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Saturday 23 November, 2013

CCI – Application and examples

This article will demonstrate a few of the ways the adaptive CCI can be used to trade. There are many chat rooms and web sites that deal with this one indicator. Most are derivative of the one main room that promotes using this single indicator. While this main room claims to have discovered certain pattern using the CCI, most of these patterns have been talked about for many years by other technical analysts, perhaps not using the CCI, but using another indicators triggering similar signals. Therefore, there is much information available to study regarding patterns on indicators.
If you’ve read the two preceding articles on the CCI you will understand the basic structure of the indicator and the need to have the proper input parameter. When I began using this indicator, mostly for trading intraday charts, I felt the main chat rooms were way off base in their understanding of this indicator for all the reason I described in the first two articles. My first attempt at improving the indicator was to plot several different parameters, such as 21 period, 34 period, 89 period, etc. I stuck with fibonacci numbers just to keep the choices manageable, not because I believed there was any special power to these numbers. I thought I could spot the cycle and its corresponding parameter and just trade that particular version. There is something to be said about keeping the parameter consistent. But after much testing I found I was more trusting of an adaptive technique where I only had to watch one CCI and let the computer estimate which cycle length was dominant. Another thing became clear, and that is: one indicator can’t do everything. But too many indicators will cause confusion. There’s a balance of what you can watch and react to. If you are a daytrader you need fewer indicators because you don’t have time to over analyse. The trade will pass you by. If you are a position trader you have more time, but still too many indicators can cause analysis paralysis. And, you can always find some indicator that will give you the answer you want based on your bias.
For now, I will focus on this one indicator, although I don’t trade with just what I’m going to present on these charts. Since this is an article on application of the CCI it would be confusing to show other indicators and then try to explain their useage at the same time. I will have an article in the future that puts all the elements together. But the following patterns are the basis of what I look for when I analyse a market using the CCI. There is much more to consider before a trade can be put on, such as overall trend direction, trend strength, etc. So please don’t use these examples out of context. They do not represent a complete trading approach.
The CCI that I use on a daily basis is a combination of the adaptive CCI along with smoothed version of the CCI, as described in the previous article. This is done to help smooth out the bumps so I can get a better picture of the pattern that is setting up. The adaptive CCI does filter out much of the noise, but the smoothed version filters out even more, with very minimal lag. I plot the smoothed version as a thick black line along with the histogram bars that change color depending on the trend. I usually plot the unsmoothed adaptive CCI as a thin blue line, although in some of these examples the unsmoothed line will be thicker yellow line to make it easier to see on the smaller charts.
When Lambert created the CCI his idea was to trade excursions outside the one hundred lines. In actual testing in the early days, back in the early 80′s when the PC and trading software started appearing, the results of doing this were so bad that most technicians reversed the rules and started trading crosses back into the hundred lines. If only 20% of the prices would go beyond the hundred lines, it made sense to trade when the CCI crossed back inside where 80% of the prices were to occur. Of course they were using a static parameter in those days. If they could have extended the parameter as the cyclic component started to abate as it does when a trend appears, Lambert’s original idea may have gained more of a following.
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The chart above is the smoothed adaptive CCI along with the thin blue line, which is the basic, unsmoothed adaptive CCI. The point of showing this chart is to demonstrate how, with the correct cycle and input parameter, the market will stay in a trend mode as long as the CCI is beyond the 100 line, as perscribed by Lambert. It didn’t catch every tick out of the trend, but it did do a very good job. All divergences should be ignored as long as the CCI is over the +100 line in an uptrend, or below the –100 line in a downtrend. In this example I’m sure there are many other methods, such as a simple moving average, that would have also kept you in this trend. In this case prices stayed over about one and a quarter standard deviations above the moving average that represented the cycle in play (meaning the 100 line represents about 1 ¼ SD).

