Happy Friday everyone,

It has been another big week inside TheStrat Lab.

Over the past year, I’ve spent an unhealthy amount of time looking at price action, market structure and what sits behind some of the more explosive moves we see in individual stocks.

While researching Triple-Digit Breakouts, I’ve been studying over 700 stocks that doubled in less than 3 months. The deeper I got into the data, the more I kept coming back to a fairly simple question:

What actually moves price?

‘Only price pays’ , but something has to move it

As a price action trader, I’ve always worked on the principle that only price pays. (Stolen from Brian Shannon)

Ultimately, price determines when I enter, where I’m wrong and how I manage the trade. That hasn’t changed.

But price doesn’t move in a vacuum.

Behind many of the biggest moves is some kind of catalyst that forces the market to reassess what a company is worth. Sometimes that is a gradual change in expectations. Other times, new information lands and the gap between what the market expected and what actually happened is enormous.

That gap is where things start to get interesting, and it has changed the way I think about searching for setups.

Rather than scanning thousands of stocks and looking for interesting price action in isolation, what if we could be more deliberate?

Find the stocks where something meaningful has changed first, and then look for the price action that tells us the market is responding to it.

The catalyst narrows the search. Price still makes the decision.

That led me down two fairly deep rabbit holes.

1. Earnings acceleration & surprise

The first is earnings.

This probably sounds obvious, but what interested me wasn’t simply whether a company beat earnings. I wanted to understand the magnitude of the change.

What happens when earnings suddenly accelerate? What happens when revenue comes in materially above expectations? Most importantly, what does the market do when the numbers are completely different from what analysts had priced in?

If a company suddenly reports 1,000+% quarterly earnings surprise, Like today for example, that is new information the market has to digest. Institutions have models to update, analysts have estimates to revise and funds may suddenly find themselves underweight a stock whose fundamentals have changed very quickly.

Historically, I’ve traded those situations almost entirely through the price action. What I haven’t had is a systematic way of identifying the fundamental change sitting behind that price action.

So we’ve started building one.

The new Earnings Calendar & Acceleration Engine inside SwingTheStrat lets you track upcoming earnings, consensus estimates and historical surprises, but more importantly, it surfaces changes in earnings and revenue alongside the subsequent price and volume reaction.

The idea is not to predict earnings. I have very little interest in trying to guess whether a company will beat or miss before it reports.

The idea is to wait until we have new information and then be much more specific about where we search for price action.

Has something materially changed? Were earnings significantly better than expected? Has revenue suddenly accelerated? Was the magnitude of the surprise unusual? Are stocks in a particular theme always beating earnings?

If so, then I want to look at the chart.

We are still trading price action. We are simply trying to search for that price action in places where there is a reason for the market to behave differently.

2. The Biotech rabbit hole

The second rabbit hole surprised me a little more.

When I started analysing stocks that doubled within three months, biotech kept appearing.

I’ve largely avoided biotech throughout my trading career. From the outside, these stocks can look like lottery tickets. A clinical trial result lands, a stock gaps 80%, and unless you understand the science it can feel almost impossible to develop any kind of repeatable process around them.

But when I started digging into the data, I realised I had probably been looking at them the wrong way.

Clinical trial readouts, FDA decisions and other pipeline milestones don’t simply appear from nowhere. Many of them have timelines. The catalysts can be categorised. The historical reactions can be studied. And, potentially, that means some structure can be put around what initially looks like randomness.

Again, I’m not interested in trying to predict whether a clinical trial will succeed or whether the FDA will approve a drug.

I’m interested in what happens after new information enters the market.

If a meaningful catalyst lands and fundamentally changes the story, can we then use price action, volume and market structure to identify whether the market is beginning to reprice that company?

That feels like a much more interesting problem to solve.

It doesn’t suddenly make biotech predictable; but it does make it more researchable and that is enough to get me interested. :)

So we’ve built a Biotech Catalyst Tracker inside SwingTheStrat to start mapping these events and, over time, understand which types of catalysts, setups and market conditions have historically produced the most interesting opportunities.

Both are now live and I need some help

Both the Earnings Acceleration Engine and Biotech Catalyst Tracker are now live in beta inside SwingTheStrat.

Have a play with them, break them, question the data and let me know what you think. As always, the feedback from people actually using these tools is what helps shape what we build next.

I also have a slightly different ask this week.

I’m a researcher, data geek and product builder. I am not a biotech or pharmaceutical expert, and I’m very conscious that there will be nuances in this data that aren’t obvious from the outside.

So, if you work in biotech, pharmaceuticals, clinical research or somewhere adjacent to that world, or you know someone who does, I’d really like to speak to you.

I’m particularly interested in understanding the nuances around clinical catalysts, the quality of the available data and, importantly, where someone approaching this purely from a trading and data perspective is likely to get things wrong.

If that sounds like you, just reply to this email or drop me a message.

I suspect there is quite a bit more to uncover here.

Have a great weekend.

Nigel

Earlier this year, one of my Substack readers recommended 100 Baggers by Christopher Mayer. [Thanks Sreeni]

If you’re getting into investing and haven’t read it, I would highly recommend it.

The premise is simple: find a handful of exceptional businesses, buy them, hold them through everything the market can throw at you — recessions, crashes and decades of drawdowns — and let them turn into a 100-bagger.

As a swing trader, I have to be patient. For vast chunks of the year I’m used to waiting; sitting on my hands until the direction of the market regime is clear and an opportunity suited to one of my edges makes an appearance, instead of forcing one.

But even for me, I don’t have it in me to sit calmly for 26 years waiting on a single position to mature. Equally, I’m not an adrenaline junkie who wants to trade every single day either. That’s not the life I’m building.

So I closed the book with a different question in my head:

How do I find stocks that double — not over decades, but in a timeframe I can actually work with?

In data, we trust

I went and pulled the data: Every stock that doubled in price since 2020 across my universe of ~2700 tickers till Q2 2026. I split the results into three equal groups, sorted by how long each double took.

The fastest third I called Zone 1: stocks that doubled in 66 trading days. Call it three months.

Something about that number just clicked.

Three months is short enough to hold my attention, long enough to build a process around.

So, that is the origin story of the book I’m now writing: Triple Digit Breakouts.

What the research turned into

The full study covers ~700 stocks and ~1,800 separate instances of a stock doubling in under three months.

I want to be upfront about what that is and isn’t. This is not a promise to predict the next 100% winner.

Hindsight makes every big mover look obvious after the fact.

What I actually wanted to know was what was true before the move: the price structure, the relative strength, the volume, the market conditions — the things you could have actually observed while the outcome was still unknown.

That’s the central theme the book focuses on:

• The story behind the move. The common narratives, catalysts and market conditions that recur across the fastest winners.

• Scanning for the setup. What price, relative strength, volume and market context looked like before the move began.

• A better entry process. How to define an entry, size genuine risk and avoid chasing extended names.

• Holding winners properly. How to manage volatility, distinguish noise from failure, and add to strength without destroying your average cost basis.

A sneak preview from the book.

One of the chapters I’ve been working on recently covers trade management.

Specifically, how you actually hold a winner once it’s working, without either panicking out too early or giving the whole move back on a normal pullback.

The exam question is:

Once you have a profit cushion, how do you maximise the move?

A hard exit on a close below the 10-EMA or 21-EMA is simple. But with the sort of volatile stocks this book studies, is that close actually a signal that the trade has changed character or is it just ordinary noise?

I would have loved to test Jeff Sun’s original Opening Range Low (ORL) approach directly. His method uses 5-minute data to decide whether a moving average break has actually failed, rather than automatically exiting at the close.

But there was a practical challenge. My testing rig holds daily data for roughly 3,000 stocks across six years. It does not hold five-minute data for every stock in that universe.

So I had to find a workable substitute.

Rather than using the literal 5-minute ORL, I tested a volatility adjusted proxy: the Daily open minus either 0.25×ADR or 0.50×ADR.

In plain English, instead of selling immediately when the stock closes below the 10-EMA or 21-EMA, the system gives it a defined amount of room based on how much that stock normally moves in a day.

It is not Jeff Sun’s exact method.

It is a proxy for the same idea: don’t confuse an ordinary volatility shakeout with a broken trend.

When I backtested it across roughly 1,800 Zone 1 doubling runs, here’s what came out:

• A hard exit on a 10-EMA close below kept you in the trade about 44% of the time by day 66, capturing a median of ~72% of the eventual move.

• A hard exit on a 21-EMA close below did better, around 58% of trades still active by day 66, capturing ~81% of the move.

• A tighter ORL volatility buffer (0.25x ADR) pushed that up further ~67% of trades surviving and median move capture climbing to ~92%.

• A wider ORL buffer (0.50x ADR) edged it higher still ~70% survival, ~95% of the move captured.

The gap between the crudest exit and the most refined one isn’t small.

It’s the difference between giving back roughly a quarter of each winning move to ordinary noise and staying involved long enough to let a genuine leader work — while also taking your participation rate from around 44% of trades all the way up to roughly 70%, and capturing a much larger share of the move when you do.

You’re not only participating in more trades; you’re capturing more juice.

As every trader knows, these are the nuances that make you profitable.

That’s what I want this book to be full of: Less opinion, More evidence.

The full chapter goes further, combining this volatility buffer with a four-phase stop framework designed to protect capital early while giving genuine leaders room to develop.

How far away is the book?

The research is done; that was always going to be the hardest part. Six years of market data, thousands of doubling events, breaking it down into the patterns and playbooks that actually repeat.

The 21-chapter outline is finalised, and every chapter now has a complete draft. The manuscript is not polished yet, but the structure, research and first full pass are in place.

What’s left is to select and annotate the charts, then take each chapter apart and rebuild it.

That’s the work ahead of me over the next three to six months, targeting a release in mid-Q4 this year, though that could shift depending on what I find along the way.

Your help is needed

There are a few decisions I’m genuinely grappling with: format, price, colour optionality and how involved I want readers to be while I finish this. Rather than deciding alone in a vacuum, I’m hoping you, as readers, can help me get there via this poll.

One quick favour before you vote: answer the poll in order and go with your first instinct. Don’t overthink it. Your immediate reaction is more useful to me than a perfectly reasoned answer, and the polls are anonymous.

How can I get involved?

If you want to follow along as Triple Digit Breakouts takes shape, you can register your interest below.

I’ll be sharing snapshots of the research like the one above, annotated charts from real triple digit breakouts, samples from the manuscript and the occasional “here’s what I just found” as I work through the final edits.

Later, when I’m ready to open the beta reader process, this is also where I’ll share the details: when beta reading opens, what it involves, how to apply and when I’ll be shortlisting readers.

There’s only one list. So whether you simply want to follow the research or you think you may want to be a beta reader when the time comes, register your interest below.

I'm interested in Triple Digit Breakouts

Thank you.

While you wait

If you want to understand the price action principles behind my approach, TheStrat Protocol is the place to start.

TheStrat Protocol is my first book: a trading novel about a trader given thirty days to prove he can trade systematically, and how he discovers a system that forces him to trade structure instead of instinct.

Buy TheStrat Protocol

The problem is focus.

When market breadth improves and the major indices are aligned, there can be hundreds or even thousands of stocks showing bullish structures. At that point, the objective is not to chase whatever has already made a large move. It is to exercise restraint, identify quality and concentrate on stocks with the type of characteristics that can attract sustained institutional demand.

In this article, I will show you the scan I use to identify institutional-quality stocks: liquid companies with recent fundamental acceleration that may be experiencing analyst repricing and renewed institutional interest.

Start with market regime

Before running any scan, establish the market regime.

Even the best stock setup is less attractive when the overall market is fighting against you. Conversely, when the major indices are aligned and market breadth is strong, you have a much larger pool of actionable long ideas.

The purpose of the scan is therefore not to predict the market. It is to help you focus your attention once the market environment gives you permission to participate.

A bullish environment can leave you with more than 1,000 technically positive stocks. Narrowing that list simply by liquidity may still leave you with close to 200 names.

That is still too many.

We need a way to identify companies with the kind of recent fundamental strength that institutions are more likely to care about.

What institutions want to buy

Institutions generally need two things:

1. A compelling reason to own the company

2. Enough liquidity to build and manage a position

The compelling reason often appears in the numbers. Companies that report strong earnings and revenue growth can force analysts to revisit their assumptions. That can lead to upgrades, target-price changes, estimate revisions, and sustained buying interest.

The scan therefore looks for stocks with:

• Recent earnings reports

• Strong quarterly earnings growth

• Strong quarterly revenue growth

• Sufficient daily dollar volume

• A meaningful share price and average trading volume

This is not a buy signal on its own. It is a way to create a higher-quality universe of stocks for technical analysis.

The institutional-quality scan

Here is the exact logic.

1. Earnings reported within the last 30 days

Start by filtering for stocks that have reported earnings recently.

Filter: Earnings reported in the last 30 days.

Recent earnings matter because this is when the market receives fresh information. If a company materially exceeds expectations, institutions may need time to build positions as the market reprices the business.

This first filter will still produce a large universe. In my example, it returned roughly 1,200 tickers.

2. Quarterly earnings growth above 40%

Next, filter for meaningful earnings acceleration.

Filter: Last-quarter earnings growth greater than 40%.

We are not looking merely for companies that are profitable. We are looking for companies demonstrating a significant improvement in earnings power.

This helps remove lower-quality names and focuses the scan on businesses that have delivered a meaningful fundamental result.

3. Quarterly sales growth above 40%

Earnings growth is useful, but I also want strong revenue growth.

Filter: Last-quarter sales or revenue growth greater than 40%.

Revenue growth helps validate the earnings result. A company can sometimes improve earnings through cost-cutting, one-off gains or accounting effects. Strong sales growth provides additional evidence that demand for the underlying business is improving.

Using both earnings growth and revenue growth reduced the list in my example to around 40 stocks.

Now we are getting somewhere.

4. Average dollar volume of at least $25 million

Next comes liquidity.

Filter: Average daily dollar volume of at least $25 million.

Dollar volume matters because institutions need to enter and exit positions without creating excessive slippage. A stock may show impressive fundamentals, but if it trades only a small amount of value each day, it is less suitable for serious institutional participation.

This filter reduced the list further, from around 40 names to approximately 26.

5. Price above $10

I also prefer to remove lower-priced names.

Filter: Share price above $10.

This is not a universal rule, but it helps avoid many low-quality, highly speculative, or excessively volatile names. It also makes the final list more consistent with the type of liquid, institutionally tradable stocks we want to review.

6. Average volume above 1 million shares

Finally, add a share-volume requirement.

Filter: Average daily volume of at least 1 million shares.

Combined with the dollar-volume filter, this gives us a tradable universe with both sufficient turnover and consistent participation.

After applying all six filters, my list was reduced to just 16 names.

That is the power of the process: moving from more than 1,000 possible ideas to a short, focused list of companies worth reviewing.

The scan is the beginning, not the trade

A stock meeting the fundamental and liquidity criteria does not automatically belong on your buy list.

The scan identifies companies that deserve attention. The chart determines whether they are actionable.

Once I have the shortlist, I review each name for structure:

• Is the stock extended after a sharp move?

• Is it consolidating cleanly?

• Is it pulling back in an orderly way to a key moving average?

• Is there an actionable inside day, 2dG reversal or continuation setup?

• Is the stock trading in a strong industry group?

• Is the risk defined and acceptable?

The best candidates go on the buy list. Others go on a watchlist or short list until they provide a better entry.

For example, a stock might have excellent earnings and revenue growth but be too extended to chase. Another may be pulling back neatly into the 21 EMA, offering a more favourable entry with clearer risk.

Strong companies can be poor entries. A great setup in a poor company can also be problematic. The goal is to align fundamentals, liquidity, technical structure and market regime.

Look for industry clusters

One of the most useful things to do after creating the final list is sort it by industry.

If several qualifying stocks appear in semiconductors, oil and gas, construction or leisure and entertainment, that tells you something important: capital may be rotating into that area of the market.

You do not need to trade every stock in the group. In fact, you should avoid concentrating too heavily in one theme. But industry clustering can help you prioritise research and select the cleanest technical structure among comparable names.

In the scan example, there were notable clusters in:

• Semiconductors

• Oil and gas

• Leisure and entertainment

• Engineering and construction

That gives you a useful map of where to look more closely.

The complete filter checklist

Use this as a starting framework for your own institutional-quality scan:

You can adapt the thresholds to suit your own trading style, market and risk tolerance. The important point is not the precise number. It is the logic behind the scan: find recent fundamental strength, confirm liquidity, then use technical structure to time the trade.

Final thoughts

The purpose of a scan is to reduce noise.

When the market is healthy, there will always be more potential opportunities than you can realistically trade. Rather than chasing extended names or reacting emotionally to whatever is moving, use a structured process to identify companies with the combination of growth, liquidity, and institutional relevance.

Then let the chart do the rest.

The scan finds the candidates.
The market regime provides the context.
The technical setup provides the entry.
Risk management determines the outcome.

This article is for educational purposes only and does not constitute financial advice. Always conduct your own research and manage risk appropriately.

Can you do me a favour?

If this piece resonated with you and you’d like to support the work, TheStrat Protocol is available now on Amazon. If you’re already a Kindle Unlimited subscriber, it won’t cost you a penny, just your time.

If you’ve already purchased The Strat Protocol — thank you, genuinely.

Buy TheStrat Protocol

It’s probably one of the shortest pieces I’ve published here. Also, far more qualitative than quantitative. But I felt the observation was interesting enough to put out there for introspection.

From hypothesis to qualitative observation

I’ve spent a fair amount of time studying Jeff’s 50 SMA extension heuristic, but most of it was always to the upside, focused on overextended rallies snapping back.

Over the last few days, when doing my end-of-day scans, I noticed something interesting: when price is -3σ extended to the downside, it starts to behave the same way. This morning, I looked at three or four charts that hit the −3σ extension and saw price bounce almost immediately.

It felt like the mirror image of the upside extension behaviour I’d already studied, the same rubber-band dynamic, just inverted.

From observation to a possible edge

Call it serendipity, but it was three charts back-to-back and too obvious to ignore, so I decided to go away and scan for tickers that had shown very high relative strength in the past but are now −3σ extended over the last 1–2 weeks.

In other words:

• Previously strong names (outperformers in the prior trend)

• Now sharply oversold relative to their own 50 SMA/ATR profile

• Sitting around −3σ extension territory

It was a short list of around 30 tickers and as I moved through them, the connection to TheStrat really stood out.

Broadening Formation

A lot of these names were not only inside broadening formations; they were sitting right at the lower boundary of the pattern, with a really huge range still available to the other side.

• Broadening formation = expanding range, two-way opportunity

• Price at the lower trendline = potential exhaustion of the downside leg

• −3σ downside extension = added mean-reversion pressure

• If price snaps back, the measured move is not just back to the 50 SMA, but potentially all the way across the broadening formation to the upper boundary

So what started as a simple “oversold 50 SMA” screen quickly looked like a specific Strat-flavoured edge: previous high-RS names, currently at the lower edge in broadening structures, tagged at −3σ to the downside, with potential for a snap-back.

Let your eyes be the judge

Below is the list of tickers and charts that came up on my scan. I’m not calling these “buys” or “setups” in any prescriptive sense—just sharing what the screen returned and what I’m watching.

A cautionary note

This piece is deliberately light on rules and heavy on observation. I’m treating this as a hypothesis generator, not a finished system.

The following will go on my research backlog (but will take a back seat as I’m working on my next book project):

• Quantify how often −3σ downside extensions lead to meaningful snaps back

• Measure the average follow-through over the next 3–10 bars

• See if the edge is stronger in specific market regimes or sectors

For now, I wanted to put this out while it was fresh: the downside extension behaviour looks like the symmetrical cousin of the upside 50 SMA ATR extension edge.

If you’re watching any of these tickers, I’d be curious what you’re seeing on your own charts.

Can you do me a favour?

