Friday, March 15, 2013

Trading after the signal

Slippage is an important issue for market timing investors. It is a costly consequence of almost all transaction-heavy trading plans – certainly more of a concern than commission costs. Briefly defined, slippage is the difference in the price at which you intend to buy or sell an instrument and the price at which the order is filled. There are multiple reasons for this divergence, including the time that passes between order placement and execution and the relative liquidity of the instrument. Closing the gap between your expectation of what price a stock will be bought or sold and the price you actually get filled should be an important part of your trading practice.

But what about the slippage that occurs between a market signal and trade order? This is an especially important consideration when following a system published by a market-timing advisory service. Can the rates of return achieved by a trading system be closely simulated? How does the model function in real trading? Readers here would ask how do the model Stock Trends Portfolio trading systems rate against actual or achievable trading results? The  signals generated by the Stock Trends trading systems are issued after the close of trading on Friday, but the model portfolios register the Friday closing price as a transaction price. Can subscribers to the service attain the same returns registered by the model portfolios by trading post-signal in the following trading sessions?
First of all, a clear statement can be made about exactly duplicating any portfolio: It would be highly improbable that the transactions of a trading strategy can be matched. Even high frequency trading systems generating split second orders cannot be regenerated exactly in the marketplace because every fraction of a second represents a new market for an instrument. Of course, the differences in results may tend toward insignificance when the time between signal and order execution is small, but actual order regeneration at the same price is still highly improbable over numerous trades.
However, we would like to see that the results generated in a particular model are reasonably simulated in actual trading. What is acceptable in terms of variance between the model results and actual results will depend upon the over-all profitability of the system. In other words, will a trading system provide returns high enough to make the differences in actual results acceptable? If a system gives you 5% returns you won’t be too happy with a 2% difference in actual results. If the system gives you 20% overall returns, 2% slippage might be acceptable.
Periodically, I do get asked how the Stock Trends model portfolio performance holds in a post-trigger market  - what would be the real trading results for subscribers who want to mimic the trading activity directed by the published strategies? The answer to that question depends on the trade efficiency of the investor and on the type of stock traded. Poor trade order practice and illiquid, volatile stocks make a bad combination.
Certainly, placing ‘market orders’ on trades in this category of stock will very often result in less than optimal results. As a point of reference I will direct readers to the Stock Trends Handbook chapter on executing trades, but there are many other sources that can educate investors on how to properly make a trade. It is important that every investor know that regardless of their source for a trade signal – whether from an advisory service, your own analysis, or your taxi cab driver’s – the responsibility is on you to execute the trade as optimally as possible. A signal to buy or sell is not a signal to go to the market unprepared. It is essential to be tactical with every trade order.
For now, let’s assume that trade order best practice is being used. What can we learn about the differences in trading results possible for investors who go to market after the Stock Trends Portfolio trade signals are issued? I will only do analysis of this question on the weekly data series I maintain and will only seek to approximate possible results on the assumption that the trade is executed in the following week. As a result any differences that are highlighted in this analysis may in fact be lower if the trade data was for the following trading day instead of the following trading week. Nevertheless, I believe this analysis should give us a pretty good idea of what kind of replication of trading prices is likely and whether there is a significant difference in results from the model portfolios published here.
How can we truly approximate actual trades made? Even if I presented trade tickets for each trade it would not be an accurate representation of the population’s (every subscriber who transacted on the signals) trade record. It is necessary to approximate as a central measure, to estimate a price at which a transaction would have tended toward. If we take the mid-point of the range of the stock price in the following period we can estimate a central point, although without actual inter-period data to see what the distribution of prices tells us it would be an imprecise estimate. For instance, a stock may have traded mostly above the range mid-point. This analysis cannot tell us how individual stocks traded on a daily or intra-day basis.
If we take a sample (data, csv) of over 5,200 transactions directed by the six active Stock Trends model portfolios currently published and extract trading data for the following week after trade signals (both buy and sell), we get the following statistics of the results for post-trigger trades made at the midpoint of the weekly range :
Mean of the difference between the post-trigger price change (%) and the published price change (%):-0.18
Median of the difference between the post-trigger price change (%) and the published price change (%):0.19
Median Absolute Deviation of the price difference from the published trigger price:3.84
This tells us that approximately 50% of the transaction prices obtained in the post-trigger period (the following week) at the midpoint of the price range had overall trade results within the range of 3.65% lower and 4.03% higher than the published trade price changes. Roughly restated, if an investor bought or sold the stocks posted in the Stock Trends portfolio transaction reports the week following, and obtained a price near the midpoint of the weekly price range, the overall results of the trades would differ only marginally from the posted results.
Of course, there will be varying experiences on individual trades, and some traders will obtain better or worse prices than the midpoint of the range. A mythical trader who somehow managed to enter each position at the lowest price and exited at the highest price post-trigger would have experienced a 47% improvement in overall returns. Conversely, a mythical trader who somehow managed to enter each trade at the highest price and exited at the lowest price post-trigger would have experienced a 44% drop in overall returns.
These two highly improbable scenarios only serve to expose the range of experiences that are possible given the broader parameter of this analysis – that the trade takes place within the following trading week of a buy/sell signal. The ranges expressed here would likely be tighter if the analysis were done solely on trades the day following the buy/sell trigger. One hopes that the typical trader would tend toward the midpoint (although it would also be a mythical trader who makes all trades at the midpoint of a range) and the results experienced over an extended period would be in line with that published in the model portfolio reports.
This exercise serves two purposes: first it reaffirms that post-trigger trading can simulate the model performances. But more importantly, it reminds us that investors trading their own account should be certain to always engage the market tactically, use limit orders regularly, and to make every effort to exact the best price possible in every trade. 

