like to look at the technical patterns that a large number of traders emphasize. Those especially include support/resistance areas. When you see an obvious support area and you also see large traders hitting bids and pushing the market lower, you know that the locals, at the very least, will gun for the support area and take out the stops placed by relatively naive traders. I’ll get short with those large traders and, when the stops are hit, cover my position.
By studying different instruments and intermarket relationships–and by constantly asking yourself who is going to have to disgorge their positions if the market moves up or down–you can often frame longer-term and day timeframe trading ideas. Discipline keeps traders in the game, but careers are built out of creative, flexible thinking.
I recently wrote about the issue of getting broader as an alternative to getting larger in the face of market success. This is not only a trading challenge, but an emotional one.
One of the ways I plan to broaden is by researching and trading four-day patterns in the stock indices. This is expanding my typical time frame, but remaining with my usual trading vehicles. My research suggests that several of my indicators, viewed over a four day horizon, nicely anticipate returns over the following four days.
Some of the emotional hurdles to moving to this horizon include:
1) Avoiding the temptation to act on short-term data to quickly take profits or losses;
2) Tolerating the ambiguity of overnight holding periods;
3) Ensuring that the longer timeframe perspective does not color intraday trades;
4) Achieving proper position sizing to ensure that the new, longer-term trades don’t dominate the P/L from the shorter-term trades.
Most important of all, lengthening one’s time frame means separating oneself from perceptual and behavioral patterns that have been built over months and years. At this point, I am “wired” to see trading through a short-term (intraday) lens. To overcome old learning and reflexes will be an especial challenge.
There is no better way to learn about trading psychology than to live it!
Consider the following scenarios:
|A hospital studies the treatment records of 50 surgeons and finds that 10 of the doctors have a much lower complication rate among their patients than the other 40, when adjusted for the difficulty and risk of the procedure. The Medical Chief interviews and observes these top 10 surgeons to find out what they’re doing differently from their colleagues. The results of the study lead to a set of guidelines that improve surgical care outcomes across the entire hospital.|
|An automobile manufacturer finds that one of its plants has a much lower defect rate than the others and yet is just as productive. A team of managers goes to the plant to observe quality control procedures. This leads to changes at all of the plants that improves auto quality and consumer satisfaction.|
|A pit crew for a NASCAR driver videotapes each of its practices and races and measures the time it takes to get the car back on the road following routine stops. By watching the videotapes and comparing the quick and slow times, the crew figures out how to position themselves on the track to shave tenths of seconds off their times.|
Each of these situations is one in which best practices guided performance improvement. We can think of best practices as continuous learning tools, in which we learn from what we do best–and learn to do it more often.
What are your best practices as a trader?
Do you even know?
Incredibly, many traders keep journals and records, but never think to isolate their best practices. They simply don’t know what they do well and why it works for them. As a result, they can’t learn from their successes.
Here are a few best practices I have identified in my own trading:
1) I enter a position with a relatively small portion of capital and add to the position within a short amount of time if market conditions continue to look favorable. My research of my own trades told me that many of my losing trades started out in the first few minutes as losers. The winners were either winners in the first few minutes or hovered near breakeven. With a small initial position, I get smoked when I’m small and ride winners with larger positions.
2) I religiously follow what large traders and the majority of stocks are doing. Consistently, my winning trades go with the flow when large traders are dominantly hitting bids or lifting offers. My winning trades also ride the tendency of the broad market to trade at the bid price vs. offer (as measured by the NYSE TICK).
3) I put a trailing stop on my intraday P/L. Quite simply, if I’m up a decent amount of money on the day, I’ll only allow myself to lose a portion of it before I stop trading for the day. A winning day thus never turns into a loser, but I can continue to selectively pursue opportunity.
4) I trade the most volatile instruments that are highly correlated to my main market, the ES futures. So, for instance, I’ll add a unit of capital to the Russell 2000 futures rather than double up in the Spooz. My research found that what I was trading was adding as much to profitability as how I was trading (specific setups).
5) I trade almost exclusively during morning hours, when I have my best feel for the market. My research has found my afternoon trading to be, on the whole, no better than breakeven. All my profits have come from morning trades.
Obviously, your best practices will be different from mine. The idea is not to mimic someone else, but to be more of who you already are when you’re at your best.
In psychotherapy, there are practitioners from different theoretical perspectives. Some adopt a cognitive framework; others are psychoanalytic. We have solution-focused therapists, behaviorists, interpersonal psychologists, and many, many more. Each theory offers an explanation for why a person might be having problems. Each theory also offers a set of procedures that are designed to change these problems.
Not surprisingly, a great deal of research has been devoted to the question of which approaches are most effective. It turns out that all of them are more effective than no therapy at all, but none of them are consistently better than any of the others across various people and problems. One researcher anointed this the “Dodo Bird” finding, naming it after the character in Alice in Wonderland who declares, “All have won, and all shall have prizes!”
The reality behind the Dodo finding is that all the approaches work because of their common ingredients, not because of the specifics of their theories. In other words, it’s what behaviorists, analysts, cognitive therapists, etc. are doing similarly–not what makes them unique–that accounts for psychological change.
Trading, interestingly, is in a similar situation. There are many different trading methods, ranging from chart and indicator reading to assessment of stock and market fundamentals. Each practitioner is convinced that his or her methods are responsible for success. Yet, when we look across traders, we find that successful traders employ many different methods. As with therapies, no one method seems to have a lock on truth.
The notion of transtheoretical trading is that perhaps all successful traders share common ingredients that account for their success. These common factors are independent of the explicit rationales that they employ in taking and managing trades. In other words, it’s not really about the Fib numbers, the chart patterns, fundamental data, statistical analyses, or oscillator readings. Rather, it’s what all good traders do that makes them successful.
What might these successful ingredients be?
1) Generating favorable risk/reward with price targets and stops;
2) Proper allocation of assets to trades (position sizing) to avoid large drawdowns and risk of ruin;
3) Diversification of capital to spread risk and reduce fluctuations in P/L;
4) Superior reading of supply/demand patterns (regimes; see previous entry) to enter trades at prices that incur little heat during the trade;
5) Superior reading of supply/demand patterns to exit trades at prices that take meaningful pieces out of market moves;
6) Trading with the market’s directional (or non-directional) bias;
7) Holding winning trades longer than losers to create the favorable risk/reward balance;
Reducing the frequency of trading during periods of low opportunity (low volatility);
9) Quickly recognizing shifts in market regimes (trend changes; volatility changes) and changing trading patterns based on this recognition;
10) Consistency: trading similar markets similarly.
Much of trader education focuses on setups specific to a trading method and not to these basic, core skills. Doing things right in trading means doing the right things–and these are the things all good traders do.
One thing I’m working on is a transtheoretical perspective on trading. What I mean by that is a description of what good traders do regardless of their specific methods. The transtheoretical notion suggests that traders are basically doing the same thing, whether they’re looking at statistical patterns, chart patterns, oscillator patterns, wave patterns, or Fib patterns.
My account begins with the idea of the regime. A regime is a set of rules that fit the market’s performance over a recent period of time. An active daytrader may define a regime in terms of minutes; a longer-term trader will focus on a series of days. The regime might be thought of as the rules that the market is playing by over that period. One simple description of a regime is that the market is trading in a 90-day cycle.
By its very nature, a regime is a curve-fit description of the past. Regimes come and go across various timeframes: this provides the market with its complexity. A good example is a trendline. It is a description of the market’s past action, but eventually it will be violated–until a new regime can be defined.
This brings us to two basic modes of trading: 1) regime-following and 2) regime-changing. A regime-following trade assumes that, as long as the participants in the marketplace remain relatively constant and fundamental, macro influences do not significantly change, the regime that is in place will persist. Thus, we can figure out the rules that the market has been playing with over the past X bars and use that information to anticipate the next bars. Trend following is a common example of this kind of regime trading.
Regime changing trades attempt to anticipate shifts in regimes. A market has been trading one set of rules, but because of shifts among market participants and/or macro-economic factors, is about to shift its regime. During such shifts, there will be many traders who are late to recognize the change and who will behave emotionally when the rules change. The trader who is trading regime change hopes to profit from this behavior. A good example of regime-changing trading is breakout trades.
From this perspective, chart trading, statistical trading, indicator trading–all are different ways of framing regimes and their changes. We can think of these trading approaches as different languages that traders speak. Truth lies, not in the language, but in the thoughts that are spoken. But the visual language of charts may speak to some traders better than the analytical language of statistics. What makes the trader successful is the performance skill of being able to recognize regimes and their shifts–not the charts or oscillators themselves.
This has important implications for training and trader education, which I will be exploring in future posts.
Performance pattern I’m looking at this week:
Over the last three weeks, selling a 60 minute high in the S&P 500 Index (SPY) and holding for 40 minutes has led to 13 winning trades in 16 occurrences. The average win has been $.29 (2.90 ES points), and the average loss has been -$.49 (4.90 ES points). The net profit has been $2.37 (23.70 ES points).
Also over the last three weeks, buying a 60 minute low in SPY and holding for 45 minutes has led to 10 winning trades in 15 occurrences. The average win has been $.31 (3.10 ES points), and the average loss has been -$.14 (1.40 ES points). The net profit has been $2.41 (24.10 ES points).
The pattern shows, on an intraday basis, how we are tending to revert to the mean rather than trend when the market makes a new high or low.
Hats off to Trade Ideas and the Odds Maker module for scoping out this pattern.
In the wake of my recent blog posts on addictive trading, comes this eloquent blog site from England. Rather than read what I have to say, I encourage you to read each of his posts from the last several days. This is a much more common phenomenon than those in the trading industry acknowledge. The blogger is to be commended for his honesty and courage in posting what so many others go through.
I would estimate that, since moving to Chicago in 2004, 80-90% of traders I’ve known at various firms are no longer in the trading business. Think about what that means in terms of lost money, accumulated debt, dashed dreams, and disappointed family members. Why do no trading coaches talk about those personal tragedies and losses? Why don’t the stories of the vast majority of traders ever grace the pages of trading magazines and trading books?
When I wrote my new book on Trader Performance, I wanted to find a piece of music that captured my experience with traders in Chicago. The piece that fit best for me was Neil Young’s classic “Needle and the Damage Done”. When you read the blog from England, you’ll know why.
As readers know, I like to track the number of stocks making new highs or lows when the broad market averages make fresh highs or lows. The degree of participation in market moves has been a very useful gauge of the likelihood that those moves will persist, as my recent research has highlighted.
I’ve been asked how to track intraday new highs and lows in a way that does not distract a trader from following price and volume action. The way I do this is to create a basket of 17 stocks that are drawn from the consumer, cyclical, financial, and technology sectors and that, as a group, nicely mirror the price action of the S&P 500 Index. I’ve consistently found that if half or more of the stocks in my basket are making new highs or lows when the average is making a new extreme, the likelihood of continuation is enhanced.
To track these, I use the Trade Ideas scanning program. It enables me to create my own watchlist of stocks, and then I set the alerts for 5 minute new highs *and* 5 minute new lows. Voila! Alerts pop up in a corner of my screen whenever one of my stocks makes a five-minute new high or low.
Here’s the basket of stocks: AIG, C, CSCO, FDX, GE, GM, IBM, INTC, IP, JNJ, KO, MER, MMM, MSFT, PFE, WMT, XOM.
No grand secret to the basket; I’m sure other highly weighted stocks across sectors would do as well. And, of course, if you’re trading a different index, such as QQQQ, you’d want to create a basket of representative NASDAQ 100 stocks.
During the day, I measure the number of new 5 minute highs reported by Trade Ideas during the day minus the number of new lows. That turns out to be an effective trend gauge. I’m reluctant to short markets that consistently are making more new highs than lows and vice versa.
When markets have been rising or declining and I see a waning of new highs or lows, I’m likely to take profits on the move. Check out today’s Weblog entry for a nice example on an intraday basis.
One key to performance is extending your ability to process market information. By letting Trade Ideas scan the market for me, it frees me up to watch what the large traders are doing and how order flow is evolving. In the past, I’ve used Excel sheets dynamically linked to my real time feed to obtain the new highs/lows data. This works, but I find the screening program less cumbersome and easier to read.
I’ve been taking a look at David Aronson’s excellent book Evidence-Based Technical Analysis in recent posts to TraderFeed. Here I want to ask a different question: Is there evidence for evidence-based methods?
It sounds like a whimsical question, but it’s at the heart of a major debate in applied psychology. Following medicine, psychology has adopted an evidence-based approach, investigating which specific approaches to therapy (cognitive, psychoanalytic, behavioral, etc.) yield superior results in controlled outcome studies. The idea is that practice would become more objective, as we learn which approaches are superior for particular presenting problems.
Well, here’s the rub. It turns out that all the major therapeutic approaches work significantly better than placebo treatments or no treatment, but no single approach works significantly better than other approaches. Moreover, it seems as though factors common to all the therapies–such as the quality of the helping relationship and the motivation of the client–account for more outcome variance than the specifics of the various treatment approaches. (See Bruce Wampold’s research summary, “The Great Psychotherapy Debate”, for a thorough presentation of these findings).
What if the same is true for technical analysis? What if the factors that distinguish successful from unsuccessful traders have little to do with the specifics of their methods (i.e., their chart patterns, tracking of Elliott Waves, reading of oscillators, etc.) and more to do with their implicit knowledge/skill related to factors common to all technical methods: distinguishing trending and bracketing markets, reading shifts in momentum, managing position size and risk, etc.?
Perhaps a more scientific approach to trading could emerge, not from various schools of technical analysis, but from relatively approach-neutral accounts of what successful traders actually do in practice. If, indeed, successful traders succeed because they are able to process complex patterns regarding shifts in supply/demand, the different technical approaches might be thought of as like the different schools of psychotherapy: Ways of describing, organizing, and communicating what practitioners do. That is valuable from a descriptive vantage point, but not from an explanatory one.
Success, in trading as in therapy, resides in the development of skill, not the mechanistic application of particular approaches.
My recent TraderFeed post examined lapses in trader discipline and suggested that these most often occur when traders are attempting to trade in a style that does not fit with their personality traits. A simple example of this occurred when I tried to trade on a full time basis. I became so engrossed in emailing and IMing other traders that I missed many nice setups. The problem wasn’t that I lack discipline as a human being. Rather, I was ignoring basic (social) personality needs by ignoring the very personality factors that brought me to psychology in the first place. A trading approach may be sound on paper, but if it involves levels of risk and activity that are difficult for a trader to tolerate, it will not yield profits in actual trading.
The other factor that plays into discipline lapses is our cognitive skill set. Very often traders attempt to trade in a style that simply does not play into their cognitive strengths. Highly analytical individuals will not, on average, make good intuitive scalpers. The person who can rapidly process incoming information and make decisions on the fly is not utilizing a core strength in trading longer-term chart patterns. The trader who is good at researching and developing trading systems has different skills from the trader who is good on the floor of the exchange making markets.
One of the points I make in the new book is that whatever core skills might bring you success in trading are ones that you are already utilizing in other areas of your life. Skilled quant traders invariably display mathematical competence and interests prior to their market involvements. Very often, successful scalpers found success in such pursuits as video gaming and poker prior to entering the markets. Such traders find a trading niche by applying their talents to an area of opportunity afforded by markets.
The implications of this for performance are profound. If you have never been interested in math or good at math, you’ll probably not make a successful quantitative trader. If you’ve never been drawn to situations in which you must make rapid decisions under pressure, you probably won’t succeed as a daytrader.
Motivation to succeed is not enough. Skills, talents, and interests all must come together and align with opportunity. Success never emerges on its own. It always builds on prior success.
I have found it helpful to my trading performance to follow a set routine to prepare for the market day. A typical day’s routine looks like the following:
1) Night before the trading day – I’m preparing my Trading Psychology Weblog and tracking stats on sentiment, momentum, trend, and volatility. This is also when I run my research on historical trading patterns. I’m looking to see if the market is showing extremes in sentiment, momentum, or volatility at any time frame and, if so, I examine what has happened recently as a result of such extremes. I’m also looking qualitatively to see if the market is picking up or losing strength (new highs/lows) and momentum (Demand/Supply). This information provides me with a context for the coming trading day and–sometimes–a directional leaning.