If this piece resonated with you and you’d like to support the work, TheStrat Protocol is available now on Amazon. If you’re already a Kindle Unlimited subscriber, it won’t cost you a penny, just your time.

If you’ve already purchased The Strat Protocol — thank you, genuinely.

Buy TheStrat Protocol

— Nigel

The good news? They leave footprints.

You just have to know where and how to look.

This is the exact monthly routine I run inside SwingTheStrat to decode institutional positioning before the next leg of the market plays out, using TheStrat framework and a data scientist’s approach to following evidence.

Think like a general, not a gambler

Picture an 18th-century war room:

A massive table, a map of the terrain, every army rendered as miniature pieces, every warship’s position marked, every movement tracked through intelligence from the field.

The generals aren’t guessing. They’re studying the battlefield, interpreting the opposition’s positioning and building a thesis on what the next move (or the real move) is likely to be.

That is the mindset we need in trading, too. We don’t need to predict markets; we need to observe them with enough clarity, structure and discipline to form an evidence-based view, then trade it like a business.

We need to think like a general, not like a gambler.

The market gives us that map every single month.

Most traders, however, simply don’t know how to collect and introspect that evidence.

The four-layered cake

Every month, I move through four layers of the market, in order:

• Indexes (macro health)

• Sectors (capital rotation)

• Themes (opportunity)

• Stocks (confirmation)

Each layer narrows the picture. By the end, you’re looking at a shortlist of ideas backed by real positioning data.

Layer 1: Indexes: Is there alignment or chaos?

Start with QQQ, IWM, SPY and DIA on the monthly, then check the weekly for confirmation.

If all four are moving together, conditions are clean. Size up.

If they’re diverging, the market is fragmented, and that’s your cue to slow down, not speed up.

Right now, we’re seeing exactly that: QQQ and IWM showing weakness while SPY and Dow hold up. That divergence alone tells you it wasn’t a month to force trades.

Layer 2: Sectors: Follow the money

Which sectors are up on the month? Which are lagging?

This month, Energy and Financials have been leading, while Tech has lagged, largely dragged down by semiconductor weakness.

That’s not trivia, its a a map of where capital is rotating into and out of.

Layer 3: Themes: Where trades ideas actually come from

Inside sectors live themes: AI, semis, energy, retail, biotech.

Filter for themes where the monthly candle closes in the top 70% of its range. That’s a simple proxy for “active buyers showed up here.”

Last month, that filter narrowed down themes to just 14 worth watching, including steel, oil and gas and retail.

That’s the difference between staring at 3,000 tickers and working from a shortlist of ideas with real institutional backing.

Layer 4: Stocks: Confirm with participation

A single tree falling in the forest may go unnoticed, but when an entire section starts moving, it’s time to pay attention.

One stock breaking out is noise. Ten stocks in the same theme moving together is a signal.

When 75 to 100 percent of the names in a theme are moving in the same direction, that points to coordinated buying. Now, that’s the institutional footprint you’re hunting for…

The full monthly checklist

The market doesn’t hide its intentions.

It just moves too fast, across too many tickers, for most traders to see the pattern in real time.

This routine slows it down. It turns 3,000 stocks into a handful of ideas with actual conviction behind them.

Here’s the complete framework, broken down step by step. Bookmark it, print it, use it every month-end.

1. Indexes (Macro health)
Assess QQQ, IWM, SPY and Dow Jones. Focus on the monthly for directional bias, weekly for context.

• Are the indexes up or down on the month?

• Is there alignment or divergence between them?

• Are weekly and monthly trends in agreement or conflict?

2. Sectors (Capital rotation)
Drill into sector performance to see where capital is actually flowing.

• Which sectors are leading (up on the month)?

• Which are lagging or being sold?

• Are sector moves confirming or diverging from the indexes?

3. Themes (Opportunity layer)
Identify actionable themes within sectors (AI, energy, biotech, semis, etc.).

• Which themes are outperforming or underperforming?

• Are themes moving with or against the broader market?

• Where is each theme in its lifecycle: early breakout, continuation or exhaustion?

4. Timeframe Conflict (Edge creation)
Spot disagreements between timeframes and layers.

• Are they buying on the month but selling on the week or vice versa?

• Are themes diverging from sectors or indexes?

5. Reversals (Inflection points)
Look for early signs of trend change.

• Which indexes, sectors or themes have reversed on the month?

• What has reversed on the week within a broader monthly trend?

• What are the potential targets following these reversals?

6. Broad Participation (Confirmation)
Confirm strength or weakness through coordinated movement.

• Are multiple stocks within a sector or theme breaking out simultaneously?

• Is there evidence of coordinated, algorithmic buying (ETF-driven flows)?

Run through all six steps, and you’ll go from “watching the market” to actually reading it.

Watch the full walkthrough

In this video, I walk through this entire process screen by screen using this month’s actual market data, filtering 3,000 stocks down to a handful of high-conviction ideas in real time.

For more tools, scans, and trading routines, visit SwingTheStrat.

Can you do me a favour?

If this piece resonated with you and you’d like to support the work, TheStrat Protocol is available now on Amazon. If you’re already a Kindle Unlimited subscriber, it won’t cost you a penny, just your time.

If you’ve already purchased The Strat Protocol — thank you, genuinely.

Buy TheStrat Protocol

Earlier this week, we published a study analysing how stocks behave when they stretch past Jeff Sun’s +3σ extension threshold above their 50-day Simple Moving Average (SMA).

One of our readers, Jacek, left a comment that immediately hijacked my brain:

Once a stock extends past 3σ does it grind slowly and linearly to the 50 SMA or does the rate of deceleration sharpen dramatically in the first 2 to 4 days, bleeding off the extreme momentum before settling into a slow, grinding drift?

It was a brilliant question and honestly, I have always been looking for an opportunity to use a violin chart in our research. What a wonderful excuse this turned out to be.

To answer Jacek, we built a visual machine to track the exact daily path of 23,353 individual +3σ triggers in the StratLab universe across 2.7 million daily records from 2021 to 2026.

Visualising the lifecycle of a 3σ ATR extension from the 50 SMA

To map the lifecycle of an extension, we isolated every single instance of 3σ ATR extension from the 50 SMA and tracked it day-by-day:

• Step 1: Identify Day 0 (The Trigger): We scanned our database of ~3000 tickers. We flagged every day where a stock’s close price stretched strictly above +3σ (3 standard deviations) from its 50 SMA. That day was recorded as Day 0.

• Step 2: Calculate the extension in ATR multiples: Because standard deviation adjusts dynamically for each stock, we translated the price distance from the 50 SMA into a uniform volatility unit:

  

• Step 3: Track the next 33 Days: For every trigger, we stepped forward to Day 1, Day 2 and so on, all the way to Day 33. We plotted the stock’s new ATR extension value on that relative day.

• Step 4: Draw the distributions: Finally, we stacked these distributions side-by-side as a sequence of violin plots.

The result is a complete visual history of price decay:

How do you actually read this 🎻 chart?

Forget the name for a second. A violin plot has nothing to do with music.

Picture a bell curve — the classic hump shape you see in every stats textbook. Now tip it on its side, so it stands up vertically instead of lying flat.

That’s half a violin plot.

Now mirror that same hump onto the other side, so it’s symmetrical, like a reflection. You end up with a shape that bulges in the middle and tapers at the top and bottom. It genuinely does look like the body of a violin. That’s where the name comes from.

Here’s the part that matters for trading: the width of the violin at any height tells you how many stocks were sitting at that exact level.

Imagine lining up all 23,353 stocks on the day they hit +3σ. Now imagine stacking them vertically by how extended they were — a stock at 5× ATR goes in a pile at the “5” mark, a stock at 8× ATR goes in a pile at the “8” mark and so on. Where the piles get wide, that’s where most of the crowd is standing. Where the piles are thin, only a handful of stocks made it that far.

The violin shape is just those piles, smoothed out and drawn as an outline instead of a bar chart. So:

• A wide, bulging middle means most stocks in the study landed around that ATR level. That’s the crowd.

• A thin, narrow tail stretching upward means very few stocks reached that extreme. That’s the outliers.

• A short, stubby violin clustered near zero means the group has mostly flattened out and reverted.

• A tall, stretched violin means the group still has stocks scattered across a wide range of extension.

To see this in action, let’s isolate just Day 0 — the exact moment when the +3σ extension triggers:

This single-day snapshot shows us exactly how the 23,353 triggers in our study are distributed at birth:

• The Median (4.81x ATR): Half of all stocks trigger their +3σ signal at less than 4.81x ATR and half trigger above it.

• The Belly (3.87x to 5.98x ATR): The middle 50% of the entire cohort (the bulging purple belly) is tightly clustered here.

• The Mean (5.06x ATR): The average is pulled slightly higher than the median due to the long, thin tail stretching upward.

• The Rocket Tail (6.0x to 14.0x+ ATR): This is the narrow needle at the top. It represents stocks that trigger +3σ with massive, high-volatility momentum, stretching the rubber band to the absolute limit.

Now here’s why we drew 34 of them side by side — one for Day 0, one for Day 1, one for Day 2 and so on, all the way to Day 33.

Think of it like a time-lapse. Instead of one photo, you get 34 snapshots of the same crowd, taken one day apart. If you flip through them in sequence, you can watch the whole group of stocks physically drift downward and squeeze together, day after day, as the extension bleeds out of them.

That drift is the entire story of this study. On Day 0, the crowd is bulging high — most stocks sitting around 5x ATR, with stragglers up at 7-8x. By Day 15, that same crowd has slumped lower and spread wider, like a group of runners slowing down and spacing out after a sprint. By Day 33, the crowd has settled almost entirely near the bottom, hugging the 50 SMA.

The only place this pattern breaks is around Day 22, where you’ll suddenly see a thin, needle-like spike shoot far above the rest of the violin. That’s not the crowd moving. That’s a small handful of buyout-pinned stocks doing something mathematically bizarre, which we unpack in the next section.

The Day 22 “Buyout Pin” bulge

If you inspect the violins between Day 21 and Day 23, you will notice a sudden, temporary bulge and vertical spike that shoots up to 10x, 12x and even 146x ATR (with one illiquid OTC outlier peaking at a mathematically absurd 513.66x ATR).

This is not a momentum spike. It is a buyout pin anomaly.

Here is exactly what happens behind the scenes:

1. The catalyst (Day 0): A stock gaps up massively (often 50% or more) on a cash acquisition announcement or major clinical trial success (e.g., a clinical biopharma leader that doubled overnight). This triggers the +3σ crossing.

2. The price pin (Days 1–21): The stock gets pinned strictly at the cash buyout price (e.g. pinned near its acquisition price). Arbitrageurs close it out and the daily price range (high-to-low) shrinks to a tiny fraction (often just a few cents).

3. The ATR contraction: By Day 20, the 20-day Average True Range (ATR20​) is entirely composed of these flat, pinned days. The ATR shrinks to near-zero (e.g., $0.05).

4. The math explosion: While the ATR has collapsed, the 50-day SMA is slow and takes a full 50 days to catch up to the $41.00 acquisition price. The absolute distance Close - 50 SMA remains huge (e.g. $15.00).

5. When we calculate the ATR extension on Day 22, we divide that large $15.00 distance by the collapsed $0.05 ATR, resulting in a massive 300x ATR extension!

This explains the visual bulge: it represents cash-acquisition arbitrage pins where the daily volatility (ATR) collapsed to zero while the stock remained far above its historical 50-day average.

📋 Back to the exam question

So, does the rubber band snap or grind?

The data is clear: reversion is a slow grind.

Out of all the stocks in our study that reverted to the 50 SMA, 95.2% did so via a slow grind taking weeks to complete. Only 4.8% experienced a sharp, vertical crash of 4 days or less.

If your plan is to fade a +3σ extension, you must be prepared to sit through a multi-week consolidation.

Can you do me a favour?

If this piece resonated with you and you’d like to support the work, TheStrat Protocol is available now on Amazon. If you’re already a Kindle Unlimited subscriber, it won’t cost you a penny — just your time. If you’ve already purchased The Strat Protocol — thank you, genuinely.

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In our first article, we showed that once a stock stretches past its +3σ extension threshold — 7.83 ATR multiples from the 50-day Simple Moving Average, the rubber band is at peak tension.

In the second article, we showed that the odds of reaching +3σ improve meaningfully when a stock already has strong relative strength, especially on the weekly and monthly timeframes, and when the key moving averages are aligned and rising sharply.

After we published, Dave Johnson at Trading Time Machine shared thoughtful peer research. He ran a 25-year Nasdaq backtest and found that, over a 120-day holding period, these extended stocks massively outperform the general market.

At first glance, that looks like a contradiction.

Nigel Johnson says: “Be careful. It’s extended.”

Dave Johnson says: “Buy it. It’s a momentum monster.”

Who is right?

This recent study aims to resolve the debate.

By analysing the exact pathways of all 1,171 +3σ runs in the StratLab database, we found that both views are correct. They are two parts of the same market lifecycle.

Here is the story of how the rubber band stretches, digests and re-launches — and how to trade it.

Part 1: The cooldown rule

If a stock stretches to +3σ — 7.83 ATR above the 50 SMA — does it snap back?

The data says yes, two-thirds of the time.

Once a stock exits its +3σ peak and falls back below the 7.83 ATR line, 66.30% of the time — 724 runs — it resolves by pulling back and touching its 50 SMA.

The remaining 33.70% of the time, it makes a micro-pullback: it drops, then turns back up and re-enters +3σ without touching the 50 SMA first.

But here’s the important part.

Reversion to the mean is slow. It is a grind, not a crash.

For the 66.30% of stocks that revert, the median time to touch the 50 SMA is 32 trading days or about 1.5 calendar months.

This curve shows the distribution of days required to touch the 50 SMA. The peak is broad, clustering between 20 and 45 days, which tells us mean reversion is a slow digestion process.

Part 2: Stay durations and the Tension curve

How long does a stock actually stay in that extreme +3σ zone once it gets there?

Our state machine tracked the consecutive days spent above 7.83 ATR for every run:

• 43.00% of runs last exactly 1 day.

• 16.89% of runs last exactly 2 days.

Nearly 60% of +3σ entries are one-to-two day wonders.

The rubber band stretches, hits the exhaustion wall and snaps back.

Still, 40.11% of runs persist for 3 or more days, and an elite 6.91% walk the line for more than 10 trading days.

Tension curve

Think of +3σ as the point where the rubber band starts resisting hard. The Tension Curve shows the maximum extension reached during these runs.

Notice how the probability density clusters tightly between 7.83 and 8.5 ATR. That is the Peak Mode Range. Once a stock crosses the threshold, it rarely goes much further. The buying power simply runs out of gas. Only a tiny fraction of runs ever push past 9 or 10 ATR multiples.

Part 3: The +2σ Super-Momentum slingshot

If two-thirds of extended stocks revert, how do we identify the ones that become the elite 33% — or the subset that spends 3 or more days in +3σ territory?

A random stock crossing +2σ has a 30.23% baseline probability of reaching +3σ. The other 70% fail and revert.

But if we filter for Super-Momentum stocks — Weekly & Monthly RS above 80, with the 10 EMA, 21 EMA and 50 SMA all rising more than 30 degrees — the physics change:

• Success rate spikes: Probability of hitting +3σ rises to 35.55%.

• Velocity accelerates: Average time to reach +3σ drops to 5.54 days, 25% faster than control.

• Returns double: Mean 10-day return post-trigger rises to +4.34% versus +1.91% for control.

This is not just a cleaner entry filter. It identifies stocks with enough structural force to keep stretching into the market’s most extreme persistence zone.

Part 4: The repeat offender watchlist edge

What happens after a stock completes its 33-day correction and touches the 50 SMA? Is the trend dead?

No. Reaching +3σ is a rare tail event—only 25.03% of tickers ever achieve it in their history. Once a stock proves it has this explosive institutional buying power, it joins the Repeat offender cohort.

Here is what happens after they touch the 50 SMA (completing their macro pullback):

• The re-extension (18.23% repeat rate): Once a Repeat Offender completes its pullback to the 50 SMA and resets, 18.23% of them will build a new base and stretch all the way back to +3σ again during their next trend cycle.

• The breakout edge (+40% lift): Because they have proven explosive capability, their subsequent breakout attempts are far more reliable. When a Repeat Offender crosses the +2σ trigger line again, its success rate of reaching +3σ rises to 14.20% (from 9.39%)—representing a +39.67% relative edge lift over a stock breaking out for the first time.

Visualising the Repeat edge:

When we look at subsequent breakout attempts (crossing +2σ again), Repeat Offenders consistently outperform first-time breakouts across all time horizons, proving that previous +3σ achievers hold a persistent structural edge:

Part 5: The Johnson’s harmony story

Now we can reconcile our shorter-swing-term data from the first two articles with Dave Johnson’s long-term outperformance study.

• The short term, 1–5 days: reaching +3σ is a climax state. The stock pauses or exits quickly, with a median stay of 2 days. That makes chasing it a trap.

• The medium term, 15–45 days: the stock digests the tension by slowly consolidating, taking a median of 33 days to touch the 50 SMA.

• The long term, 60–150 days: reaching +3σ is a powerful filter that selects the market’s strongest growth leaders. Once the 33-day consolidation is complete, structural momentum resumes, which explains Dave’s 120-day hold outperformance.

The micro-tactics we add to Dave’s macro-momentum findings:

1. Avoid the +3σ buy.

2. Add +3σ hitters to a Repeat Offender watchlist.

3. Buy them on the pullback consolidation or on the next +2σ repeat trigger.

4. Hold for the intermediate-to-long term, 60 to 120 days, to ride the structural regime change.

The Full Playbook

Step 1 — The +3σ Cooldown Rule

• Once a stock extends past +3σ and exits, 66.30% of the time it resolves by pulling back and touching the 50 SMA mean. Reversion is not immediate. It is a slow grind, with a median of 33 trading days.

• Do not chase or buy stocks in +3σ territory. If you already hold the position, lock in partial profits or tighten stops the moment it enters the zone. Expect a 1-to-2 month consolidation period.

Step 2 — The +2σ Super-Momentum Slingshot Selector

• When a stock crosses +2σ, filter for Super-Momentum markers: Weekly & Monthly RS above 80, and EMA/SMA slope angles above 30 degrees. This lifts the transition probability from 30.23% to 35.55%, speeds up the move to +3σ, and doubles the mean 10-day return.

• Use the Super-Momentum scanner at the +2σ crossover to identify the highest-velocity slingshot setups. Buy and hold for intermediate trends of 60 to 120 days.

  

Step 3 — The Repeat Offender Watchlist Edge

• Reaching +3σ once is a permanent signature of high-momentum capability. On its next +2σ trigger, the success rate rises to 14.20%, a 39.67% relative lift over first-time breakouts.

• Build a dedicated Repeat Offender Watchlist of tickers that have reached +3σ in the last 12 months. When one of these names reverts to its 50 SMA and sets up a new breakout, prioritise it over all other assets.

SwingTheStrat includes this filter automatically.

Can you do me a favour?

If this piece resonated with you and you’d like to support the work, TheStrat Protocol is available now on Amazon. If you’re already a Kindle Unlimited subscriber, it won’t cost you a penny — just your time. If you’ve already purchased The Strat Protocol — thank you, genuinely.

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I’d traded EPs before, but every time I tried them they pushed me into my worst habits: chasing, oversizing, breaking rules and getting greedy. The setups themselves weren’t necessarily bad; the way they interacted with my personality was. So I quietly removed EPs from my toolbox and moved on. EP9M fell into the same bucket as well.

Then, over the weekend, I heard Pradeep walk through EP9 Million at the TraderLion conference and it forced an uncomfortable question:

“Did I dismiss this because it truly doesn’t fit my edge, or because I never actually did the homework?”

So I spent a weekend doing the homework.

I pulled every EP9 Million trigger I could find in my basket of ~2900 tickers from 2020 through 2026.

