Recent behavior as a guide in small-cap stocks

Guessing, randomness vs behavioral pattern recognition

If removing guessing from trading is one of the priorities that everyone should be striving for, then finding methods to reduce guessing and increase probabilities should be as well on the one's priority list. Any clues that price action and its behavior can leave you might be beneficial as it removes the guessing just a little bit. Typically no single clue can remove the speculative aspects enough but if it can increase the probabilities just enough to create an edge that might just be enough. It isn't so much about finding clues that provide very highly reliable probable scenarios, but finding just enough of those so that probabilities are raised above the guessing/randomness enough so that edge can be developed. Often 10 or 15% increase in probabilities could be enough, for example, a 15% increase from 50% is already potentially good enough to create an edge.

While that isn't surprising and many aims to seek such clues there are issues on how to do it. What to look for? How to tell if a clue is just cherry-picking and forcing beliefs based on a small sample size, or is it something that is significant enough in terms of statistics and accuracy that matters and has a good chance to repeat, with larger sample size in focus. Those answers aren't really easy to find.

When it comes to recent behavior, the number of repetitions matters as well. If the behavior was to be more trustworthy and repeatable, it needs to prove itself at least a few times. This might sound logical, but too often traders draw conclusions just from something that happened only once. Only one example on left is a potential for behavior to repeat, two repetitions are better than just one, and three might already signal decent consistency, meanwhile anything above the four could be already firm enough to confirm that behavior indeed is repeatable.

So the basic rule is, if you are unsure whether some behavior or some pattern on left should be used as a clue, the larger the sample size, the more trustworthy it could be. Especially for beginners, it can be difficult to tell if the single pattern is trustworthy because typically trustworthiness of a single or low sample size will be the devil is all in the details. The less the sample size, the more extrapolation of details is needed typically, the bigger the sample size, the fewer details are needed. This is a good starting ground for someone trying to deploy recent behavior into the edge, make sure that a small sample size of behavior is only applied as a guide on future expectations once you have already decent experience in practicing that. When in the initial stages of experimenting with it, focus as much as possible only on behaviors that repeat as many times as possible, but the general guide will be more than three times. The reason why the sample size is relatively small (3= is because we are talking about small-caps specifically and this market niche will have limitations of just how many price patterns it can provide within a limited period, let's say 2 weeks. Typically because there are only so many tickers in play and only so many candles of price action that will develop in that time, you won't get a chance to see 100s of patterns in a short amount of time, the math will be a limiting factor preventing that from happening. This especially means that it requires a lot more adaptivity from anyone on spotting those repeatable behaviors because chances of picture-perfect replications in small sample sizes have a lower probability than they do in very large sample sizes.

The image above addresses some of the issues that you might run into using recent behavior in small-caps as a guide for future expectations. Small sample sizes have credibility issues because the credibility of data is best proven through larger/sheer numbers of validation. In other words, the smaller the sample size, the more potentially subjective conclusions could be. Additionally, overfitting might be an issue, you already might have pre-bias on where you wish the asset to go, and then the previous pattern "on left side of chart" might be set along with that same direction, where the currents pattern conclusion of direction will then also be made same, just so that you overfit it to a same desired result. To sort out those issues takes a lot of experimenting and experience, at least from my experience there is no other way to improve that method in the short term.

Small sample size issues therefore:

-subjectivity

-overfitting

"Rather than luck give me the full truth instead"

If you are like me then you might dislike randomness and luck. If seeking order is your thing then patterns are often a good tool to bring you the necessary clues to highlight order from the randomness. One of the major reasons why one should be using the recent behavior of tickers as a guide, (if it is repeatable behavior) is that it provides you better expectations and insight on what price levels to focus on, or where the tops or bottoms might be relative to just completely guessing it when you don't have recent past behavior to lean on. In short, it reduces the guessing and increases the accuracy of "predictive ability" and i use the word "predictive" in quotes because it's a sleazy word too often used in markets. If you want to involve any sort of serious data-driven or more science-driven approaches predictability should be carefully used so it doesn't become just fortune-telling show.

When you don't have any prior clues of price action on the left side of chart or near history it can be difficult to build any sort of realistic expectations on what the ticker should be doing today or in the next few days in the first place. Additional help can be if similar tickers in the recent past have been moving and providing those clues, this would be the so-called themed-tickers.

So clues for price behavior can be found potentially either:

-on the same tickers chart on the left side,

-or prior tickers (if they belong to the same theme) that have traded over the past few days by checking their charts and applying their observations on the current tickers chart.

Typically traders make mistake by either applying too much of the first (same tickers behavior) or too much of the second (prior tickers behavior), not mixing them both well.

For example, trying to extrapolate what the current ticker might do only by using its historical behavior as a guide, without actually checking what other similar tickers from the past few days have done. This would qualify as an example of using too much one without the other. The more likely accurate behavior guide will come from using both as a mixture rather than just only one of those methods.

Remember that, tickers trade within the cycles, and cycle momentum is contagious, which means the tickers influence each other back to back, but each ticker has also a certain own character that drags from the past, which binds its possibilities somewhat but not completely. Keep in mind that even a very strong "character" can be broken or changed if market conditions change enough, this explains why the hybrid approach is always the most accurate to adjust to change when needed.

Both of those methods should be combined as a method of defining the potential price movements.

Either of those two approaches is valid to reduce the luck, however obviously, only if one compares the right tickers together and doesn't bulk apples and oranges to make extrapolations. It's there where the whole thing complicates a bit. For example in small-cap stocks, traders will use a (1). 15 USD priced (2). 80 million market cap, (3). strong rallying ticker from recent days and extrapolate what the current (1). 4 USD ,(2). 10 million market cap, (3). slightly fading ticker might do aftermarket open, which could lead to complete noise in expectations because the characteristics of each are too mixed. Reducing luck and increasing insight on tickers is much more difficult than it seems, many models that traders use are not refined enough to provide credible accurate insights, and most models will just be misleading. Most models are also never tested at their accuracy which makes them questionable in the first place.

For themed tickers make sure the tickers are as similar as possible, within all the criteria they possess and display.

Focus on repeatable behavior

As we have established the behavior should repeat few times to be worthy extrapolation of potential future outcomes. It should repeat once, twice, three times, or more. The more times it does, the better, the more it creates the character of the asset, each asset has one particular niche of behavior that is most cohesive.