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In this chart I changed the raw adaptive CCI color to yellow and made it thicker for clarity. The pattern inside the pink ellipse is a micro M pattern. I don’t trade these types of momentum reversals if they are against the main trend, which I determine with other indicators. But for this example I’ll assume that I did want to go short this market. If I just relyed on the basic adaptive CCI I may have gone short on the first downturn, or the left side of the M. I find it dangerous to go short on the unsmoothed CCI momentum reversal if the smoothed CCI is still trending up. On the second reversal, the right side of the M, the smoothed CCI turned down resulting in a much safer entry point.
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Here is another example but in the other direction. This would be a micro W on the unsmoothed adaptive CCI. When it finally turned up again on the right side, the smoothed version turned up as well, resulting in a much better and safer trade.
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Perhaps the best and safest trade is the first pullback. I first learned of this concept from Linda Raschke. She favored trading the first pullback after a trend appeared. She offers a few variations. Her grail trade is a pullback to a moving average. She trades bull flags with various triggers such as by using her 310 oscillator. This CCI trade is essentially the same type of trade. When the CCI bars, as defined by the smoothed adaptive CCI, have been on one side of the zero line and then the CCI goes to the other side of the zero line, one can enter on the first pullback to the zero line as momentum shifts back in the direction of the trade. In this example the bars turned green, then pulled back to the zero line, and then on the first CCI uptick a long trade can be entered. I use the smoothed line as the signal, but prefer to have the unsmoothed CCI line lead the way up. In this example they both turned on the same bar. In some cases the unsmoothed line will turn first, and the following bar will show the smoothed line turning. The zero line reject is somewhat different, as that will require, from the basic rules using a 14 period CCI, a high level of the CCI, usually over the 100 line, preceeding the pullback to the zero line. With the static 14 or 20 CCI you get many of these, and most result in losses. With the adaptive CCI it is common to have this setup, but without the need for the CCI to first have been at a high level. There are many continuation trades that do have the adaptive CCI coming from a high level, put in the case of the first pullback it is not required.
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Here is an example of a zero line reject that does have the adaptive CCI starting the descent to the zero line from a high level, in this case from between the 100 and 200 lines.
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The next pattern is simply three drives to a bottom. Sometimes it’s three drives to a top. The best patterns seem to occur when the three CCI lows create a divergence with price. In the case of a bottom, three lower lows on price and three ascenending, or higher lows on the CCI. Sometimes the divergence is create with the first and third low and the middle low falling somewhere in between. In some chat room this concept is only traded when the second low is the lowest of the three, which would be a head and shoulder pattern. I find those to be less reliable in general. When I do see them I prefer to also use the detrended CCI as confirmation. Examples of this will follow.
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Here is an example of three drives to a top. Here I have the unsmoothed and smoothed adaptive CCI. The CCI is like a flat head and shoulders, while there are three very distinct impulses up. On the right side on the price bars, where I drew the horizontal blue line, you can see a slight attempt for the market to push back up, but the CCI was already heading to new recent lows. When the kink in the raw link turned back down in the direction of the smoothed line, the market made a nice move to the downside. Not all of these reversal trades work this nicely. Reversal patterns are the most unreliable and difficult to trade. It is always easier and safer to trade in the direction of the trend.
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The next pattern is the hook around the zero line. It has a different name in various chat rooms. I find it to be an excellent pattern. In this example the unsmoothed adaptive CCI is making the hook while the more stable smoothed adaptive CCI is trending in one direction. If find these especially powerful. Sometimes the smoothed adaptive CCI will be dominant in making the hook.
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Here is an example of the micro M top from near the extreme 200 line (the left ellipse). If one missed this entry, or passed on it because the trend was still up, the next chance to get in on the downside was a slight hook of the unsmoothed CCI near the zero line. I didn’t highlight this as it was quite subtle. The more realistic trade was the first pullback, in this case a pullback up toward the zero line, which is highlighted with the ellipse on the right side of the chart.
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Here is a micro M and a zero line reject combination. There was also a kink in the unsmoothed adaptive CCI against the declining smoothed adaptive CCI. I don’t often take these, but when I see them they can influence a continuation of a trade, or sometimes be part of another pattern. It is marked with the red down arrow. If I had taken the zero line reject and prices failed to move in the anticipated direction within a few bars, I might be tempted to exit the trade. However, when I see this kink in the unsmoothed CCI in the direction of the smoothed CCI, I will assume that the momentum will continue in my direction and that prices should follow. Sometimes there is a series of these zig zagging kinks against, and then with, the direction of the smoothed line. That probably signifies a choppy environment so I will go with the flow, but will keep my finger close to the mouse button.
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Here another example of the micro W, probably with divergence (but don’t have prices up to be sure) and a hook around the 100 line, as denoted by the first up arrow. The second up arrow is another kink of the unsmoothed CCI in the direction of the smoothed CCI.