(Eagle‑eyed readers might notice a small jump from ~2,700 to ~2,900. You can thank Jeff Sun for that. I added a few leveraged instruments to the universe.)

I treated it like a clean-slate research project:

• What exactly is this setup detecting?

• How often does it appear in different market regimes?

• What do the forward returns look like with and without risk management?

• Which catalysts actually produce the big winners?

• Which names are just noise and should be ignored?

• How can I improve expectancy?

This article is the result of that research. It’s not about proving Pradeep right or wrong. It’s about answering a simpler question:

“What does EP9 Million really look like in the wild and how can a retail trader use it without blowing themselves up?”

What EP9 Million actually is

Classic Episodic Pivots are rare, catalyst-driven breakouts: neglected stocks with game changing earnings or fundamental news that suddenly explode on volume. They might only show up a handful of times per year.

EP9 Million takes the same core idea—sudden, abnormal participation—but flips the starting point. Instead of beginning with fundamentals and then confirming with price, it starts with one simple volume event:

A stock trading roughly 9 million shares or more in a single day, far above its normal volume, usually on some kind of catalyst.

In other words, EP9 Million is a volume-first discovery framework:

• Step 1: Let extreme volume bring candidates onto your radar.

• Step 2: Use catalyst, price action and prior accumulation footprints to decide which ones deserve your risk.

That distinction matters. EP9 Million is not “buy every 9M-volume spike.” It’s:

“Use 9M-volume spikes to quickly narrow a huge universe down to a handful of names worth deeper inspection.”

How I tested it

To keep this useful and not purely theoretical, here’s the high‑level test harness I used:

• Timeframe: 2020–2026.

• Universe: A basket of ~2900 US stocks with all the constituents of QQQ, SPY, IWM, DIA, plus a handful of leveraged ETFs

• Trigger Day = Day 0: The day the EP9M condition fired.

• Forward Returns: Tracked cumulative returns from the next open (Day 1) out to Day 60.

• Two Modes:

  • Unstopped: No risk management; just “what happens if you hold.”

  • Stopped: A more realistic model where we:

    • Enter at Day 1 open.

    • Use a hard stop at the Low of Day (LOD) from the breakout day.

    • Track the resulting distribution of outcomes.

Later, for the 2026 cohort specifically, I overlaid news and catalyst data so I could classify each breakout by catalyst type and see which ones actually paid and which quietly destroyed accounts. I limited this catalyst analysis to 2026 because the StratLab database only has complete news/catalyst coverage from this year onward.

How often do EP9M show up in the real world?

One of the first things I wanted to understand: Is EP9M a daily toy or an occasional signal?

When you apply a sane EP9M filter set across 2020–2026, you get 5,453 valid EP9M triggers in total. That’s enough to make the stats meaningful, but not so many that the scan becomes “everything and nothing.”

What matters more is how that count breathes with the market:

• 2022 bear + early 2023 recovery:
  Average of roughly 37–39 setups per month.

• Speculative breadth expansion of 2024 and the peak bull environment of 2025:
  Setups surge to about 66–83 per month.

That makes EP9M setup frequency a kind of market breadth thermometer:

• When EP9M triggers are scarce, speculative capital is hiding. You should be picky.

• When EP9M triggers explode higher, speculative capital is aggressive. It’s safer to push risk—if your process is robust.

Raw forward returns: The “Outlier Illusion”

Before talking about stops or catalysts, it’s useful to look at what happens if you simply buy every EP9M breakout and hold.

Across all setups (no stops, no filters), the forward returns from Day 1 to Day 60 look something like this:

The key takeaway:

The typical unstopped EP9M trade loses money. The median is negative almost the entire way.

The only reason the average return looks attractive by Day 60 is because a small number of trades double, triple, or more and they pull the mean up while most trades quietly bleed or churn sideways.

That’s what I call the Outlier Illusion:

• If you trade EP9M blindly with no risk management,

• You’re essentially playing a game where most trades underperform,

• And you’re hoping that the occasional monster winner bails you out.

Without proper stop‑loss logic, EP9M as a raw “buy the trigger and hold” system is negative‑expectancy.

Why “Risk” looks bigger than it is

Next I looked at the volatility of returns—how wide the outcomes actually spread over time.

Two snapshots:

• Day 15 (about 3 weeks):

  • Standard deviation ≈ 27.07%.

  • A naive 1σ range (mean ± 1 standard deviation) runs roughly from −19.76% to +18.74%.

• Day 60 (about 3 months):

  • Standard deviation ≈ 58.98%.

  • 1σ: about −32.76% to +36.73%.

  • 2σ: around −59.55% to +152.39%.

If you took those bands at face value, you might conclude:

“I can lose 60% on this in 3 months. That’s insane. Pass.”

But EP9M returns are heavily skewed to the right—a handful of trades go +200%, +500%, +1,000%. Those outliers massively inflate the standard deviation and therefore the “theoretical” lower σ boundary.

When we look at the actual percentiles under a stop-loss, a very different picture emerges.

What happens when you use a hard stop?

To move closer to reality, I ran a second test:

• Entry: Day 1 open.

• Stop: hard Low‑of‑Day (LOD) from the breakout candle.

• Forward horizon: at least 5 days, out to 60 days.

Under this stop‑loss regime, a few important things happen:

1. The left tail is truncated.

   • By definition you can’t lose more than the distance from entry to LOD, except via overnight gaps below your stop.

   • In practice, over 92.69% of all trades lose less than 10% under this rule.

   • The rare deeper losses are almost entirely due to meaningful gap‑downs.

   

2. The theoretical σ bands stop matching reality.

   • At Day 5, the standard deviation is about 41%.

   • A naive 2‑σ calculation (mean − 2 × σ) suggests you could see something like −71.4%.

   • But the actual 16th percentile loss is only about −5.14% (empirical 1σ).

   • The 2.5th percentile (empirical 2σ) is only about −18.55%.

   In other words, the actual downside experienced by 97.5% of trades is closer to −18%, not −70%. The wild right‑tail winners are what make σ so big, not endless catastrophic losers.

3. The distribution shifts from negative to positive expectancy.

   • Because the left tail is capped and the right tail is still allowed to expand, the system’s average expectation flips positive over reasonable holding windows.

That leads to a critical practical point:

Do not interpret standard deviation as “how much you can lose.” Treat it as “how noisy this game is” and then use intelligent stops to define your actual risk.

Why tiny stops don’t work here

If you look at the early‑day volatility:

• Day 1 standard deviation is around 9.23%.

• The average daily range (ADR%) of many of the big winners is 4–10% or more.

In that context:

• A 1–2% stop is just a way to guarantee you get whipped out.

• You’re trying to play a high‑noise game with extremely low tolerance for noise.

The data backs this up when we test the impact of when you move your stop-loss to break-even (entry price). We tested five stop management rules over a 1-month holding horizon:

1. Static LOD Stop: Stop-loss stays at the trigger day’s low and is never moved.

2. Day 2 BE: Stop is moved to break-even on the very next day.

3. Day 3, 4, and 5 BE: Stop-loss is moved to break-even on Day 3, Day 4, or Day 5 respectively.

Look at the statistical progression in the dashboard below:

• The Day 2 BE Trap: If you aggressively move your stop to break-even on Day 2, your win rate collapses from 29.79% to a measly 7.25%, and your average return falls into negative territory at −0.55%. You shield yourself from some losses, but you shake yourself out of almost every single monster winner.

• The Recovery Trend: As you delay the break-even adjustment (moving it on Day 3, 4, or 5), the metrics steadily improve. By waiting until Day 5, the win rate rebounds to 13.31% .

Why does this happen? Delaying the adjustment gives the stock room to establish a price cushion. If a breakout is a true momentum leader, by Day 5 it has likely extended far enough away from the entry price to survive normal intraday pullbacks.

This also explains why Pradeep, these days, tends to favour delayed EPs. A delayed entry or a delayed risk adjustment bypasses the maximum noise window of the initial Day 1 breakout, letting the stock establish structural support before you trail your stop.

Decompressing the Winners: Why holding time matters

To understand the winners, I isolated all LOD Stopped trades that ended positive and divided them into three equal‑sized buckets by return:

• Zone 1: Base wins (lower third of winners).

• Zone 2: Strong wins (middle third).

• Zone 3: Outliers (top third).

Then I tracked how these zones “decompress” as you stretch the holding period:

• 1‑Week (5‑Day) Horizon:

  • Zone 1: < 9.26%

  • Zone 2: 9.26% to 22.46%

  • Zone 3: > 22.46%

• 1‑Month (20‑Day) Horizon:

  • Zone 1 shifts to < 14.79%

  • Zone 2: 14.79% to 37.32%

  • Zone 3: > 37.32%

• 3‑Month (60‑Day) Horizon:

  • Zone 1: < 26.56%

  • Zone 2: 26.56% to 64.63%

  • Zone 3 outliers push to > 64.63%, with absolute monsters far beyond that.

That’s the essence of momentum decompression:

• If you cut losers quickly (LOD stop),

• But let winners run for 1–3 months,

• The right tail expands enough that a minority of trades can carry the entire campaign.

This is the mathematical justification for the classic rule:

“Cut your losses fast. Let your winners run.”

In EP9M language, it becomes the basis for the 80/20 scaling rule, which I’ll come back to.

How can I improve the EP9 Edge?

One of the most interesting findings came from looking at the nature of the volume spike itself.

We ran a Pearson correlation study on all trades with over 300 unique variables and asked:

“Which price or volume characteristics actually correlate with being a big winner (≥ 20% swing return over 3 weeks)?”

This cluster of volume metrics, showed some interesting results:

• hve (Highest volume ever recorded):

  • Correlation with Win (r) = +0.0099 (Pure Noise)

• hv1 (Highest volume in past 1 year):

  • Correlation with Win (r) = +0.0148

• hvq (Highest volume in past 1 quarter):

  • Correlation with Win (r) = -0.0251 (Negative Correlation)

• hv30 (Rolling high-volume count in preceding 30 days):

  • Correlation with Win (r) = +0.0447

• hv90 (Rolling high-volume count in preceding 90 days):

  • Correlation with Win(r) = +0.0410

• hv180 (Rolling high-volume count in preceding 180 days):

  • Correlation with Win (r) = +0.0661 (Highly Significant Edge)

The takeaways:

• Single‑day climax volume is basically noise.

  • hve (lifetime high volume): r ≈ +0.0099 → pure noise.

  • hvq (quarterly peak): r ≈ −0.0251 → slightly negative correlation.

  Huge, one‑off volume peaks often signal exhaustion or distribution, not sustainable momentum.

• Repeated prior high‑volume days matter.

  • hv30: r ≈ +0.0447

  • hv90: r ≈ +0.0410

  • hv180: r ≈ +0.0661

  As you extend the “lookback window,” the correlation with big winners strengthens. Big winners had, on average:

  • About 5.15 high‑volume events in the prior 180 days,

Translated to plain English:

Whether today’s volume is the highest ever doesn’t tell you much.
Whether the stock has been attracting repeated institutional interest over the last 3–6 months does.

The role of ‘Catalysts’

In Pradeep’s framework, EPs aren’t just “big candles.” They need a real catalyst—something that explains why the market suddenly cares.

I wanted to quantify that for EP9M, so for the 2026 onward data (where I had full scraped news coverage), I split EP9M triggers into two big groups:

• Catalyst‑backed EPs: Earnings, guidance, biotech/FDA, substantive product launches, major fundamental developments.

• Technical‑only EPs: Clean price + volume breakouts with no meaningful news.

Here is the visual proof comparing the 5-week average returns, win rates, expectancy curves, and return distributions of catalyst-backed vs. technical-only breakouts:

Under a stop‑loss regime, the difference was stark at the 5‑week horizon:

• Catalyst‑backed EPs:

  • Win rate ≈ 23.87%

• Technical‑only EPs:

  • Win rate ≈ 16.53%

Put bluntly:

Without a genuine catalyst, most EP9M moves are wealth‑destroying traps over a swing horizon.

2026 Catalyst Breakdown: Not All News Is Equal

For 2026, I had complete price, volume, fundamentals and news coverage, so I drilled further into the catalyst types.

Out of 556 total EP9M breakouts in 2026:

• 399 (71.8%) had a validated catalyst.

• 157 (28.2%) were technical‑only noise.

When we grouped those 399 catalyst‑backed EPs by catalyst category and looked at 3‑week results (LOD stop), several patterns jumped out:

• Biotech Approvals & Trials:

  • Average ≈ +10.63% over 3 weeks.

  • About 25.7% became big winners (> 20% swing in 15 days).

  • But stop‑out rate ≈ 54.3% → very high risk, very high reward.

• Earnings & Guidance:

  • Highest consistency.

  • Win rate ≈ 36.8% with the lowest stop‑out rate (~52.1% in this cohort).

  • For many traders, this is the most reliable core EP9M category.

• Contract Wins & “Deal” Announcements:

  • Statistically awful.

  • Average ≈ −2.72% with a 70.4% stop‑out rate.

  • These are often used to distribute shares into retail strength.

• Technical‑Only / No News:

  • Average ≈ −1.55%.

  • Stop‑out rate ≈ 65.6%.

  • Occasional lottery‑ticket winners but terrible cohort stats.

The message:

The catalyst is not optional. It’s the difference between a positive‑expectancy campaign and a grind of slow, repeated cuts.

Day 0–3 timing: When is it too late to buy?

A common real‑world question:

“If I miss the breakout day, can I still buy Day 1, Day 2, or Day 3? Or is the edge gone?”

To answer this, I tracked 588 EP9M breakouts in 2026 that had a full 30 days of post‑trigger data and grouped them into:

• Catalyst‑backed EPs (n = 263).

• Weak/Noise news EPs (n = 162).

• Technical‑only EPs (n = 163).

Then I looked at cumulative returns vs the Day 0 open over 30 days and paid special attention to median vs average:

• Technical‑only EPs:

  • Average looks fantastic on Day 20 (+19.48%), thanks to a handful of insane squeezes.

  • Median tells the truth: it peaks around +8.47% on Day 5, then decays to +1.88% by Day 30.

  • The typical trade gives most of it back.

• Weak news EPs:

  • Median peaks around +6.89% on Day 0, then bleeds to +0.49% by Day 20 and slightly negative (−0.07%) by Day 30.

  • This is what distribution into retail FOMO looks like on a chart.

• Catalyst‑backed EPs:

  • Median holds around +4.58% on Day 30.

  • Positive outliers lift the average to roughly +16.27%.

The execution rule that falls out of this:

• If there is a verified, strong catalyst, you have a multi‑day window. You can buy the Day 1 follow‑through or Day 2 consolidation and still be trading a positive‑expectancy cohort.

• If the news is weak or there’s no news, you either:

  • Enter on Day 0 intraday using a tight, disciplined playbook or

  • Walk away.

Anatomy of ‘Big Winners’: Where the Outliers live

To make all of this more actionable, I isolated the 40 “Big Winners” out of 286 EP9M setups year‑to‑date in 2026, using:

• Big Winner: Any EP9M trade that gained ≥ 20% over 3 weeks (Day 15), under the LOD stop regime.

Then I looked at them across four dimensions:

1. Sectors and Industries

Three areas dominated, accounting for 77.5% of big winners:

• Financial Services (27.5%)

  • Heavy in crypto‑linked and leveraged asset management names (COIN, various 2x/3x ETFs, etc.).

• Technology (27.5%)

  • Concentrated in Software – Application and Semiconductors.

• Healthcare / Biotech (22.5%)

  • Primarily Biotechnology, often on Phase 2/3 data or FDA decisions.

Actionable note:

If your EP9M scan flags Utilities, Real Estate, or Consumer Staples, the odds of a major swing run are statistically low.

2. Volatility Footprint (Pre‑Trigger ADR%)

Big winners almost never came from sleepy names:

• 95% of big winners had a preceding 20‑day ADR% of ≥ 4.0%.

• 75%+ had ADR% of ≥ 6.0%.

• About 25% had ADR% above 10.0%.

Actionable note:

If a stock has ADR% < 4.0% going into the EP9M day, it’s statistically dead money for big momentum moves, even if it trades 9M shares on the trigger.

3. Price Sweet Spot

Price mattered too:

• $3–$10 range: about 37.5% of big winners.

• $10–$20 range: about 22.5%.

• $20–$50 range: about 20%.

• Above $50 produced some winners, but much less frequently.

Lower‑priced names have the volatility and elasticity to make 40–100% swings in a few weeks.

Actionable note:

Don’t auto‑filter out the $3–$20 names if you’re hunting EP9M outliers. That’s where a lot of the real action is.

4. Catalyst Mix and Leveraged ETFs

Within the Financials big‑winner cohort:

• Roughly half of the winners were leveraged ETFs or sector proxies (e.g. 2x COIN, 3x crypto baskets, etc.), not individual companies.

Actionable note:

If your focus is on idiosyncratic corporate catalysts, you may want to filter leveraged ETFs out of the EP9M scan and treat them as a separate strategy.

The EP9M Playbook:

All of this research is only useful if it turns into something you can apply at the desk.

Let’s start with Pradeep’s playbook first:

Next, let’s layer in what we have learnt from this stack:

1. Scanning & filtering

Start with a daily EP9M scan that finds:

• Volume spike around ≥ 9M shares traded and far above normal.

• Price between $3 and $50, with focus on $3–$20.

• RVOL ≥ 3

• ADR% ≥ 4% over the preceding 20 days (prefer ≥ 6%).

• Sector in Financials/Crypto, Technology or Biotech/Healthcare (you can include Industrials/energy for the occasional outlier, but keep them as second tier).

• Signs of prior accumulation (multiple high‑volume days over the past 180 sessions, not just a one‑off lifetime volume spike).

  • HV180 > 5

Then, filter hard on catalysts:

• Strong catalysts to prioritise:

  • Earnings beats with raised guidance.

  • Biotech/FDA approvals, successful trial readouts.

  • Real product/tech launches that matter to revenue or positioning.

• Weak/no catalysts to treat with suspicion:

  • “Contract wins,” vague “business updates.”

  • Sympathetic moves on someone else’s news.

  • No news at all.

If there is no meaningful news, treat the EP9M as a technical lotto ticket at best. For most swing traders, the best move is to skip it.

2. Entry execution

Two primary entries:

• Immediate Entry – Opening Range High (ORH):

  • Let the first 15 minutes print.

  • Buy when price breaks above the high of that first bar.

  • For very liquid mega‑caps (COIN, SMCI, etc.), consider a 30‑minute ORH to filter out early noise.

  • For very fast growth names, aggressive traders can test 5–10 minute ORH variants—but know you’re trading into more raw volatility.

• Delayed Entry – Post‑Breakout Consolidation:

  • Wait 2–5 days for an orderly pullback or tight sideways consolidation after the volume spike.

  • Enter when price breaks out above that consolidation, ideally on renewed volume.

Here’s how to scan, with a single click, for reversal entries on delayed EPs inside SwingTheStrat.

If there’s no catalyst, the data says the edge evaporates quickly. In that case, the only scenario where entry makes sense for most people is intraday on Day 0 with a very tight, well‑rehearsed plan. Otherwise, pass.

3. Stop loss rules

Use stops to cap the left tail without strangling the right tail.

• Primary stop:

  • Place your initial stop just below the LOD of the breakout day.

  • Accept that this may be 5–10% away in many names; that’s the price of playing this game.

• Alternative tight stop (ideally for the Delayed EP):

  • Use a static 2.5–5% stop below your entry only if:

    • The stock is deeply liquid,

    • The ADR% is on the lower end of the EP9M spectrum,

    • And you’ve tested this variant separately.

• Do not rush to breakeven.

  • Moving stops to breakeven on Day 2, on average, kills the system expectancy.

  • Let the trade breathe through normal volatility for at least several days before tightening stops.