Smallcap tickers often have their own niches, and certain nuances of behavior, the more you can figure those out the more accurate your read on ticker might be. Trying to zoom into behavior that repeats the most on the ticker is often a good way to achieve an increase in the read of the asset. Every ticker will have one particular behavior that stands out as its most repeatable, however that won't necessarily be always very clear in real time, in some cases things are clearer to see in hindsight with a calmer mind.

You should always look on 1-minute charts and then 15-minute charts to see differences of micro or macro behaviors that the ticker displays so that a better picture can be framed, on just which behavior stands out the most on that particular ticker. Always compare different time frames together to see what stands out, rather than being zoomed strictly just on one.

The reason being, is some tickers have more clearer behavior consistency on higher time frames, and some have more on recent intraday action.

Examples of repeatable behaviors might be:

Distributions: some tickers like to form consolidated ranges often that result in breakdowns. Accumulations: Some tickers have very shallow trend pullbacks and tend to keep running higher every day until they start to hit the parabolic stage. The shallow pullback therefore will be its repeatable behavior over 5 10 or even more samples in a row.

Breakout continuations: Some tickers form consolidated structures that breakout and each time there is strong squeeze following. And such behavior could repeat 3,4,5 times in row.

Etc...

There are many different variations of just what that repeatable behavior might be. It will also depend a lot upon your eye, can you see it or not. Charting experience and research are needed for this, there is no shortcut to that. This is also strictly price-based behavior, you will need to study price action and structures, as using indicators will not highlight enough of details needed to spot behavioral repetitions with enough insights in most cases. Its not that behavior of price relative to indicators does not repeat, because it does, it is that the accuracy of those replications is too low to be applied unless some additional good reasons are used.

Think of the whole behavior of price on the specific ticker in zones, rather than as a whole combined total. Your task is to spot a few zones that repeat within the whole total to extract the edge. Each ticker might have 70% of price action that is noisy or seemingly random, your task is to spot a few zones where the repetition does stand out.

Example of noise vs repeatable recent behavior on ticker GOVX, where in second case solid long play was set using prior behavior on left as a guide on squeeze.

The above example is good one to highlight why you need multiple variables that define behavior in one particular zone, so that subjectivity and noise are reduced. Having only one condition for each zone (price above X moving average for example) will yield too many failures and non-exact guides (big behavioral disconnects). Think of it as stereotyping mistakes. Most stereotyping mistakes come due to too simplified definition that isn't well enough defined through enough angles. If you aim only through one angle your hit and miss rate will be terrible and close to randomness. To define edge above randomness specificity is often needed, so define behavioral zones through more variables than just one.

Focus on observing those micro patterns on the left side of the chart as potential guides for repetition of price structures:

-accumulations

-liquidation swipes (clearouts)

-failed breakouts or successful breakouts

-distributions

-ranges versus impulsive moves

Most of those have been explained in previous articles, so they should give you a good idea on just how each structural pattern is constructed of a few key variables rather than just one thing.

Figure out from the above 5 micro components what image current ticker suits the most, so that you have a rough guide on its character.

The idea is to figure out how much of each micro pattern above it likes to repeat, to see which ones to focus on. It should go without saying, that this requires a lot of practice before it gets deployed well, so if you feel lost for a while that is normal.

As mentioned above, each ticker has a bit of a different character. Your focus should be to roughly define just which behavior tends to be the most in play currently or has highest repetition rate. Once you spot that pattern again in development in real-time it might provide you important insight into repeatable behavior coming. However don't expect that from all tickers, some tickers are going to be more randomized and won't repeat on its behavior too much. Some will and some just won't, the idea is to hunt for those that do recycle its behavior as much as possible.

Conceputal example:

But remember do not be zoomed too much into a single ticker and its past price guides, it is also important to be in tune with the current theme and what other tickers of same theme are doing or did do recently because one ticker might feed sympathy flows to another ticker if they are somewhat similar tickers. For example, two multiday runners can each set accumulations at the end with breakout and squeeze higher, where the first MDR ticker gave such pattern and then the second one developed such accumulation later on same or next day, but the first ticker was a key guide for it. Patterns often go in themes, so recent behavior does not just apply to the same ticker itself, but across different tickers, if they belong to the same theme and have similar attributes.

Below are examples of three tickers (OLB, CRLX, BCDA) with repeatable behaviors across the day, where behavior is split into different segments (red and orange boxes). The reason why observing behavioral repetitions across different tickers rather than within the same ticker is often more useful, is because there will be a limited amount of price data available in each US market session, so behavior has less chance to repeat within the same asset and more chances it repeats within few assets because one needs enough candles/bars in the first place for behavioral clues. 1 ticker has a total of 8x60 1-minute candles across US session, but 3 of them have three times as much. So the math does the job on the rest.

Behavior composition for those three tickers above goes:

-cheap 1-2 USDish ticker (under 4 USD)

-medium gap size in pre-market (under 70% but above 30%)

-lower pre-market liquidity (volumes per 1 minute are under 200k)

-some push in the first 30 minutes (smaller % extenson before topping)

-distribution within the first 30 minutes

-neutral range for 2 hours (mid day)

-range breakdown and unwind until market close

-all tickers are all-day faders but with a soft fading tilt, not a harsh one

Above three tickers are therefore good examples, where one doesn't look for recent behavior as a guide from within the same ticker but looks at other tickers that belong to the same theme and have similar attributes, and then uses their behavior as a guide, as long as it is somewhat already showing the similarities to current ticker in play. This is especially useful when you try to form an opinion on what the ticker might do before the market already opens in the premarket. Because current ticker wont have enough of the current days price "printed" yet, it is only possible to draw conclusions or observations from similar recent tickers that already traded full day and "printed" the needed chart data.

This might seem like a complicated mess to establish just what qualifies for repeatable behavior, but sadly that is an approach to go for if one wants higher accuracy. There is only so much accuracy that can be extracted from the opinion on "two tickers that went up 50%" as the only identifying factor.

Recent vs older historical behavior, advantages and disadvantages of each

Some are already familiar with the fact that my prioritizing of recent price action movements is much higher than a historical one. Whether its recent resistances vs older ones or recent maximums vs prior older historical ones and similar things of such, recency is prioritized often.

If comparing the past observations it is often needed that both past and recency are compared from similar market conditions (or cycles) and by focusing on recency the need to define those conditions well is reduced. It is just more likely that over recent days the same cycle and market conditions are present, while historically it could be that past historical situation was unusual one or its conditions were different to current market conditions. This explains why recency is prioritized against "older" history because it's less likely to make overfitting mistakes.