2) Early morning before the open – I check to see how we’ve traded in Asia and Europe, with a particular note to see if those averages have traded with a volatile range or not and whether those indices hit new highs/lows, moved outside of trading ranges, etc. I then quickly check the overnight US Globex market, also to see if we’re trading within the prior day’s range (and value area) or if we’ve developed a directional move due to overnight influences. This is also when I check the news online and glance at the headlines in the Wall St. Journal and the Financial Times, my two daily reads.
3) Break for exercise – I’m convinced that my energy level and level of mental clarity is an important factor in my trading success. Exercise includes stretching, basic calisthenics, and either a jog or a session with the weights. I am much more likely to push myself mentally if I’m pushing myself physically.
4) Back to the screen before the open – I prepare my TraderFeed blog entry, often on a topic that is relevant to that day’s trade or to my own trading. It’s a way of making myself sort out my thoughts–much as a journal might. I also update European trade and the US preopening market and check to see which economic reports are due out when and what the expectations are. I find Briefing.com helpful for this.
5) Time to watch the market – I typically follow the market closely (but not tick for tick) from about 7:30 CT through the open, but I typically don’t trade. I’m tracking the DAX and S&P, NASDAQ, and Russell via e-Signal and watching the ES futures on Market Delta. By this time, I’ve formed a pretty good preliminary idea for whether we’re trading in a range, whether we’re likely to see volatile trade or not, etc. This is my time to adjust expectations and entertain hypotheses. For example, I’ll integrate my current observations with my historical research and say to myself, “Hmmm. if we can hold above the lows from 2-3 AM CT when the DAX was weak, we’ve got a good chance to break above yesterday’s highs.” I also see if any economic reports move us outside the overnight or previous day’s range. This may greatly impact my hypotheses, as the market adjusts to new information and participants.
6) Trading – I watch every tick in the market and especially track the presence or absence of large traders. If I’m leaning long, I wait for selling to hit the market and show me that it cannot push the market below a key reference point. If I’m leaning short, I wait for the reverse. Very often my trades will be framed as breakouts or tests of ranges either from the overnight range or from the previous day’s range. My entries will often occur when I detect transitional structures in the market that reveal waning buying or selling. Because I’m picky, I may not trade if the day is slow and I may only trade once or twice if my setups aren’t there. Conversely, if I see that my hypotheses and research are panning out, I may double up and trade large, generally by diversifying my position to include Russells along with Spooz. (Once in a great while, in a distinctively strong or weak TICK environment, I may trade Russells against Spooz, with Russells trading in the direction of the TICK). Often, my early trades are short-term: about 20 min average duration. I find that even losing early trades provide me with solid market data; I only exit these if the market disproves my basic hypothesis. Most of my longer-term trades typically play for a break of a specified level and either involve holding to the last hour of the day or to the next day’s AM.
7) Review – This is actually intertwined with trading, as I will review a losing trade before placing a new one. The focus of the review is twofold: a) what has the trade told me about the market; b) what do I need to work on to improve how I’m trading. Not infrequently, my end of day review will finish with my setting a goal for the next day’s trade. I always review my daily and weekly P/L and the growth of my account. To the penny. Every day.
Well, there it is in a nutshell. The important elements for my performance are: 1) research; 2) framing meaningful hypotheses about ranges, breakouts, and moves to and away from value areas; 3) tracking real-time ebbs and flows in buying and selling; 4) targeting edges of ranges or important price levels as profit targets; 5) exiting losing trades only when the underlying idea behind the trade is proven wrong; and 6) learning, learning, learning from what I do right and wrong. The routine is designed to make trading an ongoing process of development. P/L is simply a way of keeping score.
Your routines will differ, depending on your trading style and markets. I suspect, however, that creating a structure for your trading will pay off for you in performance development.
Research suggests that, in most performance fields, mentorship plays an important role in the development of expertise. One important role of mentors is to provide students with new ways of approaching a field. In my latest post, I suggest that a trader’s development is a function of expanding cognitive complexity. Much of this growth can be facilitated when a mentor shakes up a student’s world-views and models alternative ways of approaching performance. When I have worked with traders, it has been most helpful to get them to talk aloud about what they see in the markets, the ideas behind their trades, and their rationales for managing those trades. Such talk-aloud samples reveal the underlying cognitive processes of the trader and allow a mentor to spur growth by introducing fresh observations and ideas. Trading “coaches” spend considerable time expounding upon emotional self-management, but the mentor’s most important role is facilitating new ways of approaching the performance field. Think of the teachers of concert musicians, the coaches of basketball players, and accomplished movie directors: all have content expertise in their fields and directly help students reach higher levels of performance by tackling their work in new ways.
This is perhaps the greatest obstacle to success faced by the independent trader. Trading individually, the trader is isolated from the kind of feedback that could spur cognitive development. Online trading rooms, such as those coordinated by Woodie, offer one source of external development. Structured training programs at trading firms, supervised by dedicated mentors, offer yet another path. My expectation is that, in the near future, we will see virtual trading groups in which serious independent traders join online in real time to process market data, exchange ideas, and engage in mutual mentorship. Imagine 5-10 experienced traders, each engaged in original research and analysis, sharing information, trade ideas, and results each day, with dedicated review of results each day and goal setting. How quickly might that accelerate the learning curve, conferring benefits for all?
My recent TraderFeed post tackles the S&P 500 Index and what appear to be orthogonal (independent) components of its performance: the day session and the evening/pre-opening session. The Index exhibits very different and uncorrelated price behavior at these two time periods. Further research, which will appear on the blog shortly, establishes that these differences affect traditional technical indicators. When, for instance, traders track the advance/decline line or the number of stocks up or down on the day, they are combining the impact of the opening price gap (close to open) and the actual day session (open to close). It turns out that the behavior of advances/declines is quite different–and also independent–for those time periods. I would expect that other market indicators, such as TRIN and most oscillators, would suffer from the same shortcoming.
This raises a very interesting performance issue: Might it be possible to construct new technical indicators that simply track open to close performance? This would more accurately reflect the environment faced by the daytrader and might capture patterns not otherwise noticeable in the data. One of my upcoming projects is to study the Daytrading Index from my TraderFeed post and see if it displays historical trading patterns that would provide a daytrading edge.
Of course, the second performance implication of this is that it is the night session that displays the most consistent trending behavior. We think of daytrading as a strategy to minimize the risk of holding positions overnight, but a strong case could be made for the fact that this also minimizes opportunity. Particularly for long positions, it makes sense to consider holding setups into the next open rather than closing them at the end of the day–especially if historical patterns can maximize the odds of predicting when those overnight periods will be up.
I just finished reviewing the book Markets in Profile, a long-awaited follow-up to the very successful and enlightening Mind Over Markets. The new book reflects author Jim Dalton’s many years of applying Market Profile principles to a variety of markets and timeframes. What I like most about it is that it offers a framework for thinking about market behavior and making sense out of the patterns that we observe each day.
Early on in their education, medical students and psychologists learn diagnostic systems for classifying various disorders. Without such systems, health care professionals would be faced with a bewildering array of symptoms and complaints. By clustering these together in a fashion that reflects their underlying pathology, doctors can rapidly make sense out of presenting problems and triage effectively.
It is much the same for traders. Markets consist of a bewildering array of movements and chart formations. Auction theory provides a conceptual framework for thinking about what buyers and sellers are doing and what their actions mean. By organizing market data into meaningful categories such as “value”, “attempted direction”, and “excess”, auction theory enables traders to diagnose market action as it’s occurring.
There’s a very nice statement in Chapter Seven of Markets in Profile: “. perhaps the most important skill you must master to become a successful trader is the ability to distinguish ‘price’ from ‘value.'” Value, in auction terms, is the range of prices at which most transactions have occurred. Price is either within that value range or outside it. Once we depart from value, one of two possibilities present themselves: the auction will either fail to facilitate trade at the divergent prices and the market will revert to its value area or the auction will generate acceptance of a new definition of value and we’ll get a trending movement.
That is the question you need to answer to be a successful trader: When is a market that is away from value failing to facilitate trade and when is it facilitating trade? Or, stated otherwise: When are we in a bracketing, range bound market and when are we in a trending one?
There are very few sources of guidance for this crucial question. Markets in Profile, I believe, is one of them. The book is scheduled for February publication.
Last week I asked the question of whether trade setups could be combined with a reading of order flow to produce successful trades. On Friday, the combination worked out well.
Here’s the pattern from Odds Maker. It told us to fade upside breakouts of the opening 30-minute range in SPY and hold the trade for 30 minutes. As you can see at the bottom of the screen shot, trading that setup over the last 3 weeks produced 8 winners in 14 trades, with the average win size more than twice the average size of the losing trades. My expectation is that, by tracking volume flows after the trade signal is given, I can improve the batting average of the setup by simply eliminating occasions in which breakout moves attract increased buying volume from institutions.
Here’s my Market Delta screen that I used to track volume flows once we had the upside breakout on Friday:
You can see that buying dried up after the attempted breakout, with fewer contracts purchased at the offer at 1336 and higher. Holding the trade for 30 minutes provided a full point of profit with minimal heat. Holding the trade to the midpoint of the AM trading range (a different pattern that I’ve researched) provided even more profit.
By trading tested patterns and only entering them when current market conditions are favorable, I believe traders can meaningfully improve their ratio of winning trades to losing trades; eliminate many bad trades; and trade with greater confidence.
Can tested trading setups be combined with a reading of market order flow to produce successful intraday trades? One of the nice things about extending the time frame of my personal portfolio is that enables me to research questions such as these.
The experiment I propose is to combine a reading of order flow with the new Market Delta program (see my recent TraderFeed post and updates for examples) with a tested trading setup from the Odds Maker program offered by Trade Ideas.
Here are the basics behind the trade setups I will track initially:
|I will follow only equity index ETFs at first, with an emphasis on large caps (SPY) and midcaps (MDY);|
|All trades last 30 minutes in duration, unless order flow considerations take me out on a discretionary basis. No stops; no holds overnight.|
|The setup requires a several step process: 1) an upthrust or downthrust with significant momentum; 2) a pullback from that initial thrust that displays less momentum; 3) an entry in the direction of the initial thrust once the pullback has retraced much–but not all–of the impulse move.|
For the past three weeks, this setup has provided 25 sell signals (16 profitable; average win = $.35, average loss = -$.10; net profit $478 per 100 shares traded). On the long side, we had 26 entries (17 profitable; average win = $.34, average loss = -$.21; net profit $387 per 100 shares traded).
The largest loss on the short side was -$.24; on the long side it was -.$48. The largest win on the short side was $1.30; on the long side it was $1.17.
While these performance stats look good, let me offer some caveats:
|Until we follow the setups over changing market conditions, not just a three-week window, we won’t truly know how robust they are. A fully developed trading system would have to test the patterns over longer time periods (to cover a full range of market conditions) and would need to test the patterns over separate, non-overlapping time periods to ensure their stability;|
|A large portion of the total profits came from a relatively small number of large winning trades. Filtering signals could degrade as well as enhance performance;|
|There are many occasions in which the market gives no signals whatsoever. Very strong trending markets don’t provide enough of a pullback from momentum moves. Very slow, range bound markets don’t provide enough momentum to qualify as upthrust or downthrust;|
|Slippage and commissions are not included in results.|
That having been said, I have tracked the setups over the past week and have found no performance decline. Moreover, I’ve tested the setups on several baskets of stocks, including the Dow Jones Industrial Average, NASDAQ 100 issues, and the basket of large caps that I use to mimic the S&P 500 Index, and all have tested out quite profitably, in line with the results above.
In sum, I believe that the setups provide reasonable trade hypotheses that we can then confirm by tracking order flow and market participation. I will post morning signals for SPY and MDY starting this coming week and see how we might blend system trading with discretionary judgment.
I’ve begun a program of research to catch market moves of intermediate-term duration (weeks) and hope to have a very basic trading model ready after the holiday period. The idea is to allocate capital across a variety of time frames and trading instruments, with historical odds guiding each of the trading decisions. To the extent that the methods and instruments are non-correlated, the portfolio should help to spread risk by diversifying capital across a variety of opportunities.
Part of my interest in pursuing this evolution of my trading, however, is psychological. Very little has been written or studied regarding the process by which traders re-learn markets. How do we undo years of looking at markets one way, acting on one set of risk management criteria, and implement with consistency a wholly new method in entirely different markets?
My experience with traders is that they are generally reluctant to make such changes, even in the face of evidence that their current methods or markets are not working for them. It seems as though it’s harder to learn new approaches than to initially learn a particular approach. No doubt, that’s because the former involves un-learning and re-learning. It takes considerable effort to reprogram our instincts and behavior patterns.
What learning procedures most effectively facilitate re-learning? Can un-learning and re-learning be accelerated through psychological methods? Is it even possible to reprogram performance skills learned over a period of years? By serving as my own student/client, I hope to generate a few preliminary answers to these questions. I’ll report on my progress here, as the project progresses.
Blogging has become an important source of publishing, owing to its timeliness and ready availability. Print publications take hours or days for assembly and distribution; blogging can post information on the fly. As such, blogging is ideal for traders who follow the markets closely, as blog posts can be read within seconds of market-moving events.
How popular is blogging? The search engine Technorati tracks 52 million blogs worldwide. A total of 349 blogs followed by Technorati relate to “trading”; 78 specifically to “day trading” and 110 to “futures”. If it took you only a minute to read each of the blogs devoted to “stocks”, you’d be tied up for about 8 hours. That’s one way to avoid daytrading!
Clearly there are more blogs out there than we can hope to follow regularly. Accordingly, I thought I’d pass along a few of the ones that I have found to be particularly worthwhile. These sites offer four basic features:
1) Frequently updated content–at least daily;
2) Unique content–fresh perspectives on markets, the economy, stocks, trading, etc.
3) Knowledegable content–information based on clear reasoning, research, and experience.
4) Participation in the blogger community – linking content to broader material in the blogosphere and on the Web for enhanced depth of coverage.
What do these criteria eliminate? They filter out blogs that are mainly personal trading journals, and avoid blogs that have a primarily commercial or promotional focus. This isn’t to say that these blogs are without value; it’s just that you’re less likely to find important strategic insights there. The blogs I find of greatest value fall into four categories: research blogs, global/macro blogs, trading blogs, and aggregator blogs. Let’s look at a few examples of each:
I started my TraderFeed blog because I detected a shortage of practical, trading-oriented, original research on the Web. The focus of TraderFeed is historical analysis: I look at what the market is doing at present (for instance, making a five-day high with volume highly concentrated in advancing stocks) and then go back into history to see what has happened in the past when the market has been at a similar juncture. Often, such analyses detect edges to the next several days’ trade that can be useful in formulating trade ideas. TraderFeed also offers morning updates of the S&P 500 index market, to see if the AM trade is following up on those historical tendencies.
A different kind of research blog is offered by CXO Advisory Group. There you’ll find research pertaining to everything from behavioral finance to seasonal calendar effects in trading to market models based on earnings yields and returns to value. The site links to primary research sources, but also reports original findings. A recent example of their work was a look at whether sentiment in the financial news predicts future stock returns.
Still another research-oriented blog is Abnormal Returns. This site offers a variety of links to primary sources of information and also reports on significant research in the trading world. For instance, their recent Research Roundup featured a number of links to sources of quantitative insight into the markets. There are several sources of blog links that I particularly trust and value; this is one of them.
Finally, a very interesting research blog is Ticker Sense, which is published by Birinyi Associates, Inc. A recent feature is a sentiment poll of market bloggers. I’m a participant, so there’s hope that the poll will be an excellent contrary indicator! In addition, TickerSense reports on economic events, sector development, and original studies of market performance.
