a subtle way to find stocks beginning to quietly outperform their peers within the Dow30 industrials.
Using a formula for computing quality that considers both the gains and the relative volatility over various time periods allows us to examine relative performance improvements in a way that cannot be duplicated with reading individual charts
the first chart examines how to frame a favorable reward:risk ratio trade in PG (Proctor & Gamble) for a short term tradeis a performance table, and the second chart shows the analysis table that identified PG as a favorable candidate.
here is my take on the IITM System Quality NUmber idea (SQN): and fat right tails. it’s what I said to chuck whitman on this topic
on my -1R loss exits: this is the result of a single trade decision cycle on that trade that is very effective
now, on the possibility of a 10R win that “skews” the histogram of results and increases the variability of the data set and therefore lowers SQN and therefore lowers recommended risk
the essential question is this: was the 20R a consequence of the act of entry, of the fact of entry, independent of any trader decisionmaking along the way?
if you have no influence over the achievement of 20R in the trade, then the argument that the increased variability suggested by the 10R return should reduce the SQN is warranted
because upside variability that is a result of the market’s “decision” ought to imply the potential of the same kind of downside variability suprising you
that is the essence of the marble game, that once you decide to play that the result is pure random function generator
BUT: what if you had played the washout pattern in SPY on 10 March and followed every rule and were able to manage the trade thru 6 iterations of successive Washout Cointinuation patterns and as a result of 7 cycles of trade decisions, were able to bring in 10R, yet never risked more than 1R on the downside
everything is now a function of how you classify that trade batch
if you say: that is one episode, and the trade is 10R, and the 10R implies i could have taken a 10R loss which is beyond my management ability, then i will say i disagree
if we have traded a WO pattern system 200 times and my 100 losses show an avg loss of less than 1R, AND my worst loss was 1.4R, and my trading mgt skills enabled me to manage risk properly all the way up the ladder, then simply treating the WOCs as 6 separate trades on avg of 1.6R (6x 1.6R = 10R) gives a true picture of the quality of the system
the Sortino ratio which examines the stats of just the losses with StDev is the right way to understand the losses and since computation is free you SHOULD do both the standard SQN AND a Sortino to better understand your loss pattern to make your decision on how much risk to take on
now, if i can get a 10R thru intraday trade mgt by getting a carefully engineered, risk controlled, very manageable morning hook which gives me a boost and my trade is a single continuous episode but Inever was in a position to experience a 10R loss, then you would be nuts to penalize that system
you MUST really know your edge, and where the 10R comes from and decide if it truly represents the possibility of expereincing a 10R loss
you MUST know your system thoroughly in order to create meaning, to fully understand its risks (as much as it can be understood)
portfolio heat rules and other heuristics must be in place to protect us against -10Rs beyond our control such as power outages, discontinuity in the mkts, so that we never commit the hubris error of LTCM
we are no where near that in our application
here is where SQN is VERY VALUABLE: when i have 5 sets of mechanical rules that i am testing, iindependent of trader discretion, as a check on the robustness of the mechanical framework
SQN can give me the basis for deciding which to pursue, to revise and extend
what your studies are revealing is that you are understanding how the SQN math works
this is a good thing
example: this morning in the seminar we looked at a Triple Screen on GOOG which if executed mechanically would have given a .8R win going in to the close, but which had “open risk” ie trader initial cpaital at risk AND was in the red for 5 hours until near the close
by applying trader Quality to it, we engineered the entry timing in order to get a 2R iStop, and scratched the 1st trade, and then earned 6R on the identical setup on the next leg up.
the open risk was 15 minutes, when we moved to no lose, and then we spent 4 hours in the green until deciding to cash a 5R before the close
i guarantee that the 5R win should not be interpretted as implying increasing downside volatility and thus lowering SQN
coming to that conclusion would demonstrate IN MY OPINION, an inferior understanding of interpreting SQN
let me suggest what true Quality measure could/should include: amount of open risk x # of minutes
1st GOOG example: 15 min red all of which was <1R and then all green rest of the day up to a 5R win
correction that was the 2d GOOG example: the 1st GOOG exmaple eas mostkly red all day = lots of pain, low quality; compare the “time area in the red” to the “time area in the green” to really understand the “quality of each system.
charts to follow
bottom line: you better know where your outsize R comes from
you must be ruthlessly focusing on your R losses to ensure you are calibrated in identifying mkt risk to your idea
you must be open to the potentials of achieving risk managed high R wins, ie fat right tails
Chuck LeBeau developed a powerful analysis tool for traders called the exit efficiency index. Briefly, it’s an analytical process to examine the quality of your average trade exit within a reasonable timeframe around your actual trades. Understanding the technique will help you tune your system to typical market conditions where you are seeking to exploit your edge.