4. Scaling: The 80/20 Rule

To capture big outliers without exposing your full size for months:

• Sell ~80% of the position into initial strength:

  • Typically within 3–5 days.

  • Targeting the zone around 8–15% gains, which corresponds loosely to the boundary between Zone 1 and Zone 2 winners.

• Let the remaining 20% run:

  • Use a dynamic trailing stop such as a rising 10‑day EMA or a swing‑low based structure.

  • Hold as long as the trend stays intact, up to 1–3 months.

  • This is where the Zone 3 outliers (often +60% and beyond by Day 60) show up.

The stats justify this:

• Even though roughly 76% of trades ultimately stop out or end negative by Day 60,

• The right‑tail decompression in the top third of winners is so extreme that the overall system average remains strongly positive when managed correctly.

Classic EP vs. EP9 Million

It’s worth underscoring the relationship between classic EPs and EP9M:

• Classic EPs:

  • Low frequency, high conviction.

  • Require clear, powerful fundamentals (triple‑digit earnings or sales growth, major game‑changing catalysts).

  • Only a handful of A‑quality candidates per year.

  • Research burden per name.

• EP9 Million:

  • Higher frequency, more opportunistic.

  • Starts from volume, then layers in catalyst, volatility, sector and accumulation filters.

  • Offers hundreds of opportunities per year at different quality levels.

  • Functions more as a discovery and filtering framework than a binary “buy signal.”

The ultimate “So What”

After dismissing EP9 Million for years and then backtesting it across thousands of trades, here’s where I landed:

• EP9M by itself—“buy every 9M volume spike and hold”—is not a good system. The median trade loses money and the outlier illusion hides that reality.

• With a strict LOD stop, a realistic understanding of volatility and no rush to breakeven, the distribution flips: the left tail is capped, the right tail is allowed to decompress and the system’s expectancy turns positive.

• Catalysts are non‑negotiable. Earnings, guidance upgrades, biotech approvals and real product launches dramatically outperform technical‑only spikes and weak “contract” headlines.

• Context filters matter. High ADR%, specific sectors (Financials/Crypto, Tech, Biotech), price between $3 and $50 and signs of prior institutional accumulation all stack the odds in your favour.

• EP9M’s real power is as a discovery framework:

  • Volume gets a ticker on your screen.

  • Tightness, volatility, catalyst, sector and prior footprints tell you whether it deserves your risk.

If you treat EP9 Million as a magic buy signal, it will punish you.

If you treat it as a structured way to find and qualify potential momentum campaigns, and you respect what the data says about risk and catalysts, it becomes a very useful tool in a swing trader’s toolbox.

Can you do me a favour?

If this piece resonated with you and you’d like to support the work, TheStrat Protocol is available now on Amazon. If you’re already a Kindle Unlimited subscriber, it won’t cost you a penny — just your time. If you’ve already purchased The Strat Protocol — thank you, genuinely.

Buy TheStrat Protocol

Right now, we are in a period of chop, and the major indices are diverging in a meaningful way. On one side, QQQ and SPY are failing to hold key moving averages and rolling lower. On the other, the Dow Jones and IWM are holding up well, finding support, and even testing the top of their broadening formations.

When half the market is breaking down and the other half is holding firm, the message is simple: caution is warranted.

If you are a swing trader, this is not the time to be forcing trades or increasing size. If you try to push hard in a mixed market, you are increasing the chances of getting chopped up.

Instead, this is the time to step back, protect capital and observe. We need to let the dust settle and identify the leaders of the next market cycle.

To do that, I rely on two high-conviction scans.

1. The Anchored VWAP + Time Frame Continuity Scan

In a mixed market, true leaders will continue to show full time frame continuity. For this scan, I filter for stocks where the close is greater than the open on the daily, weekly, and monthly charts.

But continuity alone is not enough.

I also apply an Anchored VWAP across those same time frames — day, week, and month — and only want to see stocks that are actively closing above that anchored volume-weighted average price.

If a stock is maintaining full TFC and holding above multi-timeframe Anchored VWAP while the broader indices are failing, it deserves attention. That is the kind of behaviour that often appears before the next major move.

You can read more about the origin of the scan here

2. The Absolute Relative Strength Scan

My second scan is designed to find absolute power.

I filter my universe using the following criteria:

• Relative Strength above 90 on the daily chart.

• RS Leader Phase, meaning the stock has shown leadership over the last 21 days.

• Strong closes, with price finishing in the top 20% of the candle range on both the daily and weekly charts.

This scan helps me isolate names that are not just holding up, but actively outperforming. In a market like this, that matters more than ever.

Visualising the rotation

Running the scans is only the first step.

Once I pull the results into the Market Map, I can quickly see where institutional money is rotating. That visual context is incredibly useful because it helps turn a list of strong names into a structured watchlist.

Right now, we are seeing clear strength cluster in a few specific areas:

• Banks: JPMC, WFC, BAC.

• Cyber Security: RBRK, PANW.

• Retail: EBAY, ETSY.

That kind of clustering is important. It tells you where strength is broadening and where the next pocket of leadership may be forming.

Final thoughts

This is not the time to force trades. It is the time to stay patient, let the market show its hand, and focus on quality.

The next leaders are usually hiding in plain sight while the broader market is still trying to decide what it wants to do. If you have the right scans, you do not need to guess.

You just need to wait for the strongest names to reveal themselves.

I’ve also released a full video walking through exactly how I run these scans, analyse the sector clusters, and build the watchlist for the next bull cycle.

Stay patient, trust your scans and have a great trading day.

Can you do me a favour?

If this piece resonated with you and you’d like to support the work, TheStrat Protocol is available now on Amazon. If you’re already a Kindle Unlimited subscriber, it won’t cost you a penny — just your time. If you’ve already purchased The Strat Protocol — thank you, genuinely.

Buy TheStrat Protocol

They typically tend to ‘Undercut and rally’. Price dips below a key level, shakes out weak hands and then snaps back above support before the breakdown can gain traction. It is one of the cleanest ways to spot stocks that are still acting well even when the broader tape is under pressure.

What is an ‘Undercut and rally’

An undercut and rally happens when price briefly trades below a meaningful level and then closes back above it. That level might be a prior low, a moving average or another obvious support point on the chart.

The move below support is the shakeout. The reclaim is the signal. In TheStrat terms, this often shows up as a 2dG or a hammer-style reversal, both of which suggest sellers pushed too far and buyers stepped back in.

Why it matters now

This setup becomes especially interesting when the broader indexes are pulling back into support. In that kind of environment, a lot of stocks are being tested at the same time. Some truly break down. Others get pushed lower briefly and then start to reclaim key levels.

Those are the names worth focusing on. A good undercut and rally is not just a random green candle after weakness. It is a failed breakdown inside a stock that still has structure, participation and a realistic chance of resuming trend.

How to scan for it

The strength of this scan is that it is built with layered logic. It does not rely on a single candle or a single indicator. The ANY and ALL groups each have a job to do.

Trigger

This group is set to ANY:

That gives the scan two valid ways to catch the reversal. Not every undercut and rally will print the exact same candle structure, so the trigger needs flexibility.

Location

This group is also set to ANY:

This keeps the setup anchored near an important moving average. The goal is to find stocks being tested at levels that actually matter.

Trend strength

This group is set to ANY:

This is a good way to make sure at least one part of the trend is showing real strength. The stock does not need every moving average to be steep, but it does need evidence that the trend is still alive.

Momentum filter

This group is set to ALL:

This helps keep the list focused on names with stronger candle quality and better closes. It is a useful filter for avoiding weak charts that happen to bounce for a day.

DCR (Daily Closing Range) measures how strong a stock’s close is by showing where the current price sits within that day’s high-to-low range.

Trend Confirmation

This group is set to ALL:

This is the broader structural check. It makes sure the stock is not trying to undercut and rally inside a declining trend.

Participation and Relative strength

This final group is set to ANY:

This is an important addition. Relative volume tells you there is participation. The relative strength filters help keep the scan focused on names that have already shown leadership.

Why the group logic matters

The real edge in this scan comes from how the logic groups are combined. ANY groups give the setup room to show up in different valid ways. ALL groups enforce discipline where confirmation matters most.

That balance is what makes the scan practical. Too tight and it misses good names. Too loose and it starts surfacing random bounce candidates that do not have the structure to continue.

How to read the results

Scan results for 9 Jun

DELL, JHX, WDAY, PBI, RDDT, NTAP, TWST, SWK, EVER, STRL, QLYS, ZD, ECG, GRDN, SAH, WLY, TRNS, IESC, ADV, MSBI, MPTI

This scan should be treated as a shortlist, not an automatic buy list. Once a name appears, the next job is to inspect the chart and confirm that the undercut happened at a meaningful level and that the reclaim has real intent behind it.

The checklist is simple:

• Did price undercut a meaningful level?

• Did it reclaim that level quickly?

• Is the larger trend still intact?

• Did the move happen with participation?

• Is there nearby overhead supply?

If the answer is yes, then the stock deserves closer attention.

A few that caught my attention

And here are some examples that meet the criteria, but don’t look right

Keep the ‘Market’ in context

Undercut and rallies are interesting, but they should never be read in isolation. The broader market still sets the tone and that should shape both expectations and position sizing.

If the indexes are deteriorating, caution matters more than enthusiasm. In that kind of tape, even strong-looking reclaim setups deserve smaller size, tighter risk and more selective execution.

The goal is not to ignore the setup. The goal is to respect the regime around it. A strong stock can still bounce, but when the broader market is unstable, it makes sense to stay measured and size accordingly.

Entry and Risk

More aggressive traders may enter on the reversal candle, while more conservative traders may wait for a retest of the reclaimed area or a break above the reclaim candle’s high.

The stop usually sits just below the undercut low. If price loses that level again, the setup is likely invalid. That is one of the pattern’s best features: clear invalidation and well-defined risk.

Final thought

The undercut and rally is really about finding stocks that were pressured, not broken. Weak names stay underwater. Strong names act like that balloon: they get pushed down, hold their character and rise again when the pressure starts to come off.

In a market pullback, that is where the opportunity often sits. Not in guessing bottoms, but in identifying the names that prove they still want higher prices.

Can you do me a favour?

If this piece resonated with you and you’d like to support the work, TheStrat Protocol is available now on Amazon. If you’re already a Kindle Unlimited subscriber, it won’t cost you a penny — just your time. If you’ve already purchased The Strat Protocol — thank you, genuinely.

Buy TheStrat Protocol

Your stop’s been hit, your position closed and you’re left wondering what you missed.

You probably didn’t miss anything obvious.

What do institutions have that you don’t?

Institutions are not reading the same thing you are

Think about it this way.

You have a headache. You take a paracetamol and it gets better. But what if something more sinister was behind it? There are moments when only an MRI can detect the tumour that is actually causing that headache.

The paracetamol was not wrong. It told you something real. It just could not see beneath the surface.

Classic TFC is the paracetamol.

It tells you something real. It works. But it only has two data points to work with: the open and the close.

And there are moments — the ones that take your stop — when what is actually happening beneath the surface requires a different tool entirely. A different layer of information that the surface symptom cannot reveal.

Institutions do not move price randomly. They move it with purpose. Market makers are fully aware — the open and close are the most visible, most heavily watched, most retail-depended-on numbers in the entire session.

If you were on the other side of the trade and you knew exactly where retail traders were watching, where their stops were clustered, what signals they were waiting for — what would you do?

You’d engineer those numbers.

So what if you had a radar that detected what was actually underneath? Something that couldn’t be engineered as easily, because it doesn’t rest on two moments in time, but on every share traded, at every price, all session long.

That’s what we’re getting into today.

Now, if you are not familiar with how TFC is used within TheStrat, here’s an excerpt from the book to quickly bring you up to speed.

The drawback with classic TFC

Full Timeframe Continuity is a foundational pillar of TheStrat for good reason.

When the hourly, daily, weekly and monthly candles are all aligned — all green or all red — it gives you a clear, actionable read on directional control. Buyers or sellers.

It is simple and mostly reliable. It has been one of the most useful lenses in my own trading for years and it remains a core element in the StratLab stack.

But it rests on two data points: the open and the close.

Here is a question worth sitting with:

After more than a year of running a parallel experiment — measuring TFC through a completely different tool — I have found something worth sharing.

That is the Anchored VWAP based TFC.

I’ve spoken about it a couple of times on X in passing. Today I wanted to make it more public.

Volume does not lie the same way two points in a candles can do

Volume Weighted Average Price(VWAP) tells you what the average participant actually paid.

It’s not just where price opened or closed, but where every buyer and seller in that session, on that timeframe, actually transacted — weighted by how much they traded. That’s harder to engineer, and it’s closer to how Portfolio managers at large institutions are measured and evaluated by those who pay their wages!

Brian Shannon is the ‘Godfather of AWVAP’ and the reason most traders today know what it is. If Rob Smith put TheStrat on the map, Brian did the same for Anchored VWAP.

The anchor is the key. Pin the VWAP calculation to a meaningful moment in time:

• Daily anchor — is the average buyer from today’s session in profit or underwater?

• Weekly anchor — where does the average participant stand this week?

• Monthly anchor — at the monthly level, who has the structural edge right now?

While Brian discusses WTD, MTD, QTD and YTD in his book, he does not frame it explicitly through timeframe continuity lens. He briefly touches on multi-timeframe alignment in the appendix, but that idea was the real unlock for me.

(Brian, in the unlikely event that you ever read this piece, I would respectfully encourage you to expand the appendix to include a discussion of continuity, in addition to the alignment framework you already cover.)

Price above the anchor: average buyer in profit. Buyers in control. Price below the anchor: the opposite.

Stack that across timeframes and you have a volume-weighted read on directional control — one that does not care just about where price happened to be at 9:30am or 4pm.

AVWAP TFC dots

The breakthrough for me came when I turned that idea into a simple set of dots. Instead of manually anchoring multiple separate VWAPs on every ticker and manually moving them around and preventing clutter on my charts, I built a visual that told me at a glance whether the daily, weekly and monthly anchors were above or below price. Each dot represented one timeframe and its colour showed whether that timeframe was in alignment or not. The size of the dot told me how strong the trend was.

It took a process that was useful but painfully manual and turned it into something I could scan instantly across hundreds of charts.

Rather than rebuilding the structure from scratch every morning, I could immediately see when the shorter-term picture was in harmony with the higher timeframe anchors and when it was starting to fracture underneath the surface. For someone looking at 300 to 500 charts a day, that difference was huge. It made AVWAP TFC less of a theory and more of a practical tool.

The dots did not just save time — they changed the way I read the chart.

The moment Classic TFC lies to you

Let me show you exactly what I mean. We're going to put (Russell 2000 ETF) on the table, run a bar by bar replay and see what the MRI actually picks up beneath the surface…

Act One: The whisper

Classic TFC? It’s broadly green. Quarterly and yearly both sitting at +5.71%. By every measure, buyers are in control. The structure intact and there’s nothing to worry about, right?

Except the Anchored VWAP panel tells a completely different story.

The quarterly and yearly anchors have just flipped red. The average participant across both timeframes is already underwater — while Classic TFC is still waving the green flag.

So what happened the next day?

Full timeframe continuity to the downside. A clean, decisive reversal — and if you were sitting in that trade, you felt every pip of it.

Classic TFC never blinked. Right up until the move, it was still green.

The classic signal was only showing you half the picture.

Also carefully observe how the background colour on the Anchored VWAP is constantly switching between red and green. That is control constantly flipping between buyers and sellers.

That also gives you a really clear indication of indecision and a potential regime change. That is what Rob would call ‘battle’, and it is that extra level of nuance that you necessarily don’t necessarily get to witness on the classic TFC.

Act Two: The confession

Price drops further. Classic TFC starts to catch up. The weekly and monthly turn red. But quarterly and yearly still showing -1.32%. The picture Classic TFC is painting: the quarter has barely moved.

But on Anchored VWAP quarterly and yearly: -3.97%.

The average participant on the quarter is not down 1.32%. They are down nearly 4%. Anchored VWAP had already priced in three times the damage — the day before.

Act Three: The first trap

Price gaps down hard. The previous pivot gets taken out and the broadening formation stretches wider. But then buyers show up. They show up with real conviction. That long lower wick isn’t subtle — it’s a deliberate statement that someone stepped in and defended price.

Most timeframes on Classic TFC flip green. The structure starts to feel like it’s healing. To a retail trader watching the candles, it looks clean. Dip bought. Sellers rejected. We’re fine.

But pull up the Anchored VWAP and nothing has changed.

Monthly, quarterly, yearly — all red. The average participant across every timeframe that institutions actually care about is still underwater.

Classic TFC gave you just enough green to feel confident. Anchored VWAP didn’t flinch.

This is the first trap.

Act 4: Repeated traps

This pattern repeats — three more traps. — Classic TFC offered enough green to justify optimism. Each time, Anchored VWAP was unmoved. And each time, it was a trap.

Trap 1

Trap 2

Trap 3

This one is the most seductive of the lot.

Classic TFC quarterly and yearly sitting at exactly 0.00%. Dead flat. On paper, that looks like a clean slate — a proper structural reset. New buyers coming to that chart see what they’ve been waiting for. The macro timeframes neutralised, short-term green starting to emerge. The selling pressure gone. Maybe, just maybe, this is the bottom.

It’s an easy story to believe.

But the Anchored VWAP quarterly and yearly tells you something that zero doesn’t. Down 4.33%.

The average participant on the quarter is still sitting on a loss.

That flat reading on Classic TFC isn’t recovery.

The open and close happened to land in the same place. The volume-weighted reality underneath? Completely unmoved. The pain is still there. It’s just been tidied up on the surface.

This is not a coincidence.

Act Five: The First Real Signal

And then, for the first time in this entire sequence, both panels say the same thing.

Classic TFC: H, D, W, M, Q all green.
Anchored VWAP: H, D, W, M, Q all green.

This is the signal that none of the previous bounces produced. Every prior recovery had Classic TFC flashing green while Anchored VWAP stayed red. This time, both are green simultaneously.

One caveat worth naming — the average participant on the year is still underwater, and the yearly anchor is always the last to heal. This is a confirmed tactical long, not a declaration that the bear market is over. Full conviction would require the yearly to come along for the ride.

But five timeframes aligned across both tools is the highest quality signal this framework has produced across this entire sequence. The yearly will do what it does in its own time.

Is this one is enough to act on?

I probably would have waited!

Act Six: The Confirmation

A couple of days pass and the alignment holds.

Not a single-candle flash, but a sustained, multi-day consensus. Both Classic TFC and Anchored VWAP green across five timeframes, for three consecutive days. The yearly still red on both — but everything else confirmed.

Act Six: The Payoff

Price explodes. $265.07. Short sellers are covering. The structure has flipped completely — the HTF broadening formation is being reclaimed.

For the first time in this entire sequence, all six timeframes are green on both panels simultaneously. Every average buyer — daily, weekly, monthly, quarterly, yearly — is in profit. The two tools are in full agreement at every timeframe.

Every previous bounce was a trap. This one was not. And the only tool that told you the difference in real time — before the move proved itself — was the one that could not be engineered as easily as an open and a close.

One plus one is greater than two

In most cases, Classic TFC and Anchored VWAP tell the same story. When the daily, weekly, and monthly candles are all green, price is typically above all three anchors too. Both the methods are in agreement.

And when they agree, conviction compounds.

Think about what that actually means. One tool reads the verdict of the candle — open to close. The other reads the verdict of every participant, weighted by every share traded at every price, all session long. Two completely independent methods, speaking entirely different languages, arriving at the same conclusion. That is the highest quality directional signal these tools can produce together.

One plus one becomes greater than two.

Structural confirmation and volume-weighted reality, both pointing the same direction at the same time. That is when you can afford to be aggressive — bigger size, more wiggle room, the conviction to hold through the noise.