This category includes blogs that cover a variety of asset class and trading markets worldwide. One of my favorites is The Big Picture, written by Barry Ritholtz. He covers everything from the economy to geopolitics to trading strategy. A recent focus has been the housing market and implications for the economy and stocks. His “Apprenticed Investor” series models how to think about markets, drawing upon his experience as a professional money manager.
Another very stimulating site is from Random Roger (aka Roger Nusbaum), who similarly brings a portfolio manager’s perspective to his blog. Roger covers housing, foreign markets, oil, interest rates–just about anything that affects markets. Recently, he has been posting on ETFs. He, too, models a way of thinking about markets and tries to educate readers even if they don’t agree with his conclusions.
Straddling the line between trading blog and global/macro blog is Tom Lydon’s ETF Trends site. He covers the broad universe of ETFs–an important and growing area of investment, given the movement of exchange traded funds into the precious metals, currency, interest rate, and energy markets. A recent post covered a housing ETF that allows traders to hedge the value of their homes. Recently, Tom has been adding guest contributors to the site, adding a variety of perspectives.
Another global/macro blogger with an interest in teaching traders how to think about markets is Bill Cara. Oil, mining, housing–Bill tracks all of it and closely follows the performance of market sectors. Also part of his site are longer educational articles on such topics as fixed income, technical and fundamental analysis, economics, gold, and international equities. He describes the site as “a virtual community of students-of-the-market united by their commitment to the pursuit and sharing of knowledge.”
These are the blogs that talk shop: traders talking to traders about stocks to buy/sell, what the market is doing, how to manage risk, etc. One of my daily reads is Charles Kirk’s blog, The Kirk Report, a site that goes the extra mile to dig up interesting links to news items, blog entries, and topics relevant to markets and the economy. His “Members” section posts lists of stocks matching screening criteria and offers detailed Q&A each month. A recent interview with Laszlo Birinyi was particularly memorable.
Another daily read is Mike Seneadza’s Trader Mike blog. He very effectively links to a variety of blogs and news sites, emphasizing trading-related topics. His annotated charts allow traders to see markets through his eyes and more than once have given me trade ideas. He summarizes major market trends in his market recaps and offers watchlists of stocks for daytrading based on his trading philosophy.
Adam Warner offers one of the few blogs for options traders, and it is regularly updated with a variety of perspectives on markets, trading, and sports. His perspectives on volatility are unique, and he writes in an engaging and thought-provoking manner. Recent topics have included put/call ratios, calendar spreads, and deep call buying.
Most blogs are written productions with a splash of graphics. Brian Shannon’s Alpha Trends blog, however, utilizes video to provide daily summaries of the technical conditions of equity markets. What I like about Alpha Trends is that it is a kind of teaching tool: Brian explains what he’s seeing in the markets, illustrates it in the videos, and explains what it means. He selects individual stocks for analysis, providing daily trade ideas, and covers multiple time frames in his analyses.
These are blogs of blogs: sites that summarize and organize a large amount of information across a variety of blogs. Perhaps the best known is Seeking Alpha, which organizes articles by topic, including different international markets (China, Japan, India) and sectors (telecom, biotech, retail). Other categories include long ideas, short ideas, and market overviews. It’s an excellent way to stay on top of the online financial press.
The Stockblogs site focuses exclusively on market-related blogs and categorizes these by content. These categories include general market, technical analysis, fundamental analysis, options, and commodities. It’s an excellent introduction to the market blogging world; the blog of the week feature is a good way to discover new blogs.
There are many more fine sites that are worthy of mention. These include the Trader X site, which does an unusually good job of outlining an interesting and successful trading approach. Billing itself as “views from a distorted mind”, it certainly meets with my approval. (I am listening to Ministry’s “Every Day is Halloween” as I type this–honest). MaoXian’s site offers excellent coverage of stocks, tracking issues making new highs, ETF performance, and interesting charts. The blog at the StockTickr site includes interviews with experienced traders and bloggers–a kind of online “Market Wizards” facilitated by Dave Mabe. The folks at Trade Ideas have created an informative blog that helps users get the most of their market screening software (keep an eye on that blog for a new development that will integrate screening and market research).
It is a testament to the free market of ideas that so many excellent sources of information have come to the fore in just the past couple of years. Bloggers receive comments on their work every day from readers, providing immediate feedback on what is helpful, what is not; what is needed and what is superfluous. As a result, the best blogs evolve over time, becoming ever better at meeting traders’ needs. Blogging is truly the Open Source of market information, the result of creativity and initiative unleashed by technology unimagined in decades past.
Starting this coming week, I will be posting modeling results to the Trading Psychology Weblog based upon the most promising Weblog measures. I call this the Micropsychology Modeler (see posting below), because it tracks variables directly related to the psychology of the market itself, including volatility, sentiment, and momentum. The purpose of these postings will be to establish whether or not there is a significant bullish or bearish edge to the market over a 1-5 day time horizon. The target market will be the S&P 500 Index. Given that many individual equities (and other equity indices) correlate quite well with the S&P, this information should be relevant to a variety of short-term traders.
With the publication of the Model results, the material offered in the Trading Psychology Weblog and in TraderFeed will be tightly integrated in an effort to help traders improve their performance. Here is how I suggest you utilize the information that will be provided in the two sites:
1) Trading Psychology Weblog – This will provide the big picture for short-term traders. There will be links to valuable information sources, an assessment of intermarket forces, and an ongoing evaluation of strength and weakness in the market based on the aggregated trading patterns of thousands of stocks. Many of the market metrics summarized in the Weblog are available nowhere else, to my knowledge. The Weblog will continue to offer an assessment of the market’s short- and intermediate-term trending, and it will conclude with an overarching trading plan for the coming day. In the section that has been labeled “Market Context” will appear the Micropsychology Modeling results for that day. At a glance, readers will be able to determine whether the models are bullish, bearish, or neutral at defined time frames. The Weblog will generally be posted by 10 PM CT, summarizing the past day for traders and orienting them to the coming day.
2) TraderFeed – This will continue as a blog site for original historical market research, with occasional articles relevant to an understanding of trading and markets. Most of the posted studies will focus on market variables widely available to the trading public (unlike the Trading Psychology Weblog, which is modeling with proprietary measures developed over many years) and will have some relevance to the upcoming trade. For instance, TraderFeed may look at what happens when we’re at a particular level of price change and VIX level and compare those historical expectations to what was discovered in the recent Micropsychology modeling. The TraderFeed posting will generally be on the site before 7:30 AM CT. The new feature of TraderFeed will be a second, mid-morning posting that tracks how the market is actually trading that day. A particular focus will be a tracking of large traders and how they are leaning. I find that this information is most often missing in traders’ awareness and is of greatest assistance to their performance. Knowing how large traders are trading will keep you out of bad trades and help you assess whether or not the historical modeling findings are likely to pan out in the day session.
As a result, traders can expect three pieces of communication to help them with their trading:
1) An evening market summary from the Trading Psychology Weblog providing the big picture;
2) Premarket research from TraderFeed offering an additional assessment of historical edge;
3) Midmorning update from TraderFeed providing the immediate, intraday trading picture for very short-term traders.
If you think this information would be of interest to you, I suggest that you add TraderFeed to your feed list, so that updates will come to you automatically. You can subscribe to Bloglines and add TraderFeed to your list that way, or you can simply click on the RSS feed on the TraderFeed site and add the blog via FeedBurner. Of course, subscription is free and does not expose you to spam or other commercial solicitation.
You might also want to bookmark the Weblog.
To the best of my knowledge, the integration of the Weblog and TraderFeed will provide the first real-time, quantitatively driven trading guidance widely available to traders. The information on how large traders are trading and the Micropsychology modeling results will be data that promise to supplement your existing trading/analytic methods.
I encourage you to take a look at the information and not be too much in a rush to act upon it. See what makes sense to you, and see what might fit with your existing trading strengths and methods. Then provide me with feedback about what you like and don’t like; what’s useful and what’s not. That will no doubt help me fine tune the offerings over time.
The idea is to improve trader performance, and my hope is that additional trading tools will aid in that goal. Consider the sites a source of decision support: data to be considered, but not blindly relied upon. I’d like to augment what you already do well, not turn you into a Dr. Brett clone!
Thanks for your continued support of the two sites. I look forward to hearing from you.
The Micropsychology Modeler is nearing the point where I can roll it out and provide historical models based on the most predictive Trading Psychology Weblog measures. The final inputs to the Modeler include price change, volatility, sentiment, and momentum. The recent breakthrough examines configurations among these four variables based on two separate time frames. The output covers a 1-5 day period and simply indicates whether or not there is a bullish, bearish, or non-existent edge in the market. I’m still working out the routine for selecting the optimal lookback period for modeling. The key is selecting a stationary period that incorporates enough instances for meaningful assessment.
As I mentioned in the last post, the output of the Modeler will simply indicate if there is a historical edge to the current market. It is then up to the trader to examine the trading day’s action as it is occurring to determine whether or not the market is following its historical norm or not. A market that has a historical edge but is not living up to that edge is providing important information. In that sense, I like to treat the model results merely as a set of normal expectations, not as a mechanical trading signal.
The recent TraderFeed articles on the NYSE TICK suggest one way of tracking ongoing market action to determine if bears or bulls have the upper hand. See also the posts on tracking large trader behavior. What I am advocating is neither mechanical trading of models nor discretionary tape reading, but rather an integration of the two, in which trades are taken in the direction of historical odds when the tape is also moving that way. So far this approach is serving me well since my return to regular trading.
A while ago, I developed something that I called the “Micropsychology Modeler”. It was a statistical routine that examined past markets similar to the current one across several different market psychology variables. These variables included price change, volatility, breadth, sentiment, etc.
I recently discovered that the Modeler’s ability to capture short-term historical patterns in the market is greatly aided by incorporating two different time frames into the modeling: one quite short-term (hours), the other longer-term (days). Moreover, the Modeler’s identification of historical trading edges also greatly benefits from using variables that are not commonly examined by traders. An example would be some of the measures I follow on the Weblog, such as the number of stocks trading above and below their Bollinger Bands (Demand/Supply).
The Modeler, so configured, provides three basic readings: bullish edge going forward, bearish edge going forward, no edge going forward. I think of this, not as a mechanical trading system, but simply as a statement of odds based on past market action. My goal as a short-term (intraday) trader is to determine whether or not the current market is following its historical pattern. In that sense, the output of the Modeler is not so much a prediction as an indicator. If I know that the odds of a rising market are quite good over the next two trading days and *then* I notice that sellers cannot push the market lower and large traders are beginning to lift offers in the ES, I will certainly press the long side.
What I like about the Modeler is that the variables it utilizes truly capture the psychology of the marketplace. I call it “Micropsychology”, because it is a reflection of very short-term patterns of sentiment and emotion.
Stay tuned. If the development of the new Micropsychology Modeler is as promising as it appears to be at present, I will begin presenting its outputs daily on the Weblog.
I’m pleased to report that my new book, Enhancing Trader Performance, is in its final phase of editing, with publication on track for this fall. Here is a summary of the book’s contents:
Chapter One: Where Expertise Begins – This chapter summarizes factors responsible for the development of expert performance across a variety of fields, with applications to trading.
Chapter Two: Finding Your Performance Niche as a Trader – Whether you succeed or fail is not just a function of your talents and skills, but finding the right fit between those and the kind of trading you undertake. This chapter helps traders find the right fit.
Chapter Three: Building Competence – Research tells us that the ways in which we structure the learning process is a key factor in producing competence, with major implications for traders. An important part of this success is a function of the mentorship process and how we mentor ourselves.
Chapter Four: Strategies for Cultivating Competence – Here we take a look at how traders can mentor themselves and structure their own learning for maximal performance. Included are specific resources that help traders go from the stage of advanced beginners to the stage where they can consistently cover the costs of trading.
Chapter Five: From Competence to Expertise – What makes experts different from competent performers? It’s not nature or nurture alone, research tells us, but a particular blending of the two that transforms performers by changing how they perceive and think. The training programs of elite performers in various fields can be directly applied to trading, though this is rarely undertaken.
Chapter Six: Mechanics, Tactics, Strategies – Here we look at the performance pyramid and how traders can systematically improve their game by working on skills pertaining to trading mechanics (how trades are executed), tactics (generation of trade ideas), and strategies (identification of ongoing edges in the marketplace).
Chapter Seven: Performance Dynamics – This covers a variety of tools and techniques that traders can use for themselves to monitor their trading performance, set goals for improvement, and make steady progress toward expertise. All of these techniques are utilized by professional traders and are readily available to individual traders.
Chapter Eight: Cognitive Techniques for Enhancing Performance – You didn’t think I was going to ignore traditional trading psychology altogether, did you? This chapter is a self-help manual for using cognitive techniques to change negative patterns of thinking and cultivate more constructive modes. All techniques are validated by extensive research and outlined step-by-step.
Chapter Nine: Behavioral Techniques for Enhancing Performance – Many times, traders experience disruptions of performance because of the impact of highly emotional episodes on subsequent trading. This idea is perhaps the most important in the entire book and is overlooked by practically every trading psychology text out there. This chapter provides a research-based, step-by-step self-help manual for dealing with those disruptions.
Afterword: The Making and Remaking of an Expert Trader – A real-life account of the successes and challenges faced by a successful professional trader pulls together the themes from the book.
I wanted to write a book that took trading psychology to a new place, with a practical, research-based focus on improving trader performance. Unconsciously, I think I also wrote a performance plan for my own trading!
A while back I posted on the topic of the “minimum data set” needed to make accurate trading decisions. My goal was to distill all the market information (but nothing more) that I need so that everything could fit onto a single computer screen.
Since returning to regular trading, I have found that maintaining a higher degree of concentration and focus with respect to fewer market variables is more effective than trying to keep up with multiple screens and a large number of indicators, indices, news items, etc.
I also find that, when I’ve done my homework prior to the market open, I know which variables are moving the market (e.g., interest rates, oil), which allows me to focus on only the most relevant markets while my market is trading.
Everyone trades differently, so what I look at may not be relevant to those who operate in other markets and timeframes. But here’s what I’ve found most helpful with respect to the intraday trading of the ES (S&P 500 Index) market:
|Historical Patterns – I conduct historical studies, such as those in TraderFeed to see if there is a bullish or bearish edge to the coming market day. These studies also include assessments of recent marketing trending and strength, as documented in the Weblog and studies of which markets or sectors are tending to lead my market.|
|Price Levels – There are certain ranges that short-term traders–especially those dealing in size–tend to look at. These include the range of the previous day, the overnight session, and the market’s opening range. Many times, I’m looking for a breakout or reversal near the edge of one of these ranges based on the patterns above.|
|Activity of the Market’s Largest Traders – I use the Market Delta program to track large trades coming into the market and whether or not they’re dominantly on the buy or sell side. I want to follow the activity of the market’s largest participants, per my recent post.|
|Participation – I am much more likely to fade market movement if relatively few individual stocks participate in that movement, and I’m more likely to go with a trend if there’s broad participation among equities. The NYSE TICK is my shortest-term measure of participation; I also track the number of issues/sectors making new highs/new lows over varying time frames.|
All that being said, I’m finding several aspects of execution to be as important to my success as what I’m looking at:
|Defining Firm Exit Points – Every trade has a target price based on price levels, historical patterns, and market volume/volatility. I hold the trade until the target is hit as long as large traders are going my way, and I exit after that target is hit as soon as large traders stop going my way.|
|Exiting Positions Quickly – I have been framing my trades by timeframe as well as by anticipated movement. In other words, I will use my historical studies to assess the odds that the market will reach a target price within a particular period of time. If time passes and we’re not getting the movement, I’m likely to exit the position and move on rather than wait for a stop loss point to get hit.|
|Only Trading When Volume is Above Average – Volume correlates very well with volatility. When volume dies out, I stop for the day. I don’t widen my timeframe. In practice, this leads me to trade active morning hours and then move on, which also keeps me from getting fatigued or bored.|
In practice, this has me trading a couple of high probability patterns each morning. Less is more, I’m finding, if that means you concentrate harder on the things that matter.