The reason why past (recent) behavior plays a large role in increasing the edge within trading, is that without any references much about trading can be often just guessing. Pretty much any edge will be built on historical data or behavior, however, since market conditions over a long period can change drastically (but typically dont stay in that very changed state for too long) it is often rendering such historical data lower in performance, and often noisier. The shifts of market cycles over time introduce the noise into patterns of behavior and render its commonality in behavior more chaotic, therefore decreasing the use case of such data into prediction-based discretionary trading models where one has to build expectations on traded assets.

Example of the problem caused where very old historical behavior is used as a guide without addressing the fact that the market might have been in very different cycle conditions and strength of momentum previously than it is in recent pattern example:

This especially is a common issue for those that focus on gap% fade or gap % rally historical data to draw extrapolations on current expectations vs past performance of ticker. Or perhaps saving charts of intraday behavior on the same ticker (and its past gaps and intraday moves) but they do not adjust expectations based on how current flows of the market are stronger or weaker versus the past examples. This often skews the accuracy of those expectations a lot more than traders would like to admit. But in reality, many do not even notice it, because it takes extreme market conditions to start observing this in action. Since more times than not the market will stay within its more usual/normal behavior, it will require more unusual behavior to notice just how much performance of a single ticker can escape its past norms. Since most traders do not track cycle momentum and market strength, these differences are never noticed until in hindsight. This is why focusing only on recent behavior rather than very old historical behavior can often help to solve some of those issues, since it is less likely the market conditions have shifted a lot recently.

This is not to say that old historical data has no use case or is misleading, not at all. It can be very useful, the issue is it has to be carefully used. It is often not handled with enough care. Gap% fade as example guide given above, has to be used when market conditions of past are similar to current ones, to use that gap % data as guide. In strong cycles tickers will perform often much stronger % moves than in weaker cycles, so not addressing that is a major mistake.

Example of how patterns behavior performance might change relative to its history if market conditions currently are much different to past ones:

Focusing on recent behavior only, rather than historical therefore has the major advantage that it automatically solves that problem for the trader, where the trader no longer mixes the data from strong or weak cycles and very different market conditions into building the expectations. Over a short while (1 week for example) chances are that the market will not shift in its flows from very strong to very weak, but rather stay within more similar conditions, it creates recent behavior as more trustworthy. The noise is somewhat reduced. It is not a final solution, but it is one of those steps that every trader should implement at least somewhat to reduce the surprise effect, which typically comes from relying too much on historical behavior without understanding the context.

The disadvantage however is that not all assets will provide recent behavior guides to be deployed. Historical data will have an advantage there where no matter what ticker it is, chances are that there will be someday in the past where its data will exist and might provide you reference. Or past macro chart itself could be such a reference too. Therefore historical (old) data is always ready for you to be used, but the question is its quality and reliability (if past market conditions aren't defined). If market conditions right now are very different from the ones you are using the data from it could be that data might be very misleading, and the asset will therefore behave very differently. In small-caps stock trading, this is a highly common issue. It is one of the reasons why I shifted more focus towards recent data rather than very old historical data because it solves a lot of issues for my trading and accuracy of expectations but as well for many other traders that I communicate with daily.

Perma-bears use more historical behavior, while perma-bulls use more recent behavior

An interesting observation is that perma-bears often focus strictly on historical behavior because they feel like it's the end-to-it-all type of data from which smallcap tickers can't escape (since it's more fade tilted rather than squeeze and hold tilted). It simplifies the reasons why everyone should be bearish biased in their view.

Perma-bulls on another hand, especially beginners will focus on recent behavior mostly, seeing a recent ticker that squeezed tons and use that as a projection of what the next tickers might do. In most cases, the issue will be, that it might be too much driven by greed, and it might often be too late. In most cases, bulls will use only extremely bullish recent behavior as a guide, which will be near the peaks of strong cycles, just before things inverse. This explains two common traps that each group steps into.

But the point is that every so often each side will be very right and the other side very wrong. Which explains why for accuracy one should know when to use either expectation and adjust well, hence the hybrid approach as outlined on the article above.

If there is such an advantage in using recent data then why it is not much more used in small caps?

1. It is more difficult to be objective with recent data guides (takes plenty of experience) to understand the difference between cherry-picking expectations from the single repetition of a pattern, versus where it does matter and it isn't cherry-picking because other confluence factors align as well.

2. You have to know what to look for. Behavioral patterns such as fake breakouts or floor breakdowns and reclaims are not well known or discussed, therefore chances that many are paying attention to needed micro behaviors is already low from the get-go.

3. Day1 gapping tickers will not provide any recent data (until mid day), and since many focus on trading on specifically just those (freshest and highest liquid ones) the advantage of using recent behavior as a guide decreases by a good amount (almost 50%). As mentioned previously, ticker first needs to trade several hours to print enough of behavior to even seek for clues intraday (on recent data). And since most short-sellers like to build their thesis on asset within pre-market that makes use of recent data less appealing.

Tickers of focus to seek recent behavioral clues

Tickers that qualify:

1 Multiday runner (same ticker across multiple days repeating behavior)

2. Themed tickers split across a few days

3. Same ticker with replication potential within the same day

Split into probability areas or clusters, most repeatable behaviors will be present on multiday runners, mostly because those will have more price data and therefore chances to present repeatable behaviors. Another section of tickers just behind those will be any tickers really that have a common theme behind them. For example, day1 tickers on gapping catalysts could fit such criteria, but mind that behavior on those will become themed and repeat within patterns only every so often, and often it might be completely disconnected. Markets will go in phases. The last section but typically the most reliable one is when on the same ticker the behavior repeats. This will classify as replication. This is most reliable because on the same ticker the liquidity conditions or market cycles won't change and it is most likely the same market makers will reuse the same trick from before, making such behavior repeat more trustworthy.

Below is an example of two different thinkers within two days back to back (Monday/Tuesday) with repeatable behavior. Another ticker followed with the same behavior where the first two were used as potential behavior guides. This is an example of 2nd example of themed behavior.

Fake breakout (fbo) behavior

One of the behaviors that any smallcap trader should track is the HOD breakout or fake-breakout performance/response rate. It is one of under-deployed edges that if done correctly has often a good risk to reward ratio.