In a nutshell, you take your actual profit in each trade and divide it by the perfect exit from a timeframe that represents twice your actual trade to come up with a score between zero and one. If you can reliably get 30 to 40% of the perfect trade in your normal timeframe then you’re doing well.
With a few refinements it is possible to examine the quality of the entry as well as the quality of the exit. In this way you can consider how well tuned your trading system is to the general market conditions you seek to trade.
With another refinement you can look at the worst possible exit in the same timeframe to determine how well your profit preservation and capital preservation exits are protecting you against losses.
The same refinement will also show you if there are profit opportunities by reversing your trade direction and going the other way when the first trade is over.
Maybe the hardest piece of analysis to do is to look for opportunities on the other side of the trade when you have a positive expectancy system. It would not normally occur to you to see if you are missing profits by trading in exactly the opposite direction, when you have a system that makes money.
By doing so, however you may be opening yourself up to some real surprises concerning profit opportunities when you normally trade. You may discover that you can trade both sides of your idea in sequence. An example of this would be to stop and reverse instead of just stopping out of your trade.
I have found that there are certain market conditions favor stop and reverse strategy, while others it is appropriate to simply stop and scan for new opportunities.
By examining your trading statistics from all angles you’ll improve your bottom line as a trader.
Chuck LeBeau is a master trader and teacher who want ed to examine the quality of his exits, based on the belief that exits are far more important than entries in a trading system.
Since there were no existing measures available to use for his analysis he decided to invent his own, which he called the exit efficiency index.
The idea behind this concept is to examine the quality of your actual exit against the perfect exit in retrospect. He decided to look at all possible trades in approximately the same timeframe as the original trade. Comparing the perfect exit to the actual exit would allow the trader to determine if the system was in tune with the market when the trader is looking to exploit his edge.
Here’s how to do it. First, define the time period between entry and exit as the variable “t”. Now examine all price action within double that timeframe and call it “2t”. Find the highest possible price you could’ve sold at in the “2t” timeframe. In retrospect, that would be the perfect exit. Because we are creating an index of efficiency the scores will range between 1.0 for perfect and 0.0 for worst.
In order to develop the efficiency rating for each trade, divide the actual return by the perfect return and you will come out with a number between zero and one. Convert this to a percentage to see what percentage of the perfect return you actually received.
Based on Chuck’s analysis, a trading system that on average can extract 30 to 40% of the perfect trade is doing very well. This is important information for traders who are depressed about leaving too many profits on the table. Regret at missing profits is one of the most powerful psychological forces at work making you change your trading plan. Armed with this information, a trader can maintain the willpower and discipline to stick to good trading rules in the future.
There are two ETFs that focused directly on gold, the commodity. The first one has a symbol of GLD and is by far the most heavily traded of the two ETFs, probably because it was the first one to market. The second symbol is IAU. These two ETFs trade so closely together that it would be hard to fit a razor blade in between them.
When you have a statistical edge in the market, the first thing you want to know is how reliable it is. If it turns out that your edge is robust, that means it can be relied upon to work for you in most if not all types of markets. When you have such an edge, the best strategy to adopt is that of a Las Vegas casino. You want to be the house.
Being the house means that you want to play a positive expectancy game with as many iterations as possible, in order to achieve the expected average return of your system.
The statistical edge in the games of chance played in Las Vegas casinos are very small. These small edges though are mathematically certain because of a tightly controlled conditions and the environmental attractions the casinos offer.
Because the conditions are so carefully controlled, the house can’expect for its mathematical edge to work in its favor precisely because of the large number of iterations. If you are the house, you want 1 million people playing blackjack for one dollar hand, rather than one person playing a single hand of blackjack for $1 million.