In a recent review of my book The Psychology of Trading, Dave Mabe made particular mention of my contention that emotions should not be eliminated or minimized from trading. Rather, we should develop enough self-awareness (and restraint) to become observers to our emotions and assess whether these feelings are accurate guides to action or exaggerated responses to transient market conditions.
I have personally worked with a number of traders who have made one million dollars or more (after expenses) in a year for multiple years. Of these, I would describe none as emotionally restrained. In fact, just the opposite. They tend to be highly competitive and quite emotional about losing. They channel this emotion into making themselves better, however.
I think you’d find the same to be true of elite athletes or chess champions. Michael Jordan, Muhammad Ali, Bobby Fischer–hardly non-emotional types.
Yes, we saw at the World Cup how emotions can get the better of us. The answer is not to eliminate emotion, however. It is to use emotion as information.
Let’s use fear as an example.
Last week I was long ES and had a three-tick profit on the trade. I was looking for two full points. A 2000 lot lifted the offer and, for a moment, the market just sat there. No one jumped on board when the big buyer entered the fray. I felt a brief stab of fear–the feeling of “this isn’t right!–and quickly took my three-tick profit. Almost immediately after, the trade was a full point under water.
Fear was my friend. It alerted me to danger. If aggressive buyers can’t get the market up in the short run, it isn’t going up. My feelings were accurate guides to market conditions.
But fear ultimately is a response to perceived danger. Sometimes we perceive threat when none is present. We can’t really know if our emotions are trustworthy unless we can gauge the threat that they detect. That’s where enhanced awareness–not just getting lost in emotion–is crucial.
To eliminate emotion–even if that were possible–would be like covering the warning light on our car dashboard so we don’t see it. Maybe the warning light is defective and there’s no engine problem. But maybe the engine needs to be looked at. The answer is not to eliminate our warning signals.
And that’s what fear is: a warning signal.
Frankly, IMHO, a lot more traders have perished from overconfidence than from fear. A healthy respect for market risk and those multi-sigma events that can move against you–a basic fear born of the recognition that the market is always bigger than you are–is a good thing. Eliminate that and you’ve got a recipe for the kinds of blowups that truly end trading careers.
Potpourri of performance topics today:
I met a very successful trader a while back; someone with superior research, long experience, and a track record of years of success. Between 50 and 60% of the trader’s trades made money and the size of the losing trades was about two-thirds the size of the winners. But the trader was consistent and compounded money year after year, sticking with carefully researched ideas. Perhaps most interestingly, the trader only traded about once a week. It’s been a while since I met with that trader (who, BTW, still is making money), but after hearing another breathless claim of 100%+ annual returns and huge winning percentages for a mechanical trading system, I had the thought: If a hugely successful trader with superior research can only succeed a little over half of the time and only finds superior opportunity once a week, what are the odds of sustaining success with these curve-fit systems?
Someone I respect told me that trading psychology was a dying field, as professional trading is increasingly conducted algorithmically by computers. I actually agree. The idea that sustaining market success is a function of objective talent and superior information/research is threatening to both traders and trading coaches who lack access to data. So they implicitly collude in the insistence that success is predominantly a function of mindframe, although that is not the case in any other human performance field.
My research finds the greatest edges in holding trades several days. My trading results show the greatest profits in trading short-term ES patterns in the morning. I find myself increasingly using explicit research for context and implicit knowing for execution. The best trades have been occurring when the implicit and explicit come together.
Exits are the hardest for me. I’m good about setting targets for trades based on the research, but gauging how long to wait to let a trade work itself out is still more art than science. I’m definitely at my least successful when I let pain take me out of trades. At those times, the risk of losing possible good trades exceeds the risk of being in a bad trade. I trade best when I’m loose enough to let possible good trades go and instead limit myself to great trades. I’m also at my best when I take what has been discretionary (exits) and make it more rule-governed. It’s so easy to get taken out by noise if those rules aren’t top-of-the-mind. There’s something to be said for augmenting the implicit with the explicit.
What you see is what you get. I’ve seen a lot more morning markets over the years than midday or afternoon markets because much of my trading has had to be part time in mornings, when I haven’t had other work obligations. As a result, I’ve built up a much larger set of pattern-recognition experience with morning markets than later ones. Lately I’ve been trading like a relief pitcher: I come in for a couple of innings (hours), make my good trade(s), and then leave the mound for the day. There was a funny experience on Friday afternoon, however, in which the late day trade picked up and became more morning-like. Almost immediately, I started picking up the patterns. The implicit learning seems to be specific to particular levels of volume/volatility. How many traders have I met who make money during the day but can’t keep it, precisely because they keep trading during market periods that no longer capture the patterns they’ve internalized?
There’s a long tradition in the philosophy of science that differentiates understanding from prediction. We can predict something without necessarily understanding why it occurs. Not surprisingly, market participants tend to be more interested in predicting than understanding. The result, however, can be predictions that don’t truly capture underlying market dynamics, as in the case of curve-fit trading systems and spurious correlations (Super Bowl indicators).
I’ve been using the framework of what might be called “qualitative cycles” to provide a conceptual framework for my research and trading. The term qualitative denotes that these cycles are not of a fixed frequency and vary considerably by amplitude. What makes them cycles is not their periodicity, but their underlying structure. Markets trend because there are periods of time in which the vast majority of issues are being accumulated or distributed by locals and institutional traders. They consolidate when there is a mixture of accumulation and distribution among the stocks and market participants.
The breadth momentum indicators that I post to the Weblog seem to capture the dynamics of these cycles quite well–most notably the Demand/Supply Index. (See also my TraderFeed post on the Cumulative Demand/Supply Index). A cycle begins when we have a rising number of stocks displaying significant upside momentum. This broad momentum crests and we enter a period of continued rising prices, but fewer issues with strong upside momentum. The market then enters a decline, with a growing number of stocks displaying significant downside momentum. This broad downside momentum bottoms out and we begin a period of continued lower prices, but fewer issues with strong downside momentum. At that point, the cycle repeats itself.
These dynamics appear to play themselves out on multiple time frames. That is the thrust of my current research. What is significant for trading performance is two things:
|I’ve found superior returns from aligning my positions with longer-term qualitative cycles and being willing to hold core positions over these cycles;|
|I’ve found it easier to stick with good trade ideas, riding out market noise, when I have a level of understanding as well as prediction.|
Knowing where we’re at in a market cycle (and where cycles are located with respect to each other) appears to be a valuable source of edge, as the TraderFeed post on the Cumulative Demand/Supply Index suggests.
It’s been interesting getting back into trading on a regular basis. Since 2004, I’ve been immersed in working with traders in Chicago and New York, on top of writing a new book. Those have left little time for daily trading. Recently, however, I’ve been in the trenches. Here’s what I’ve observed.
Trading the S&P 500 Index intraday is much harder IMO than a few years ago. There is much larger size in the order book, and large traders can move the market quite a few ticks at a time–even with relatively little volume actually trading at each price. These episodic, violent moves are interspersed with considerable backing and filling, creating a jerkiness that makes short-term trend-following exceedingly difficult.
I quickly found that placing stops near entry points resulted in a death by a thousand cuts. Quite simply, there is too much noise around any price point to be able to limit losses to a few ticks. Perhaps there are traders far more able to read the order book than I, but the rapid shifting of orders–with much pulling of bids and offers–makes it difficult to engage in top and bottom picking based on order flow.
My profitable trades have had several features in common that I am codifying into rules, per the previous posting:
|Fading extremes – When there have been sharp runs in the market (up or down) that fail to take out major price levels, we’ve seen quick runs for the exits among the bulls or bears, creating nice countertrend opportunities;|
|Alignment of time frames – I recently conducted a research project in which I studied the predictors and time frames that provided the best directional edges in the S&P market. Interestingly, I found that breadth-based strength indicators (such as the Demand/Supply followed on the Weblog) performed best, and that a several day time frame was ideal–not intraday. Accordingly, my best trades have come when I have first established an edge over a several day period and then followed intraday setups in the direction of the larger trend.|
|Utilizing structural relationships to define intraday setups – This will be the topic of my next post. I’m finding that the concept of a “market cycle”–differentiating segments of upmoves and downmoves based on indicator strength, volume relationships, relative activity of buyers/sellers, etc.–is extremely useful in framing entries and exits and determining when trade ideas are wrong. Here is a chart from a recent article that illustrates this “thinking in cycles”.|
Net net, what I’m finding is that my best trades are well planned and conceptually grounded. This provides a logical advantage, but also a psychological one, as it provides me with greater confidence to ride out the market noise. After I return from NY, I’ll be working on systematizing this to a greater degree.
I just finished reading Jeffrey Liker’s book “The Toyota Way”, which details the manufacturing processes and philosophy that have typified one of the world’s most successful business organizations. The book is especially insightful about lean processes and the values that support these.
A recurring theme in the book is that you cannot improve the quality of a process until you have standardized that process. That is, only once you’re doing something the same way with consistency can you accurately evaluate its outcomes. It’s really just a reframing of the need for experimental control in science.
That had me thinking, however. Do I really *know* which elements of my trading style and decision-making are responsible for profit and loss? If I view my trading as a manufacturing process in which I am using raw materials (information) to produce trade ideas (a product) to generate a return on capital above and beyond the riskless rate, then I need to standardize my process in order to adequately evaluate it, identify its shortcomings, and make progressive improvements.
Toward that end, I’ll be starting an experiment in which I write down my rationale for each trade (entries, money management, etc) and make note of the trade’s P/L, max. drawdown, etc. Over time, I will be able to conduct lexical searches of my entries and correlate the trade results with the various facets of the trade decisions. The idea, drawn from lean manufacturing, is to eliminate all elements of decision-making that do not add value to P/L.
I hope to report on this effort in the near future.
In my forthcoming book, Enhancing Trader Performance, I emphasize the concept of learning loops: monitoring your trading performance, identifying strengths and weaknesses, and using this information to make progressive improvements in your trading methods and in your understanding of markets. An excellent example occurred on Friday, when I took an unusual (for me) amount of heat on a trade that ultimately was profitable. Although we were making higher highs and higher lows in a choppy fashion, with strong bursts in the NYSE TICK and ES premium to cash, I could not shake the sense that the buying was not “real”. Sectors were moving out of sync with one another, and the upward bursts were on conspicuously low volume. This uneasy feeling led me to investigate the market further, which led me to the observation (noted in the Weblog) that Money Flow in SPY was negative even as price was moving higher. It thus appeared that buying in the futures was being accompanied by selling in the ETF, almost certainly as part of an arb trade. The buying didn’t seem real, because it was in fact not part of a directional trade.
This, in turn, has led me through the weekend to work on direct measures of arb activity in the S&P, with separate measures for arb buying and arb selling. To my surprise, the daily figures correlate at a near zero level. There are days of high arb buying and high arb selling; there are days low in both; and there are days in which one dominates the other. What seems especially important is the relative balance between arb trading as a whole and directional (non arb) trading. Market rises that occur with a high proportion of arb trading are less likely to continue than market rises that are accompanied by low arb activity. Interestingly, when arb activity is skewed to the buy vs. sell side, this seems to affect short-term market forecasts.
The important thing is to view trading as your personal laboratory, in which you are continually working on yourself, and continually refining your understanding of markets. In the book, I talk about a Fundamental Law of Performance: in every performance field, expert performers spend more time preparing for performances and practicing performance than in live performance itself. When you think how much time Olympic athletes, performing artists, and chess grandmasters put into their preparation–and then look at how much time the average trader spends in learning loops–it is not surprising that so few traders reach elite levels of performance.
In my previous entry, I explained my planned return to active trading. In that post, I stressed the performance process I wish to undergo to refine my trading tactics and execution. In this post, I will explain my basic strategy.
Two concepts are central to the strategy: asset classes and trading instruments. Asset classes consist of clusters of individual, related trading instruments. Moreover, the relationship between asset classes and trading instruments exists at a variety of levels of abstraction.
Thus, for instance, we can consider consumer stocks to be an asset class. The trading instruments comprising this asset class might be the shares of the individual companies in the consumer sector, such as P&G, Coke, etc. At a broader level of abstraction, we can view American equities as an asset class, with sector ETFs as constituent trading instruments. Still broader, world equities might be our asset class, with the bourse indexes of individual countries (Dow Jones Industrial Average, FTSE 100, DAX, etc.) comprising the trading instruments.
Within the asset class of fixed income, trading instruments exist all along the yield curve for American government debt. We also have corporate, mortgage, and government debt as trading vehicles, At a wider level of abstraction, we can view the debt of various countries as trading instruments in a worldwide fixed income asset class.
Currencies, energy, agriculturals: all are asset classes consisting of individual trading instruments.
It turns out, in my research, that the relationship between the whole and the parts has an extremely important bearing on the price performance both of the broad class and of its trading instruments. Instruments within a given class are normally correlated with one another in their price performance. When this set of correlations varies significantly from historical norms: that is where inefficiencies–and distinct trading edges–can be found. My recent Weblog and TraderFeed postings on sector correlations illustrates this idea.
The key is figuring out:
1) How to condense a matrix of correlations among various instruments and time frames into a single composite statistic, equalizing weightings from the instruments and time frames;
2) When a market is significantly out of line with respect to its normal set of intercorrelations;
3) What time frames offer the best predictive horizons for situations in which intercorrelations vary from their norms;
4) Which asset classes offer the best profit potential when multiple classes vary from their norms at a given point in time;
5) How to allocate funds to these asset classes to create trades that are truly statistically independent and to maximize profit potential.
What I’m finding is that the number of asset classes that are out of line at any given time is itself a market indicator that has tremendous relevance for identifying shifts in regimes (e.g., changing market cycles). When we see intercorrelations among currencies, fixed income, commodities, and equities vary from their norms at the same time, *that* is when we’re likely to see broad shifts in capital flows and market themes.
With the rise of ETFs across asset classes, it is easier than ever for individual traders with access to capital to create their own hedge funds. That, in essence, is what I’ll be doing, focusing on part/whole relations across multiple asset classes and time frames.
I am planning a return to active, daily trading some time during the second half of the year. My goal is not just to get back into the markets, but to implement a performance plan and track my progress. My plan will have several components:
|A refined methodology for identifying market edges based upon multiple, independent historical analyses and a core strategy of grounding my market strategy in these analyses;|
|A set of tactical rules for implementing the market strategy, including diversification of capital to independent trading vehicles and ideas and allocation of capital based upon the identified edge in these trades;|
|An ongoing program of review and learning to master trade execution: entering and exiting at favorable prices, effectively scaling in and out of trades, etc.|
So few traders I encounter truly approach their trading with what psychologist Ellen Winner at Harvard (referring to experts in their fields) calls the “rage to master”. There is little or nothing that is systematic and programmatic in their efforts at self-development.
Also, few traders that I encounter differentiate between the development of strategy–finding a verifiable edge in the markets–and the tactical execution of that strategy, which requires an ability to read markets in real time. There are quant traders who are almost completely tone-deaf in reading markets, and there are discretionary traders who have a feel for real time action and order flow but have almost no analytical skill.
So many performance fields require an integration of explicit and implicit knowing. A fighter pilot needs to know his aircraft intimately, but must be capable of making sophisticated snap judgments during a dogfight. The poker champion needs to know the odds of various hands, but also possesses an uncanny ability to read opponents–and prevent them from reading him.
Can a performance program integrate the strategic/explicit with implicit tactical and execution skills? Think of how emergency room physicians are trained–and how they develop expertise–and the structure of a comprehensive performance plan becomes clearer. We learn by doing–and then by altering our doing based upon what we learn. I look forward to the challenge.