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Here are many of the previously discussed patterns. The left ellipse is a micro M, followed by a hook of the raw CCI around the zero line in the direction of the smoothed CCI (first red down arrow). Following is a near perfect zero line reject that triggered via the unsmoothed and smoothed CCI on the same bar (second red down arrow). Finally there is a micro W with divergence (the rightmost ellipse).
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Here’s a chart of the smoothed only version of the adaptive CCI with prices. There was an up zero line reject to the left when the histogram was green. But more important was the first pullback after the trend had evidence of being down, as the histogram turned red. This is my favorite trade. Notice how prices made a halfway attempt at a rally before giving up and falling. The smoothed adaptive CCI caught the move nearly perfect. The following examples will display only the smoothed version of the adaptive CCI for clarity. I usually have the unsmoothed version on my charts, but it is displayed only as a faint blue line. Sometimes I prefer the simplicity of only having the smoothed version in my charts.
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Here is another example of a first pullback after the trend turned down. This one went a slight bit over the zero line. Nothing is perfect, but this is nearly perfect.
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Returning to the three drives pattern, I often will use the detrended smoothed adaptive CCI for confirmation. Notice on the leftmost three drive pattern that the CCI had a divergence pattern, but the lower subgraph, with the detrended CCI, shows a very clear higher level on each of the three drives. The bars turned positive long before the CCI. On the third drive I prefer to see the detrend stay in the green. In this case it went slightly negative, but was close enough to confirm the third drive. The three drive pattern on the right is a bit more perfect. The CCI formed more of a head and shoulders, but notice how the detrended CCI (bottom subgraph) had a very high level at point 1, much lower level at point 2, and had long been red and negative by point 3. The smoothed CCI did stay over the +100 level when this occurred, so I would have waited until the CCI returned to under the 100 line.
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Here’s a first cross down, followed by a first cross up. The down signal didn’t produce much price movement. You never know the extent of the move that will follow a signal. Sometimes that gives you a clue that the next signal in the opposite direction will work out better. In this case it did.
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Back to the detrend. Here are two examples of divergences with confirmation via the detrend. The sell divergence on the left side the detrend went just a hair to the positive before turning down, but was close enough. This isn’t an exact thing. You have to allow for a little room, as long as the concept is there. On right side example the detrend stayed in the green for the buy confirmation.
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Here is a bear flag that broke to the downside. As a pure chart interpretation, just looking at the price bars, it might have been possible to see this in real time. It is always very clear after the chart is drawn, but while the pattern is developing it can be difficult to spot, especially if you are daytrading. The zero line reject made the pattern much easier to spot. The CCI only got about halfway to the zero line, but the reversal in momentum made this a valid trade. In the middle of the CCI was the three drive pattern I referred to earlier, where the divergence was between the first and third pivot, with the second pivot in between the two. Another reject occurred within this pattern, without much follow through in price. The three drive pattern was followed by a classic first pullback with a nice follow through in price.
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The left side of the CCI shows a first pullback, then in the middle is another first pullback to the upside, followed by a zero line reject. The reject went a bit below the zero line, but the trend of prices was clearly up. Rejects after the first pullback can be a little less reliable. They become more and more unreliable the more they occur within the same trend. There are other indicators to watch as a clue as to when it is getting late in the game. One method is the use of standard error bands that I discuss in another article.
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Here’s another three drives pattern, this time with another head and shoulders on the smoothed adaptive CCI, with a confirming CCI detrend with three higher bottoms. It was followed by another first pullback, which is no surprise as they will often follow the three drives pattern.
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Here is an interesting pattern. The detrend CCI displays the head and shoulders formation with the right should break occuring right as the smoothed CCI is sitting on the extreme 200 line. I would only view this as a warning at this point. I don not go short if the smoothed CCI is still above the 100 line, and in this case it is at the 200 line showing extreme upside momentum, although overbought. When the smoothed CCI finally broke below the 100 line it was accompanyed by a reversal under the zero line by the detrended CCI. Price followed down shortly thereafter.
The examples and ideas presented here are not meant as a trading system. An indicator just indicates the possibility, but there is no assurance that any of these patterns will result in a successful trade. You must do your own research and tabulate your own stats to gain confidence in your trading. I have been purposely vague in giving exact formulas and parameters. That is an area of research you need to do on your own charts, on the markets you trade, and for your own, personal trading style. My parameters and stats won’t help your trading. It takes a lot of time and effort to put a trading plan together. I’m hoping just to encourage and inspire some ideas for your own research and testing.