The reason for tracking different sections of behavior around HOD levels is that it creates a better picture for an observer on just what kind of expectations are best to form on the current potential fake breakout if the ticker was to signal one happening as a good probability.

Not all fake breakouts are the same, and the fact that they are quite different in their composition is what makes it more difficult to trade, and often quite a frustrating process (unnecessary losses due to poor read). It becomes somewhat more controllable and smooth when the trader focuses on just the right components that together form the right expectations. Keep in mind, when trading fake breakouts it's not about that one thing you have to pay attention to, but usually a combination of a few variables at once.

There are two approaches to trade fake breakouts, one is to avoid losses on the long side by spotting fbo correctly, and the other approach is to actually short it. Regardless of the direction of a trade there is applicability.

There are three key sections to track:

-the chances of fake breakout (FBO) around HOD level

-the distance (in cents) of overshot on FBO

-survival time

-speed of reject on fbo

1. Defining the chances of a fake breakout happening:

Without getting this done right everything else might be forced. The way how chances of FBOs are primarily determined is by looking at past tickers or trading past tickers' price behavior around HOD levels. Did many breakouts result in quick rejects and unwinds, therefore FBOs, or did they have a decent squeeze? The overall stat rate on the past 10 tickers will provide you some insight if we are currently in market conditions that are more prone to fake breakouts or not. Start with this, and if the answer is yes progress towards defining the other 3 conditions for better accuracy on just how approximately the price should respond if another FBO should happen. And keep in mind, try to be selective, if the market is suggesting currently that stats are quite neutral and mixed, do not deploy any strong opinions or avoid forming on whether the next breakout might be a good result or fbo. Be selective, wait for the market to shift into theme where one type of behavior becomes relatively better at consistency and only form opinion there.

2. Distance of overshot:

Due to different liquidity conditions distances of FBOs can be very wide or in some cases very small. Some FBO distances can only go 2 cents above HOD level especially on cheaper and thicker tickers, while other tickers typically higher-priced and more illiquid ones will have overshot distances wider, such as 20 to 40 cents. If distances in past have clear similarities use that to know where your ideal short entry might be so you do not force entry too quickly and then get slipped to the upside. This can also be helpful if one is long into HOD swipe and you are trying to decide where to sell that long, the FBO distances on overshoots are a very important deciding factor for that. 5cents extra squeezed distance could be the difference between an extra 1 R.

3. Stall time (survival time above HOD):

Some tickers survive above HOD levels for a longer time, while others reject back under very fast. Some will form micro shelves above HOD levels and then slowly unwind and complete fake breakout, while others will reject fast. Survival time does not typically impact your entry or exit price decisions since it will not impact how far the price goes, but it can spare you some extra minutes of mental power, by not entering the position too early and having a ticker chop around before it delivers the move requested.

4. Speed of squeeze and reject:

This will let you know what kind of aggression the MMs like to use on a particular ticker to initiate a squeeze into the HOD level and how quickly the selling will begin after it spikes above the HOD. This can vary from ticker to ticker by huge variation. Some tickers have very sharp and fast FBOs that reject within just a second or two, while others will be much slower and have total speed distributed to around 3-5 minutes of the whole break and reject the move. Being observant of if there is any consistency in the speed of the FBOs in tickers' recent behavior can be a very important guide. The last thing you would want is to miss on short entry because of thinking that you have a whole minute to decide on an entry while recent tickers' behavior suggests that all FBOs have rejected back down within just 10 seconds or less.

Above 4 conditions are just one example on how to do this for fake breakouts, using recent behavior as a guide, but this should in fact be deployed to any type of price behavior, the method is universal and used as a guide on fbos for sake of one example.

Let's use the conceptual example of a ticker that has the consistency of fake breakouts that deliver similar overshot distances and also have a similar speed rate of push-pull. This is how a trader would use past behaviors consistency to form a decision on the next FBO in terms of entry and exit prices along with risk levels:

To put the above 4 key points into use, let's use the ticker ABVC from the image below.

-chances of fbo: High. This was present when many fbos were happening daily.

-overshot distance: Large. Swipes above HOD more than the whole depth of the structure.

-survival time: Very short, ticker spends little time above HOD.

-speed: Fast. Ticker swipes into FBO fast within 10 seconds, bigger distance.

The above is an example of how you should always think about tickers and measure their activity on a particular behavior. So let us say that theoretically today is Monday and ticker ABVC has traded with such fake breakout as above, and 3 key identifiers point to where they are theoretically above. This data should then be used on the next 2-3 days of similar tickers because there is some chance the behavior might follow that suit. Again if there is only one such ticker with a guide it is not as trustworthy as if there are two or three, the more reliable it can be as the market shows it is respecting the theme more.

So the idea to grade the behavior of fake breakout or any other specific behavior is to set the tone on expectations on what might be following for the next breakouts. Having these specifics laid out well can create a better plan on just where to enter positions and where to set stops. This is a practice type of activity, not learn it once and be done with it. It is not hard to learn the method outlined above, and many traders will actually notice those things without the need of anyone to explain them. But what really makes difference is the adaptability rate. To deploy it successfully it all depends on how quickly you are able to spot theme change, as that is the real difficult part that most wont get done right without the practice and well laid out method as the one explained above.

It should be as well noted one important thing, do not overdo it or force it!

For example, if a ticker has fake breakout behavior from the recent past, but those fbos are very different in their behavior (distances, survival time...), do not try to pick some clear guide out of it. In some cases for whatever reason, the behavior will be very noisy and all over the place and you just have to accept it as it is, and not force some precise expectations on what should happen on the next potential fake breakout, because that is a sure way to oversize and get into a frustrating battle with the asset. Know when to stay away from forming an opinion. Only form opinions when behavior has decent consistency and similarity, using the above 4 key variables as a crossover determining factor.

A conceptual example of too noisy behavior in recent past for FBOs specifically:

Let's highlight an example of RDBX which was a recent ticker with plenty of FBO behavior consistency. One of the cleanest examples of 2022. Tickers that attract fbos are often:

-Between 4-8 USD price range,

-Dynamic (jumpy) spreads (4 cent spread distance to 8 cent spread distance for example),

-Liquidity peaks (high volumes) near highs at obvious scale

Those are going to be typically tickers that will display more FBOs than other tickers, however the ratio is not a holy grail obvious at scale. For example, the advantage might only be 10-15% in total, but those small add-ons do matter in long run. RDBX ticked the box on those conditions quite well straight away.