Wednesday, February 27, 2013

First steps toward quantitative trading

Finding the right combination of indicators is an important launching point for technical traders. Fine tuning different charting constructs, different momentum and trend indicators and back-testing results - that's the life of many market timers. Indeed, optimizing indicators and trading systems is a growing part of the investment business, as much with wealth management institutions as among the many retail traders who live by the success of their algorithms. In its own way, Stock Trends is part of this movement toward quantitative trading.

Most quantitative trading runs through finely tuned statistical models that guide both trading systems and portfolio management. The Stock Trends analysis is more static in terms of the indicators used, but seeks to optimize applied trading systems within the given parameters. The toolset used here is basic: a moving average study, a momentum indicator, and a volume trigger. But together these elements provide a powerful - and simple - means of engaging the market on a quantitative level. Each report Stock Trends publishes can be used within the framework of a trading system.

Optimizing results through statistical modelling is a multi-step process. The first thing that must be achieved is to understand your data. It is important to be able to describe its characteristics and to determine the independent and dependent variables. I have started that process in recent editorials by generating some statistical output of the Stock Trends data. The next step is to break down those descriptive statistics by category. Each subset of data that is pulled from the data history can be grouped and assessed for differences in results of the output variable - namely, the post-observation performance of individual stocks or ETFs, or the probable outcomes based on statistical predictive models.

With a given combination of Stock Trends variables, for instance, are there sub-groups that have different characteristics. We can seek to ask questions like: what are the differences in central tendencies and the distribution of results for low priced stocks compared to high priced stocks? Does price momentum propel small cap stocks higher compared to large cap stocks? Do certain sectors perform better for the momentum trader? There are many of similar questions we can evaluate, and all would be important ones to answer when developing a trading model.

Building on last week's editorial, let's take another sample of the Stock Trends data and see what it can tell us about different categories of stocks. Of particular interest is whether we can identify any divergence of performance statistics between stocks based on their share price and the Relative Strength indicator. The Stock Trends Picks of the Week report (available to subscribers of Stock Trends Weekly Reporter) provides an interesting sample of the data for discovery of important relationships between the variables. Last week we looked at statistics of the entire sample; here we group the data and compare results.

Again taking a sample of Picks of the Week selections since the beginning of 2012 until February 8,2013 we can create new categories for the share price and Relative Strength indicator and group by ranges.