The results of an individual iteration of a game of chance is not knowable before hand. However, the laws of large numbers work to your advantage when you have the edge.
In the same way, if people in your neighborhood like to gamble, then the best strategy for the whole neighborhood is to pool their money into a single pot and send that money to Las Vegas to play a single hand of blackjack, winner take all. A single iteration at blackjack is the best possible return for your money in Vegas, although it is a slightly negative expectancy gain under most circumstances.
A short-term trader who has plenty of opportunities is better off taking five positions at 1% risk per position than a single position at 5% risk no matter how he decides to rank order the signals by quality. This assumes of course that the signals that pass the screening criteria are equally reliable in the long run.
The greater the number of iterations in the sample size, then the greater your chance of achieving the average expected return a positive expectancy system. There is a natural tendency among traders to try to concentrate their capital on what they consider to be the best trade available. If your system is generating multiple signals though then you are better off taking all of the signals at reduced risk, provided that you have done your back testing work and admin costs of trading as low.
Behavioral psychologists have pointed out that people do not act like rational actors when it comes to investing and trading in the stock market. In addition to fundamental factors like the business cycle, there appear to be psychological motivations at play that help to explain market performance and price fluctuation.
A short term or intermediate-term trader can take advantage of the power of human psychology to improve their trading practice with respect to timing market entry and exits.
One strategy that can be very useful is the idea of the stealth trade.
The stealth trade occurs when a market sector that is represented by an ETF for example, is in a position where it is no longer the worst performer in the market but has not yet rebounded strongly enough to be considered as a new headline story.
Newspapers, magazines and television shows that cater to traders make their money with exciting news stories and headlines. Headlines sell advertising, so there’s always a hunt for the newest and most interesting story.
Consider what might happen to a sector such as semiconductors that is beginning to outperform all other market segments dramatically. The headlines will talk about semiconductors as the way of the future and momentum money will chase this sector until there’s nobody left to buy. Semiconductors will then be at an intermediate high and then begin to lose ground as people try to lock in profits or avoid further losses if they are the latecomers to the party.
Once semiconductors have lost their shine and are beginning to descend back into the pack or lower, they are no longer news and no one is talking about the. They will probably continue to fall until they find something like a natural level where only value players are interested. There may be some headlines concerning semiconductors if they achieve bottom rung status by being the worst performing sector available. That is news and they will be back in the headlines.
Now suppose that they have started to attract value investors. Because of their buying pressure, the panic selling will be clearly over. There’s nothing exciting about semiconductors anymore as they start putting in a bottom. Institutional money is probably quietly buying at these price levels in such a way as to avoid attention. Only after semiconductors have started to move back into the pack will you start to see headlines about the rebirth of semiconductors.
The trading psychology cycle will begin once more. Institutional money which acquired semiconductors at a value price is now unloading those positions in measured amounts to momentum money that is chasing the sector once more.
The stealth strategy then is simply trying to find the sectors that used to be the worst performers which are now not quite so bad but before they have become headline news one. Still traders are acting like institutional money, taking the other side of the trade from the masses.
This is an attractive way to use ETFs since you are able to offset or avoid individual company risk while playing the larger macro economic trends.
The difficult part of the stealth strategy is in selling a position that is now finally beginning to catch fire and you should be selling to momentum money. The temptation is always to overstate your wealth of insight that sector but this is a nice problem to have.
Trading psychologists generally agree that there are two dominant emotions in the area of behavioral psychology that influence market price and performance. These two emotions are fear and greed. It is also generally agreed in the scholarly literature that fear is about three times more powerful than greed. It distorts our normal rational analytical mind when it comes to considerations of reward and risk.
Knowing this fact, a short term trader could reasonably conclude that market mispricing occurs more likely in moments of great fear that great greed. A short term outlook is much more likely to be influenced by emotion the longer-term rational approach to the markets.
For these reasons, a short-term trader could consider the idea of “Max Pain” in the search for tradable short-term opportunities.
Here are some different ways this idea could be used to find low risk tradable ideas.