Divergence is a different conversation entirely. When Classic TFC and Anchored VWAP start telling different stories, that is where the edge lives — and for me, it means the opposite. Trim the position. Take some profit. Reduce exposure. The framework is waving a yellow flag and experience has taught me to listen to it.

Great, but is this statistically significant?

While there was enough anecdotal evidence from using Anchored VWAP-based TFC alongside Classic TFC for over a year, the question remained whether the pattern would hold up under proper testing or simply reflect confirmation bias.

I pulled 1,299 signals from 100 QQQ components (NASDAQ 100 stocks) over 6.4 years (January 2020 to June 2026) and categorised them into one of three distinct groups: Classic only, AVWAP only or Full Alignment:

1. Cohort A (Classic Only): Daily, Weekly and Monthly candles are all green. But price is below the anchored VWAP. This is the “Trap” — price action looks bullish but is stuck under institutional supply.

2. Cohort B (AVWAP Only): Price is trading above the Daily, Weekly, and Monthly anchored VWAPs, but the candles are not all green.

3. Cohort C (Full Alignment): The intersection. Candles are green and price is above the anchored VWAPs.

For each signal, I tested whether price reached the magnitude target before hitting the stop loss, defined as the previous day’s low. When a prior candle high existed, magnitude was set to that high at the daily, weekly, or monthly level. When the setup occurred at an all-time high, magnitude was defined as a volatility-based extension using ATR.

The Trap is real

The first thing the data exposed was ‘The trap’ we saw from the IWM charts above.

When Classic TFC turned bullish on its own, but price was still sitting under Anchored VWAP, the setup often looked ready before it was actually ready.

That is where the whipsaw lives. This is were your stops get taken. The candles look green, the structure looks intact, but price is still pushing against institutional supply and that is why traders keep getting stopped out

Once AVWAP joins Classic TFC, the picture changes completely. The signal is no longer just structurally bullish — it is now supported by the volume-weighted position of participants. That is why Full Alignment consistently posts the strongest win rates across the predefined magnitude targets.

Anchored VWAP appears to be a stronger confirmation layer for TFC than Classic TFC alone. It does not replace the structural read, but it helps distinguish the setups that merely look bullish from the ones that are actually being supported by volume-weighted participation.

Blue Sky Breakouts (All-Time Highs)

When a stock triggers at an All-Time High, there is no previous high to use as a target, so I switched to ATR-based magnitude levels.

One note on the sample size: Classic-only produced just one trade, so that result is directional rather than statistically meaningful. The real comparison here is between AVWAP-only and Full Alignment, where the sample sizes are larger and the behaviour is more informative.

[I could probably do a separate study on stock behaviour at ATH]

How to use it

In the MTIF article, we built on an existing distinction:

Anchored VWAP fits into a similar framework:

Think of it as a second opinion. Classic TFC is your primary read. Anchored VWAP is the confirmation layer. The practical workflow builds naturally:

The practical workflow:

1. Use MTIF to identify where structural commitment is stacking across timeframes

2. Use classic TFC as your primary directional orientation

3. Layer Anchored VWAP — daily, weekly, monthly — as a volume-weighted cross-check

4. When both align: highest conviction. This is where you build positions with confidence

5. When they diverge: pay attention. The divergence is a flag. Take a minute to investigate before you act

Scaling the Workflow: How to Scan for Full Alignment

Knowing the framework is only half the battle; the real edge comes from execution at scale. If looking at hundreds of charts every morning to manually check for this dual-layered alignment sounds daunting, it doesn’t have to be.

If this approach resonated with you and you are looking for a seamless way to scan the markets using these exact parameters, you can use SwingTheStrat.

As you can see, the platform allows you to combine Classic TFC filters directly with multi-timeframe Anchored VWAP conditions. With a few clicks, you can filter out the “Traps” and bubble up only the highest-conviction Cohort C setups that possess full volume-weighted institutional support.

• Ready to scan? You can head over to SwingTheStrat to build your filters.

• Want a test drive? If you are not signed up on SwingTheStrat yet but want to get a real feel for the application, you can use this shared scan link to see it in action. (Please bear in mind: this shared link is a point-in-time scan reflecting the market exactly as it was when this article was published. If you are viewing this at a future date, the specific data and results will be different.)

Closing thoughts

This is the beginning of a longer conversation. I’ll keep testing the idea as more data comes in.

The broader question remains the same: is the open and close enough to tell us who is really in control or does Anchored VWAP give us a cleaner read on what is happening beneath the surface?

Can you do me a favour?

If this piece resonated with you and you’d like to support the work, TheStrat Protocol is available now on Amazon. If you’re already a Kindle Unlimited subscriber, it won’t cost you a penny — just your time. If you’ve already purchased The Strat Protocol — thank you, genuinely.

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Huge thanks to Solanki, Snowman and Kyna for reviewing this and helping me whip it into shape.

One comment in particular stood out, especially Chris Huber’s suggestion that we should segment the study by stock performance level.

That was a really good call, because it pushed the question in the right direction:

When stocks are already more than two sigma extended from their 50 SMA, what is the likelihood that they continue on to three sigma? How does that compare with the general population?

That was the exam question we set out to answer.

Monthly RS

We all know the textbook version of the story.

A stock is ripping. It has been leading the market for weeks. Then it reaches a major milestone, extending more than two standard deviations above its 50 SMA (ie 66% probability that it will revert back to the mean) or roughly 5.27 ATRs above the moving average.

At that point, the usual warning lights start flashing. The stock looks stretched. The rubber band looks too tight. The expectation is that mean reversion is close.

So you wait for the pullback.

But sometimes the pullback never comes, at least not straight away. Instead, the stock keeps climbing, accelerates again and pushes straight into the three sigma zone, roughly 7.83 ATRs above the 50 SMA.

The market’s strongest leaders do not always behave like the average stock. Sometimes they operate under a different set of rules.

So we started with the first layer: Monthly RS.

Monthly RS tells you whether a stock has been a strong intermediate-term leader. It is the broad trend filter. It shows whether a stock has already been outperforming over a meaningful time horizon.

When we looked only at stocks with monthly RS above 80, the probability of moving from +2σ to +3σ improved versus the control group. The curve shifted to the right. The mean days to +3σ shortened slightly. The mean reversion rate fell.

That is the first clue. They also had enough fuel to push the forward return further.

A stock with strong monthly RS is not just stronger in general. It also appears to tolerate more extension before gravity takes over and also pushes the envelop further.

Weekly RS

If monthly RS tells you about stability, weekly RS tells you about urgency. It captures short-term acceleration. It shows whether the stock is not just strong, but strong right now.

That is where the second layer comes in.

When we added weekly RS > 80, the picture became more nuanced. The continuation probability was still above the general population, but the real change showed up in forward returns. Weekly RS seemed to act like a velocity filter. It did not just identify leaders. It identified leaders with immediate momentum behind them.

Monthly RS gives you the broader trend. Weekly RS tells you whether the move has fresh fuel. Together, they start to build a more complete picture of extension behaviour.

The super-momentum layer

Then we pushed the test one step further.

If a stock has strong relative strength on both weekly and monthly horizons, that is already interesting. But what if the moving averages themselves are also aligned and rising sharply?

That is where the slope filter comes in.

Slope alignment means the 10 EMA, 21 EMA and 50 SMA are all rising at more than 30 degrees. In other words, the short- and intermediate-term trend structure is all pointing in the same direction with real strength behind it.

When those averages are all rising together like that, the move is no longer just extended. It is being supported by a much stronger structural backdrop. That is the super-momentum case.

And that is where the most interesting results appeared.

The super-momentum cohort showed the highest probability of reaching +3σ, the fastest average path to +3σ and the strongest forward returns.

In practical terms, that means the strongest leaders do not just extend further. They often do it faster.

The Aha chart

Now we get to the chart that really matters.

This is the Aha chart.

It does not just show that high-RS stocks continue more often. It shows something bigger: their entire extension distribution shifts right.

The general population sits near the middle of the chart, which is what you would expect. The high-RS cohort shifts the centre of gravity to the right. The super-momentum cohort shifts it even further.

The curve is not just moving a little. It is moving enough to suggest that extension is behaving differently depending on the type of stock you are looking at.

For the broad market, +2σ is still a warning zone. For the strongest leaders, it is closer to a continuation state. That does not mean there is no risk. It means the odds are no longer the same.

What the cohort statistical mean means

The cohort statistical mean is just the average extension level for each group.

In the general population, the average sits close to the moving average itself.

In the high-RS cohort, the average sits much further above it.

In the super-momentum cohort, it moves farther right again.

That is powerful because it means the average state of a strong stock is not the same as the average state of the whole market.

Put simply:

That is why the chart matters so much. It tells you that “extended” is not a universal condition. It depends on the stock’s underlying strength.

The conditional probability point

Shout out to @SubtleInduction, for flagging this.

One important clarification: this is not an unconditional probability study.

Once price reaches +2σ, the stock is no longer in a neutral state. From that point onward, the question becomes conditional:

That framing is essential, because strong stocks do not all behave the same once they are stretched.

This is why the +2σ to +3σ transition is such a useful test. It does not ask how a random stock behaves from a neutral starting point. It asks what happens after a stock has already entered a state of extension and demonstrating leadership traits.

The continuation matrix

Here is the full picture:

The base case shows the average stock reverting most of the time. Monthly RS improves the odds. Weekly RS improves short-term returns. And the full super-momentum filter gives you the strongest continuation profile of all.

What the results mean

The main takeaway is simple: high-RS stocks do not behave like the average stock once they become extended. They are more willing to keep stretching.

That does not mean you should chase them blindly. It means you should stop treating every +2σ event as if it carries the same message, because context matters.

A +2σ event in a weak stock is usually a warning. A +2σ event in a stock with strong monthly RS is a different setup entirely. Add weekly RS and slope alignment, and you are no longer looking at a tired move — you are looking at a stock that may still have fuel left in the tank.

How do I scan for Super-Momentum stocks?

All of this is great — but the question probably going through your mind right now is: how do I actually find these stocks?

Don’t worry, we’ve got you covered.

Within SwingTheStrat, you have the ability to build highly custom screeners tailored specifically to your needs — including the ability to filter by slope and angle alignment, which makes hunting for super-momentum setups far more precise than a standard screener.

Here’s a sandboxed link you can use to get a feel for the application:

👉 https://swingthestrat.com/api/share/s/mRRAV7at

Please note: This is a point-in-time scan. If you’re viewing it at some point in the future, the data may not reflect current market conditions. Use it as a feel for the tool, not as a live signal.

A little gift for you

If you’ve read this far — thank you.

Many of you have asked for access to the ATR extension cloud from my previous article, so as a small thank-you for making it all the way to the end, here’s the link. The thresholds are built from the full basket of 2,700 stocks.

For those interested in the variable ATR multiple, the version that adapts to the individual personality of each stock — I’m bundling it as a free gift with TheStrat Protocol until June 5th 2026.

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If you’ve already purchased TheStrat Protocol, use the link below to claim your copy before the June 5th 2026. After that date, it will be available separately for $49.

👉 Claim your free indicator here

Buy Adaptive ATR Extension Cloud $49

I pulled up the chart and looked at the distance from the 50 SMA in ATR terms. It was sitting at about 6.7x ATR, so I replied, “It looks like it has some more room to run, and once prices start reaching 7.5x to 8x ATR, that’s when it usually starts pulling back.”

He challenged me straight away and fairly so.

“How can you be so certain?” he asked. “How can you be certain when there’s no certainty about anything in trading?”

He had a point. Trading rewards humility. The market has a way of making fools of anyone who speaks in absolutes. But while there may be no certainty, there is probability. After years of staring at these charts and using Jeff Sun’s ATR extension indicator, I had experiential evidence — a strong intuition built from repetition.

The question was whether it was anything more than that.

SwingTheStrat has five years of historical data across roughly 2,700 tickers. That’s millions of candles. And for the first time, I realised we could actually test it properly — find out whether this was a well-worn heuristic or something genuinely empirical.

Why extension matters

At its core, the market behaves a bit like a rubber band.

When price stretches away from a moving average, tension builds. Sometimes that tension keeps expanding for longer than your gut thinks it should. Sometimes it snaps back fast. The key is learning the difference between a normal stretch and an extreme one.

That’s what ATR extension multiple is trying to capture.

Instead of asking, “How far is price from the moving average in dollars?” we ask a more useful question: “How far is price from the moving average relative to this stock’s own daily volatility?”

That matters because a $500 stock and a $10 stock can both be stretched, but not in the same way. ATR lets you compare them on equal terms. It standardises the move.

So if a stock is 7.5x ATR above its 50 SMA, that doesn’t mean the same thing as 7.5% or $7.50. It means price has stretched 7.5 times the stock’s normal daily range away from the anchor.

The 50 SMA bell curve

The 50 SMA is where this whole thing becomes especially useful, because it sits right in the middle of trend behaviour.

It’s fast enough to matter for swing trading, but slow enough to smooth out the noise. When you plot the extension of thousands of stocks against the 50 SMA, you don’t get randomness.

You get a bell curve.

That bell curve tells you what normal looks like.

We took over five years of daily candles across roughly 2,700 tickers and measured the 50 SMA extension in ATR multiples for every single candle. Then we plotted the whole lot. The result was exactly what you’d hope for if markets were behaving like a statistically messy but broadly understandable system: a clustered middle, thinner tails and a few extreme outliers.

Most candles live near the middle. The further you go out, the rarer the event becomes.

Translated into the numbers from the dataset, that means:

• 1 sigma is roughly 2.5x ATR.

• 2 sigma is roughly 5x ATR.

• 3 sigma is roughly 7.5x to 8x ATR.

What the sigma levels mean

It’s worth being precise here, because people often throw sigma around without really explaining it.

If we say a move is within 1 sigma of the mean, we mean it sits inside the range where about 68% of observations live. That’s ordinary behaviour.

At 2 sigma, you’re covering about 95% of the distribution. Anything beyond that is unusual enough to merit attention.

At 3 sigma, you’re out in the tail. That’s the territory where only about 0.3% of observations should live if the distribution is behaving normally. In plain English, that’s extremely rare.

That gives you a practical framework:

• 1 sigma means normal.

• 2 sigma means stretched.

• 3 sigma means you are in the extreme tail.

This is why the 7.5x to 8x ATR zone matters so much on the 50 SMA. It’s not just “a bit extended”. It’s the point where the market is telling you the move has already travelled a long way from its mean and is now vulnerable to mean reversion.

And mean reversion is often where traders get caught.

They don’t buy when the stock is still building. They buy when the stock has already done the heavy lifting.

So when a stock is sitting near 7.5x to 8x ATR above the 50 SMA, you are deep into statistically rare territory. It doesn’t guarantee an immediate reversal, because trading doesn’t work like that, but it does tell you the odds are no longer in your favour if you’re still chasing late.

That was the answer I was really giving about .

It wasn’t, “This stock will definitely pull back here.” It was, “This is where the odds start getting ugly.”

That’s a much better way to think.

The other moving averages

The 50 SMA is only the beginning.

SwingTheStrat also captures extensions for the 10 EMA, 21 EMA and 200 SMA. Once I plotted those too, a more complete picture started to emerge. Each moving average has its own personality and the numbers make that personality concrete.

The 10 EMA is the tight leash. Because it reacts fast, the extension bands are narrow. One standard deviation sits at just 0.93 ATR to the upside. By 3 sigma, you’re only at 2.69 ATR. That might not sound like much, but it means the 10 EMA reaches its extreme zones quickly. A stock doesn’t need to travel far before it’s statistically overstretched on this timeframe.

The 21 EMA gives a stock a little more room to breathe. The 1 sigma boundary sits at 1.45 ATR, and 3 sigma stretches out to 4.17 ATR. It’s still a responsive measure, but the bands are wider, which means you’re capturing a slightly more developed move before the signal becomes extreme.

The 200 SMA is a different animal entirely. Because it moves so slowly, price can travel a very long way from it before the extension becomes statistically significant. One sigma doesn’t arrive until 5.42 ATR. Two sigma is 10.72 ATR. Three sigma is 16.02 ATR. These are not small numbers.

But the more interesting thing about the 200 SMA is the shape of the curve itself. Look at it closely and you’ll notice it’s slightly skewed — the right tail is a little heavier than the left. That asymmetry is telling you something real about long-term trending behaviour. Stocks can spend a surprisingly long time extended to the upside.

Standard deviation extension bands for key moving averages

Using the bell curves, we can now set proper thresholds for positive and negative extension across the different anchors.

That table is more useful than a single number because it shows both sides of the distribution.

A lot of traders only think in terms of the upside extension. But the downside matters too.

A stock that is deeply extended below the 200 SMA is not just “cheap”. It may be broken, capitulated or in the middle of a violent unwind. Likewise, a stock stretched far above the 50 SMA may still keep climbing, but the odds of a sharp pause or mean reversion are rising fast.

A better way to think about extension

The best way to use extension is not as a yes-or-no switch.

It’s a context tool.

If a stock is only just starting to stretch, that doesn’t mean it has to reverse. It just means you should start paying attention.

If it becomes stretched on one moving average, that’s useful.

If it becomes stretched on two, that’s more interesting.

If it stretches on three or four at the same time, you start getting real conviction that mean reversion is getting closer.

A stock can be extended from the 10 EMA and still continue higher. It can even be extended from the 50 SMA and keep grinding. But when you start seeing the 10, 21 and 50 all moving into stretch or trap territory together, that’s when the move starts to look exhausted.

Jeff’s indicator that73K traders around the world have viewed is super useful, but it’s still one-dimensional. It tells you the extension in a column, not the shape of the move. The extension cloud fixes that by plotting the stretch directly on the chart, so you can see how price behaves around the 10, 21, 50 and 200 moving averages in real time.

This is why the extension cloud is so helpful visually.

You don’t just want a number. You want to see how the candles behave inside the cloud, how quickly they move through it and whether they’re stretching on one anchor or all of them at once.

AMD: a stock that kept stretching

AMD is a good example of why extension shouldn’t be treated as a hard stop.

A stock can look extended in one dimension and still have fuel in another.

That’s why people get into trouble when they look at one moving average in isolation. The 50 SMA might be saying, “This is stretched”, while the 10 EMA and 21 EMA are still telling you the trend is alive.

In that kind of situation, the right response isn’t to panic. It’s to pay attention.

AMD kept running even as the extension cloud started to thicken. That told me the stock was strong enough to absorb the stretch. The real question wasn’t, “Is it extended?” It was, “How many of the anchors are now showing strain?”

That’s a much more useful question.

CAR: when the rubber band really snaps

CAR gave a very different picture.

That was one of those charts where multiple moving averages were all flashing at once. The 10 EMA, 21 EMA and 50 SMA were all stretched. That’s where the rubber band analogy really earns its keep.

When the shorter anchors are all showing the same kind of stress, the stock is no longer just extended. It’s becoming mechanically vulnerable.

That doesn’t mean the reversal must happen immediately. But it does mean the stock has reached a point where the probability of continuation is lower and the probability of exhaustion is much higher.

That’s the sort of move where you stop thinking in terms of chasing and start thinking in terms of protecting capital.

And if you’re looking for the moment when a stock is most likely to capitulate, that simultaneous stretch across multiple timeframes is exactly what you want to watch for.

Apple, revisited

So let’s come back to Apple.

The important point here is that I didn’t need the extension cloud to know Apple still had room to run. Jeff’s heuristic already told me that. Once a stock gets up into that 7.5x to 8x ATR zone from the 50 SMA, that’s where the odds start shifting against fresh chase entries.

That was the original insight. The cloud doesn’t replace it, it just makes it easier to see.

What the extension cloud gives you is a visual way to watch the stretch build in real time. Instead of relying on a number in a column, you can see how price moves through the bands over time, how the candles behave inside the cloud and when the move starts to look crowded.

On Apple, that matters because the stock is not yet fully compressed across every anchor. The 10 EMA and 200 SMA are still relatively calm, whilst the 21 EMA and 50 SMA are starting to show more stretch. That tells you the move is still alive, but the pressure is building.