Share price ranges
$2.00 - $4.99
$5.00 - $9.99
$10.00 - $19.99
$20.00 - $49.99
$50 - max($800)

Relative Strength Indicator (RSI) ranges:100-104

These groupings are arbitrary, and not based on any statistical method to create bins (groups). Nevertheless, they help us categorize the data in terms or ranges that are easy to understand.
So how do the statistics for the percentage change in price (to the current end date, February 22, 2013) break out for these groupings?

Below is a table, ranked by median percentage change of the groups (median represents the middle value of observations). It gives us a breakdown of the number of observations, as well as some important metrics of central tendency and distribution of results.

price group($)RSI groupnumbermeanstandard deviationmediantrimmed meanminmaxrangeskewkurtosis

See full table and article at

As with most tables, the first thing that jumps out is the extreme values. In particular, at the bottom half of the rankings are stock picks that had share prices between $2 and $5, across RSI values. The Picks of the Week report during the period was least populated by stocks in this price range. It is clearly the most variable price group, and also the poorest performing as shown by the low values for the median and trimmed mean (trimmed mean here excludes the top and bottom 10% of observations).

However, with variability also come the outliers - those observations we trim in statistical analysis. In the statistical world outliers are problematic. For some traders, it's their holy grail. It's the grand slam home run they celebrate. Here it would include the 500% gain on the November pick of Novogen Ltd. (NVGN), or the 277% gain of the January 2012 Santarus Inc. (SNTS) pick - among a number of other very profitable trades.

Nevertheless, when modeling a trading system it is always most important to understand the risk-reward balance. Attempting to capture higher returns on individual trades can be self-defeating. It is far better to model a system that keys on more predictable results, with low variability and a distribution that offers the best chance of long-term profits. The groupings in the top half of the table presented here would represent the more controlled parameters.

Below is a plot that compares the mean (average) percentage change in price for Picks of the Week in the sample (with a 95% confidence interval represented by the vertical bar). Also presented are Box Plots that give a visual representation of the distributions of the percentage change in price by Stock Trends RSI category range for each share price grouping.

Based on this sample of data we could focus on certain price and RSI ranges when evaluating the Picks of the Week report. For instance, we notice that relatively higher priced shares with high price momentum (high RSI values) performed better. More generally, though, it seems that stock picks with a share price above $20 provide the most consistent results. If we take a subset of the Picks of the Week sample with share price >= 20, we find the following distribution:

If we limit our analysis to Picks of the Week with a share price of $20 or greater, what kind of performance could we expect? Assuming that an investor could randomly select 5 different Picks of the Week over the period, the statistical breakdown is as follows:

This distribution is for 1000 random samples of 5 Picks of the Week, share price >= $20. Here we can see that the distribution is closer to a normal, bell-shaped distribution.

Minimum1st QuartileMedianMean3rd QuartileMaximum

numbermeanstandard deviationmediantrimmed meanminimummaximumrangeskewkurtosis

 See full table and article at

These descriptive statistics of categories are just a starting point. We should also take other samples of the Picks of the Week data. This simple analysis is presented to show that the RSI indicator can be modeled in different ways to optimize on specific variables. In order to develop a trading model we need to delve deeper into the variance and correlation of the Stock Trends indicators as well as other market data variables like trading volume.

This particular exercise, though, helps us look at the Picks of the Week report (which is a specific sample of stocks that exhibited a particular combination of Stock Trends indicators) more constructively in terms of quantitative trading. Although there are levels of charting analysis that help pinpoint technically attractive trades within the Picks of the Week report, systematic traders should understand the importance of discovering the variable constraints that correlate with minimizing variance in trading results. In particular, we can learn how the Stock Trends indicators can guide us toward developing effective trading systems.

Wednesday, February 20, 2013

Stock Trends Picks of the Week: a statistical look

The primary action matrix published here is the Stock Trends Picks of the Week report. These reports are groups of stocks, organized by exchange, that match a defined criteria or combination of variables. Each of these observations are results (roughly stated here) of the following query: select stocks and ETFs, valued over $2, that have a Stock Trends Weak Bearish () or Bullish Crossover () trend indicator, a minimum level of trading volume, and a Relative Strength indicator of at least 100. Those that are Weak Bearish must also have a high probability of being a Bullish Crossover within three weeks.