Don't box yourself, go long or short depending on what fits best for recent behavior

This article is focused on repeatable recent behavior, which means that the aim for traders should not be to deploy short-only fbo trading ve e , or long-only breakout approach. But rather as the article suggests to be adaptive based on what previous behavior suggests, to deploy the best method for the current job or environment. Instead of forcing a single approach, be adaptive to what might have a bit better chance to work currently.

-go short when conditions and recent behavior fbo is more likely to happen above HOD level rather than an explosive breakout.

-go long when current market conditions favor strong momentum and recent behavior also suggests breakouts have delivered.

Rather than forcing the same approach each time, be adaptive based on what suits best the current situation based on recent behavior. Sometimes certain theme might be in play for a while before it shifts, so juice it up before it runs dry and then only switch.

Pushing the same approach (short or long-only) into HOD breakouts can be a very frustrating way to trade because in some months the market becomes very consistent in delivering only either strong breakouts, or each HOD breakout will result in a fail. It is much healthier for your own sake of nerves and morale to keep your pulse on when the market shifts to such a theme early and then adjust your approach. Chances are that at least for while such a theme might remain present before it shifts again.

And sure while being flexible is more difficult than to be completely robotic about trading using the single-only approach to not get confused, that is not the point of this article. It is to argue that flexibility and adaptivity are superior qualities that traders can deploy, but obviously, all at expense of more study, time dedication, vision, and short-term pain before things start to click.

Adjusting behavioral expectations on range expansion

Using fake breakouts as an example of why adjusting expectations is a must if volatility has increased a lot or decreased for that matter on tickers example RDBX below.

Often a typical mistake is made by those who try to observe patterns but they fail to adjust their expectations now that the ticker range has expanded a lot. If this is not addressed correctly you will likely set false expectations and have a wrong read on the price. With other words, the setup will perform with much wider or smaller range to the point where failure or success of pattern becomes questionable to a trader who has not recognized much more expanded or contracted volatility from previous left example.

Therefore always be mindful if you are drawing current expectations on particular behavior from tickers past, but current volatility is much higher or lower than it was in the recent past for that ticker, you have to adjust your expectations (plus if volatility has expanded, and minus if contracted). This is why you should always focus on price structure itself, price behavior always scales relative to the structural size.

For example, that means, if volatility currently is much higher, the fake breakout distances could be much larger to the upside before the price comes back down again. It could be that breakouts could also lift price much higher if they are successful breakouts when volatility is expanded, versus prior breakouts that were when the ticker was much tighter in volatility. Because of this, implementing volatility adjustments of price relative to context is important to build more accurate expectations for price moves.

Looking left without realizing that the current right is many different inflows than the recent left was is a sure way to get in trouble and create under-expectations or over-expectations.

A good basic rule therefore is:

-if volatility (range) is 2X currently than what it was in the recent past, add 2X to distances in cents of all pattern targets, and as well 2x in time length in terms of delivery reactions. It's very rough and won't apply to every ticker that clearly, but it is a good start non the less.

The length of structure impacts the behavior conditions

Below are two different images of the same ticker CPOP on a different day. In the first example, the ticker delivered two FBOs, with stall (survival) time above HOD around 5 minutes on average and similar price distances. In the second image example, the stall time was much shorter as the ticker rejected much faster and the distance was a bit shorter too.

Overall we can conclude that ticker is still very prone to fake breakouts since it delivered very clear ones on those two nearby days, but still, certain critical differences are there, typically those happen due to structural length and consolidation. Understanding this can improve your timing on behavioral replications, more about that lower.

As those two examples highlight, it is not just about identifying chances of fake breakouts or clearouts, but it helps, even more, to anticipate just how the price will behave to improve edge and expectations, for which structural consolidation will be a major contributor to get this done correctly.

To highlight, survival time:

-1st example: 8 minutes on average

-2nd example: 30 seconds on average

1st example on the image below with fbos that hold longer before unwinding.

2nd example on the image below for the same ticker CPOP on few days later with much quicker fbos.

To understand just how the length of structure can impact the behavior of fake breakouts, let us use conceptual example of the image below:

The lengthier the structure:

-the longer price can linger above HOD due to more liquidity exchange after swiping the HOD level

-the bigger the distance can be above HOD swipe, the more liquidity is likely to be trapped in a bigger structure which makes the possibility for stronger cascade liquidation move with a spike above HOD lasting longer and traveling a bigger distance

-price is slightly more likely to form micro shelf, because market makers can trap more fresh liquidity above HOD

The shorter the structure:

-the quicker the price might reject and come under HOD because there won't is enough liquidity exchange above HOD to sustain it for enough.

-the spike distance might be more limited because cascade of liquidity will be smaller on scale

-price is less likely to form micro shelf (and rather reject faster without shelf) because their wont be enough fresh liquidity exchanging above HOD level

But please keep in mind, that the ratios are not massive at the difference. The differences are noticeable, but to the untrained eye, it might not be so, as there could be only 10-30% differentiation no more. For example, just because the structure is lengthy at consolidation (4 hours) it doesn't mean that now the price has 400% more chance to linger above HOD for a long time as compared to if the structure was much shorter consolidated (30 minutes). The total difference might only be 10-30% no more. But those differences are what make or break the edge.

Understanding this concept is significant because it helps to form better adjustments to expectations. For example, even if one is tracking the same ticker and using past behavior as a guide currently if structural consolidations are currently much smaller or much larger this should impact the expectations and the trader should adjust a bit. That is what the above conceptual image and the examples for ticker CPOP signify.

This is the example shown for fake breakouts, but this concept should be used for really any other micro behavior where one is using guides from the recent past. Whether it's breakouts, bounces, squeeze distances, accumulations, and their projected targets. The same adjustments should be done, depending on the structural length of consolidations, if one is using a pattern that relies on structural composition, which the majority of patterns from my personal playbook and those explained on this blog do.

However it goes without saying, one has to test and research all of that, rules outlined on the article are done through years of personal research, you have to make sure that those rules of your own patterns and behaviors of price and conclusions are first properly tested before applied. Behavior always leaves clues, but to the untrained eye it might not be visible, nature and markets keep things hidden to the eye of the beholder often, the agenda has to be first thought of, then tested, and if passes a test of certain consistency to be applied.