So the message is simple: the heuristic told me there was still space. The cloud lets me see that space.

That’s why the cloud is useful. It turns a rule of thumb into something you can watch unfold on the chart.

The heuristic gives you the threshold. The cloud shows you the stretch.

Stock-specific thresholds

The cloud had already been useful, because it let me visualise extension instead of just reading a number. But I wanted to take it one step further. I didn’t want to rely only on thresholds built from the full universe of stocks. I wanted a model that could adapt to the stock in front of me.

What if you could treat each stock on its own merit?

What if, instead of using one fixed set of thresholds for every ticker in the market, you could look at a stock’s own previous behaviour and ask, what is normal for this ticker?

That was the exam question.

That’s the point of ‘adaptive thresholds’. It doesn’t compare Apple to the whole market. It compares Apple to Apple’s thresholds. It uses that stock’s own price history to work out what stretched, extreme or exhausted actually looks like for that name.

So when you look at the chart, the comparison is the point.

On the left, you’ve got the out-of-the-box model, where Apple is measured against the broader population. On the right, you’ve got the adaptive model, where Apple is measured against its own historical behaviour. Same stock, same chart, two very different ways of reading the stretch.

That matters because stocks do not all behave the same. Some are volatile. Some are smooth. Some can keep stretching for ages before they mean-revert. Others turn much sooner. A stock-specific threshold lets you see that difference clearly.

So the real question is not just whether the stock is extended.

It’s whether it is extended for that stock.

And that’s what makes the adaptive model such a big step forward.

A stock like Apple will have one personality. A stock like SNDK will have another. MU will behave differently again. HOOD will have its own rhythm.

A stock-specific model lets you study the stock on its own terms.

And once you do that, the extension cloud becomes much more powerful.

SNDK, MU and HOOD: studying the winners

If you really want to learn how to use this properly, study the winners.

That’s where the real lessons are.

Big winners do not move in a neat straight line. They stretch, pause, re-accelerate and stretch again. If you go back through historic multi-baggers, you can see how price behaves as it pushes deeper into the cloud. That tells you a lot about how much fuel a move still has and when the pressure is starting to build.

The point is not whether extension exists. It’s how far a stock can stretch before the move finally gives way.

That’s why SNDK, MU and HOOD are so useful. They show you three different versions of the same basic story. SNDK shows a strong trend that kept finding fuel even as it stretched. MU shows a steadier move that ground higher, paused and then expanded again without falling apart. HOOD shows what happens when momentum and narrative come together and the stock starts pushing through the cloud faster than most traders expect.

That’s the kind of chart you want to study.

The important thing these charts show is this: big movers often keep stretching longer than you expect before they finally snap back. That doesn’t mean you chase them blindly. It means extension is not just a warning light, it’s a way of reading pressure.

How to use this in practice

Here’s the practical part.

First, identify the anchor you care about. For swing trading, that’s often the 21 EMA or 50 SMA. For longer context, the 200 SMA matters more.

Then look at the ATR multiple.

If the move is still inside the normal range, fine. If it is stretching, pay attention. If multiple moving averages are stretched at the same time, take that seriously.

A simple framework would look like this:

• 10 EMA stretched: short-term momentum is hot, but not necessarily finished.

• 21 EMA stretched: swing momentum is becoming extended.

• 50 SMA stretched around 7.5x to 8x ATR: the move is entering statistically rare territory.

• 200 SMA stretched by 15x or more: you are looking at a major structural move or a late-stage exhaustion event.

Then combine that with price action.

If the stock is stretched and you see a reclaim candle, a hammer or a bullish engulfing bar after a washout, that’s when the setup becomes interesting.

If it’s stretched and all the moving averages are flashing at once, that’s when you start thinking about exits, not entries.

The real lesson

The biggest mistake traders make is thinking they need to be first.

They don’t.

They need to be right enough often enough.

ATR extension analysis helps with that because it keeps you from forcing trades at the wrong point in the move. It also stops you from dismissing great trends too early just because one moving average looks stretched.

You want to know when a stock still has fuel. You want to know when it’s starting to overheat. You want to know when the rubber band is still elastic and when it’s about to snap.

So the next time you look at a stock like Apple, or AMD, or one of the real monsters like SNDK, MU or HOOD, don’t just ask whether it’s up a lot.

Ask how stretched it is.

Ask which moving averages are stretched.

Ask whether you’re looking at a normal trend, an extended trend or a stock that has entered the sort of zone where prices have run too far, too fast.

And most of all, don’t confuse extension with certainty.

It isn’t certainty.

It’s a map of probability.

One more thing: scanning for extension

There’s one more capability worth knowing about.

Once you understand extension thresholds, the natural next step is to scan for them. Instead of manually checking stocks one by one, you can run a screener that finds every name currently sitting beyond a specific ATR multiple across any combination of moving averages.

That’s exactly what SwingTheStrat lets you do.

The ATR extension scan inside the platform lets you build fully customised filters around ATR multiples. You can screen for stocks that are beyond 2.5x on the 10 EMA, beyond 4x on the 21 EMA, beyond 7.5x on the 50 SMA or beyond 16x on the 200 SMA. You can combine them with ANY logic (at least one condition true) or ALL logic (every condition must be true). You can mix and match however the setup demands.

The screener shown here, for example, is scanning for stocks where any of the four anchors has breached its approximate +3 sigma threshold simultaneously. That’s the multi-anchor trap scan, and it’s exactly the kind of filter that surfaces the CAR-style setups before the reversal happens, not after.

The individual stock view goes even further. You can see the ATR multiple for each anchor at a glance, alongside the slope, the status and the zone. ARM, for example, shows a 10 EMA at 2.7x, a 21 EMA at 3.8x, a 50 SMA at 6.0x and a 200 SMA at 7.7x. All four anchors in HOLD territory, all climbing. That’s the kind of picture that tells you a move is real and building, not just a one day spike.

You don't need to build it yourself. It's already there, running across more than 2,700 tickers every day, ready to surface the setups when they appear.

And if you enjoyed this article, please consider supporting the work by picking up a copy of TheStrat Protocol. TheStrat Protocol is currently ranked #36 in Best Sellers in Stock Market Investing on Amazon, which is beyond anything I could have imagined. If you already own a copy, an honest review and a share in your trading communities would mean the world.

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If you found this article valuable, here’s the follow up article on how to detect ‘Super Momentum’ Stocks based on ATR extension:

I found one: Encyclopedia of Candlestick Charts by Thomas Bulkowski. It’s a bulky 950+ page book. Every pattern catalogued, named, tested and ranked — Doji, Hammer, Shooting Star, Evening Star, Three Black Crows and many more. I read it cover to cover and came out the other end knowing more about candlestick names than I knew what to do with.

There were many patterns and memorable names, yet no framework to apply or act on them.

The simplicity and elegance of TheStrat fixed that for me.

The classic Strat scenarios

Most traders who come to TheStrat learn four candle types:

• 1 — The inside bar (no new high, no new low).

• 2u — The 2-Up (higher high, no lower low; bullish).

• 2d — The 2-Down (lower low, no higher high; bearish).

• 3 — The outside bar (takes out both sides).

These four types are enough to describe every candle ever printed.

But at TheStratLab, we wanted more resolution under the microscope. A candle that makes a higher high and closes red is not the same animal as one that makes a higher high and closes green. Throwing away the close means throwing away vital information you could be trading with.

So, we work with seven distinct candle states:

• 1 — Inside. Coiling. No new range.

• 2u — Directional up. Higher high, bullish close.

• 2uR — Directional up, red close. Effort without conviction.

• 2d — Directional down. Lower low, bearish close.

• 2dG — Directional down, green close. The dip absorbed.

• 3u — Outside up. Full expansion, bullish verdict.

• 3d — Outside down. Full expansion, bearish verdict.

Reading them in succession, in groups of three candles, gradually reveals the market as a structured conversation, rather than mere noise.

343 possible combinations

With 3 sequential candles and 7 possible states each, the math is simple:

We ran a database query on the Daily QQQ chart from its inception in March 1999 to May 2026, analysing 6,825 + daily bars. Every bar was assigned to one of seven states and all three-candle sequences were extracted and stack ranked.

Out of the 343 mathematically possible combinations, only 317 have actually printed in QQQ’s history. More importantly, the vast majority of price action is explained by just a small fraction of them.

We took the Top 100 combinations (which do the real heavy lifting to cover ~ 80 % of the tape) and classified them into five core categories:

1. Bullish reversal: Characterised by an initial bearish phase that transitions into a bullish outcome. The final candle breaks upward.

2. Bearish reversals: Initially bullish, these reversals ultimately turn bearish. The final candle breaks downward.

3. Bullish continuations: Begins with a neutral or bullish note and concludes on a bullish note. The trend persists.

4. Bearish continuation: Begins neutral or bearish and concludes with a bearish outlook. Sellers stay in control throughout.

5. Consolidations: The final candle is a 1. The market has compressed, coiling for the next break.

The Distribution of the Top 100

Here is how those Top 100 combinations split across the five categories on the Daily QQQ chart

Three striking things stand out immediately:

• Bullish continuation leads, but only by a slim margin. At 23.9%, it is the largest bucket in the top 100 combos, with bullish reversal close behind at 22.0%. Bearish continuation and bearish reversal are tied at 21.1% each, which keeps the structure remarkably balanced. The message is clear: trend-following still matters, but it does not dominate the way you might expect.

• More striking: continuation and reversal are nearly evenly split in the strongest setups. Continuation patterns make up 45.0% of the top 100, while reversals account for 43.1%. Nearly half of the best sequences are directional shifts rather than clean follow-through moves. If your process is built only around continuation, you are leaving a meaningful part of the tape on the table.

• Consolidations, at just 11.9%, remain the smallest category. That means compression is rarer than expansion or directional movement among the top combos. When the market does tighten up, it deserves attention, because the resolution is going to be a directional move. (More on this later - look at bullish pivots later in the article)

The 100 Strat Combos that matter most

We went one step further. We stack-ranked all 100+ combos by raw occurrence count and identified the scenarios that contributed to the top 80%.

• The top 40 combos account for ~52% of all three-candle sequences in the dataset
  More than half of everything QQQ has ever done on the daily chart fits into 40 patterns.
  

• Push that to 109 combos and you reach 80.1%. Out of 343 possible combinations, just 109 explain four-fifths of the tape.
  

Breakdown of the Top 100 based on category

Next we took each of the 5 board categories and stack ranked them based on their occurrences.

Key Insights from 27 Years of QQQ Data

• QQQ has a clear bullish persistence bias, but not an overwhelming one.
  The most common combo is 2u-2u-2u, which appears 352 times and accounts for 5.15% of all daily history. That is far ahead of the second-ranked pattern, 2d-2d-2d, at 174 occurrences and 2.54%. The market does trend upward more efficiently than it trends downward, but it still leaves plenty of room for reversals and pauses.

• A red close inside an up sequence does not automatically end the uptrend.
  Patterns like 2u-2uR-2u and 2u-2uR-2dG show that a red close after strength can be absorbed without breaking the broader move. The market can pause, shake out weak hands, and still continue higher. A single red candle is not enough to call the trend broken.

• A green close inside a down sequence does not automatically rescue the downtrend.
  Bearish continuation patterns such as 2d-2dG-2d and 2d-2u-2d show that a green close can appear inside weakness and still fail to reverse the tape. The market can bounce briefly and then continue lower. Without confirmation, a green close is often just noise inside a larger bearish structure.

• The default state of QQQ is not random — it is structured persistence.
  The top-ranked sequence is 2u-2u-2u, which tells you that clean bullish follow-through is the single most common three-candle pattern in the dataset. That matters because it means trend continuation is not an edge case; it is a core feature of the tape. QQQ repeatedly rewards traders who respect strength when it persists cleanly.

• Reversals are real, and they resolve through confirmation.
  Bullish reversal examples like 2d-2dG-2u and 2d-2d-2u appear prominently in the top list. These are not chaotic flips; they are structured transitions that require directional confirmation back to the upside.

• The inside bar is balanced, not biased.
  2u-2u-1 and 2d-2d-1 sit almost perfectly side by side in the rankings. That tells you the inside bar is compression, not prediction. It does not tell you which way the market wants to go; it tells you the market has paused long enough to ask the question.

• RevStrat-style patterns are statistically relevant.
  1-2d-2u [0.6%] and 1-2u-2d[0.5%] both appear in the top 50, so these are not rare curiosities. They show that failed breaks and immediate reversals matter on the daily chart. The market absolutely does produce tradable transition structures — they are just not the majority of cases.

• Breakout-and-go still matters more than reversal failure.
  The clean inside-bar continuation pattern 1-2u-2u [1.2%] + 1-2uR-2u [0.3%] is more common than the RevStrat-style reversal examples 1-2u-2d[0.5%] 1-2uR-2d[0.4%]. That means compression more often resolves with follow-through than with immediate failure. In other words, acceptance beats rejection more often than traders sometimes expect. Lesson: Dont predict. Move your stops to Low of day.

• The real edge is in reading transition, not just direction.
  Reversals make up a meaningful share of the top 100 combos, which makes them a key part of QQQ’s daily structure. They show where momentum is fading, where control is changing, and where the next move is being decided. For a Strat trader, that makes reversal structure essential to read well.

Where Things Occur Matters

But here is the limitation of everything above. All of it treats the tape as a flat, context-free sequence of bars. Rob Smith’s central teaching is that this is exactly the wrong way to think about it. As he puts it: “When things occur matters.” and by extrapolate

A 2d-2d-2u printed in the middle of a choppy, trendless range is a completely different event from the same combo printed at the low of a major Broadening Formation that has held for three months. The combo is identical. The trade is not. So we asked a harder question.

So, we asked a different, much more powerful question.

Instead of looking at all Strat combos across all 6,825+ bars, what if we only looked at the combos printed at confirmed Pivot Highs and Pivot Lows, the structural swing points that actually define the market’s broadening formations and trend cycles?

Using a strict 7-bar pivot window (requiring a bar to be a local maximum or minimum with 3 daily bars of lookback and 3 daily bars of lookahead), we isolated 652 confirmed Pivot Lows and 591 confirmed Pivot Highs in QQQ’s entire daily history.

To align with the exact mathematical indicators, we analyse the 3-candle sequence centred at the pivot bar:

• Candle 1: The day before the pivot (how we entered the swing).

• Candle 2: The pivot bar itself (the structural extreme).

• Candle 3: The day after the pivot (the immediate reversal/rejection verification).

This definition is crucial. By mathematical definition, the bar after a pivot high cannot make a higher high, and the bar after a pivot low cannot make a lower low. Therefore, classic continuation sequences like 2u-2u-2u are structurally impossible here.

Here is what 27 years of daily data reveals when we look at these swing points under the microscope.

Top 24 Bullish Pivots

For the 652 confirmed structural bottoms on QQQ, a mere 24 unique centered combos describe exactly 81.44% of all swing lows ever printed. This completely covers the 80% threshold required to map out how the market forms structural pivot bottoms.

Here is the complete stack-ranked directory of the 24 scenarios:

Top 24 Bearish pivots

While capturing bottoms is the primary focus for many retail traders, identifying structural tops is just as critical for protecting capital and executing short positions. Just as we did with swing bottoms, we isolated the exact price-action signatures that print when QQQ runs out of buyers at major daily pivot highs.

For the 591 confirmed structural tops on QQQ daily, the top 24 unique centred combos describe exactly 80.34% of all swing highs. This maps out the complete 80% statistical threshold for daily index peaks.

Here is the complete directory of the 24 swing high scenarios:

The 21 EMA: Retest vs. The “Deep Flush” Bottom

To make this analysis actionable, we decided to overlay a 21-day Exponential Moving Average (EMA),the industry standard indicator for trend confirmation.

We looked at every single 2dG (Two-Down Green) candle printed in QQQ’s daily history to see how it behaved relative to the 21 EMA.

Across the entire dataset, there were 787 total 2dG bars, and they were split almost perfectly down the middle:

• 2dG Above the 21 EMA: 398 bars (50.57%)

• 2dG Below the 21 EMA: 389 bars (49.43%)

But when we filtered the data to only look at 2dG bars that printed at confirmed Pivot Lows (structural bottoms), the distribution shifted significantly:

• Pivot Low 2dG ABOVE the 21 EMA (The Trend Retest): 97 occurrences (38.34%)

• Pivot Low 2dG BELOW the 21 EMA (The Deep Flush): 156 occurrences (61.66%) 👈 Structural Bottom

When QQQ prints a 2dG that actually marks a major structural swing low, it is roughly 62% likely to be trading BELOW the 21 EMA.

This reveals a structural secret about this index:

• The Trend Retest (38%): In a roaring bull market, QQQ will occasionally pull back to the 21 EMA, print a 2dG (dip absorbed), and immediately bounce. This is a standard, low-risk trend continuation entry.

• The Deep Flush Bottom (62%): Major, multi-week market bottoms are not formed by gentle retests of the moving average. They are formed by a violent, deep flush below the 21 EMA that stops out all leveraged longs, hits extreme discount value, and prints a capitulation 2dG absorption bar deep in “oversold” territory.

If you are a swing trader waiting for a major market bottom to buy, the data says do not buy the first dip to the 21 EMA. Wait for the deep flush below the 21 EMA, and then buy the moment the market prints a 2dG absorption candle.

From Research to Practice : Putting the Tools in Your Hands

Analysing thousands of bars of transition probabilities in real time is impossible to do manually. So we built two tools to for you to internalise it — one to train your eye, one to sharpen your edge.

StratIQ — Train Your Pattern Prediction Probability

Before you can trade these combos, you need to see the probabilities. StratIQ is a game built specifically for that purpose.

The format is simple. You are shown two consecutive QQQ candles and asked one question: what is the most statistically likely candle to follow? It sounds straightforward. It isn’t. Most traders discover very quickly that their instincts are miscalibrated — overweighting reversals, underweighting continuation, or misreading what a red close inside an uptrend actually signals. StratIQ corrects that, repetition by repetition, using the same QQQ dataset this entire article is built on.

If you have read this far and want one concrete next step: play it. Twenty rounds of StratIQ will do more for your pattern recognition than re-reading this article a few times.

👉 Play StratIQ

Q

The Markov Engine — Map the Probabilities

Once your eye is trained, the Markov Engine gives you the numbers behind what you are seeing.

A Markov chain treats each candle state as a step in a sequence, and calculates the probability of the next state based solely on what just printed. Just: given that we just printed a 2d, what does the historical record say comes next — and with what frequency?

We built three levels of resolution:

• 1-Dimensional — Given the current candle, what is the probability distribution of the next candle? Your immediate next-step odds.

• 2-Dimensional — Given the last two candles, what does the third most likely look like? This is where the combo analysis in this article lives.

• 3-Dimensional — Given a full three-candle sequence, what historically follows? The deepest layer of the transition map.

All three levels are available across QQQ, SPY, IWM, and DIA so you can compare how each behave structurally and whether the transition probabilities align or diverge across indices in real time.

Together, these two tools operationalise everything in this article. StratIQ builds the instinct. The Markov Engine quantifies it.

Closing Thoughts

Thomas Bulkowski spent years cataloguing over 1,000 candlestick patterns. It is an extraordinary, heroic piece of work. But after reading 950 pages of his encyclopedia, most traders still struggle with what to do when the market opens on Monday morning.

That is not a knock on Bulkowski. It is a reflection of what TheStrat does differently.

TheStrat doesn’t ask what a candle looks like. It asks what control looks like, where control shifted, and whether the market accepted that shift.

When we ask that question across 6,825+ daily bars on QQQ, the market collapses into a surprisingly small, highly structured vocabulary:

• 109 combinations account for 80.13% of the entire tape

• 24 combos account for 80% of all bullish pivots

• 24 combos account for 80% of all bearish pivots

If you feel overwhelmed by the math, you don’t have to be. The entire framework reduces to three elegant rules that never change:

• We enter on reversals.