This weekly screen focuses on Weak Bearish and Bullish Crossover stocks and ETFs for a simple reason: this is the transitional trend moment where issues are theoretically primed to begin a new bullish trend. Although the parameters of Stock Trends are by definition lagging, the assumption is that the long-term trend has or will change category. Sometimes arriving late to the party, these selections still arrive in time to have the forces of trend work for the trade. That is the modus operandi for the Picks of the Week report.

However, the report does not filter down to deeper levels of categorizing observations, and as a result the list is too extensive to use without additional filtering. It is really up to the investor to match the trend qualities that are presented and the technical merit of each potential trade. That is an important question: how do you isolate the best trading opportunities from the Picks of the Week report?
The answer to that question does not come easy, and I won’t attempt to answer it in this editorial. Instead, let us just say for now that one way is to draw at random from the Picks of the Week report. Before we assign value to the report itself – never mind finding the optimal trade within the report – it is important to know how outcomes of the report stack up against random outcomes more generally. Will random selections from the Picks of the Week report yield better results than random selections from the broader population – namely, all stocks and ETFs? Surely, there should be a statistically significant difference in the two probable outcomes, otherwise the Picks of the Week report lacks credibility.
Before we look at comparisons between random sample performance of the Stock Trends Picks of the Week and from a broad sample of the data population, we can get an understanding of the central tendencies and distribution of random samples of the Picks of the Week report. For instance, if an investor simply bought 5 different random Picks of the Week selections – what would be the statistical representation of those choices? This kind of mean analysis of random samples is a common statistical method in probability models.
Let’s take a sample of the Picks of the Week report: all selections, across all exchanges, since the beginning of 2012. The sum of the weekly picks during this period is 6,599. That’s a big grouping and includes all picks right up to February 8, 2013. The inclusion of very recent Picks of the Week (those in the last month, for instance) is problematic in that it does affect central tendency and distribution of the positive returns, but not in a more significant fashion than the use of “end-of-period” returns.
There are obvious problems in measuring results for a given period. For instance, stocks may reach a high and subsequently retreat, thereby under-reporting possible results if a simple end-of-period statistic (based on the most recent closing price) is used. Also, in practical terms, stocks that have hit stop levels may have been sold before the end period, thereby reducing the amount lost on the trade. Nevertheless, we’ll simplify this analysis to make the evaluation on a very crude level. We are asking: if an investor blindly picked 5 different stocks/ETFs from the Picks of the Week reports at any point during the time frame and held them to the end-point (February 15, 2013), what is the mean return and what would the distribution of those average returns look like?
First, here is a summary table and histogram of these Picks of the Week returns (% change since selection):
Minimum value (PSN-T)1st QuartileMedianMean3rd QuartileMaximum value (SNTS-Q)
numberstandard deviationmedian absolute deviationrangeskewkurtosis
When we present the Stock Trends Picks of the Week, though, our expectation is not that every trade will be successful. However, we would like to see that the distribution of results is favourable. In practical terms, an appealing distribution will be asymmetrical, skewed positively with a fat tail to the right. While we can describe data with mathematical determinants that tell us of likely results, including simple measures of central tendency such as the mean and the median, in the end each pick represents a random sample of this subset of the larger population. We would want to see how these descriptive terms compare to the population itself.
We won’t make an attempt to calculate the population distribution now, but we can estimate that it would be close to a normal distribution, which would be symmetrical and possibly centered at the zero value, depending on the market’s overall direction. However, we can see that the distribution of the Picks of the Week sample is asymmetrical, that it is skewed positively – a long tail to the right. Half of the results fall with the 1st and 3rdquartile (the interquartile range)– between -4% and 20%. If an investor were to buy just one of the many selections in the Picks of the Week report since the beginning of 2011 their trade would have a greater than 95% chance of resulting in a return between -29 and 42%, which represents all those picks within 2 median absolute deviations (remember that the actual results obtained may be different in a real world scenario – profits booked at higher prices, losses capped at higher prices when a stock holding begins to retreat).
But putting all your eggs in one trade is not a trading plan sensible investors would endure. It is presumed that an investor would spread risk across trades. We should then be more interested in the distribution of average returns on samples of several picks. Let’s again assume that the investor makes a handful of trades in the period based on random selections from the reports (again, across all exchanges), and holds them until the end period. These mini-portfolio results should tell us something about the effectiveness of the Picks of the Week report.
A basic statistical method often used is sampling. By taking a random sample multiple times – actually many, many times over – we can estimate probable outcomes. Generally, this kind of resampling tends to prove a basic statistical truth: regardless of whether a statistic shows a non-normal distribution, as the sample sizes increase the statistic will tend toward a normal distribution. This is called the central limit theorem. However, let us take a first step and keep the sample size consistent with money management constraints and estimate that an investor’s portfolio would consist of at least 5 positions.
Bootstrapping 1,000 random samples (with replacement, since each of these samples is an independent portfolio) of those 5 selections from the Picks of the Week report generates the following histogram representation of the sample mean (average return of the 5 selected picks) distribution, as well as a summary table:
Minimum Value1st QuartileMedianMean3rd QuartileMaximum Value
numberstandard deviationmedian absolute deviationrangeskewkurtosis
Now we see that the measures of central tendency have moved up – the mean (of the sample means) is 19% - and the distribution is closer to a normal distribution. More importantly, most of the sample means are above 3% (one absolute deviation below the median). The following box plots of the sample returns and the sample means returns give us a good graphical representation of the data.
Differences in the kernel density plots are also represented by the horizontal shape of the violin plots.
Overall, this analysis indicates the Picks of the Week reports have a good record of delivering trades with above average performance. This analysis does not go deeply enough in the data to indicate optimization and does not accurately compare against random sampling of the broad population of stocks. Nevertheless, it gives us an idea of the statistical metrics behind the performance of this particular subset of Picks of the Week selections.
I’ll be digging further into the Picks of the Week report and try to isolate various combinations of the Stock Trends variables and trading stats that change the returns distribution significantly from the sample’s distribution. There are some things we can learn about price momentum and how it delivers different results based on the sector, market capitalization or the price of the stock, and other variables. Applying more rigorous statistical analysis of the Stock Trends indicators will be the primary goal of editorials. Also, while there may be room for a return to market commentaries, I am the first to recognize there are many, many sources of “opinions” about the market direction. I’ll try to stick to quantitative trading analysis, and statistical meat here. In the end, the decision about trading is up to the investor. The best any information service can do is provide a framework for understanding the risk and rewards at hand. There is no certainty - but it’s a lot better to know your odds.