My article scheduled for tomorrow on the Trading Markets site offers a different way of looking at price action. Instead of analyzing the absolute magnitude of price change, we can look at it in relative terms. In that article, I define relative price change as the ratio between current price change and the average of the absolute values of price changes for the past 60 periods. Thus, if the average absolute price change has been .50%, a move of 1% would constitute a relative price change of 2. If the average absolute price change has been 1%, that same 1% move would now give us a relative price change value of 1.
The theory behind this is that people respond to their perceptions of events, not events in isolation. Perception places events in their contexts. Sixty miles per hour might feel slow in a sports car; quite fast on a motorcycle. Jumping in a pool after a cold shower feels very different than after a hot shower, even though the pool temperature is the same. A 1% move in a very volatile market may elicit little trader reaction; in a very narrow market, it creates a breakout and quite a stir.
I already utilize the relative concept with respect to volume. Instead of determining whether volume is light or heavy by looking at the absolute number of contracts traded, I create a ratio between the current volume and the average volume for that time of day. That ends up being a sensitive measure of market activity, and one that is superior for analytics. Relative price operates in much the same way.
If we’re looking at historical patterns a la TraderFeed, will we identify more valid edges by incorporating the relative concept throughout all market indicators that vary in mean and standard deviation over time? I think that’s a distinct possibility.
Potpourri of thoughts this week:
|I’m completing a review of the brief therapy literature for a future chapter of The Textbook of Psychiatry. It’s a lengthy process that involves reviewing research and writings in the field and drawing themes and conclusions from the large body of work. Suppose, however, that market outcomes–within a day and across days–are viewed as separate pieces of research that can be reviewed to gain understanding of trader and investor behavior. Might the structured process of academic literature reviews uncover investment themes if applied to markets? Might the statistical techniques of outcome studies and research reviews apply to reviews of market outcomes? What would a meta-analysis of market outcomes look like?|
|From my review, it is clear to me that my speculation in The Psychology of Trading was on the money: changes in patterns of thought and behavior require intensive and extended enactment of new patterns during altered states of experiencing. Most people can’t change their behavior patterns (or, more properly, can’t sustain their changes) because their change efforts are sporadic, brief, and conducted while they are in ordinary states of consciousness. Learning appears to be greatly enhanced when we are in heightened states of emotional experiencing. This works to our detriment in psychological trauma, but can work to our benefit if harnessed. My new book, Enhancing Trader Performance, will feature two chapters that illustrate how traders can coach themselves with cognitive and behavioral techniques. What’s clear from my review, however, is that change hinges upon how these techniques are used, not on the techniques themselves.|
|One finding that is striking from the research on expertise is that experts develop schemas (mental maps of their fields of expertise) that are moderately abstract sets of cognitive representations. These representations are flexible and change with experience, guiding complex behavior. Experts not only know more than non-experts; they organize their knowledge differently–in ways that facilitate action. Having a schema for business cycles and bull/bear cycles in markets is crucial for longer-term traders and investors. Such schemas, for me, draw upon intermarket relationships and the dynamics of sector participation in rises and declines. Early in a bull market, interest rates are falling or stable, stocks are rallying hard, and most sectors are participating in the rise. After we hit a point of maximum upside momentum in the market, we tend to see dollar weakness and commodity strength, followed by rising interest rates. During this phase, market indices continue to rise, but more selectively, with fewer stocks participating in the strength. That’s where we’re at now, in the current market. It is only when rates rise to the point of threatening the earnings of the bluest chips–which tend to be the last stocks to weaken as the bull market ages–that we enter a bear phase. It helps to have a big picture schema even if you’re a short-term trader. The big picture helps identify the sectors that will have the best short-term performance.|
The six most common mistakes made by traders:
1) They assume that market conditions will continue indefinitely – As a result, when markets undergo changes (increased penetration of electronic and automated trading; changes in volume and volatility; changes in trending), traders are unprepared and go through emotional turmoil.
2) They are improperly diversified – They take one position at a time, putting their trading eggs in very few baskets and creating outsized risk.
3) They follow a market, not the markets – When macro events impact interest rates, currencies, energy, and the like, they are unprepared for the impact on equities.
4) They fail to exploit edges in the marketplace in the name of risk management – The size of their trades don’t reflect their confidence in the trades and the degree of edge present in the trades.
5) They lack a demonstrated edge in the marketplace – They don’t have a structured method of trading or haven’t tested their methods out in historical trading. As a result, they can’t truly have confidence in their trading.
6) Their trades don’t test specific hypotheses about the market(s) – As a result, traders don’t clearly identify when their trades are wrong and stay in bad trades too long.
In short, traders tend to approach markets as amateurs, not professionals. During the past few years, I have worked with successful traders at several different trading firms. I have yet to see one who trades off chart patterns. I have yet to see one who trades off common oscillators and indicators. Elliott Wave? Fibonacci? Earnings data? Candlesticks? Trend Lines? Not a single successful professional I’ve known and worked with personally–a sample of dozens–have used these methods or any of the commonly touted methods of the popular trading literature.
After a while, I begin to think that is not a coincidence.
It was shortly after I had come to Kingstree Trading, LLC in Chicago, and I was working with firm owner Chuck McElveen to set up a training program for new traders. I rode the Pace bus each day to the Metra station, where an express train picked up Lisle passengers and deposited them in the Chicago Loop. Across from me on the Pace bus was a young man reading a book about trading. He explained to me that he had come to Chicago from Texas to pursue a trading career. I was struck by his maturity, despite his young years: He had not yet turned 20.
I didn’t tell him that I was setting up a training program, but instead let him do most the talking. His name was Derek Buending, and it was clear he did not have an easy life. His sister had died in a car accident, and the loss had taken a toll on the family. I was impressed that Derek talked much more about his concern for his parents than for himself. Here, I thought, is a good human being.
In time I told Derek who I was, and he eventually applied for and received a position in the first internship class at Kingstree. He worked diligently, maintaining a daily journal and meeting with me religiously to review his performance and improve his results. He understood risk management, and he especially understood that he had to earn the right to trade size. It wasn’t long after he graduated the training program that Derek earned that right.
Like many in his family, we encouraged Derek to pursue a college education, and he decided to return to Texas and go to school. He made it clear that he would return to Chicago to continue his trading. Unfortunately, Derek did not have the opportunity to complete his education or return to Chicago. He died following a tragic car crash.
Today his family held a memorial service for Derek in Naperville. My wife, Margie, and I were there, and Chuck came in from Chicago–a gesture that showed his respect for Derek and his concern for his employees. We met Derek’s mother Ani, who displayed bravery and heart in dealing with the loss of a second child.
Derek understood that there are several keys to developing trader success:
|Finding a market you love and that fascinates and challenges you;|
|Making use of mentorship wherever it can be found, from teachers, coaches, and senior traders;|
|Having the patience to allow yourself to develop on a learning curve, first by practice trading in simulation mode; then by trading small; and only later with size;|
|Tracking your results relentlessly to figure out what you’re doing right (so that you can do more of it) and what you need to improve;|
|Realizing that markets change and that we are all students of markets who need to learn and relearn patterns of supply and demand.|
Derek understood trader performance. I will never forget him, or his eager talk over the bus rides, recounting trades and what he did right and wrong. Long life is guaranteed to none of us. Let us all resolve to live life to its fullest, like Derek: as performers who make the most of who and what we are in our chosen pursuits.
I’m in the process of writing a chapter for the forthcoming edition of the Textbook of Psychiatry, a standard reference work for psychiatrists. My topic is “Brief Therapy”. I like writing such chapters, because they force me to review the recent research and integrate my ideas. Whenever I’ve wanted to think about a topic more deeply and really learn, I commit myself to writing on that topic.
The research I’m reviewing includes just about every major controlled outcome study of various therapies for different psychological problems. It’s a fascinating look at what works and what doesn’t work in the world of psychological intervention.
Here are just a few big conclusions:
|Talk therapies don’t work equally well for all problems. Success rates, for example, are much lower for depression than for anxiety–and not all forms of anxiety respond equally well to psychological help.|
|Relapse is a major problem. Talk therapies are much more effective in producing improvement than in sustaining such improvement. People who use the techniques to sustain changes as well as achieve them are significantly less likely to relapse.|
|Chronic (long-term) psychological problems benefit from short-term help much less than more recent, situational concerns.|
|Very active, structured techniques for changing patterns of thinking and behaving are more successful than less structured talk methods across a variety of problems.|
|Therapists that adhere to these structured methods are more successful in helping clients than therapists that veer from the methods.|
So what does this have to do with trading? A number of trading psychology gurus claim that they are not doing therapy, but in fact they are engaged in efforts to alter patterns of thinking, feeling, and behaving. The fact that their clients are traders and not individuals diagnosed with emotional disorders is irrelevant. The question is: What methods are most effective in changing thought, emotion, and behavior? Sadly, I’m not sure that most trading psychology types can even answer this question, because so few are versed in the research.
Because of this, I devoted the last two chapters of my new book to manuals that show readers how to use the techniques from the two most effective forms of therapy: cognitive restructuring methods and behavioral, exposure modalities. At an upcoming Chicago seminar in May, I will present on the topic of “Becoming Your Own Therapist” and will show participants which methods work and how to employ them. Keep an eye on the Weblog for details.
I continue to find an expanding array of Web resources for traders. The proliferation of portals, news sites, online communities, educational sites, mentoring services, screening tools, and charting services ensures that we have far more data and information at our fingertips than ever before. It is truly astounding for those of us old enough to remember pre-computer days, when the weekly newsletter was the usual mode of communicating with traders.
Data and information, however, are not knowledge. In many ways, we are drowning in opinions and data and not necessarily gaining understanding. Trading is a performance activity; the kind of information that is most relevant has a strong “how-to” component. That is what is missing in most blogs and educational websites. They offer perspectives, not instruction.
Expert reasoning is distinguished by thinking in principles. That is how expert physicians make diagnoses, and it’s how scientists make discoveries. Even in the arts, principles of composition and design guide the painter and author. A mere recounting of market data or tossing out of impressions–“this looks like it’s getting ready for a breakout”–is not principled reasoning. What aids trader performance is the opportunity to model the thinking in principles that distinguishes the competent performer from the novice.
Take Friday’s market. Several principles kicked in during the trading day. The first one for me was, “If the market makes a new high (or low) and a very small proportion of issues participate in the move, the likelihood of reversal is enhanced.” The second principle that kicked in for me early in the AM was, “A market that returns to a trading range after a false breakout is statistically likely to hit the midpoint of that range.” The third principle was, “A market is most likely to make a trending move when interest rates, the dollar, and/or oil are making breakout moves; i.e., when there are fundamental economic developments.” Finally, the fourth principle was, “When a market displays highly skewed breadth on a breakout move, the odds are good that the day’s price extreme will occur in the final hour.”
Notice that these principles are generalizations that link observations to actions. Based on those principles, you would have shorted the market on Friday morning and continued selling bounces during the early afternoon. Many of the principles that I employ in trading are statistical probabilities of hitting benchmark prices during the day: the average price of the previous day, the high/low of the overnight range; the high/low of the morning session, etc. The principles usually take the form, “If X happens, Y is likely to follow”, based on historical analysis.
I am reorganizing this site to more clearly emphasize principles and the kind of reasoning that I believe would be helpful to traders. Each weekday entry will begin with a collection of links to worthwhile information and resources. That will be followed by “context”–perspectives on the market that are part of the big picture. Finally, we’ll have the analysis of the market, emphasizing principles that guided trading for the previous day and that are worth considering for the coming day. My hope is that this will help bridge the gap between knowing and doing.
In his latest book, James Altucher presents compelling evidence for the underperformance of stocks that are added to the S&P 500 and outperformance of those that are deleted from the group. Indeed, it seems as though the more popular stocks are within indexes, the worse they trend–perhaps due to their prevalence in arb trading.
Below we can see a comparison of the unweighted S&P 500 Index and the standard, weighted version since July, 2005. Notice the recent outperformance of the unweighted version. The less a stock is weighted in the large cap universe, the better it seems to be performing. This also helps to explain why we’re seeing outperformance among small and midcap issues.
An interesting question becomes: Might stocks that are not major parts of arbed indices display more consistent historical trading patterns than stocks that are in the popular trading vehicles? This would be the case to the extent that program trading/arbitrage distorts stocks’ performance relative to historical norms.
If so, this might be a unique source of alpha: Trading baskets of stocks that consist of issues that display distinct historical trading patterns. Note that this is different from trading stocks that display superior trending qualities. What you’re looking for here are stocks that display unusually large edges as a function of historical pattern testing.
For example, one could create a standard set of historical screens (such as “What happens after two consecutive daily declines?”) and then examine the trading patterns that result from these. My intuition is that stocks less prominent in the indices would display more edge than those that are highly weighted. A worthy project.
The recent TraderFeed posts, as well as my latest article for Trading Markets, have found evidence for countertrend tendencies in the S&P 500 across multiple timeframes. What is particularly interesting is that the countertrend action tends to last as long as the prior trend. Thus, for example, when we have a solidly upward five-day period, the next five days underperform; when we have a solidly downward five-day period, the next five days outperform. I’ve seen evidence of this countertrend equivalence over five hour, five day, and five week periods.
There appears to be another facet to this, however. Recall that the trend measure is looking simply at directional persistence–not the size of the directional move. Because of that, I decided to look at strong trending moves over a five day period, but break these down into trends that produced large gains vs. trends that produced small gains.
After any five-day strong uptrend, the market averages a gain of .20% for the next five days (65 up, 48 down). That is weaker than the five-day average gain of .30% (448 up, 320 down) for the overall sample. When the uptrend produces large gains, the market averages a gain of .27% (33 up, 23 down); when the uptrend produces small gains, the market averages only .12% over the next five days (32 up, 25 down).
In short, it’s not just trends that are reversed; it’s trends that produce relatively little movement. In other words, the market is moving directionally, but not generating much gain or loss over that period of direction. Those are the best markets to fade.
By comparing what a trend provides in price change terms compared to what it *should* provide based on historical norms, we might have a rational basis for fading vs. following trends.
I had an excellent talk with Jon Markman of the MSN Money site earlier today and thought I’d pass along ideas from this experienced stock picker. Jon has been especially successful finding well-managed, profitable companies in the small and midcap sectors that have been long-term market outperformers. Most have little following among analysts and so are under the radar of most traders and investors.
One of Jon’s comments to me was that flexibility is essential to long-term market success. “Three years ago, you bought strength,” he noted. “Now you sell strength.” In my upcoming book, Enhancing Trader Performance, I make the distinction between first- and second-order competence. First-order competence is knowing how to perform a task well; second-order competence is knowing how to master new tasks. If I know how to navigate the streets of New York City, I have first-order competence. If I know how to figure out my way around the streets of any new city I might visit, I have second-order competence.
Many traders have first-order competence. They have mastered a market, but have not mastered the markets. The only way you can gain the confidence and ability to navigate any city is to visit many cities, get lost in them, and eventually figure out common elements of urban design that orient you to layouts of downtowns, highways, and suburbs. Similarly, you can only master markets by experiencing many different market cycles and conditions and extracting similarities and differences among them.
What happens when traders develop first-order competence is that they experience success and ramp up their trading size and/or frequency to exploit their good fortune. They are thus most exposed to risk when markets turn and trading conditions change. This causes them to lose much of their trading stake before they can undergo the new learning that would help them navigate the new market.
The problem is not so much one of learning markets as relearning them and adjusting one’s risk exposure to the inevitable lean times that accompany relearning. Perhaps this is why, as the saying tells us, there are many old traders and many bold traders, but not many old, bold traders.
I wanted to follow up on a point made in the recent Trading Markets and TraderFeed articles that have shown trending decreasing across all time frames in the S&P 500 market. Here, for example, is the chart from the Trading Markets article showing trending behavior over the past forty years. Not only is there a downward slope that has accelerated in the last few years; we now are actually at a point where we are seeing anti-persistence. Moves up are actually more likely to be reversed than continued (and vice versa).