-Doug Tucker

Trading Methods – What Works and What Doesn’t

The are many successful traders using a variety of trading methods. However, there are a far greater number of traders who fail to produce consistent profits from the markets. I would estimate the percentage of successful traders about the same as in other professions such as acting, music, sports, etc. Many are called, yet few succeed. Over the years I’ve formed a few opinions on why trading is so difficult for so many people. Most traders I meet are intelligent. They have had success in other careers. They are usually hard workers and devote much time and energy to their trading. Yet most of these traders move from one methodology to another, never finding anything that works for them.
In the area where I live I try to attend as many trading groups and meetings that I can find. Trading can be a lonely business when you trade your own account. It is important to maintain human contact. That is face to face contact and not just communicating on Twitter. One trading group that I’ve belonged to for several years seems to be a laboratory for watching traders who go down dead end roads. I’ve tried to draw some conclusions why these traders consistently go in the wrong direction regarding their methodology.
When a trader first decides to trade for a living, that trader must go through a process of personal discovery to find a methodology that fits their personality. It is quite normal to try many different approaches to find what fits. One could decide that the perfect fit would be very short-term day trading. However, once that activity is put into practice with real money that trader may decide the stress and fatigue of watching the computer screen all day is just not worth the effort, despite how much money that approach might produce. Another trader might decide holding option spreads for a month is the perfect approach, but the lack of activity may cause that trader to be inattentive to his trades and get distracted by other activities and become bored with trading. So finding what fits is best done by trying different approaches. What seems right may not be once that methodology is implemented.
But why are so many traders changing methodologies every few months? Whenever I attend the monthly meeting at my local group, I find that they all have found a new chat room or guru to follow. I hear stories about how that new guru finally has the answer. I hear how that guru called the next days market direction perfectly and has done so every day that month. Really? I heard that the last guru also called the market turns perfectly. If the previous guru was so good, why is everyone now following this new guru, with no longer a mention of the previous guru-of-the-month? There seems to be a need and a real desire to know that there is someone out there with the answers these traders are looking for. They think someone that is successful at trading will be kind enough to give them a profitable methodology, either for free or for a small price. They are led to believe that the guru is actually successful trading his or her own money. But are they? Most of these people are not trading real money while teaching or running a chat room. They will lead you to believe they are successfully trading real money. Many have never been successful. And if these people actually are making the money they claim in their own accounts, why would they be charging a fee to run a chat room and teach their methodology in a trading school with the added work and liability. Wouldn’t their profits from trading far eclipse the relatively small fee they could be receiving from charging for their services? And if their advice or methodology were up to their claims, wouldn’t the word get out and the Internet traffic to those sites become overwhelming? Wouldn’t billion dollar hedge funds want to know those secrets that elude their own research teams? I think these traders looking for an answer are not asking themselves these questions. If they are asking these questions they may be so invested in finding the answer to their trading problems that they don’t want to deal with the hard truth that they may not be on the right path.
Is there a common denominator with many of the approaches being offered that most likely will be a dead end for the trader? Any approach that tries to predict future market direction from non-market generated information is doomed to failure, in my opinion. I’ll explain.
Several meetings ago one trader brought in an approach that tried to extrapolate future market direction from finding similar patterns being developed currently and comparing those to patterns that were developed 70 or so years ago. This trader acted like this was a novel approach. Market letter writers and technical analysts have been doing this type of comparison since the beginning of the markets. It has never worked. You can take the shape of the prices of the current market and try to overlay them on past data and you will find many similar shapes and patterns over the last hundred years or more. But making the assumption that the outcome of that pattern can be determined is just beyond nonsense. What possible relevance would there be in the shape of the price chart currently to that of 70 years ago, or even six months ago. If by chance there were a similar outcome it would be purely random chance. It would be easier and quicker to simply flip a coin.
Another flavor of the month that had everyone excited was using moon and tide cycles to predict where the market should go. I needn’t spend much time discrediting that one. Elliot wave is another approach that in my opinion is a complete waste of time. The theory of Elliot wave is actually correct in explaining and describing the mass psychology of traders. It can explain an impulse move in the direction of the trend, and then explain the logic of the reaction against a trend. On past data most Ellioticians would agree with an analysis, or wave count. However, if you put a hundred Elliot wave “experts” in a room with a chart and asked them where the market is headed, you’d get a hundred different answers and most likely three hundred alternate counts. It is completely useless in trying to forecast the future. The problem as with the moon cycles and overlaying past data, is that the trader is trying to tell the market where it should go rather than listening to the market and hearing where the market wants to go.
Fibonacci analysis is another dead end in my opinion. Fibonacci was quite popular when Elliot Wave was first being reintroduced in the late 1970′s through mid 1980′s. There currently seems to be a renewed interest, along with such techniques as the Gartley Butterfly patterns and a few other rehashes of long forgotten classic techniques. Again, these techniques attempt to tell the market where it should go. A Fibonacci retracement or extension makes the assumption that a market should stop at or go to a certain price because of some natural numerical relationship that defines the spirals of a shell or the relationship of the belly button on the human body. Pure silliness. Sometimes these numbers get hit with precision and turn the market at precise Fibonacci numbers. I’ve dropped a horizontal line on a chart at random and have hit that random number with about the same frequency as that on my Fibonacci retracement tool. Many Fibonacci experts will cluster numerous starting and stopping points on their charts so there will be many lines. Many will also include numbers in between the main Fibonacci numbers. As a result there are so many lines going across the chart that one of them is bound to be hit. The only number that I find useful on a Fibonacci retracement tool found on most charting software packages is the 50% retracement level. And 50% is not really a Fibonacci number. But it is the retracement most often used by most analysts as a guide. It is probably useful because so many watch it and price turns at that point become self-fulfilling.