• We add on continuations.

• We defend with tight stops.

Bulkowski gave you the names of the patterns. TheStrat gives you the framework for acting on them. The combos tell you what the market is doing; the three rules tell you what you must do about it.

You can keep the 950-page encyclopedia on your bookshelf as a reference. But for day-to-day execution, you now have a clean, actionable map: built on data, driven by probability, and equipped with the tools to help you spot them better.

If you enjoyed this article, please consider supporting the work by picking up a copy of TheStrat Protocol. And if you already own it, an honest review and a share across your Strat communities would mean the world.

Buy TheStrat Protocol

This one is for EasyName, who asked the question everyone is thinking but most people don't ask: "Do you have a formal logic to define the broadening formation? Something you could code up instead of going visually?" .

The broadening formation has a formal logic. Here’s how you define it, step by step and how you draw it correctly.

Step 1: Identify your ‘Pivots’

Start by identifying swing highs and swing lows, but only on confirmed, closed bars.

The pivot length is the key variable. It’s also the only subjective element of the whole thing.

Think of it as a window. If your pivot length is 8, the algorithm looks at 8 bars to the left and 8 bars to the right of any candidate bar. If that bar has the highest high across all 17 bars in that window, it qualifies as a swing high. Same logic in reverse for a swing low.

The rule is always:

• Swing High = highest high within N bars left and N bars right

• Swing Low = lowest low within N bars left and N bars right

Step 2: Choosing Your Pivot length

This is the secret sauce. The pivot length you choose determines which swings you see and which ones you miss.

First start simple. Use hardcoded values per timeframe. Fix a default pivot length for each chart and get comfortable with what it shows you before touching anything else. Different timeframes warrant different sensitivities. What works on a daily chart won’t be appropriate on a 60-minute.

Then go adaptive. Once you’re comfortable with hardcoded values, you’ll notice the “right” pivot length shifts with market conditions and the character of the stock. Instead of a fixed window, the algorithm calculates pivot length dynamically based on the rate of change of the Average True Range (ATR) over a set lookback — 10 or 14 periods are common starting points.

• Higher ATR volatility = shorter pivot length

• Lower ATR volatility = longer pivot length

The formation adjusts itself to the ticker and the market condition.

Step 3: The expansion check

Once you have confirmed pivots, one rule governs the formation:

• Each new swing high must be greater than the previous swing high

• Each new swing low must be lower than the previous swing low

Both must be true simultaneously. That expanding envelope creating the broadening formation — higher highs and lower lows. The two trendlines diverge from left to right, creating the megaphone shape. .

Step 4: Draw Trendlines

Draw the lines backwards, connecting the most recent pivot to the prior one.

• The upper trendline connects the most recent swing high back to the prior lower swing high

• The lower trendline connects the most recent swing low back to the prior higher swing low

In the example from the book, that means connecting B ($202) back to A ($198) on the upper line, and Pivot Low B ($168) back to Pivot Low A ($172) on the lower. Those two lines form the megaphone.

Step 5: Mark your ‘Reclaim’ levels

At every confirmed swing high and swing low, draw a horizontal line. These are your reclaim levels. This is the most actionable levels the formation produces and probably the most under discussed.

When price breaks below $198 in the example, it is now targeting the other side of the formation at $168.

Equally if the $198 level is not breached, the dynamic has shifted. Prepare for expansion and wait for a reversal to enter.

You can read more about ‘Reclaims’ below

Step 6: Know where you are in the BF

Not all locations inside the formation are equal:

• Action Zone — the bottom third of the range. Maximum magnitude potential. This is where you look for longs.

• Ambiguous Zone — the middle of the range. Lower probability. Avoid initiating new positions here. Great for adds.

• Exhaustion Zone — the top third. Avoid entries. Consider exits and profit-taking instead. If the BF expands, consider adding to your existing position.

Step 7: Re-evaluate continuously

Every time price creates a new swing high or swing low, erase the old lines and redraw. Broadening formations are dynamic: redraw every time price makes a new higher high or lower low.

This makes the broadening formation a continuously evaluated condition, not a one-time visual call.

If all of this sounds compelling but you’d rather not build it yourself, I’ve bundled a Broadening Formation indicator for free with the book.

Buy TheStrat Protocol

One question that stayed with me even after we finished our conversation sits at the heart of today’s stack:

For me, three of the most under-discussed concepts in TheStrat are

• Reclaim

• The Next 2

• In force

I covered Reclaim and the Next 2 in a previous articles.

The concept already exists inside TheStrat. It just hasn’t had a name. Today I want to give it one: Multi-Timeframe In Force — MTIF.

The Paradox of the “Perfect Entry”

There is a peculiar torture that belongs exclusively to traders. It is the experience of watching — weeks or months later — a stock you studied, maybe even nearly bought, go on to make 300% without you.

The usual explanation is psychological. You lacked conviction. You hesitated. You got scared.

But that is only partly true.

Most traders do not miss these moves because they are weak. They miss them because

A stock pushes into all-time highs and immediately the same thought appears: surely it has gone too far.

TheStrat too gives you an easy get out of jail card — magnitude has been hit. The move looks extended. The obvious fear is that you are the last buyer in.

That fear sounds sensible. It even sounds disciplined. But it often rests on a false assumption — that a stock at all-time highs is closer to the end of the move than the beginning.

In reality, some of the market’s strongest moves happen precisely when price enters discovery and there is no overhead resistance left. Research on momentum published in 2024 found that stocks close to historical highs generated a disproportionate share of momentum returns, contributing 70% of returns while making up 45% of momentum holdings. Fidelity makes a similar point at the index level: historically, new all-time highs have often led to further all-time highs rather than immediate collapse.

So the real question is not, is this stock too high to buy?

The real question is:

And that is where most traders get stuck. They can see price moving. What they cannot easily see is whether the move is still being fuelled across timeframes, or whether it is running on fumes.

Here is what we know about 300% moves. None of them started as 300% moves.

To make 300% in a year, a stock first needs to make 100% in a quarter. To make 100% in a quarter, it needs to make 20% in a week.

Clearly this isn't some new unique trading insight. But simple arithmetic, deployed at the right moment, has a way of becoming something much more powerful.

What it tells you is this:

That is what MTIF makes visible. It is the mechanism that lets you hold a runner all the way to 300% — not on blind faith, but on structural evidence..

The chart already knows

In the 1960s, a researcher named Amos Tversky began a series of experiments that would eventually overturn a century of assumptions about how people make decisions. One of his key findings was deceptively simple: people consistently underweight base rates. They ignore the statistical context. They focus on the vivid, the recent, the emotionally available — and miss the structural signal hiding in plain sight.

Us traders do exactly this, every day.

When AMD climbed from $190 to above $420 in the move visible on the chart above, the financial media responded — eventually. Analysts updated price targets. Retail traders noticed volume.

The stock became a story.

A timeframe is In Force when the current candle’s close has broken beyond the prior candle’s high or low. Bullish In Force means buyers have taken control — the prior range broken to the upside. Bearish In Force means sellers have taken control. If it closes within the range of the prior candle’s range means no directional commitment yet.

This is, on the surface, a simple definition. But its implications are not simple at all. It tells you whether a timeframe has made a commitment — whether the market, at that level of the structure, has resolved its ambiguity and chosen a direction.

A market that has chosen a direction — behaves differently from one that hasn’t.

The stacking of commitment

You are familiar with Full Timeframe Continuity — the condition where all the major timeframes are aligned in the same direction, one of the foundational pillars of TheStrat.

MTIF is similar. We are talking about a ticker being In Force across multiple time frames.

A stock can be aligned across multiple timeframes [FTFC] without being In Force across multiple timeframes.

Visualising MTIF

The dots panel beneath the price action has six rows. one for each timeframe.

Each dot is green, red, or grey — Bullish In Force, Bearish In Force, or no commitment. The size of the dot grows as the candle stretches further from its open, scaled to the stock’s own Average Daily Range [ADR]. A small dot means early commitment. A large dot means extended momentum. When three or more timeframes align consecutively, the background turns green or red.

How to use it

The practical workflow that follows from MTIF is straightforward:

1. Scan for Q and Y Bullish In Force — these are your macro candidates

2. Wait for M to confirm — the monthly going In Force dramatically raises the probability of a sustained move

3. Watch for W to trigger — weekly In Force with monthly, quarterly and yearly all green is your highest-conviction entry context

4. Use D and H for entries — Wait for a daily/hourly actionable signal to enter. Don’t be the first to enter. Enter on the first signs of strength after a temporary weakness.

Build your own conviction [BYOC]

But do not take my word for this. In trading, borrowed conviction never holds up under pressure. If you want to trust a structural concept like MTIF enough to actually hold a 300% move, you have to build the evidence yourself.

Open your charts. Turn on the bar replay tool. Go back to the quiet weeks before the acceleration and step forward, one candle at a time. Watch the timeframes stack. Watch the structure load before the price explodes.

Here is a practical tip for TradingView: when you run a replay on the daily chart, set the update interval to one hour. This gives you a realistic feel for how a day actually develops. A stock can do a lot of things during market hours to shake you out, but watching that intraday noise unfold teaches you why the close is the only thing that matters. The intraday movement is just negotiation. The close is the verdict.

To get you started, here are a few examples straight from my own model book.

2025

2024

2023

2021

2020

Wrapping up…

Full Timeframe Continuity describes alignment.

In Force describes commitment.

Multi-Timeframe In Force describes the condition where commitment is stacked — where every level of the timeframe hierarchy has chosen a direction and the structure is loaded, waiting, before the move begins.

Thank you to Solanki — sometimes the best research begins with a simple conversation

Can TSLA do it again?

You’ve probably spent hours watching videos, studying charts, and trying to make TheStrat click. The scenarios made sense. Broadening formations made sense. FTFC made sense — when someone walked you through it on a clean chart with the benefit of hindsight.

But then the market opened; it all got harder.

This book was written to bridge that gap. Not with more rules or another checklist but by putting you inside the decision in real time, through the story of James Hunt: a talented trader with six years of experience and no system he truly trusts.

Given a 30-day ultimatum — find a systematic edge or resign — James discovers Maximus, a dormant trading programme built entirely on TheStrat’s principles.

Every lesson Maximus teaches James, you too will learn.

Every losing trade, every breakthrough: TheStrat unfolds one session at a time.

Inside the book, you’ll learn:

• In Force: The single most important and under discussed concept in TheStrat. Understanding which timeframe is currently in control and trading only in its direction.

• Enter on Reversals: Wait for the confirmed reversal signal — 2dG, 2uR, Hammer, Shooter, inside bars — before crossing the spread. Anticipation costs money.

• Defend with Stops: A stop isn’t a panic button. It’s a structural invalidation level. Place it where the trade thesis is proven wrong, not where the pain becomes unbearable.

• Domino Trades: Timeframes tripping in sequence — hourly, then daily, then weekly. Each one confirming the next.

A thank you to this community

Since you are the ones who read my work and support what we are building at TheStrat Lab, I wanted to do something special for the launch.

For the next 30 days, the Kindle version is completely free with Kindle Unlimited subscription.

I’ve also bundled three professional TradingView tools with every copy of the book.

1. StratLab FTFC & Actionable Signals

2. StratLab Broadening Formation

3. StratLab TFC Dots

https://www.tradingview.com/script/bj6Bbjcf-StratLab-FTFC-Actionable-Signals/

https://www.tradingview.com/script/q9cBlhwJ-StratLab-Broadening-Formation/

https://www.tradingview.com/script/iZnYqhZ3-StratLab-TFC-Dots/

Every concept James learns in the book is backed by a real indicator you can put on your chart the same day. You’ll find the exact instructions on how to unlock and install them right inside the manuscript.

Here is where you can grab your copy:

• Download the E-book (Free for 30 days on Kindle Unlimited)

• Order the Paperback (332 pages)

If you do pick up a copy, whether digital or physical, leaving an honest review on Amazon is the single biggest way you can help support the book’s launch.

Thank you again for reading, for backing my work and for being part of this journey. I can’t wait to hear what you think of James’ story.

Stay focused. Stay disciplined. Stay Strat.

Best,
Nigel

Because once reclaim clicked, something else unlocked.

I started hearing Rob’s question differently.

I’d heard it dozens of times. I could repeat it back to you on day one. But I wasn’t really using it. So the question isn’t whether the next directional candle is coming — it’s coming, it always comes. The question is: when it does, what will it create, negate, or do?

The question behind the question

When Rob asks what’s the next 2, what will it create, negate, or do, he’s not asking you to predict. He’s asking you to prepare.

• Create — Would the next 2 set up a new signal on a higher timeframe? A new weekly reversal? A new monthly reclaim? A new continuation pattern? If yes, you have something worth paying attention to.

• Negate or Reconfirm — Would it change continuity, or confirm that the current trend is intact? This is where reclaim lives. If price sweeps a level and the next 2 closes back above it, it negates the breakdown. If it can’t, the level has flipped.

• Do — What job does this candle have to do? Take out a prior high? Reach the next target? Fill a gap? If the candle has no job, there’s no trade.

Why this post exists

A shout-out to Vanille, who posted this question in a forum:

“I’m scanning for a 2dG daily candle. Give me a detailed checklist of what to consider before buying — using daily, weekly, and monthly context. Take into account create, negate, or do.”

It’s a great question. But what I realised writing the answer is that the 2dG checklist is just one application of the bigger question. The real skill isn’t memorising a checklist for each candle type. It’s developing the habit of asking — before every single trade — what’s the next 2, and what will it create, negate, or do.

The checklist is the scaffolding. The question is the building.

The checklist

Before acting on any 2dG daily candle, you’re really just asking the same three questions with more granularity.

Start with the monthly:

• Is price at a monthly support or resistance level?

• Would the next 2 create a new monthly signal?

• Is there room to the next monthly target, or are you buying into a ceiling?

• Is the monthly candle trying to reclaim a prior high or low, or just bouncing mid-range?

Then drop to the weekly:

• Is the weekly trend still intact, or already weak?

• Would this daily candle create a weekly setup?

• Does it reclaim a prior weekly high or low?

• Is there enough room to the next weekly target before you hit resistance or supply?

Then judge the daily candle itself:

• Is the 2dG happening at a good location?

• Is it coming off a clear level — prior low, support, gap, 21 EMA/50SMA, prior breakout area?

• Did it close strong, near the highs?

• Did it reclaim the prior day’s high or close?

• Is there a clean invalidation level?

• Is this a real reversal candle, or just a weak bounce inside a down move?

The rule: I only buy it if it creates value on the higher timeframe, does not negate the bigger trend, and has enough room to do the next job. If it’s mid-range, extended, or buying into resistance — I pass.

Example 1: $ACIW

Let’s run the framework on a real name from the scan right now.

On the daily, price is printing a 2dG. It’s sitting just above a key reclaim level around 43.01, holding above the 21 EMA which is beginning to curl upward after the sell-off from the 46+ area. The compression near this level after the flush is constructive — buyers are defending.

On the weekly, price came down hard from the 50-53 range, made a series of lower lows into the 38-39 area, and has since pushed back. The current weekly candle is an inside — still digesting, not yet in force.

The monthly is where the conviction is. We’ve got a 2d-1-2u reversal on the monthly with three clean targets stacked above price — M Target 1 at 55.07, M Target 2 at 58.55, M Target 3 at 60.00. Price is sitting inforce and that’s our line in the sand.

Now let’s walk through a potential scenario. If price opens above yesterday’s high, the daily goes in force — and that puts a PMG on the table, similar to what $USAR did recently.

Then next week, if price takes out the current weekly high at 46.04, the weekly goes in force too. Add in the new month just starting, and suddenly the monthly can come into play as well.

Now ask the question:

• Create? A daily close above 43.55 creates a daily signal in force. If the weekly then takes out 46.04, the weekly goes in force too. With a new month just starting, all three timeframes could begin stacking in the same direction quickly.

• Negate or Reconfirm? Reconfirms — monthly continuity holds as long as price defends above the bull trigger zone.

• Do? Clear room to M Target 1 at 55.07. That’s roughly 25% from current levels with stacked pivots and a clean path.

Invalidation is simple: a daily close below the 41.16 monthly open puts the thesis on hold. Until then, this stays on the watchlist.

That’s the kind of alignment I’m looking for. Multiple higher timeframes beginning to confirm in the same direction, one candle at a time.

Example 2: $GTLB

This one has several signals stacked cleanly across multiple timeframes. A 2-down green on the daily. A 2-down on the monthly. A 2-down on the quarterly. The pivots and upside targets are sitting above price, giving the trade room to expand.

What adds even more conviction is sector strength. The broader software sector — $IGV — is also moving aggressively higher.

The daily is printing a 2-down hammer. The monthly is a 3-up. The quarterly is also a 2-down.

This is exactly what we mean by stacking the odds in our favour. When both the individual stock and the sector are aligned across multiple timeframes, those are the names that tend to participate most strongly if the move continues.

Example 3: $RBRK

Similar setup to $GTLB on the surface. Daily, monthly and quarterly charts are all printing 2-down signals. The pivots are stacked to the upside.

But I’m not as bullish on this one, and here’s why.

While the $HACK sector is printing a monthly 3-up, the weekly is still inside and doesn’t show the same level of strength we saw in $IGV.

On top of that, if you look closely, price closed back below some of the reclaimed levels from a prior swing high.

That detail matters. A failed reclaim — price coming back above a taken-out level and then closing back below it — is one of the most bearish signals you can see. It tells you the buyers tried, and they couldn’t hold it. My conviction on $RBRK is lower than $GTLB at this stage. As more evidence presents itself in the new month, we’ll reassess.

The question is the edge

Most traders look at a a setup and ask: should I buy this?

That’s the wrong question. It puts you in prediction mode. You’re trying to be right.

The right question is: what’s the next 2, and what will it create, negate, or do? That puts you in preparation mode. You’re not trying to be right — you’re building a thesis, setting your line in the sand, and letting price tell you which path it’s taking.

That shift in thinking is subtle. But it changes everything about how you sit in a trade.

If this was useful, share it with someone who’s still trading off the daily candle alone. They need this more than they know.

I was that trader. This is the roadmap I wish someone had handed me.

It turns out, the antidote isn’t more information. It’s less.

That sounds simple. It isn’t. Because everything about the trading environment pushes you in the opposite direction. The alerts, the forums, the hot takes, the “you should be watching this” energy that’s everywhere. Ignoring it feels wrong. It feels like you’re missing something.

You’re not.

The traders who learn to read markets well aren’t the ones who watched everything. They’re the ones who watched a small number of things, over and over, until the patterns became instinctive. Until they stopped thinking about what the market might do next and started seeing it.

That kind of fluency doesn’t come from breadth. It comes from depth.

This roadmap is deliberately sequenced. Each phase builds on the last. Rush through it and you’ll have surface knowledge without real understanding.

Here’s Phase 1 expanded:

Phase 1: Major indexes and Megacaps.

Before you look at a single stock, its important to understand the broad market.

Most beginners skip this. They go straight to individual names because that’s where the excitement is. But without macro context, every trade you make is educated guesswork. You might get the stock right and still get punished because the whole market turned against you.

Start with the four major index ETFs:

$QQQ · $SPY · $DIA · $IWM

• $SPY: the S&P 500’s breadth. The broadest snapshot of large-cap America

• $QQQ: the pulse of growth and technology. When risk appetite is high, QQQ leads

• $DIA: how the old economy is holding up. Industrials, financials, consumer staples.

• $IWM: Small caps are the canary in the coal mine. When IWM lags the others, something is quietly shifting before the headlines catch up. [Nov 21, Nov 24]

Think of them as a health check you run before anything else. Is the market strong across the board, or is strength concentrated in one corner whilst everything else quietly deteriorates?