Tracking fbo distances to project the ideal short entries or were to sell the longs

It is not just useful to observe what kind of behavior ticker likes to repeat, but also to measure nuances around that behavior. Distances of how much it dips before it rips, or how much it overshot a certain level before returning lower, especially measuring visual distances or fixed unit/cent distances can be very helpful. Market makers will typically repeat a lot about their behavior because there will be certain liquidity reasons for why doing it. If ticker trades X amount of shares volume per minute, it might make to push into breakout only Y amount of cents distance before pulling ticker back. And as long as that liquidity does remain within that similar ratio of X amount traded shares per minute, the market maker might be incentivized to keep a similar distance of Y amount of cents on that pattern to repeat. Because it might make economical sense to do so, and not a cent more or less.

To use the example from the conceptual image above, there are two methods one can use to measure fbo distances. Typically 1. measuring cent distances is the simplest way to go about it, but to scale with volatility through the day it is often useful to 2. measure it in structural depth units.

You can either observe that and keep it in your memory depending on how much experience you have with pattern and behavior tracking, or you can do a more detailed approach of keeping your notepad occupied with all the behavior characteristics being tracked and write them down. This is especially helpful for beginners who are trading a few tickers at once and might forget what is key behavior to track for a particular ticker.

The summary on that is since fbo distances on the same ticker sometimes tend to repeat quite closely, it is worth paying attention to that behavior because it can provide you additional insight into where the potential top on the ticker might be after it goes into the fake breakout. But as always remember that you need to see the ticker first going into fake breakout at least once before such expectation can be applied, more ideally actually not just once but twice, so that theme is more clearly set.

Example on ticker SIGA of how measuring fbo distance helped to project where ticker might top out in next fbo using that as one of reasons for short position and to sell long.

Average depth of price retrace

Another behavior that can provide a bit more insight is the depth of retraces. Again as with any behavior, it needs to show some consistency and rough similarities if it was to be useful as projection. Make sure to never use any such guides on the ticker that is all over the place, and has a very noisy price action over the past several days. This behavior just as any other requires decent symmetry to be useful when it comes to drawing future projections.

Textbook example on multiday running ticker where the past depth of pullbacks would be useful as a guide on current expectations is where trend progression is very even and symmetric:

This is especially useful behavior to track in multiday running (MDR) tickers.

It can create a bit better expectations on just where the trend should be defended, and if it is not, it could signal some insight that reversal might be due. In day1 tickers, this behavior is more rarely useful because ticker has to be on a consistent uptrend in first place, which not many tickers will meet as criteria.

Such an example can be seen on the MDR ticker ATER which had a relatively similar depth of retraces and each structural consolidation took approximately a similar time before swiping into fresh macro highs. This makes previous behavior more reliable as a signal on where the next replication (dip) has to defend. As it failed to do so, it provided an additional clue that perhaps the trend failure was due and a downtrend might start.

You would want to see at least 3 such examples of behavior to repeat if using the depth of pullbacks in your guide for assets direction or certain bounce levels. Two of them would be a bit too unreliable, so three is a better starting number just to kickstart more samples. 4 or 5 would be even better. And remember, be watchful of all components around the behavior that further confirm the behavior is trustworthy to use as replication.

Performance of breakouts (fbo or actual breakout delivery)

Tracking the performance of breakouts above very obvious macro highs is an important behavioral condition to track. It signals the strength of flows on the asset, as well as how manipulated the ticker might be against the retailers. Some assets just aren't much manipulated and others are a lot. You would want to establish some rough idea on what scale your current tracked ticker is, high or low.

Keep it in your mind, track it on paper, or just make a quick check every day by checking the chart on the left of the ticker to refresh what kind of consistency of breakout-followthrough or breakout-failure behavior the ticker is displaying. If the behavior is stacked to the failure side, you should not have high hopes for longs when trading breakouts on such a ticker. Or if trading short with expectations of fake breakouts but ticker not displaying any such past behavior (most breakouts delivering decent squeeze) you are likely fighting the odds on the wrong side as well.

Example on ticker IMPP where shorting potential swipe of HOD might be valid idea more rather than longing it because prior example confirmed a failed breakout above HOD.

This micro behavior tracking applies to all tickers, whether it's intraday tickers or multiday runners. The chart scaling is all relative make sure it is being tracked on all tickers. As usual, if there is consistency towards one side of behavior do not fight those odds, chances are the behavior will repeat more likely than the inverse. But always make sure to calibrate with other conditions to confirm better, such as market cycle and other so that expectations are not based on just one prior move.

For example, if the ticker in past had 3 macro high breakouts after some consolidation and in each case delivered a strong squeeze and never fell under that breakout level for long while, then in 4th case it wouldn't be reasonable to expect a failure, especially if ticker remained in similar price range and has not extended much since.

On another hand there could be a ticker with more mixed behavior where a total of 4 macro high breaches were present in the recent past, 2 of which had fake breakouts resulting in larger selloffs and 2 of which had strong breakouts with squeeze higher. In such a case it isn't healthy to form any strong expectations of what should happen in the 5th case if that setup was to come about because it could be too prone to guessing. As the usual only focus on tickers that show clearer consolidated behavior to one side.

Example of ticker ALF with relatively smooth delivery of breakout behavior on the image below. If one was to track such ticker in real-time it should be noted that breakouts above macro highs tend to deliver on this one, so expecting failures too early would not be the best idea.

Example of how recent behavior of consistent fake breakouts above macro highs should help the trader to stay away from trading long side of breakout:

If behavior repeats as consistently with fake breakouts as the image above highlights it is unwise to be expecting something different in the fourth example. Therefore instead of going long, it might be a better idea to trade short and expect another failure and reject back down. This is why being adaptive on bias is very helpful because one can always find a play on breakout setup whether its trading short of failure if that is more likely or longing the breakout.

AGRI is another example of a ticker where the breakout distance and time in terms of days signal how strong the overall ticker is, and it could provide some insight if the ticker was to go for the third time into the macro breakout phase, the room might be limited to the upside.

Typically you do not want to construct some convincing idea just based on two breakouts and their performance, but that certainly can be providing much more idea than not having any past behavior as a guide.

Observing ticker's behavior above HOD levels is especially important (after the structure has consolidated for a while) because those will be liquidity zones often for market makers. What happens to ticker after major HOD level is breached will signal important factors on tickers strength for further games or not.

An example of why not observing those past days' behaviors has led to some mistakes made by some traders selling longs too early on ticker GOVX, where ticker has signaled clearly in recent past that its breakouts performed with decent push before coming back lower. However, since there was only one such past example it is hard to say how much of a guide can such behavior have in real time, its one of those cases which for lesser experienced traders it might not be visible in real time.