Also, there is much effort by many of these gurus to draw conclusions from straight lines drawn from distant points on the chart up to the current prices. First, the markets are fractal by nature. Being restricted to drawing a straight line on a chart is like trying to draw a map of a coastline using a straight line. It cannot be done. There is meaning to the up and down movement of the market. If one understands the concept of price rejection and acceptance around previous pivot, or swing point, the trend is more easily understood. Drawing a straight line back in time and assuming price will react somehow once that line is met is not logical. Sometimes a major trendline will act as support or resistance for a time only because so many people are watching it. But market structure based on what the market is actually communicating is far more important. Sometimes straight lines, such as in triangle, will appear to offer valid signals, but most often on closer inspection the actual swing points are a much more reliable guide. Straight lines cannot connect with all the important swing points. Trying to force a fractal data series to a linear series is bound to miss the point.
Classic price patterns, such as the head-and-shoulders pattern, are another area where the trader is trying to tell the market where it should go, regardless of where the market actual does want to go. Like the Elliot Wave, the head-and-shoulders pattern, along with three-drives-to-a-high, can explain investor psychology very well, but again in hindsight. Many major tops and bottoms occur after these patterns are formed. In fact if you look at enough price charts these patterns seem to jump off the page at major turning points. The problem is that there are far more of these patterns formed during trends that do not turn prices back the other way. When analyzing past data on charts, it is amazing how the human eye will gloss over the many more numerous failed patterns and gravitate to the successful patterns. The trader wants to believe. Reality gets glossed over. Again, like the other methods, patterns are based on an assumption that a particular shape of previous data will have an implication for future price direction. It simply isn’t so. Any predicted outcome is pure chance, or at best self-fulfilling. You can’t tell the market where it should go based on random patterns from the past, no matter how well categorized and documented they are.
Another area that can lead to frustration for the beginning trader is the belief that a mechanical trading system can work. To my knowledge there has yet to be a successful mechanical system developed. If a mechanical system actually worked that system would soon own the entire market and would have to self-destruct at some point. Countless hours of programming on main frame computers using advance methods have failed to turn up a system that stood the test of time. The problem is that most of these system use curve fitting to once again extrapolate patterns from past data, whether using price patterns or indicators, and assuming that will somehow tell the future. The curve fitting may work for a short time, but as markets change, as they always do, the systems will no longer be in synch with the markets. All these systems fail. Trying to find a system that will work is futile, and the trader will waste much time in that search. That time would be better spend learning about how the market works and learning to read what the market is trying to communicate.
To summarize the common denominator that I find that will lead traders down a dead-end road are any of the trading approaches that try to tell the market where it should go. Most of these approaches are based on information that is not directly generated by the actual price action. Of course a trend line or Fibonacci level is influenced by price only in that it is drawn on the price from the same data. But it is backward looking. It is making an assumption that something from the past based on an irrelevant numerical relationship or random pattern will cause or influence buying or selling in the future. It just doesn’t happen with enough reliability to enable consistent profits for the trader. In fact, a coin flip has a better chance of predicting the future than any of the methods mentioned above. The very best mechanical systems have about 30% winning trades, which is below the 50% that a coin flip will produce.
If you’ve read this far you might conclude that it is impossible to trade successfully, and that there is no useful approach to trading. It is true that the vast majority of those who try trading will fail. Most stay on the dead end roads of gurus and approaches that don’t make logical sense.
I would suggest concentrating on two things. First, money management is probably the most important element in a successful trading plan. It isn’t as interesting as learning indicators and pattern analysis. But with proper money management, one could take a far from perfect trading approach, even the coin flip, and have a fair shot at making a profit over time. Even successful gamblers with terrible odds can win if they employ strict money management. It should be first on the list of techniques to master if one wants to have a long career, but this is usually the element of the trading plan that is neglected.
The next thing I would suggest is to learn the principals of market generated information. The Market Profile is a logical place to begin. By learning the Market Profile one will learn the auction process relating to all traded markets in all time frames. It is a study in learning the language of the market. Most traders are too busy trying to interpret Elliot Waves and Fibonacci retracement, and as a result they don’t listen to what the market is trying to communicate. The market is concerned about the here and now as it tries to interpret the future. It doesn’t care about some straight line drawn through price points six months ago. Often the graphic that represents the Market Profile confuses and turns traders away. The graphic is not as important as learning the concept. One can still use bar charts and moving averages and other indicators as a guide. But knowing what the market is trying to accomplish by moving up and down in what appears to be a random fashion can make the difference in how a trader views a chart. It is well worth the time spent learning what this technique is all about, regardless of the type of chart a trader uses. In fact the concept behind the Market Profile was developed to simulate the mental process of a trader in the pit. The buying and selling and seemingly random price moves do have meaning, but most traders are not paying attention.
To trade successfully is very difficult. The learning curve can be very long. The learning curve for the Market Profile approach can be quite long and difficult. Few things in life that are worthwhile come easily. I gave a three-hour lecture on Market Profile a few months ago to the local trading group. To my knowledge not one person in attendance is applying any of the principles discussed. A few people came up to me at subsequent meetings and said the lecture was interesting but it was just too much work to learn that approach. They would prefer to stick to what the group is doing by jumping from one guru to another in search of an easy method that will give precise and winning trades. Good luck to them. Unfortunately they will supply the profits to those who understand the markets.
Doug Tucker
 via - Tucker Report