- Become familiar with a small set of liquid companies and ETFs and determine what normal conditions of gains and losses are. Then, be on the lookout for moments when losses have exceeded the threshold of normal. Use these moments as opportunities to find high probability reversion to the mean trades.
- Become an expert in a single stock or ETF and develop an appreciation for how it trades in normal swings in short term time periods and look for moments of exaggerated selling with respect to the recent normal behavior. Chances are you have identified an overreaction on the sell side which may afford you an opportunity to revert to the mean.
- Pay close attention to the size and direction of the morning gap in a set of tradable instruments. Find those symbols that have lost the greatest percentage from yesterday’s close entry fees, and treat them as high probability candidates for reversion to the mean. Look for them to find support in the first 30 minutes of the market and prepared to go long with a tight stop. Even if these symbols only close the morning gap you may have made your daily profit objective in the first couple hours of trading.
- Look for adverse reactions to news events that take the market by surprise. Chances are that panic selling will miss price a reasonable company and create a moments of by value at a discount.
The idea behind this “MaxPain” strategy is that you avoid the sudden surprise losses that created the opportunity, and you are positioned to play for the rebound. This is definitely a short-term strategy for one of which happens with such regularity you may find it a valuable addition to your trading arsenal.
The Japanese stock market, the Nissei, is the world’s second-largest equity market in terms of daily dollars traded. Because it is open when the US market is closed it does offer an opportunity to put your dollars to work at different times of the day.
One of the ways that the short-term trader and they manage of the opportunities found in the Japanese stock market is to trade the ETF symbol: EWJ. This is a broad market index ETF that tracks the Japanese stock market.
While this sounds like an attractive idea on the surface, it is complicated by the fact that the ETF itself trades with very little volatility during the hours the US market is open. Almost all of the gains and losses associated with symbol EWJ are found at the open and close of the trading session.
This is because the Japanese market varies only a little when it is closed; all its gains and losses occur during the time when the Japanese market is open. So the ETF trader is advised not to use symbol EWJ as if it were just another ETF. The gains and losses you would experience with the US market is open are so small as to be almost impossible to trade intraday unless you were to use inadvisable levels of leverage.
The symbol EWJ can be traded, however by sector recycling systems work by swing trading system or by position trading systems that were to capture macro economic track. It can also be used as part of a broad strategy for asset allocation. Then, EWJ is a cost-effective way of getting exposure to the Japanese markets without having to become an expert in individual Japanese companies or opening an account that they trade directly inside the Nissei.
There are many different useful definitions of the concept of volatility. For the purposes of this essay I just want to consider volatility in a non-technical way, and that is as the amount of variation in returns around the mean, or the average.
In any bundle of returns from a trading system, you will always be able to calculate the average return. If most of the returns are tightly clustered around the average you would normally consider this to be predictable and reliable and low volatility. If individual returns were widely scattered above and below the average, you would normally consider this to be unreliable, risky and high volatility.
That is the general sense of volatility that I want to consider in this essay.
Now, I want to consider some lessons learned concerning volatility in exchange traded funds within your trading practice.
ETFs are much less volatile than individual large cap stocks. This is true even for ETFs whose components holdings are all large cap stocks. Even the big, mature companies in the Dow Jones Industrial 30 index are much more volatile than the composite ETF that holds them. This is ETF trading symbol DIA.
One of the things you get with lower volatility ETFs is the ability to engineer your position sizes more carefully because ETFs have less volatility and also tend to be more range bound. You are much more likely to have an extraordinary event in individual stock which includes the possibility of a runaway winner whereas ETFs are more likely to give you long regular cyclic waves of winners and losers.
In addition to ETFs having lower average volatility, they also have a lower standard deviation in general than the individual stocks that make up the index. What this means is that the amount of surprise you are subject to is generally less on both the upside and downside. This is true regardless of whether you’re ETF is focusing on shore conservative companies were smaller cap growth.
Of course we want to be careful in overestimating the usefulness of this information. We want to remember that even low volatility ETFs Possibility of large at first moves off of overnight surprise news.
So ETFs may isolate you from some individual company risk but at the cost of giving up the opportunity for wildly explosive moves in your favor by surprise. For many traders, this is an excellent trade-off if you are looking for regular normal returns.