Wednesday, January 16, 2013

The truth about a Bearish trend

Technical analysts assert that the market can move in an often predictable fashion. Why else do we study the patterns on a chart if not to find occasions where a pattern could be repeated. In reality, though, the only thing predictable about the market is that it will brutally squash all those deluded enough to believe it is systematically predictable. In fact, it really doesn’t matter what investment or trading approach is applied - even the most rigorous market pricing model of cause and effect is too clumsy to time the market much better than a coin toss would (check out this forecast study:  It's Official! Gurus Can't Accurately Predict Markets). Perhaps the only real merit to all of the resources that go toward modeling the market has much more to do with risk management optimization - systems and processes that help insure against bad predictions. The best offense is a good defense.
Enough about reality and portfolio management principles, though. We’re market timing believers because 1) flipping a coin isn’t as satisfying as scientific method, and 2) even a normal probability distribution of a random statistic shows there’s room for someone in the 95th percentile. Let’s just say most market timers aspire to be positive outliers – smarter than the average investor and richer for it. Stock Trends hopefully will help you be in this “heavy-tail” company and makes a proposition that the key to improved probability of market timing success is, in fact, simplicity: define a price trend, learn how the distribution of past returns of those trends favour a trade or not, and then execute an order within a trading plan that emphasizes prudent money management. Successful market timing is not so much about being a high rolling hare; it's the parsimonious trading turtle who builds wealth.
The statistical language used here may be familiar and elementary for some, but I’ll try to keep all Stock Trends commentaries as plain and clear as possible. In the past Stock Trends editorial was largely typical technical analysis storyboarding – finding existing market trends and isolating particular stocks that are in a ‘good’ technical position for a trade. Now the emphasis will be on learning if and how the Stock Trends indicators predict probable outcomes. We will seek to quantify probable outcomes based on particular observations and the combination of variables recorded. That process of discovery will be an incremental learning exercise. Let's begin!