Sunday 22 September, 2013

Trend ?

Wednesday 28 August, 2013

Candlesticks For Support And Resistance

Tuesday 27 August, 2013

Hot Potato: Momentum As An Investment Strategy

Ryan Larson
Momentum investing has important features in common with other factor-based Smart Beta strategies. For example, it has straightforward index or portfolio construction rules that are easily explained and implemented. And, although momentum investing is emphatically not a contrarian strategy, neither is it necessarily inconsistent with the Smart Beta thesis that prices are noisy and mean-reverting. In this interpretation, momentum investing is a lively game of hot potato—buying rapidly appreciating stocks, holding them for a relatively short period, and selling them before their price trends reverse direction. And in favorable conditions it works very well.
 
Nonetheless, our research raises serious theoretical and practical questions about momentum as an investment strategy in its own right. In this issue, I review the evidence for momentum investing, consider momentum in comparison with other equity risk factors, and briefly touch upon implementation issues, including portfolio construction and rebalancing policies. I argue in favor of choosing another factor for the core investment strategy and using momentum only as an ancillary trading strategy. 
 
Evidence and Explanations
Momentum has shown itself to be quite robust across U.S. and foreign equity markets, within industries and countries, and across many different asset classes such as stocks, currencies, commodities, and bonds. In 1993, UCLA professors Narasimhan Jegadeesh and Sheridan Titman (1993) published what is considered to be the first comprehensive study of the momentum effect. They found strong evidence, over the 1965–1989 period, that stock prices trend—at least in the “short-term” of up to two years. In Jegadeesh and Titman’s study, the best performing portfolio selected stocks on the basis of the previous 12 months of price returns, bought winners, sold losers short, and held those positions for the subsequent three months.
 
Other academics confirmed that momentum is at work in international equities, emerging markets, industries and sectors, mutual funds, and asset classes.2 In fact, commodity trading advisors (CTAs) have built a profitable business around trading momentum.3
 
Empirical studies have shown the momentum effect to be strong, but financial theory hasn’t definitively explained why momentum exists. Describing investors’ behavioral tendencies in the 1970s, Daniel Kahneman and Amos Tversky (1979) identified what they called the “anchoring and adjustment” heuristic.4 In the face of uncertainty, individuals estimate the expected future value of an asset by making adjustments to a reference price, that is, an “anchored” value. Investors manifest this tendency by anchoring to the current information (stock price) and being slow to adjust expected future values in light of new information. Thus, prices lag fundamental information and play “catch up” for a few quarters, leading to serial correlation in stock prices. Jegadeesh and Titman concluded that an under-reaction to firm-specific information was the likely cause of momentum. In further support of the anchoring hypothesis, Hong and Stein (1999) found that it takes time for information to be fully reflected in stock prices.
 
Other financial and psychological considerations may also prolong momentum by postponing price adjustments due to new information. Tax liabilities might make it preferable to defer the realization of capital gains. Company insiders may decide it is prudent to reduce their holdings over an extended period. Investors’ sentimental attachment to a company may discourage them from divesting the stock. (For instance, an individual might have inherited the stock, or the officers of a charitable organization may be loath to sell the stock of their founders’ company.) Serial correlation in earnings announcements might also lead to stock price momentum.5
 
Taken one by one, these insights make good sense. However, there isn’t a generally accepted theory that explains the causes of momentum in the financial markets. For example, it is not clear why the anchoring-and-adjustment heuristic would prevail over another psychological trait—investors’ tendency to overreact to new information. Nor is it clear that behavioral patterns which are perceptible in individual decision-making can be applied by simple extrapolation to untold numbers of investors interacting with one another.
 
The lack of a cogent theoretical explanation is not a trivial matter. Maintaining that, because the stock price has risen, it will continue to rise—as though the conservation of linear momentum applied, by analogy, to financial assets—is scientifically dubious. After all, an investment thesis that supports buying stocks solely on the basis of past prices violates even the weak form of the efficient market hypothesis. More than just a beauty contest, investing becomes Keynes’s (1936) third degree of speculation:
 
It is not a case of choosing those [faces] that, to the best of one’s judgment, are really the prettiest, nor even those that average opinion genuinely thinks the prettiest. We have reached the third degree where we devote our intelligences to anticipating what average opinion expects the average opinion to be.
 
Momentum as an Equity Risk Factor
From our vantage point, it appears that investors are starting to look at equity risk factors more closely. We believe there are two reasons for this new interest. One reason is to understand the nature and relative magnitude of the risks in their portfolio. This is a very sensible exercise because many actively managed portfolios have inapparent risk exposures. The other reason is that, with the growing acceptance of Smart Beta strategies, many investors are shifting their equity portfolios to capture specific long term risk premia. The commonly accepted equity risk factors are market beta (MKT – RF), value (HML), small size (SMB), momentum (MOM), and low volatility (BAB).6 Among the first four equity risk factors, over a period longer than 40 years, momentum registered the highest return and Sharpe ratio (see Figure 1).7 

 
As attractive as momentum appears in Figure 1, it must be borne in mind that all equity risk factors are time-varying. That is, risk factor exposures will not add value consistently and all of the time. There will be some periods when certain risk factors are in favor and others when they are not—including extended intervals when factor-based investing is very discomfiting. As shown in Figure 2, the momentum risk factor has earned a negative risk premium for the 13 years ending June 30, 2013. We have also observed that momentum’s strength has eroded over the past decade. Factor-based investing requires strong conviction and a steady hand.
 