Here is a follow up on my observations regarding the performance of various instruments as a function of technical trading systems. The system I chose is very simple trend and momentum following, courtesy of the Barchart website. The system enters by buying when a stock closes above its 20 day upper Bollinger band and enters a short position when the stock closes below its lower 20 day band. A close back within the bands exits the positions. The system thus profits when strength is followed by strength and weakness by weakness.
Here is how profitability looks for several instruments, from February, 2004 – present:
The same system had very different results depending on what you traded. In general, the stock indices fared poorly. The exception was the NASDAQ 100, which also had a number of individual issues that performed well. Clearly this is a retrospective look, not a predictive one. The point, however, is that trading the same market/instrument when it no longer possesses superior trading qualities is swimming uphill. What you trade may make more of a difference in performance than how you trade.
Last week’s entry mentioned the idea of each trader thinking of himself/herself as a small hedge fund and to think of trading not so much as putting on and taking off fixed positions, but as managing portfolios of trades. Such management would be based upon continuously updated research that shows whether initial edges enjoyed by trades at entry time T continue to exist at time T+1.
A second facet of this approach would be to combine research on *when* to trade with research covering *what* to trade. One way of doing this would be to only trade when there is a historical edge in the broad market (as demonstrated by TraderFeed-style investigations), but then trade the specific instrument(s) that maximize this edge their own historical studies. Thus, for instance, you might find a next day bullish edge in the ES based on strong upside momentum today. You would then investigate this pattern across a basket of stocks to determine which would be most profitable to trade. Another example would be an investigation that shows that a particular sector will outperform the broad market. You would then replicate this study for several different issues within the sector to see where the strongest results can be expected.
Another way of combining research on *when* to trade with research detailing *what* to trade would be to link completely different sources of edge. If, say, you had a validated stock-picking system, you could use it on issues that are significantly correlated with the broad market and then trade those stocks when the historical research indicates an edge in the broad indexes. Thus, for instance, if your system told you that Google was likely to outperform the market over the next three days and you had a separate market forecast for the NQ that was bullish, you could buy Google and attempt to benefit from the independent sources of edge.
The goal is to have research validated methods for entering and managing trades and maximizing returns by knowing what to trade. My very preliminary investigations into the creation of these methods suggests that such a trading approach might manage positions intraday, but would not be exploiting intraday edges. My research is clearly showing larger edges over swing trading periods–something I hope to quantify in upcoming entries.
On the heels of talking with Breon Klopp of PIT Instruction and Training regarding lean processes for achieving superior pit crew times on the NASCAR circuit (also the subject of my recent article for Trading Markets), I’ve begun further reading into lean manufacturing and service provision. The PIT site has some interesting articles; there are also several fascinating books, including Lean Solutions by James Womack and Daniel Jones and The Toyota Way by Jeffrey Liker. The reading has me thinking about market research–the generation of trade ideas–and trading more broadly as manufacturing processes. For most traders, these processes are hardly lean, as there are no efficient mechanisms for identifying, trading, monitoring, and diversifying one’s edge. If you think of the monitoring of trades as a quality control process, the inefficiencies really stand out. What would portfolio management look like as a lean process? It’s a topic I will be exploring in greater depth in a future article. Last week’s entry in this blog really kicked off my thinking. I am seeing increasing potential in building a diversified portfolio of trades based upon non-overlapping pieces of research that focus on key predictive variables: speculative sentiment, momentum, breadth, etc. One key element to this process would be a means for updating the probability of forecasts given market behavior over the next period. This would guide the management of trades, including scaling in and out to maximize returns from winning ideas. Few individual traders think of themselves as miniature hedge funds, and even fewer think about the efficiencies of their operations. I suspect this is a fruitful avenue for both research and practice.
Last week I mentioned three aspects of performance: strategy, tactics, and mechanics. Of the three, developing a sound strategy is perhaps the most difficult, because it requires a deep understanding of the marketplace. That understanding can be tacit–as in the case of expert scalpers who have a feel for the auction process within the markets–or it can be conceptual, as in the case of quantitative traders who develop formal models of market behavior.
This past week, I developed a trial indicator that captures the relative strength and weakness of two clusters of stocks: Speculative issues and Non-Speculative ones. My initial posting to TraderFeed describes these groupings in some detail. Suffice it to say here that Speculative stocks are more volatile in their price behavior and earnings than Non-Speculative stocks. Spec issues are ones you’d think about trading; Non-Speculative stocks are ones you’d consider for investment. My Spec and Non-Spec universe cuts across markets (NYSE, NASDAQ) and sectors, covering a large stock sample.
My hope is to build a trading strategy around this clustering of stocks and the information they provide. To do this, I must make many observations regarding how the Spec and Non-Spec stocks move relative to the markets I want to predict. These observations will be over multiple time frames and will involve several different target markets for prediction: S&P 500, NASDAQ 100, and Russell 2000. The observations will also cover various iterations of the Spec and Non-Spec universe to see, for example, if NASDAQ Spec and Non-Spec stocks are more predictive of the NASDAQ 100 than Spec stocks overall.
As I make these observations, I will determine if there are significant relationships between the Spec/Non-Spec stocks and the trading indexes, and I will attempt to explain these relationships. It is this explanatory model that provides the basis for a trading strategy. If the model is sound, it should lead me to uncover still further relationships among the data. For instance, if I discover that relative strength among Spec stocks precedes reversals of weakness in the S&P 500, I might examine this relationship on an intraday basis to create a tactical timing measure for intraday swings. I might also see if shifts in Spec stock relative strength might be especially predictive of sector moves that rely on speculative sentiment, such as moves in the biotech, alternative energy, and Internet sectors.
A sound strategy reflects market understanding, which in turn aids in the development of day-to-day and shorter-term trading tactics. Once you know the relationship between speculative sentiment and performance, you can begin to exploit speculative shifts on many time frames, across many sectors. As I suggested on TraderFeed, you could even hypothesize relationships between speculative world markets and less speculative markets from the established industrial nations.
Eventually, I will have trading strategies based upon market momentum (Demand/Supply, New High/Low data) and market sentiment (Adjusted TICK, Spec/Non-Spec stocks) providing independent sources of informational edge that can augment my source of structural edge (see last week’s Performance entry). My goal is to go into trading days with estimates of edge for that day, which can then guide tactical concerns of position sizing, risk management, selection of markets to trade, etc. More on this next week.
The new book is about 2/3 completed, with an anticipated March finish date. Publication is scheduled for the Fall. The tentative title is “Enhancing Trader Performance”, and the thrust of the book will be to explain what it takes to move from beginner status to competence to expertise in any performance field–including trading. I’m also pleased to say that I will be providing my first advance peek at the book’s themes during an early May conference in Chicago sponsored by Options XPress. I will provide more information as we get closer to the date.
One of the themes of the book is that performance can be broken down into three components:
|Strategy – This is the source of one’s basic edge in the marketplace;|
|Tactics – These are the ways one implements one’s strategy in particular market circumstances;|
|Mechanics – These are the concrete tasks that one must perform well to implement tactics.|
An adequate performance plan needs to address all three components, but a thorough assessment of one’s own performance also needs to occur at all levels. A strategy may be sound, but implemented with faulty tactics or mechanics, either due to a lack of trading experience and skills or due to emotional interference. On the other hand, a trader can be sound in their mechanics–managing the risks of trades well and knowing when and how to scale in and out of positions–but trading a strategy that lacks an objective edge. Either of those circumstances can elicit emotional distress, but the solutions are different–and not to be found in psychology alone.
My own strategy is based on two sources of edge in the market, one informational and one structural. The informational edge comes from the confluence of historical studies that I perform each evening (some of which are posted to TraderFeed). When multiple historical patterns point to similar directional biases over the near term, I have the basis for an informational edge. The structural edge is specific to the market I trade and the participants within that market. Because a very large proportion of participants operate with limited risk on a day timeframe, they behave in herdlike ways to jump on perceived opportunity and avoid perceived risk. By entering positions when a large number of participants are leaning one way, but can’t get the market to budge, I know that their eventual “puking” of their positions will benefit my P/L. By combining the two sources of edge–only trading against the herd in directions that are validated by historical research–I limit the frequency of my trading, but maximize my ratio of winning to losing trades.
There are many other strategies possible across many other markets. I’m in the process of exploring a very different strategy that trades over several day horizons, taking advantage of a different structural anomaly in the market. The important thing is that you have a strategy, can verbalize it clearly, and can demonstrate its validity. IMHO, if your strategy is based upon information that is readily available to all market participants who have real time charting or depth-of-market applications, it probably is inadequate. A good strategy will not necessarily be profitable–faulty tactics and mechanics can derail the best of plans–but it is difficult to imagine making money over time in the absence of an objective edge conferred by a sound strategy.
More on this topic soon to come.
I had an opportunity to interact with Olympic champion Dan Gable via email recently re: trading and performance. Interestingly, the quality that he singled out as important to traders was “the ability to act independently and stand alone.” My reply to Dan was that there is hard statistical evidence for his belief. Near-term upside returns are superior following panic selling episodes, and near-term strength has tended to reverse, not extend. As volatility has come out of the market, countertrend trading has worked better in the broad stock market indexes than trend following. Fading moves, however, takes a healthy measure of independent judgment.
One of the reasons I started the TraderFeed blog was to push myself to develop truly original trade ideas. In the analyses that precede my trading, I will develop a directional idea re: the market from a posted analysis, but then will conduct analyses with other, non-correlated predictors to see if the edge is confirmed. Each analysis is a kind of expert, telling me where market returns have been biased. By conducting multiple analyses, I am able to consult with a committee of experts. When the committee agrees, a very sound trade idea is in place.
Running those analyses and seeing that different predictive models are generating the same results provides me with the independence that Gable talks about. Even when the trade goes against me because of sudden runs stimulated by the locals, I find it relatively easy to stand alone and stick with my idea. Independent action begins with independence of mind. How many traders truly develop their own, distinctive trade ideas and test them out? It is so much easier to mimic the ideas from chart patterns and oscillator readings that others have described–or to leave decisions to subjective impressions. When you’ve developed your own ideas, however, you have seen them succeed in your own experience and know the odds for and against you. That provides a measure of confidence that is never present among those who cannot stand alone.
Here are interesting statistics from Olympic champion and highly successful coach Dan Gable regarding wrestling:
In the USA alone:
High School Wrestlers – 250,000
College Wrestlers – 7500
Potential Olympians – 500
My guess is that the ratios would not be so different for basketball and football players–or for traders. There are many thousands of traders, only a fraction of whom actually cover their expenses and keep themselves afloat. Still fewer consistently make their livings from trading. I was shocked to learn from an industry insider that the average time from when a customer opens an account to when they close it (due to running out of money) is seven months. No doubt this is a function of both undercapitalization and a lack of training among new traders.
Chess great Bobby Fischer once declared, “I don’t believe in psychology. I believe in good moves.” Here are two other quotes from Fischer: ” I prepare myself well. I know what I can do before I go in. I’m always confident,” and “Psychologically, you have to have confidence in yourself and this confidence should be based on fact.”
What Fischer is saying is that confidence that is not built on fact is false. Good moves–the result of preparation–make for good psychology, not the reverse. Dan Gable has always known this and structured his practices to continually raise the bar of expectations and expand what his wrestlers were capable of. If they could make it through hours of Iowa practices, they knew they could survive three rounds against any opponent.
My guess is that, to paraphrase Bob Knight, most of the 250,000 high school wrestlers have the will to win, but the 500 potential Olympians display the will to prepare to win.
Reading articles, books, and websites is informative and attending seminars can provide useful ideas. None of these, however, substitute for the preparation that comes from repeated exposure to performance challenges and drilling of skills. I strongly encourage you to take a look at Nolan Zavoral’s book “A Season on the Mat: Dan Gable and the Pursuit of Perfection” if you want to understand what it takes to reach elite levels of performance in any field.
Here’s a great article from the NY Times Magazine that illustrates many truths regarding expertise in any performance field.
One of the findings from the research I’m reviewing for my new book is that experts in performance fields display superior cognitive flexibility compared with non-experts. What appears to happen during the process of expertise development is that performers cultivate “mental maps”: ways of thinking about the performance domain that eliminate irrelevant information and focus attention on meaningful patterns. We can observe this among expert emergency room physicians: the decision trees inside their heads allow them to collect information in efficient ways and quickly triage cases. Studies with expert tennis, volleyball, and baseball players similarly find that they focus perceptual attention more effectively than non-experts, attending to subtle cues (the position of the opponent’s racket or arm, the opponent’s release) to anticipate where the ball will arrive. Instead of going into a performance with a fixed plan (“He’s going to throw me a fastball”), the expert is open to a flexible array of possibilities–and ways of responding to those.
The statistical analyses on the TraderFeed site are intended, not as fixed trading strategies, but as inputs for flexible response. All the analyses tell you is what has happened historically. This information, carried into real time, provides a basis for telling you if the market is behaving according to character or not. Your job is to be flexible enough to respond to either scenario. As we’ve seen from the most recent TraderFeed analyses, patterns often have different expectations during bull and bear markets. One of the best ways of determining that a bull or bear has ended is seeing that patterns that had worked well in the recent past are no longer generating the same outcomes. The flexible trader can make profitable use of such dashed expectations.
My recent lengthy article on the Trade2Win site summarizes ideas from brief therapy and how those can be used to aid trading. Research on performance suggests that expertise is the result of an intensive process of practice and exposure that generates implicit (intuitive) learning. Essential to this learning process is a high degree of sustained concentration. Emotional interference with trading is problematic in that it serves as a divider of attention, reducing the degree to which traders can absorb learning from their experience. This is especially the case among less experienced traders, who have not yet internalized their learning to the point where it is implicit. Research that I am covering in my new book finds that explicit learning is more vulnerable to emotional interference than implicit learning. Traders who try to focus on explicit trading rules, stop losses, etc. are more likely to experience emotional interference with trading than traders who have internalized trading parameters.
Interestingly, it is not emotion per se, but the division of attention in the face of emotion that might be most damaging to trading. My hypothesis is that high positive (overconfidence) or negative (fear, frustration) emotion lead to enhanced self-focused attention, which interferes with a focus on the market. The overconfident or depressed trader makes the same mistake: focusing attention on himself, when he should be market focused. This is why I don’t believe that positive thinking is the antidote to emotional problems in the market. Traders need to sustain concentration on markets and reduce all self-focused thought.
In many ways, I think the best therapy for emotional interference that inexperienced short-term traders face is to crank up a market simulator to 2x or 3x normal speed and push oneself to read markets and place and manage trades in the speeded-up environment. This helps develop the right trading reflexes and promotes an implicit processing of market patterns. In a sense, it is not the presence of emotion but the absence of market focus that is trader’s greatest challenge. The speeded simulator may be a developer, not only of trading skills, but also of one’s powers of concentration in the face of truly fast markets!
What are the qualities that distinguish successful traders from their less successful counterparts? Usually this is asked in the context of trying to identify personality traits that account for trading success. My sense is that looking for the ideal trading personality is problematic for two reasons:
|Trading, like medicine, is a broad field encompassing many specialties. Just as very different skills and personality features make for good psychiatrists and good surgeons, the talents and traits that distinguish longer-term and short-term traders–and those that characterize quant and discretionary traders–vary widely;|
|It is not personality, but the fit between personality, talents, and demands of the trading field that generate success or failure. Traits do not substitute for talents in generating success in any performance field.|
More promising than defining trading success as a function of personality features is the identification of core competencies among elite performing traders. Here are a few that have distinguished successful traders I’ve known and worked with:
|Competitive drive – An intense desire to win; resilience in the face of setbacks;|
|Drive for self-improvement – Efforts at personal and professional development even during winning periods;|
|Capacity for prudent risk taking – Aggressiveness in exploiting their edge; capacity for decisive action;|
|Superior focus – Capacity for sustained attention/concentration and capacity for impulse control;|
|Superior information processing capabilities – Rapid processing of information; ability to process multiple streams or pieces of data simultaneously and extract pertinent information.|
If I had to boil these competencies down to three factors a la factor analysis, factor one would be a personality complex that I would call “aggressive, action-oriented”; the second would be “speed and/or depth of processing”; and the third would be “executive control”. The latter two are general factors that emerge in intelligence research, although are not directly measured by traditional IQ tests. While not all successful traders may be outstanding on all three factors, it is difficult to imagine anyone succeeding at trading without distinctive gifts in a majority of these areas. I believe that the information processing differences between elite traders and novices are perhaps most important of all–and the least appreciated. Knowing how you most effectively process information goes a long way toward defining where you might find trading success.