The Stock Trends Variables

Stock Trends is essentially a handful of discrete and continuous variables. You can see them in every Stock Trends Report on the website. They include the trend (,,,,,) and volume (,) icons, RSI values, and trend counters. I will be providing more explanatory commentary on these variables and discovering underlying relationships between the Stock Trends indicators and future price performance. After all, technical analysis may be rooted in historical data, but it is future results we would like to predict.
The most important variable is the trend indicator. The trend indicator is a factor variable with 6 possible values:


Bullish Crossover (also referred to as Newly Bullish)  
Bullish (also referred to as Strong Bullish)  
Weak Bullish  


Bearish Crossover (also referred to as Newly Bearish)  
Bearish (also referred to as Strong Bearish)  
Weak Bearish  
All issues and indexes with at least 40-weeks of trading data in its time series are assigned one of these values every week. These six indicators are defined here and in the Stock Trends Handbook, Chapter 4 - Guide to Stock Trends Symbols and Indicators.
However, the major trend categories are Bullish or Bearish. Each stock - even if it were more accurately modeled to be defined as a ‘flat trend’ – must be either Stock Trends Bullish or Stock Trends Bearish, depending upon the relationship between the 13- and 40-week average price. While there are problems that go with this binary denotation, it is a simple method of grouping our weekly observations. Once we separate observations into subsets we can begin to understand the meaningfulness of the grouping. Of course, its meaningfulness can only be evaluated if the division shows different results. In this case, does the future performance of stocks categorized as Bullish (,,) differ from that of Bearish (,,) stocks? Statistical analysis of our historical weekly data tells us the answer.

Differences in Central Tendency

As an initial step toward understanding the relationship between the indicators and future performance let’s take a sample from 13-weeks ago – October 12. Since then the market has retreated, then rallied – with the S&P 500 index now up 3% from its level on October 12th. We can now analyze a basic statistic provided weekly by Stock Trends for all issues: the 13-week price change. How does the mean (average) price change vary for each trend variable? That is the simple question we will answer.
For reasons we will examine at another time, this analysis will be limited to issues valued at $5 and greater. Controlling for only those issues of common stock and trust units that had a trend indicator 13-weeks ago, there are 5,134 observations (we will ignore ETFs and indexes for different reasons). Of course, the range of price change is large – from a painful -92% (Petrobank Energy and Resources PBG-T) to 187% (Uni-Pixel UNXL-Q). The mean (average) percentage change of this sample is 4.5%. This is obviously higher than the 3% advance by the market benchmark indexes, but remember that the mean is not the only measure of central tendency. The median percentage change over this period – the value that sits in the middle of all observations – is 2.7%. Our question has to do with how one measure of the statistic (13-week price per cent change) varies between different subsets of the sample that are determined by the Stock Trends indicators.
If our binary grouping of Bullish and Bearish stocks is meaningful, there should be some variability in the median values of each group. What do we find?
Of the total 5,134 observations 3,254 (63%) had Stock Trends Bullish trend indicators – , or . The mean percentage change in the 13-weeks for these Stock Trends Bullish major trend stocks is 4.2% (median 2.3%), while the mean percentage change for Stock Trends Bearish major trend stocks (,, or ) is 5.2% (median 4.2%). Here we do observe there is a difference in the performance of the two groups (Bullish and Bearish), although it is not a result we might have expected.
Certainly, we should sample other sets of records from different times and markets to see how the two major trend categories perform relatively. Of particular interest, too, are measures of variance (like the standard deviation) that quantify the distribution of the results. But for now we’ll simply drill down into these observations from October 12 and stick with the mean statistic (average price change) as a comparison for the Stock Trends trend indicators.
Why would Bearish stocks perform better than Bullish stocks in this sample? We should have a look at the breakdown of the statistic for the six minor trend categories (,,,,,) :
 Breakdown of minor trend categories (October 12, 2012)
# of issues
Trend category
Bullish Crossover
Weak Bullish
Bearish Crossover
Weak Bearish
The most notable statistic here is the performance of Bearish () stocks – clearly the difference maker in the results recorded in the period. During the period this particular group of stocks did better than Bullish trending stocks, and better than the Weak Bearish () stocks, a group we focus on for our Stock Trends Picks of the Week selections – although a Bearish stock generally travels through a Weak Bearish category before its Bullish Crossover () and would still qualify for a Picks of the Week selection subsequent to observations on October 12. (Take note, though, of the median values and the way the differences between the mean and the median say something about the distributions of returns in each group. This is something to be evaluated at another time.)
Regardless of the reason for the impressive move of these Bearish () stocks, we should break down this subgroup further to see if there are particular characteristics recorded by the other Stock Trends variables that might indicate commonality. Those characteristics would be something we would look for in other samples.