 
The other major challenges with momentum include higher volatility and the associated left-tail risk of severe performance crashes. These traits make it difficult to adopt momentum as an investment strategy and may explain why we don’t see many pure momentum strategies in the marketplace, where value strategies are ubiquitous. Although momentum and value factors have similar Sharpe ratios over time, momentum has 50% higher volatility, whereas value is more stable and, perhaps, more intuitively appealing.
 
Additionally, the momentum anomaly works best in illiquid, smaller cap stocks (Fama and French, 2011), and turnover is very high. Jegadeesh and Titman calculated turnover at 170% annually for their long/short portfolios. The trading costs are real and can substantially erode the risk premium due to momentum. Research is mixed about the alpha net of trading costs, but there is evidence that momentum’s high transaction costs offset the alpha potential at a fairly low level of assets invested in momentum strategies (Korajczyk and Sadka, 2004).8 
 
Implementation Matters
A better form of momentum strategy can be implemented by adopting portfolio construction rules that adjust for systematic risk. Naïve momentum strategies hold high beta stocks that lead to crowding into expensive stocks during bubbles. When the inevitable market correction occurs, high momentum stocks reverse (that is, revert to the mean) strongly, and the high beta names naturally tend to overcorrect. One of our colleagues, Denis Chaves (2012), finds that the alpha produced by idiosyncratic momentum is significantly more robust than the alpha associated with traditional momentum. The Carhart four-factor model explains less than half of the return generated by an idiosyncratic momentum strategy.
 
Chaves corrects for beta in calculating momentum for the purpose of stock selection. For example, if the market rises 20% and a stock with a beta of 2.0 rises 40%, the idiosyncratic momentum of that stock is zero because the stock is expected to rise twice as much as the market. All else equal, this stock is unlikely to be selected for an idiosyncratic momentum portfolio, but it would probably be held in a traditional or naïve momentum portfolio. Intuitively, adjusting for beta allows us to differentiate between stocks whose prices are rising for “authentic” reasons, and those that are just moving with the market.9 
 
Apart from the equity market factor, the value factor is probably the best documented and most commonly targeted source of risk premium. Nonetheless, it is not entirely clear what value is. Some theorists refer to an unknown or hidden risk (e.g., default). We have a different view. We maintain that the value premium (and the size premium as well) is a byproduct of noisy, mean-reverting stock prices, and it can be captured through contra-trading.10 In the RAFI® Fundamental Index® methodology, contra-trading is accomplished by means of systematic rebalancing to constituent weights that are not related to prices. Rebalancing, in this approach, does not merely correct for style drift; it is integral to the strategy. We favor annual rebalancing because it minimizes turnover and, therefore, transaction costs. Value investing is a long-term proposition.
 
Momentum strategies, in contrast, are profitable in the short run, and they call for more frequent rebalancing.11 But, obviously, more frequent rebalancing entails higher transaction costs. In addition, rebalancing a non-price-weighted portfolio has a strong positive value factor loading and a negative loading to momentum. These opposing characteristics are hardly surprising; momentum and value strategies are themselves opposites—procyclical vs. contrarian, short-term vs. long-term, and based upon trending vs. reverting to the mean. Recognizing these oppositions, I submit that complementing a long-term fundamentals-weighted strategy with a judicious commitment to a short-term momentum strategy might, in aggregate, produce attractive risk-adjusted returns. Indeed, Morningstar found a blended portfolio of value and momentum outperformed a blended portfolio of value and growth by nearly 1% annually.12 
 
And Yet…
So what are investors to do with momentum? Our conclusion is that momentum is inadvisable as a stand-alone strategy due to the risk of precipitous losses. Rather, we suggest that long-term investors seeking to tap more than one source of equity premium choose another, more stable factor for their core investment strategy (value is certainly a strong candidate), and consider adding momentum as a short-term trading strategy when market conditions are favorable.
 