I’ve been spending some time with the NeoTicker simulator with excellent results. The simulator works from compressed historical data, so it’s possible to select any previous day of the year and replay it for practice. The simulator can be set for normal or accelerated speeds–most recently I’ve been practice trading at double speed–and it can be paused, rewound, and fast forwarded. The pause feature is especially helpful: whenever I made a poor decision, I could pause the simulation and more closely investigate where I went wrong. After simulating at double speed, normal markets seem slow. This works to my favor: it feels as though I have lots of time to get good prices, jump into trades, etc. It reminds me very much of drills at Duke where we’d practice 25-foot jumpers. After a few of those drills, the top of the arc at 19’9″ looks very close.
My recent simulations have made use of a very minimum data set. I limit myself to ES price data, volume, and the NeoTicker TICK16 for the S&P 500. This summarizes the net upticks minus downticks for the last 16 trades across all ES stocks. It is thus truer to form and updates more quickly than the traditional NYSE TICK. Most of the trades that I was making were buys following periods of selling where the selling dried up and the reverse. By refusing to buy when the TICK16 was strong (and vice versa), I greatly minimized getting chopped up in slow markets, although I did miss out on some trending moves. A lot of the best trades entered at the market where selling (or buying) would dry up and exit with an order a few ticks above (below) the market. Using the TICK16 for timing, I pulled off several strings of these 2-3 tick winners in relatively slow markets.
I just learned that Market Delta will most likely be coming out with replay capability in 2006. At present, you can archive each day’s MD info, but not replay it for simulation purposes. With the new feature, you’ll be able to archive and replay any day of your choice and trade with your platform’s paper trading feature (such as the basic functionality found in e-Signal).
I am convinced that a tightly designed trading curriculum utilizing intensive simulation and real time practice can significantly accelerate one’s expertise development. Once I finish my book, I will become my own guinea pig and test that notion out.
One of the unexpected and most delightful surprises in writing the new book has been the opportunity to read about the training efforts of elite coaches across a variety of professions. So far, I’ve read training manuals from most of the military special forces, Chris Carmichael (Lance Armstrong’s coach), Dan Gable (legendary wrestler and coach), Nick Bolletieri (coach to a number of tennis pros), and others. What I’m looking for is how they structure learning experiences to generate expert performance. Here are some of the common threads:
|Periodization – They devote blocks of time to individual skills and work those skills thoroughly before moving on to other skills;|
|Breaking Skills Into Component Parts – They teach skills in a piece by piece, step by step manner, demonstrating and explaining the parts before having performers attempt them on their own;|
|Reassembling Skills Into Performances – They encourage performers to utilize the new skills in simulated situations;|
|Creating Realistic Simulations – As performers master easier simulations, they are given more realistic and challenging ones to help prepare them for real-time performance conditions;|
|Building Confidence – Mentors create a match between the skills of the performer and the challenges of performance tasks, so that learning is challenging–not boring or overwhelming.|
In the book, I describe how to accomplish the above in trading–and some of the unique challenges in doing so. The key to training success is maintain the “flow” state described by Csikszentmihalyi, where learning challenges are so absorbing that they capture the heightened attention of performers. In that heightened state, maximum learning occurs and performances that start out as effortful become automatic. I will be highlighting this topic in my article series on turning around trading slumps for Trading Markets.
Ellen Winner of Harvard University wryly observes that no one pretends that mental retardation stems from a lack of hard work, yet we often assume that extraordinary performance will result from vigorous efforts. Her point, supported by considerable research, is that effort only produces results when it builds upon inborn talent. Studying visual artists, she uses the phrase “rage to master” to characterize elite creative artists. They do not simply work hard; they are driven to master the performance domain and spend significant time and effort immersing themselves in it. The point I make in the new book is that the competence developed by elite performers is not the same as (or an extended version of) the everyday competence displayed in tasks such as typing, driving, etc. Because of the immersion that results from the rage to master, expert performers internalize their disciplines in ways that cannot be achieved through normal competence-building mechanisms (taking classes, repeated experience, etc). One of the consequences of this internalization is creativity. There are not many discussions of the role of creativity in generating trading performance, but my sense is that it is a significant factor. Experts don’t merely perform better than others; they see the world differently and process information differently. I hope to show this–and its relevance for trading–in the new book.
Denise Shekerjian’s book Uncommon Genius is an interesting look at 40 winners of the MacArthur “genius” Fellowships. Interviewing all 40, Shekerjian attempts to find common features that account for their success. Her list has interesting implications for trading. Among her findings are that these expert performers exploit personal strengths that have been present from an early age. Paleontologist Stephen Jay Gould, in his interview, sums it up as, “If I have any insight to contribute, it’s this: Find out what you’re good at and stick to it.” This notion of finding a niche that draws upon native talents is also a theme in Howard Gardner’s research with creative innovators, such as Freud, Gandhi, Picasso, and Stravinsky. All underwent a “crystallizing experience” in which they forged a strong emotional bond with a field of work based upon a fit between their talents and the demands of the performance field. Many put themselves through years of intensive effort, rewarded by nothing more than the learning experience. Much of this work was exploratory, not explicitly goal oriented. Shekerjian makes a great deal of this seeming dichotomy: the driven nature combined with a tolerance for ambiguity and a willingness to learn through doing, rather than by pursuing a fixed purpose. Dean Keith Simonton describes the contributions of elite artists and scientists as a function of sheer productivity: great individuals and lesser ones produce similar ratios of significant and less significant works. We remember the great individuals simply because they produce more works–and thus leave behind more memorable ones. Only the emotional bond between the performer and his/her work that comes from crystallizing experiences can sustain this productivity.
It is common among trading psychologists to attribute a trader’s lack of success to emotional factors, such as a lack of discipline and fears of loss. Research on performance, however, suggests a more profound possibility: perhaps traders do not succeed because they never find that niche that draws upon their most basic talents. We tend to believe that competence is derived from learning skills and gaining information. The research suggests that it is the immersion in a performance field that yields deep and continued learning. Mere interest or desire for success is not sufficient to sustain this immersion. If there is not a deep emotional bond between performer and performance field, competence is not likely to blossom into expertise.
Programming Note: I will be moving this blog to a weekend basis of publication. The Trading Psychology Weblog and the TraderFeed site will continue on weekdays.
Check out the latest entry on the TraderFeed site. There are some interesting ramifications for trader performance–even for discretionary traders. The gist of the argument is that many technical indicators are so highly correlated with price change during strong and weak markets that they offer no new information. A good example would be many oscillators (RSI, Stochastics, etc.), which simply give “overbought” or “oversold” readings for long periods of time during strong or weak markets. During relatively neutral periods of price change, however, many of these indicators are much more weakly correlated with price and offer unique information. It may well be that traders can improve their performance by only focusing on technical indicators during relatively flat periods of market action and relying on price/volume patterns alone during strong and weak markets. I plan to implement this strategy on the TraderFeed site.
We often think of people not being able to be successful at trading because of their weaknesses, such as a lack of discipline or excess emotionality. What if people fail at trading, however, because it does not encompass their strengths? On the test mentioned yesterday, my strengths were labeled “achiever”, “learner”, “relator”, “analytical”, and “maximizer”. I have channeled the achievement motivation, as well as my drive for analyzing and learning, into the markets, as my blogs attest. My relator and maximizer strengths, however, have been channeled into my work with others as a psychologist. These are the needs and strengths that I don’t find fulfilled in trading, and it is a major reason why I have not pursued trading as a full-time occupation. Were I to trade full-time (which I did two years ago), I would find myself emotional and unfocused, not because that’s how I am as a person, but because I am no longer fulfilling my needs and abilities in key areas. (When trading full time, I used to joke that I missed market moves because I was too busy on the phone helping traders during market hours). My sense is that traders who aren’t succeeding may be spending too much time worrying about their psychology and not enough time asking themselves if they are truly in a niche that exploits and satisfies their abilities and interests.
I recently read the book Now, Discover Your Strengths by Marcus Buckingham and Donald O. Clifton, Ph.D. The book makes a few very good points, foremost of which is that people develop by emphasizing and building their strengths–not by spending lots of time and effort remediating their shortcomings. Successful individuals, they argue, develop work-arounds for their weaknesses, but find ways of capitalizing on their strengths. They define strengths as a joint function of talents, skills, and knowledge–with talents being the most important. This is because talents represent our inborn, enduring abilities and interests. Skills and knowledge, the authors contend, can be taught. Talents are relatively fixed.
Where I find this most relevant is in the ways that traders select their markets and trading approaches. Very often traders are in markets that are not well suited to their temperaments, and even more often they are trying to trade according to styles that do not draw upon their talents. Indeed, I find it rare when I see a trader who has made a conscious effort to try different markets and trading styles, the way someone might investigate different college majors or career fields. My new book makes the argument that a high percentage of emotional problems that traders experience in the markets–including difficulties with discipline–are the result of poor fits between traders and their markets and approaches. A good analogy would be romantic relationships. When there is a good fit, the “disciplines” of monogamy and good communications are no problem; when the fit isn’t there, it is much easier to stray and lapse into arguments.
One unique element to the book is that it comes with a code that can be used to take an online assessment of one’s strengths. In upcoming entries, I’ll share my test results and expand upon their implications for trading.
I followed up my analysis of Russell 2000 vs. S&P 500 trading patterns on the TraderFeed site with an article for Trading Markets that should be posted Friday AM. The gist of the article is that strength/weakness of IWM relative to SPY tends to yield strength/weakness the next day in SPY. This is very different from the TraderFeed pattern, which looked at eight day periods of performance. There, we saw IWM eight-day underperformance lead to outperformance by SPY over the next eight sessions. The important performance point here is that any sort of patterns–chart, oscillator, quant–that seem to produce an edge at one time frame don’t necessarily generate the same edge at other time frames. We like to assume consistency, but the relationship between small and large cap stocks in which small caps lead their larger counterparts seems to be a short-term phenomenon. Generalizing to other time frames without prior testing might be hazardous to our wealth.
This was an excellent trade for me on Wednesday, and it was all based on recognition of a recurring market pattern. The pattern is that of a market “cycle”, which–in this case–begins with a high volume rise and momentum peak, is followed by significant selling (the “separating decline”, as Lindsay called it), and then produces price highs with divergences (many stocks not participating in the second rise). These price highs take the form of a “false breakout” and are generally retraced all the way back to the lows of the separating decline. I’ve outlined Wednesday’s example below:
The pattern, however, occurs across multiple time frames. For instance, November 23 represents a momentum peak for the most recent runup in the market. The current new high is a price peak with divergences. If the pattern resolves in the usual way, we should see a retracement to at least the lows of 12/8.
Becoming sensitive to patterns so that you can identify them in real time and act on them promptly and with confidence is a huge part of the performance challenge. Everything I have read and experienced tells me that this is a function of experience/exposure. That is why I’m preparing for my 2006 return to full-time trading by working out with the NeoTicker simulator and archiving the data (as above) from Market Delta for pattern review. More on this shortly.
I want to credit a reader, Nick, who wrote to me about an interesting solution he had found to the overtrading problem. He perceptively mentioned that he would get “lost” during the day and look back on his trading, only to discover that he had done a much larger than planned number of round turns. That “getting lost” is a loss of self-awareness that allows impulsivity to take over. It is hard to imagine following any trading plan without some degree of self awareness.
Before I mention Nick’s creative solution, let me share my simple strategy for identifying overtrading: Look at the correlation between your round turns per day and the daily range of what you’re trading that day. The daily range is a quick and dirty measure of volatility, which–for a short-term trader–is a simple barometer of opportunity. If you find you’re trading just as actively on slow, range bound days as on more volatile, trending occasions, it’s a good sign that you’re overtrading those markets. You want your trading activity to be proportional to the opportunity the market is giving you.
Nick purchased an erasable blackboard and planted it by his monitor. He required himself to write down all his trades and their profit/loss. This kept him aware of how often he was trading, and it also kept him in touch with his P/L on the day. In the state of awareness, he’s been less likely to “get lost”. Writing in a journal during the day, making notes to yourself, talking situations out loud–all of these are strategies to force you to become more aware of what you’re doing. Why do basketball teams call time out during a game? Why do pitchers and catchers suspend play and confer at the mound? Why do football teams huddle between plays and hold meetings at halftime? All of these are strategies for self-observation: looking at the performance and making mid-course corrections. During the plays, the basketball, baseball, and football teams want to be acting on instinct, not overanalyzing. But between plays, they return to a state of self awareness. That’s a lesson Nick is teaching us. Thanks.
I just submitted an article to Trading Markets and posted some findings to the TraderFeed site regarding changes in how markets have responded to Fed announcement days. Just as markets change their trading properties over time, we see shifts in how markets respond to events. Knowing how markets have been responding to events can aid in preparation for those events–especially, in this case, in helping traders temper their expectations given the lack of volatility associated with recent Fed days. One tendency that’s been interesting is that moves on Fed day have tended to be reversed over the next three trading days. A big reason for this is that the recent Fed moves have not represented major shifts of policy. I would expect a big breakout move from this coming meeting only if the decision represents such a shift.
The latest research findings posted on the TraderFeed site continue to document momentum effects in the market, with volatile, trending markets exploiting these patterns best. An interesting twist on the investigation was the view of what happens when momentum is moderate (neither 1 standard deviation strong nor weak). It turns out average returns are subnormal in those situations–which account for more than 2/3 of all trading days! In other words, the 1/4 – 1/3 of all trading periods with the strongest momentum (upside and downside) account for the majority of price change in the various markets.
What does this mean for performance? One possible implication is that low momentum markets are less likely to lead to trending markets in the near term, raising the odds that market moves will be reversed. A trading strategy of fading strength and weakness may be most effective in low momentum environments, while trend following works best following strength and trend reversal works best following weakness. These studies provide an interesting and potentially valuable context for intraday trading.
The TraderFeed site once again demonstrated that a trading pattern with a measurable edge elicits superior returns from an index that displays superior trending and volatility relative to the standard, large cap indices. Of course, the next step in this investigational program will be to see, once we have a core group of trading patterns, whether they elicit even better returns from synthetic indices made up of stocks from superior trending sectors with high volatility. The finding from the most recent piece of TraderFeed research–that momentum strength (its presence or absence) is more predictive of short-term price change than momentum weakness–was unexpected. It would be interesting to see if that finding holds up in a bear market environment. The deeper I get into this, the more I’m convinced that trading success is a joint function of trading the right way and trading the right markets.
I promised to update the site once I had tried out the NeoTicker program that I mentioned on December 2nd. I’ve only walked through a few of the features, but I’ve seen enough to give an initial impression. Actually I have three impressions:
1) This is the best simulation capability I’ve yet seen. I say this because the program very realistically simulates live markets using historical data, so that market days can be played and replayed. Moreover, they can be played and replayed at varying speeds, with a variety of indicators updating with the data, and with combinations of symbols. I had the market indicators updating every five seconds, depicting true to life market behavior.
2) There are some indicators available on NeoTicker that I have seen nowhere else. (See the charts on today’s Weblog entry). These include a measure constructed just like the NYSE TICK but using only S&P stocks for the construction. Even better, similar “TICK” indexes can be created for any other market average or basket of stocks you construct. In fact, I followed the TICK index for the ES only, which simply tracks the upticks minus downticks for the past N trades. Very interesting to integrate the interpretation of that index with the traditional TICK. I spent a good amount of time watching the S&P TICK and ES TICK in simulation mode and could easily use both for trading decisions.