But overall keep one very important factor in mind. The success rate of completed breakouts increases in strong cycles and decreases in weak cycles. Be already well prepared with the right expectations on breakout depending on what cycle the market is currently in and how hot the momentum is. In general, if the weak cycle is present, breakouts will likely exhaust earlier and the failure rate will be higher. And vice versa for strong cycles.

The behavior of price in first 15 minutes after market open

Another type of behavior to focus on is what happens in the first 15 minutes, in the same particular ticker if it is a multiday runner.

Or what happens in the first 15 minutes on different tickers that belong to the same cathegory, such as day1 gappers, day2 plays, etc.

In general, some tickers on some days might have very themed behavior and in such cases, it can be a great additional guide. but there are as well days where no behavior has any consistency to it, and tickers within the first 15 minutes can all behave very differently.

Therefore to apply these conditions to expectations of price directions, the theme needs to be established first, that is an absolute must. The behavior has to show it is already relatively consistent for 3 samples at least. Once such a theme is established, it can increase the chances that the next cases that follow it could follow the same or similar suit.

Tilt towards failure early in weak cycles:

The reason why focusing on the first 15 minutes and not any other 15-minute chunk of the day, is because when the market is weak and faders are more likely to deliver, often they top out in the first 15 minutes. So it makes sense to seek for the theme to get established and then follow and use it as a better guide. Additionally, liquidity will be the best in first 15 minutes often, making it higher RR trade than within random times durring the day.

When the market is very strong, chances increase that the first 15 minutes will not deliver good faders, therefore it makes sense to seek for the counter theme, or non-fading consistency, which tells the trader to be less forcing on shorts, and rather seek for a long edge.

Not respecting this first 15 minutes rule based on the cycle the market is currently in, is a frequent mistake being made by smallcap traders. Strong cycles especially can stack multiple failures of tickers to top out in a row (and tickers actualy squeeze alot in those early minutes without topping), and a short seller not being aware of that could get into trouble.

Replications (deja-vu)

One of by far the most useful examples of when the recent past behavior matters and can provide a strong edge is on replications. What is replication? It is when recent behavior as can be seen in multiple stages starts to repeat its previous steps and signal that the whole pattern itself might develop again. Pattern valid for replication has to be defined well through several segments, not just one.

For example, if the pattern was to be a construct of 5 segments, then if 4 of those are already matched in the second example there are good chances that the 5th segment and result will also deliver. However, it highly depends on just how accurate eye one has to define each of those stages.

Example of how replications should be seen in chunks:

Replications are typically seen only on tickers that are repeating something that hasn't happened so long a time ago, it's fresh. So do not be seeking replications on ticker's behavior from 50 days ago, most replications will happen within the same week, or in fact within the same day or past 3 days tops.

For valid replication one needs at least 5 defining characteristics of pattern, and they have to match well on the second setups example (or third one) if replication was to be in play. The earlier one can spot it the better because the key is not just to extract the last piece (5th for example) but also some prior ones such as 4th or maybe even third. This means spotting replication as early as possible provides an extra edge, however, it is highly correlated to your screen time and experience. The less experience the longer it might take you to find and figure out replications and that's completely fine. Pattern recognition skills will be highly subjective and differ from individual to individual, some are better at it than others.

Let's look at some of the replication examples on smallcap tickers of this year, and how this was used in edge to trade those tickers. Replications are not some specific pattern, they could be any pattern that is more complex in its structure. For example, a "wedge pattern" is not pattern that would suit the replication guide, because it needs to be a pattern that has more structural components that are very specific to suit so.

An example of replication pattern in small caps should be structured and very specifically defined in each component, this is an advanced pattern defining technique:

(lets call this pattern X, since replications pattern is dynamic and different each time)

-Dilution fillings needs cash

-Tickers price 5 USD

-Stays in neutral consolidated range for 2 hours

-Had a stronger early morning squeeze of 50%

-Squeezes past HOD level by a decent amount

As you can see ideal replication pattern uses a few different components which are not highly correlated (as you can see not all of those variables are strictly priced action related). Tickers price and fillings and mid-day squeeze are all different behavioral components.

However keep in mind, that often replications will be cross ticker used, not per the same ticker. This means two different tickers will often set replications for the same pattern, not necessarily the same ticker setting replication of the same pattern. However both could be the case, it is just that the first cases have more examples than the second. Replications can happen on the same ticker within the same intraday chart where two particular setups replicate, or it could happen on day 1 ticker of X on Monday, and day 1 ticker of Y on Tuesday (two day1 gappers on two different days).

Example of replication on ticker DTST and PPSI. The second example of PPSI was on the day after DST traded, where its replication served as a good guide on where to place short entries (first on the micro shelf before open) and second one shorting push into HOD but not expecting the squeeze past HOD because DTST did not perform one either.

Example of three replicated tickers, all day 1 tickers on gapping catalysts that ended being all day faders with the very specific progression of behavior in 4-5 stages. Once two tickers set the tone on replications this was used on the third ticker to time the short entry and adds along with the trend, as well as the final fade targets. Replications make it much clear on all sides because they provide very round conclusions on where the tops might be, how deep fades might go etc...

As another case is intraday replication on same ticker OST where two patterns replicated.

As one can see, there is no particular pattern example that qualifies as replication potential, just about any structural price pattern could be a replication setup, as the above images highlight different patterns altogether, some fade patterns, some squeeze and chop patterns, and others. But it is really important to have patterns structured in several components, otherwise one will start forcing seeing things that are not there!

One needs a carefully structured in-stages checklist for replication to be valid. It isn't enough to just draw some conceptual line on the chart and say if the ticker is twice following it, that might justify replication and potential same directional result. This kind of mentality can get you quickly into trouble. For replication, behavior has to be broken down into several components which need to be specific, and then they have to replicate stage by stage in the second pattern just as they did in the first one.

Keep in mind: Replications are not very frequent! If you see them on every chart you are doing something wrong no doubt. Try more like every 15th or 20th chart and you will be closer to reality.

Replications are therefore the best and strongest example where tickers' recent behavior has a very strong use case and provides signals on the potential direction.

Another example of using recent past behavior as a guide on where the ticker might more likely top out, and if so in what kind of structure it might set so is on ticker BWV. If behavior from the left cleanly repeats, it can provide more clues when potential rug pulls might come due, if the way how currently ticker is setting is similar to the last 2 or three times. For example on ticker BWV where rug pull from the consolidated structure was somewhat to be expected due to how the two heavy pulls came last two times from similar consolidated structures. Example of replication on the same ticker within 3 days back to back.