via - Fundamentals
Endnotes
1.     For the record, the opinions expressed in this piece of writing are the author’s; they do not necessarily reflect Research Affiliates’ views.
2.     See, for example, Rouwenhorst (1998), Griffin, Ji, and Martin (2005), Rouwenhorst (1999), Moskowitz and Grinblatt (1999), Carhart (1997), and Asness, Moskowitz, and Pedersen (2009).
3.     For a fee on the order of 2 + 20%, CTAs will gladly provide you with the momentum returns across assets.
4.     Kahneman devotes a very readable chapter to anchoring in Kahneman (2011).
5.     Soffer and Walther (2000); Chordia and Shivakumar (2002).
6.     Beta, value, size, and momentum constitute the classic “four-factor” Fama–French–Carhart risk model.
7.     The risk factor portfolios are courtesy of Ken French at Dartmouth. Risk factor returns are calculated for zero-cost long/short portfolios. Momentum is calculated by taking the returns of all stocks from 12 months ago to 2 months ago, ranking them and selecting the top returning 30% of stocks for the long portfolio and shorting the worst 30% performing stocks.
8.     The authors estimated that, for a single fund, momentum loses its statistical significance at $1–2 billion, and its profits at $5 billion.
9.     Of course, traditional momentum portfolios have alpha beyond the beta risk factor, but idiosyncratic momentum dampens volatility, resulting in a more attractive risk premium.
10.   If the stock market is not perfectly efficient for any reason, half of stocks are overpriced and half are underpriced. As market participants seek fair value, prices mean revert resulting in a return that has been shown to be approximately 2% over the capitalization-weighted index in developed markets such as the United States (Arnott, Hsu, and Moore, 2005).
11.   Vayanos and Woolley (2013) determined that the Sharpe ratio of the momentum strategy is a function of the length of the window over which past returns are calculated, and they found that the highest Sharpe ratio was achieved using a window of four months. This implies a rebalancing frequency of three times per year.
12.   Beginning in 1993 through June 2013, an equal-weighted Russell 1000 Value Index and AQR Momentum Index returned 9.53% relative to an equal-weighted Russell 1000 Value Index and Russell 1000 Growth Index that returned 8.63%. They had similar standard deviations of 15.4% (Bryan, 2013).
 
References
Arnott, Robert D., Jason C. Hsu, and Philip Moore. 2005. “Fundamental Indexation.” Financial Analysts Journal,vol. 61, no. 2 (March/April):83–99.
 
Asness, Clifford S., Tobias J. Moskowitz, and Lasse Heje Pedersen. 2010. “Value and Momentum Everywhere.” American Finance Association 2010 Atlanta Meetings Paper.
 
Bryan, Alex. 2013. “Does Momentum Investing Work?” Morningstar (April 10).
 
Carhart, Mark M. 1997. “On Persistence in Mutual Fund Performance.” Journal of Finance, vol. 52, no. 1 (March):57–82.
 
Chaves, Denis. 2012. “Eureka! A Momentum Strategy that Also Works in Japan.” Research Affiliates Working Paper (January 9).
 
Chordia, Tarun, and Lakshmanan Shivakumar. 2002. “Momentum, Business Cycle, and Time-Varying Expected Returns.” Journal of Finance, vol. 57, no. 2 (April):985–1019.
 
Fama, Eugene F., and Kenneth R. French. 2011. “Size, Value, and Momentum in International Stock Returns.” Fama–Miller Working Paper; Tuck School of Business Working Paper No. 2011-85; Chicago Booth Research Paper No. 11-10.
 
Griffin, John M., Xiuqing Ji, and J. Spencer Martin. 2005. “Global Momentum Strategies: A Portfolio Perspective.”Journal of Portfolio Management, vol. 31 no. 2 (Winter):23–39.
 
Hong, Harrison, and Jeremy C. Stein. 1999. “A Unified Theory of Underreaction, Momentum Trading, and Overreaction in Asset Markets.” Journal of Finance, vol. 54, no. 6 (December):2143–2184.
 
Jegadeesh, Narasimhan, and Sheridan Titman. 1993. “Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency.” Journal of Finance, vol. 48, no. 1 (March):65–91.
 
Kahneman, Daniel. 2011. Thinking, Fast and Slow. New York: Farrar, Strauss, and Giroux.
 
Kahneman, Daniel, and Amos Tversky. 1979. “Prospect Theory: An Analysis of Decision Under Risk.”Econometrica, vol. 47, no. 2 (March):263–292.
 
Keynes, John Maynard. 1936. The General Theory of Employment, Interest and Money. London: Macmillan Cambridge University Press.
 
Korajczyk, Robert A., and Ronnie Sadka. 2004. “Are Momentum Profits Robust to Trading Costs?” Journal of Finance, vol. 59, no. 3 (June):1039–1082.
 
Moskowitz, Tobias J., and Mark Grinblatt. 1999. “Do Industries Explain Momentum?” Journal of Finance, vol. 54, no. 4 (August):1249–1290.
 
Rouwenhorst, K. Geert. 1998. “International Momentum Strategies.” Journal of Finance, vol. 53, no. 1 (February):267–284.
 
———.1999. “Local Return Factors and Turnover in Emerging Stock Markets.” Journal of Finance, vol. 54, no. 4 (August):1439–1464.
 
Soffer, Leonard C., and Beverly R. Walther. 2000. “Returns Momentum, Returns Reversals, and Earnings Surprises.” Working Paper (January).
 
Vayanos, Dimitri, and Paul Woolley. 2013. “An Institutional Theory of Momentum and Reversal.” Review of Financial Studies, vol. 26 no. 5 (May):1087–1145.