3) I have not yet fully explored the ability to create “user defined symbols”, but the ability to generate your own basket(s) of stocks, chart them, develop indicators for them, etc. strikes me as very promising. It could easily take advantage of the theme of profiting from *what* you trade, as well as *how* you trade it.
There are many other functionalities I’ve yet to explore, including the ability to define and test trading systems and the ability to place trades via the program, using links to supported brokers. I’ll report on my experiences with other features shortly.
I currently have an archive of every tick of every trading day from 2005 available for replay in NeoTicker. That is a phenomenal learning tool, enabling users to practice trading almost any kind of market imaginable. There is much to be said for paper trading, such as the paper trading function available in e-Signal. Practicing trading with live data and keeping score with each practice session can be very helpful in getting a feel for markets. The ability to practice specific kinds of markets, however, at your own pace and with repetition potentially intensifies the learning.
Allow me to say that the people I’ve talked with at NeoTicker have been more than accomodating and helpful, but have never asked me to promote their product. Every week I get requests to endorse products or mention them on my sites. The vast majority of these requests I decline. I initiated the offer to mention NeoTicker on this site because I think it could be of value to traders who seek to further their development. If you decide to give it a look, email me with your impressions and experiences. It would be interesting to compare notes.
My current thought is to use the TraderFeed site to define a limited set of tested market patterns and then provide forecasts based on these, emphasizing the trading vehicles that can best exploit the patterns. This would be seeking an edge, therefore, both by defining trades to take and by funneling these ideas through the best markets. The patterns I’m looking for are ones that are conceptually grounded and therefore most likely to persist. An example would be the NYSE TICK pattern mentioned in my recent Trading Markets article. Because the TICK is grounded in the very auction structure of the market–reflecting the activity of bids and offers–it is more likely to impact prices than patterns that have little conceptual grounding (such as, IMHO, day-of-week patterns). In coming days, I will be exploring other such patterns and reporting results of my tests.
Here are two excellent sources of ideas for *what* to trade, as opposed to *how* to trade:
1) The book The Almanac Investor by Jeffrey Hirsch and J. Taylor Brown (Wiley, 2006) has useful information about historical market tendencies and seasonal patterns. A large chunk of the book, however, is a complete compilation of all exchange-traded funds, the sectors/markets they cover, their symbols, recent price performance, and top holdings. The table of contents for the ETFs by itself goes on for three pages of small type. It is *amazing* how this area of trading/investing has grown. I mentioned recently in the Weblog that you could gather most of the information I obtain from some of my market indicators with nothing more than simple charting applications. The way you do that is by following key ETFs. If you see that a distinct majority of ETFs are participating in a move to new highs/lows, you can have more confidence in that move than if a number of important sectors are lagging. With so many ETFs covering every market sector, it is much easier to count the number of new short-term highs and lows in the market. By analyzing which ETFs are showing superior relative strength and volume change relative to the ES and NQ/SPY and QQQQ, you can build a portfolio of sectors with superior trading qualities and exploit your own trading vehicle quickly. Many of the ETFs are trading enough volume to make such a strategy doable. The Almanac Investor can give you a good idea of the kinds of ETFs you can assemble into such a trading vehicle.
2) Carl Swenlin of the Decision Point site recently alerted subscribers that he had added sector ETFs to his “straight shots”. The straight shots are series of charts for different market indicators. By reviewing the indicators for a single trading instrument in a “straight shot”, you can quickly pick up on the relative strength or weakness of that sector. The S&P sectors included in Swenlin’s work are the consumer discretionary stocks, consumer staples, energy, financial, health care, industrial, materials, technology, and utilities. What is nice is that the indicators are derived from the sector components, so that you’re getting a pure look at the behavior of the stocks within the sectors. Once again, this can enable you to identify promising sectors for trades and combinations of sectors to exploit trade ideas.
In my next entry, I’ll take a look at whether analyses derived from traditional large cap averages might be of use in trading sector ETFs.
A subscriber to the TraderFeed site from Guatemala, Paulo de Leуn, wrote an interesting comment on the site regarding the loss of volatility in the NASDAQ market. His point was that, not only has the overall market lost volatility: the NDX has lost volatility relative to the rest of the market. I am preparing an article for Trading Markets on the topic, drawing on research from Cornell University’s finance program that documents a “Momentum Life Cycle” among stocks. Lee and Swaminathan found that low volume, winning stocks tend to outperform the broad market over a long time frame (measured in years). High volume winning stocks outperform the broad market over a time frame measured in months, but underperform over the longer period. To the degree that volume and volatility are correlated, this suggests that the best stocks for investment (holding for years) are not those that are best for trading. Performance is not only a function of how you trade, but *what* you trade–and matching your markets to your chosen time frame. In a follow up posting, I will look at potential sources of future winning stocks.
In the new book I’m writing, I’m covering a topic that I believe is important. Research suggests that performance expertise is a developmental achievement. It begins with a playful encountering of an activity, then progresses to a more serious development of skills, and then to a commitment to mastery. This is much like the development of a relationship, which begins with dating and then progresses to a more serious romance and finally proceeds to a marital commitment. Exploring relationships through dating is essential to making a suitable commitment, and the same is true in performance fields. Initial exploration and enjoyment of an activity provides the emotional fuel that eventually sustains the quest for development. Many traders settle on a market, trading style, and time frame without really exploring the full array of alternatives. They, in essence, try to begin their development by starting at the final, mastery phase. The result is similar to an arranged marriage: it may work; it may not. My sense is that many instances of lack of discipline actually reflect a lack of readiness to commit to a particular trading style because the alternatives have not been explored. It also often reflects the attempt to hold oneself to a trading style that does not truly fit one’s personality. Instead of working on discipline, the question becomes, “Am I trading the way that’s right for me, and have I really explored alternatives?” Faithfulness to your aims, in relationships and trading, comes a lot more naturally when you’ve found a good fit.
A reader of the Investing and Trading Online Newsletter published by the ShareTradingEducation site in Australia recently asked an interesting question regarding the difference between daydreaming and active visualization. Here is my reply, as forwarded to the Newsletter. The use of imagery has a longstanding history in the field of performance enhancement and is much different from passive daydreaming. One of my favorite applications of imagery involves actively imagining stressful events and then mentally rehearsing coping strategies for these. Many emotional difficulties during trading–particularly those that are triggered by specific events/situations–can greatly benefit from this technique. With repetition, the strategies invoked during mental rehearsals can be overlearned and then become automatic when the stressful situations actually occur. It’s a wonderful performance strategy for those who are able to make money when they’re cool and focused, but tend to give it back under conditions of frustration.
Merrell, a reader with a background in chess and the markets, wrote to me recently after reading one of the performance articles that compared trading and chess. I find a number of similarities, in that both involve a limited number of moves you can take a one time, but an endless array of strategies. Both trading and chess, in a manner, are easy to learn and difficult to master. The rules for participation are simple, but the patterns that must be processed for winning are complex. Merrell describes a strategy for trading preparation in which he reviews an array of charts outside of market hours. ” I look at the weekly gainers charts, as well as monthly, and yearly, for the Russell 1000 components, and rely on my mind to eventually recognize patterns,” Merrell reports. The variables of interest from a performance perspective would be how many charts he scans and how long he spends with each one. Interestingly, it may be more effective to scan many charts for less time each than to study fewer in greater detail. The quick scans would inhibit explicit processing of the information and create learning conditions more conducive to the development of implicit knowledge. As I mentioned in a past article, repetitive exposure to structured situations allows us to recognize complex patterns within those situations, even though we cannot verbalize those patterns. Recent research suggests that such implicit learning is more robust than its explicit counterpart–and especially less prone to emotional interference. Merrell’s strategy of reviewing multiple charts and timeframes might be a mini-laboratory for the development of implicit learning among traders.
TraderFeed begins a look at market breadth and whether it matters to trading decisions.
Here is this week’s article: a look at what we need to do to improve trader performance. Thanks to Trade2Win readers for their comments on the article on site and off.
I will begin posting analyses to TraderFeed prior to Monday’s open.
One of the topics I’ve encountered in the research with expert performers is their efficient processing of information based upon being able to focus on the most relevant details of their environment. A physician examining a patient can quickly eliminate irrelevant detail and hone in on body systems related to presenting problems. A poker champion notices small tells in an opponent, using these to decide when to call a bluff. Expert baseball hitters focus on details of the pitcher’s delivery to anticipate what will be thrown. Much of expertise lies in the training of perceptual ability. One of my hopes with the TraderFeed site is that it will focus on what is essential to the day-to-day trading of markets by using statistics to filter out noise and randomness. A good part of the expert’s efficiency of perception stems from anticipation: knowing what to look for in advance. Let’s see if a statistical, directional edge in markets can help discretionary traders become more alert for their setups and more confident in acting upon them. The analyses in TraderFeed will not be mechanical trading signals. Rather, they are designed to enhance perceptual efficiency by focusing the attention of traders prior to the start of trade. In this sense, analyzing markets before the open is like analyzing tapes of an opponent prior to a big game. You are more likely to know what to do in the heat of battle if you have rehearsed it in advance. Statistical studies don’t provide certainties, but they do suggest most likely scenarios and alternate ones. And those are valuable.
I want to thank readers who have been so generous as to share link information for the Trader Development page; I’ll be following up on each suggestion in the very near future. Today I followed up on the suggestion of the NeoTicker program, which has a rather extensive simulation capability. What caught my attention is that NeoTicker operates in two simulation modes: real time (so that you can practice trading live markets) and from a simulation server (so that you can practice trading historical markets). This latter functionality is crucial, because it allows students of the market to not only trade markets, but actually reload the data and re-trade them. It also allows traders to practice trading when markets are closed and practice trading particular times of day, types of markets, etc. One of my favorite performance strategies is to archive types of market days (trending, non-trending, economic news days, volatile, etc.) so that one could practice trading different environments. What better way to be prepared for changing markets than practicing under different market conditions? This is the “performance inoculation” approach I mentioned in my CQG presentation (which, by the way, I understand will be archived on the CQG site).
The other thing that interested me in NeoTicker is the availability of a full array of market data during historical simulations. So, for instance, if one trades the Russell futures off the Spooz or uses the NYSE TICK to trade the Dow futures, all those tick data can be part of the simulation. Apparently NeoTicker has some ways of simulating the filling of orders that are user-programmable to create realism in the prices that you get.
I emailed the company and was referred to a gentleman named Lawrence Chan, who–it turns out–is a company founder. He was very responsive to my questions and seemed genuinely interested in the performance applications of the software. I will be trying out the software next week and will report on my experiences on this blog.
In his presentation for CQG yesterday, Mike Glista made some interesting points and previewed new functionalities of the CQG software. First, to put my cards on the table for the umpteenth time, I do not receive any promotional consideration or compensation from CQG and they have not asked me to hype their product. I agreed to speak for their event because I see that they are making an effort to incorporate performance features in the trading software–a direction I believe will be very positive for traders. One functionality that Mike mentioned was the marking of charts to show exactly where you entered and exited trades. Moreover, there is a text field that allows you to annotate those trades. What that means is that your trading journal–or at least an important component of a journal–can be embedded in your charts for quick performance review. You can see at a glance whether your entries and exits were timed well, and you can review why you might have missed good prices. This is essential to the “learning loops” I described in my talk, where expert performers review their performances to make targeted improvements.
Mike also mentioned something that I tried to highlight in my talk: He pointed out that CQG was trying to integrate chart data, performance features, and order entry functionality in a single screen. This is huge, and I’m not sure a lot of traders get it. Research indicates that expert performers possess significant perceptual advantages over non-experts: they know what to focus on and fix their gaze quickly on relevant details, allowing them to respond quickly under conditions of time pressure. By integrating data on a single screen, a software vendor gives traders a perceptual edge. A lot of time and effort goes into providing traders with more data, but rarely is the issue of how data are organized the focus of attention. I’m going to see if I can work with John Conolly of Teach Me Futures to address some of these performance issues in a Webinar event. More on that shortly.
Below is a chart from Excel as I followed the market in the morning. Note that we had an early rise in the market, but that fewer stocks were generating short-term uptrends. That immediately cued the idea for me that we’d follow statistical odds and return to the prior day’s average price. It’s an example of how traders can learn pattern recognition and combine that skill with a basic rule set to make effective trading decisions. When you think about it, that cognitive process is no different from what physicians do when they see patients: they look for patterns among the symptoms and then link their diagnoses to rules for treatment. A quarterback who sees a shift in the defense and calls an audible is doing the same thing–as is the chess grandmaster who scans the board, sees a weakness in the opponent defense, and then generates guidelines for attack. There are commonalities in expert reasoning that can be taught, and it’s one of the most exciting areas in trading psychology.
Here is a program that I’ll be trying out in the next few days. It’s called TSim+ and it’s a trading simulation that works off data from Interactive Brokers. The simulator utilizes live data, so it is realistic in its training. It allows you to monitor and trade multiple markets at one time, and it tracks your P/L. While it cannot precisely simulate the order queue if you’re working orders in the book, it does allow you to specify a delay from the time your order is placed to simulate slippage. I found the developer, Henry, to be quite responsive to my questions and interested in refining his product. This is more than commendable, given that the product is free! There is also a TSim Lite that allows traders to trade indexes and thus work off free data from the Web. Obviously this will not precisely simulate futures trading, but as a beginner’s step it could be worthwhile–especially since the entire setup costs nothing.
I would say that practice at recognizing market patterns through the use of simulation, review of trading through videotaping, and collection of metrics to document strengths and weaknesses in trading are the three most important steps traders can take to improve their performance. The TSim programs might be worth checking out in that regard. Note that the Trade Performance program I reviewed earlier in the month also imported data re: one’s trades from IB, which–combined with TSim+–would provide a nice little performance suite.
I’m pleased to announce a new page for the website focusing on resources for trader development. Research suggests that expert performers have both a larger database of domain specific knowledge than non-experts and are more adept at applying this knowledge base. In putting together the Trader Development links, I have focused on sites that offer genuine educational content that can expand traders’ knowledge base. If you have additional links to suggest, please send them my way at the email address on the Development page.
I’m working with John Conolly of Teach Me Futures on a December followup to my CQG presentation tomorrow. It’s in the planning stage right now, but the concept is intriguing: John, a professional trader, will trade the mini-Dow futures and he and I will engage in a live performance review of his trading. The most interesting part is that participants in the online seminar will be able to download the same CQG software that John is using and trade the market at the same time he is. The performance review will thus offer an opportunity to compare your trading with that of a pro. I hope to have more details shortly.
Meanwhile, here’s a performance opportunity that might be worth checking out. John is affiliated with Advantage Futures and has offered to make professional and very affordable brokerage services available to readers of this site. Advantage is used by a number of professional traders, and it prides itself on its commitment to speed and reliability. Trevor Harnett of Market Delta recently switched to Advantage with John’s encouragement; he recently told me that he was very satisfied with their service and execution. John’s contact info can be found here. (Disclaimer: As always, I have no financial/proprietary relationships with CQG, Advantage, Teach Me Futures, or any other site or vendor mentioned on this site. I do not accept promotional consideration from vendors, as I consider it a violation of the dual-role prohibitions that apply to psychologists).
$ЂҐ $ЂҐ $ЂҐ $ЂҐ $ЂҐ $ЂҐ
My Original Posting to this Blog – Oct. 31, 2005
The book I am writing will make trader performance a major focus. Trading psychology has typically looked at emotional patterns and their effect on trading. The trader performance focus is different, examining the acquisition of trading skills and the development of trader competence and expertise. I have reviewed dozens, if not hundreds, of studies dealing with performance across a variety of fields: the common themes have significant (but unappreciated) relevance for the development of trading success. I hope to use this Weblog to summarize relevant research and practice, so that traders can create their own interventions and programs to improve their performance.
Top Binary Options Broker 2020!
Best Choice For Beginners!
Big Sign-Up Bonus!
Free Trading Education!
Free Demo Account!
Only For Experienced Traders!