Trend Counters

Stock Trends keeps track of the time period of the major trend – number of weeks an issue or index has been categorized in a Bullish or Bearish major trend – as well as the time period of the minor trend. (See Trend Counters) The minor trend counter is the number of weeks an issue or index has been in its current trend indicator (,,,,,). The minor trend is more sensitive to price movements and is both an indicator of strong, as well as weak and changing trends. How does the length of the major trend and minor trend indicate the post-observation results from this subset of Bearish () stocks from October 12?
Separating the major trend counter into two groups offers some immediate information.
Of the 1,342 (Strong) Bearish () observations, 263 had Bearish major trends over 26-weeks long. Most of the (strong) Bearish () observations on October 12 had been in a Bearish major trend for less than 26-weeks.
# of issuesLength of major trendmedianmean
263Bearish major trend at least 26-weeks long0.41.5
1079Bearish major trend less than 26-weeks long5.66.4
Those results again tell us something interesting: the better performing stocks were relatively short-term Bearish stocks. The mean 13-week percentage change of Bearish () stocks in a Bearish major trend for less than 26 weeks was 6.4% - much better than the 1.5% recorded by those above 26-weeks long in their Bearish major trend. 
Here we see that entrenched long-term Bearish trends are not stocks that this analysis would favour, and that a contrarian long trade has long odds. And some Bearish stocks are not as entrenched in a trend as others. 
That a trend ends is no surprise. They always do… eventually. Nor is it surprising that some trends are not as pronounced or as forceful as others. How can we evaluate the character of a trend? We can drill down further into the trend counters to see if there is something about the quality of these Bearish trends that perhaps would provide a cue to the change ahead.
The minor trend counter is an indicator of the quality of a long-term trend. If the minor trend counter is relatively low compared to the major trend counter, we know that the major trend has included shifts into the “Weak” iteration of the trend – here a Weak Bearish () trend. A low trend counter for this group indicates a recent switch back to (Strong) Bearish (). This obviously shows that the long-term trend is not as entrenched as those with a minor trend counter that is relatively high.
What does the data from October 12th show? The best performance from these Bearish () stocks (major trend < 26 weeks) are those that have been in a Bearish minor trend for a relatively short time.
Bearish stocks with major trend >= 26 weeks and minor Trend < 4 weeks
# of issuesMinimumMedianMeanMaximum
Bearish stocks with major trend >= 26 weeks and minor Trend >= 4 weeks
# of issuesMinimumMedianMeanMaximum

Bearish trends bend 

We’ve laboured through this elementary statistical exercise to illustrate that while Bearish trends do reverse, that stocks eventually complete their downward drift, transformations from bear trends are often part of a process that is akin to a bending of a line, rather than a breaking of it. Finding potential breakout stocks in the Bearish () group is risky, though - regardless of how statistically appealing this analysis of observations presents. This is the reason Stock Trends focuses on Weak Bearish () and Bullish Crossover () categories in our stock selections and trading strategies. They are transformational categories by definition. However, Weak Bullish () stocks are also transformational and certainly of interest since many of these stocks rally back to their strong Bullish trend (). We'll take a statistical look at that category soon.