There are no guarantees (being open-minded)

The fresh bag of issues and side effects that might come from tracking recent behavior on tickers is complacency and getting stuck in being too close-minded. Thinking that ticker has to do what it has shown to do before many times as that becomes status quo. There is delicate navigation that one has to do between using research and past behavioral data as a guide versus starting to get blinded by it. Because strong consistency of past behavior can serve as a trap often to those not experienced as it creates too static expectations and false high probabilities. Always be mindful that past behavior, even if very consistent and coherent can and often will fail to repeat eventually.

This especially is important for long-term investors or swing traders, because they chew on the same chart for a very long time. They see the same picture time and time again, and the strength of their conviction keeps increasing even though they are not absorbing any new information, the chart remains the same and past behavior replication samples are the same, but with time passing that thesis could be strengthening. The reason why this happens is that it is harder to let go of something that you have dedicated a lot of time to, it's easier to let go of something that hasn't yet eaten much time. Just as this applies to relationships, it applies often to trading thesis and expectations of the price move to the same extent.

It is one of the reasons why I much prefer trading short-term time frames, as your adaptivity and neutrality are by default going to be better. It's easier to let go of view on the chart that was observed for 10 minutes, versus the chart that was on and off observed for a month, due to a longer time horizon.

When behavior fails to replicate it also potentially provides a signal of strength increase or decrease from the past, which could signal directional change altogether. So just because behavior failed to replicate for the first time it isn't necessarily the end of the world. It can be just as much good insight but in the opposite way. Many trend changes in general start with the first major behavioral disconnect, where past behavior fails to deliver replication. Use that as potential insight.

Automated quick spreadsheet data vs timid visual behavior tracking

If you are still skeptical of the value content of this article, keep this in mind if you would prefer to look away towards easier to get behavioral conclusions from spreadsheets and such, the way how many prefer to do it:

Most of the data collected in small caps that is more reliable and value-providing (less noisy) is timid to collect and structure well, often time-consuming to do versus something that can be quickly pulled automatically by some software which is usually going to be just average noise at its best in most cases. If you want some edge it often means pulling some sleeves up. It should be added that subjective input will also be present since each trader might label macro breakout levels a bit differently for example, which could impact results, but the key takeaway should remain valuable regardless, as in most cases the data will show significance.

The point of tracking multiple micro behaviors and finding the clues out of it, is to reduce noise that you would otherwise get from something like a spreadsheet that bulks everything together without any particular targeted aim to separate micro behaviors in the first place.

The expiration date on repetition/consistency rate

If the ticker displayed one particular behavior many times in a row very clearly and consistently, and those samples are let's say in numbers close to or above 10, then the chances for failure start to increase a lot. This applies to small-caps specifically and how their timing applies to multiday runners, as well as intraday tickers. Mind that this mostly applies to multiday runners, but chances are that no behavior will get a chance to repeat intraday for 10 times in a row, there just are not enough candles and minutes in total US market session for that to happen. The numbers of candles are limited. So this mostly applies to multiday runners.

If specific behavior keeps repeating many times in a row and the number of samples are 10 or larger, then one should soon start expecting pattern failure and the reason is relatively straightforward. Think of small caps as assets with an expiration date, like milk. That clock is ticking when the liquidity gets the rug pulled out of assets, the market makers wave goodbye and the ticker becomes buried under a pile of social media history until the light shines again on it, a long while into the future usually, typically within months if not more than a year (in some cases a bit quicker).

Because of that, the time is always running out on MDR (multiday running) tickers, and especially if we are focusing on behaviors like successful bounces to the upside, or successful breakouts, and the ticker has displayed many times such successful behavior in recent history, then after prolonged samples, one should disconnect expectations from expecting this to repeat. This means with the right timing to take everything said in the article above, and flip it upside down, because after too consistent repeatable behavior the inverse is likely to follow soon, due to the "expiration date" on MDR tickers. To check that expiration date typical check the article on MDR tickers specifically.

For example, if 4 consecutive breakouts delivered squeeze and you used on 5th one that as guide successfully, but now the ticker is at its 9th replication of such behavior, and the price is extended much higher by now - well chances are that the failure of breakout is likely just around the corner. So this is where the expiration date comes into play and one should render neutral all behavioral replication expectations. Fresh behavior replication, therefore, has a bit higher chance to repeat while an older one has a higher chance to sooner or later fail because nothing stays very consistent in markets for a long time.

However keep in mind, that this mostly implies the repetition of 10 samples or 8 samples in a row, and not just 3 or 4, because that would be against the points made in the article above. It is also very relative because 10 is a very rough guide, it could be less because it largely depends on just how much ticker is extended in % over past days and how many days in total it has been a runner. This can adjust the total sample size down or up depending on the initial two variables.

It should be also added that this is just a basic foundation on "counting the behavior". It is not an absolute mathematical model.

In strong cycles the numbers of repeatable patterns before failure takes place can stretch much higher, and in weak cycles and weak momentum conditions, it is the lower sample size of repetitions that will be quickly exposed to inverse or failure much quicker. So the number of repetitions can scale with cycles and the overall strength of momentum in the market.

Conclusion (zoom in, track different behaviors, and buy some glasses?)

The idea behind this article is to highlight what traders should be tracking in small-caps and how recent behavior often leaves plenty of clues to traders but a lot of those tend to get dismissed and overlooked if one doesnt know what to look for in first place.

The majority of those observations are all based on 1-minute charts and require a trader to zoom in, if that is not your cup of tea and you don't like to look behind the curtain then it won't be your thing of preference most likely.

The recipe is relatively straightforward: To increase accuracy you need more data usually, but with more data comes more responsibility, more homework, and also a bit more mental drain trough the day, until you optimize it trough time. The more micro behaviors one tracks the more applicability it's possible to get, but that has to be carefully tested so that one doesn't over-extend him/herself and apply too much too quickly to the point where it becomes unbearable to do any conclusions, because too many different things are tracked at once too early. So do it bit by bit, start perhaps with tracking certain things about fake breakouts, then add a few other micro behaviors so that in each market cycle you have something under your belt to lean on as potential trade. The point of looking closely at micro behaviors is that no matter what market conditions are present there might be trade to be made.