When all four are aligned (trending together, confirming each other) the market is speaking clearly. You can trade with more confidence. When they diverge, it’s whispering something worth hearing and it usually pays to listen before you act.

Once these feel familiar, layer in the five megacap stocks large enough to move the indexes themselves:

$TSLA · $NVDA · $MSFT · $GOOGL · $AMZN

These names don’t just live inside the indexes. In many ways, they are the indexes. Their combined weighting in SPY and QQQ is so significant that watching them move is often watching the index move before the ETF price fully reflects it.

This is your first real edge. When NVDA starts showing weakness before QQQ rolls over, you’re getting a warning. When MSFT and GOOGL hold up whilst the broader market wobbles, you’re seeing a floor being built. The megacaps telegraph what the indexes are about to confirm.

Study how these names behave relative to each other. Do they move together? When does one lead and another lag? What does it mean when TSLA is ripping whilst the rest of the group is flat? Over time, the relationships become as informative as the price action itself.

⏳ Spend 2–3 full market cycles here before expanding. A market cycle includes a meaningful rally, a meaningful pullback and the recovery (or lack of one). That’s not a matter of weeks. It’s months, sometimes longer. This takes time. That’s the point.

Patience here isn’t passive. Every day you spend watching these nine instruments, you’re building a mental model of how the market behaves under different conditions. That model is the foundation everything else rests on. Build it properly.

Here’s Phase 2 expanded:

Phase 2: The 10 major sectors

Once you’ve internalised the broad market, move to the sector layer.

The indexes tell you what the market is doing. The sectors tell you why and more importantly, who is moving it.

The ten major sector ETFs are:

• $XLK: Technology

• $XLF: Financials

• $XLP: Consumer Staples

• $XLE: Energy

• $XLV: Healthcare

• $XLI: Industrials

• $XLY: Consumer Discretionary

• $XLB: Materials

• $XLRE: Real Estate

• $XLU: Utilities

Think of these ten sectors as the market’s internal organs. The indexes show you the patient’s vital signs. The sectors show you which organs are working hard and which ones are under stress.

Sector rotation is one of the highest-leverage skills you can develop.

Not because it tells you exactly what to buy, but because it tells you where you are in the market’s emotional cycle and that context changes everything about how you trade individual names.

Here’s the core dynamic to understand first.

Some sectors are growth-oriented. Technology (XLK) and Consumer Discretionary (XLY) tend to lead when investors are optimistic. People buy discretionary goods when they feel flush. They pour money into tech when they believe the future is bright. These sectors outperform when risk appetite is high.

Other sectors are defensive. Consumer Staples (XLP), Utilities (XLU) and Healthcare (XLV) are businesses people need regardless of the economic environment. You buy food and medicine in a recession. You pay your electricity bill in a bear market. When money rotates into these sectors, investors aren’t abandoning the market; they’re repositioning within it. Quietly. Before the headlines catch up.

When you see money flowing out of XLK and XLY and into XLP, XLU and XLV, the market is telling you something about risk appetite. That’s not a bearish signal to panic over. It’s information signalling that institutional money is adjusting its weight before the retail crowd notices.

A few relationships worth paying particular attention to:

• XLF (Financials) tends to lead when the economy is genuinely healthy. Banks make money when lending is active and credit is flowing. When XLF is strong and leading, it’s often a sign of real economic confidence rather than just liquidity-driven speculation

• XLE (Energy) dances to a different beat. It often diverges from the broader market, driven by oil prices, geopolitics and supply dynamics that have nothing to do with US equities (Like we are doing now Mar 2026). When XLE moves in a completely different direction to everything else, pay attention; it’s rarely coincidence. Remember 2022.

• XLRE (Real Estate) and XLU (Utilities) are sensitive to interest rates. When rates rise, both tend to underperform. Watching how they behave gives you a read on what the bond market is doing, even if you’re not a fixed income trader

• XLI (Industrials) and XLB (Materials) move together with global growth expectations. Strong readings in both suggest the market believes the real economy is expanding, not just financial assets

Study the relationships between sectors as much as the sectors themselves. Which tend to lead the market higher? Which hold up when things sell off? Which pairs move together, and when do they decouple?

That last question is often the most revealing. When two sectors that normally correlate suddenly start diverging, something is changing beneath the surface. Finding that divergence early is one of the cleanest edges available to a market reader.

The goal here isn’t to trade the sector ETFs directly (though you can). It’s to build a mental model of where the market’s energy is concentrated at any given moment. When you eventually zoom into individual stocks, that model is what separates a trade with context from a trade taken in a vacuum.

⏳ Spend 1–2 full market cycles here. Sector fluency can’t be shortcut. You need to watch the rotations happen in real time: the slow grind from growth into defensives, the sudden spike in energy when a macro event hits, the quiet accumulation in financials before a leg higher. Reading about it isn’t the same as seeing it unfold across weeks and months. You need the reps.

Here’s Phase 3 expanded, with the fuller ETF list organised into thematic clusters for readability:

Phase 3: Industry-specific ETFs & Equal weight ETFs

With sector fluency established, zoom into the industry layer.

If the sectors are the market’s organs, the industry ETFs are the individual systems within them. You’re not just watching Technology (XLK) any more, you’re watching which part of technology is leading, which is lagging and what that tells you about where the real money is moving.

This is where the market’s narrative gets specific.

The list below is longer than the previous phases. Don’t treat it as a checklist to master all at once. Use it as a reference. As you develop sector fluency, you’ll naturally start gravitating towards the industries that sit within your strongest sectors. This isn’t the full list, there about 150+. This more a flavour of what’s hot at the moment. [Mar 2026]

Technology & semiconductors

• $SMH: Semiconductor Leaders. The heavyweights: NVDA, TSM, ASML

• $SOXX: Broad Semiconductors. Wider exposure across the chip ecosystem

• $ARKK: Innovation. High-growth, high-risk disruptors

• $ARKQ: Robotics & AI. Automation and the physical layer of the AI trade

• $BAI: AI & Tech. A more concentrated AI-focused basket

• $IDGT: Digital Infrastructure & Data Centers. The pipes behind the AI boom

• $HACK: Cybersecurity. Beneficiary of every escalation in digital threats

Energy & resources

• $XOP: Oil & Gas Exploration & Production

• $FCG: Natural Gas E&P. Often moves independently of crude

• $OIH: Large-Cap Oil Services. The picks-and-shovels side of energy

• $XES: Oil & Gas Equipment & Services. Smaller, more volatile than OIH

• $GUNR: Upstream Natural Resources. Broad commodity exposure

• $GNR: Natural Resources

Clean energy & infrastructure

• $TAN: Solar — highly sensitive to policy, rates and sentiment

• $ICLN: Clean Energy — broader than TAN, includes wind and other renewables

• $PBW: Clean Energy — alternative weighting to ICLN, worth comparing

• $FDRV: EV & Future Transport — electrification of the vehicle fleet

• $IGF: Global Infrastructure — roads, utilities, airports, the long-cycle plays

Healthcare & biotech

• $XBI: Biotech (Equal Weight) — no single name dominates; pure biotech sentiment

• $PBE: Dynamic Biotech — active weighting, slightly different risk profile to XBI

• $ARKG: Genomic Revolution — ARK’s long-term genomics thesis

Telecoms & communications

• $IYZ: Telecommunications Services. Traditional carriers and infrastructure

• $XTL: Telecom

Agriculture & emerging markets

• $MOO: Global Agriculture, food, fertiliser, farm equipment

• $EWZ: Brazil Equities

Digital assets

• $WGMI: Crypto Mining. One of the highest ADR% plays in the entire market

Space

• $UFO: Space Industry. Early stage, speculative, but growing as a genuine sector

This is where context becomes your edge.

When you already know XLK is in a period of sector strength and you see SMH breaking out to new highs, you’re not guessing. You’re reading a story that’s been unfolding for weeks. When XLE is strong and XOP is leading within it, you know it’s the exploration and production names driving the move, not the refiners, not the equipment companies. That specificity matters, when you want to position your self in the right tickers.

The relationships within this layer are just as important as the ETFs themselves.

TAN and ICLN often move together, but TAN is more sensitive to US solar policy whilst ICLN has broader international exposure. XBI’s equal weight structure means a breakout there is genuinely broad based biotech strength, not just one large-cap name carrying the sector.

The critical shift at this phase: start studying the individual constituents of these ETFs.

Look at what’s inside SMH. Learn which five names account for most of the weighting. Understand which are driving the move and which are lagging. Do the same with XBI, with ARKK, with HACK. You now have the why behind individual stock moves, rather than chasing price action in a vacuum.

This is the moment the whole structure clicks into place. A single stock breaking out means one thing in isolation. The same stock breaking out whilst its sector ETF is strong, its industry ETF is leading and the broad market is supportive. That’s a completely different ball game. That’s confluence. And confluence is what separates high-quality setups from noise.

This is also a good point to introduce the equal-weight ETF.

You’ve already seen the concept at work with XBI, earlier on. Equal weighting strips out the dominance of the largest names and tells you whether a move is genuinely broad or carried by one or two heavyweights. Now apply that same lens to QQQ.

$QQQ weights by market cap, which means the top five names (NVDA, AAPL, MSFT, AMZN, GOOGL) account for a disproportionate share of its performance. When those names rip, QQQ rips. When they sell off, QQQ feels it hard, regardless of what the other 95 names are doing.

$QQQE is the NASDAQ-100 Equal Weighted ETF. It holds the same 100 stocks but assigns each roughly 1% weighting. No single name dominates.

The comparison between the two is one of the cleanest diagnostic tools available for reading the health of a tech rally:

• QQQ outperforming QQQE: the move is being driven by the mega-caps. Narrow leadership. A smaller number of names pulling the index higher whilst the rest participate less or not at all

• QQQE outperforming QQQ: the move has genuine breadth. Money is flowing into the Nasdaq broadly, not just into the handful of names large enough to move the index on their own

When you’re watching a technology rally and trying to decide how much conviction to give it, this single comparison tells you a lot. A broad rally with QQQE leading is a different environment from a mega-cap-driven melt-up where everything else is quietly lagging.

The same principle applies across other sectors as you develop fluency. RSP is the equal-weight version of SPY. EWCO is the equal-weight energy ETF. Wherever you find an equal-weight counterpart to a cap-weighted index, you have a tool for measuring how wide the participation really is. And breadth, in the end, is what tells you whether a move has legs.

Here’s Phase 4 expanded:

Phase 4: internalising price action

By now you have macro context (the indexes), cyclical context (the sectors) and thematic context (the industries).

That’s the foundation. What you build on it is — price action fluency.

Most traders try to skip straight to this part. They learn a methodology, apply it without context and wonder why it doesn’t work consistently. The methodology wasn’t the problem. The foundation was.

With the foundation in place, the methodology becomes the tool rather than the whole answer.

It means you shouldn’t measure your progress by how well you understand the rules. You should measure it by how quickly you see what’s happening before it’s obvious. That gap between knowing the rules and seeing the setup before it fully forms is where the real work happens.

The four things to master

These aren’t sequential steps. They happen simultaneously, in real time, every session. But learning them in order helps.

1. See the bigger picture from higher time frames

Before you look at a 5-minute chart, you need to know where price is in the broader structure.

Is it at a major decision point — a weekly level, a monthly high, a multi-month consolidation boundary? Or is it mid-range, between levels, in the kind of chop that punishes traders who mistake noise for signal?

Most beginners do this backwards. They find a setup on a lower time frame and then look at the higher time frame hoping for confirmation. The direction of that process matters. Start at the top. Work your way down.

2. Understand price discovery unfolding on lower time frames in real time

Price doesn’t move in straight lines. It moves in broadening formations.

Watch how price approaches a level — does it come in fast and aggressive, or does it grind in slowly, running out of momentum? When it hits the level, does it reject cleanly and immediately or does it stall and then push through? Is volume confirming the move or quietly contradicting it?

This is price discovery. The market is genuinely working out whether a level holds or breaks, in real time, with real money. Your job isn’t to predict the outcome in advance. It’s to read the process as it unfolds and respond to what you’re actually seeing rather than what you expected to see.

3. Execute the higher-TF view precisely on the lower TFs

The higher time frame gives you the direction. The lower time frame gives you the entry.

This sounds straightforward. In practice it’s one of the hardest things to do consistently. Because the lower time frame will always offer you earlier, more aggressive entries that seem compelling in the moment. And the market will sometimes reward those early entries — enough to reinforce the habit, not enough to make it profitable over time.

Precision matters because risk management depends on it. A well-defined entry on the lower time frame gives you a clear level to be wrong at. If price was supposed to hold here and it doesn’t, you know. You get out. The loss is controlled. A sloppy entry in the middle of a range gives you nothing clean to lean on, and when the trade moves against you, you’re making it up as you go.

Managing risk throughout means knowing before you enter where the trade is wrong, how much you’re risking to be in it and whether the potential reward justifies that risk given the broader context.

4. Stay open

This is the hardest one.

You’ve done the work. You’ve read the higher time frames, identified the structure, found the entry. You have a view. And then the lower time frame starts doing something that doesn’t fit the story.

Most traders ignore it. They’ve committed mentally. They want the trade to work. So they reinterpret the conflicting evidence, find reasons it doesn’t matter and hold on.

Lower time frame action can completely rewrite the higher time frame story. Not always. But sometimes the market is telling you something, and the cost of not listening is that you hold a loser long past the point where the evidence stopped supporting the trade.

The trader who can’t update their view as new evidence emerges ends up holding losers too long. It’s the main reason most trades that start as controlled risks turn into significant drawdowns.

Conviction is a virtue. Rigidity isn’t. The best traders hold a directional bias loosely, willing to flip it entirely when the evidence demands. That’s not weakness or indecision. It’s the most sophisticated thing a trader can do: maintain a clear view whilst staying genuinely responsive to new information.

Why this takes years, not weeks

You can write down these four points and understand them intellectually in an afternoon.

Internalisation is different. Internalisation means your eyes move to the right part of the chart without being told to. It means you feel the difference between a healthy pullback and the start of a reversal before you can articulate why. It means the higher time frame context is already loaded in your mind before the lower time frame setup even forms.

That only happens through repetition. Hundreds of sessions. millions of bars. The same names, watched over and over, through different market conditions, until the patterns stop being things you identify and start being things you recognise.

There’s no shortcut. But there is a path. And the phases before this one are what make the repetition meaningful rather than just random screen time.

You read the setup perfectly. Monthly and weekly continuity, daily confirmation, clean 2u-1-2u at a key level. You take the trade. By morning the stock is down 6% on no news, just a bad tape and thin overnight volume.

This is the experience that separates theoretical TheStrat knowledge from actual swing trading results.

Most traders who come to TheStrat arrive because the framework is genuinely brilliant. It’s objective, rules-based, timeframe-agnostic and built on pure price action in a way that removes most of the subjective noise and lagging indicators destroying retail traders.

But there’s a gap between understanding TheStrat and understanding how to swing trade with TheStrat. That gap is stock selection, and it’s where most traders quietly haemorrhage capital without ever diagnosing the real problem.

This article is about that gap. It’s about the layer of context and qualification that has to exist before TheStrat’s entry tools come into play, particularly for trades you intend to hold overnight. It’s also about a specific framework for deciding, at the end of your entry day, whether a position deserves to be carried into the next session at all.

If you're an experienced TheStrat trader who keeps getting stopped out on swing attempts despite reading the setups correctly, this one's for you. And honestly, a good reminder for myself too.

Why swing trading is structurally different

There’s a category of risk in swing trading that most traders underestimate until they’ve been burned by it several times. It’s not the risk of being wrong about direction. It’s not the risk of poor entry timing, or of a tight stop getting ticked before the move resumes.

It’s simpler and more fundamental than any of those things: it’s the risk of what happens while you’re asleep.

The overnight hold introduces a class of market events no technical setup can fully absorb.

• A Federal Reserve comment.

• An earnings miss from a sector peer.

• A macro data print arriving at 2am and repricing everything before the US open.

• A geopolitical headline landing on a Saturday.

These events don’t care about your weekly continuity. They don’t care about your stop placement or your 2-1-2. By the time you’re awake and at your desk, the market has already made a decision on your behalf, and depending on your position size, that decision can be expensive.

This isn’t an argument against swing trading. It’s an argument for approaching overnight exposure with a different level of rigour than intraday exposure.

When you’re day trading, your maximum theoretical loss on any given position is bounded by the session. You can close. You can adapt. You’re present.

When you’re swing trading, you’re extending exposure across a period where you have zero control and zero ability to respond. That changes the nature of what you’re doing, and it should change the nature of how you select the trades you take.

The problem most ‘Stratters’ who swing traders face is applying intraday selection logic to overnight positions.

They find a setup, confirm the timeframes, enter, and they don’t ask the deeper question: is this stock the kind of stock that deserves to be held overnight? That question requires a different framework entirely. It’s not a price action question. It’s a stock selection question.

The reset problem: TheStrat’s hidden challenge for swing traders

To understand why pure TheStrat can struggle in swing trading applications, you need to understand something structural about the methodology’s four key timeframes.

TheStrat’s core power comes from Full Time Frame Continuity(FTFC), the alignment of directional signals across the 60-minute, daily, weekly and monthly charts simultaneously.

When all four timeframes are in agreement, you have a high-conviction directional bias that removes much of the guesswork from entry decisions. Rob Smith designed this system to be objective and mechanical, and FTFC is the closest thing in TheStrat to a north star.

The problem, however, for swing trading is this: two of those four timeframes reset every single day. The 60-minute chart resets several times during a session as bars open and close. The daily resets overnight. By 9:31am, a new bar is open and yesterday's continuity is already history.

The directional alignment you identified when you entered a trade on Monday afternoon could look entirely different by Wednesday morning, not because the stock has changed fundamentally, but simply because the timeframes have reset and produced new bar formations that alter the FTFC picture.

In day trading, this doesn’t matter much. You’re working within a single session. The 60-minute reset is your operational tool, not a structural liability. But in swing trading, where you’re holding a position across multiple daily bars, through multiple overnight resets, sometimes across a full calendar week, the fragility of lower-timeframe continuity becomes a real problem.

You can’t rely on the FTFC picture you saw at entry to remain intact. The market will reset around you, and pure Strat analysis of the lower frames doesn’t account for that.

This is a problem most TheStrat swing traders feel without being able to name it. They notice their swing trades get stopped out on what look like routine overnight moves. They adjust their stops, change their timeframe analysis, tweak their entry criteria, and the problem persists. Because the problem isn’t in the execution. It’s structural.

The solution operates at two levels: going higher on the timeframe stack and going deeper on stock selection. Both are necessary. Neither alone is sufficient.

Going higher: quarterly and yearly as your anchor frames

The first adaptation experienced TheStrat swing traders make is extending the timeframe stack upward beyond the standard four.

Quarterly and yearly charts operate on a completely different rhythm to the 60-minute and daily. A quarterly bar represents three months of price data. A yearly bar represents a full year. These timeframes don’t reset daily. They don’t get disrupted by overnight gaps or single-session volatility.

They provide directional context that sits above the noise of lower-timeframe resets and anchors your swing bias with a level of stability the daily chart alone cannot offer.

When you’re approaching a swing trade setup, start with the yearly chart and ask a simple question: is this stock in a year of expansion or a year of contraction? A yearly outside bar, making both higher highs and lower lows than the previous year, signals a broadening formation and active price discovery.

A yearly 2-up continuation tells you the institutional trend is intact at the highest meaningful timeframe. That context doesn’t reset overnight.

Drop to the quarterly chart with the same lens. Are you approaching a quarterly outside bar? Are you looking at a quarterly reversal setup, a 2-down quarterly bar followed by a signal the next quarterly move will be up?

Quarterly reversal setups often produce some of the most reliable swing entries in TheStrat, precisely because the timeframe is long enough to be insulated from the daily noise disrupting lower-timeframe continuity.

A weekly 2-1-2 reversal is a high-quality signal. A weekly 2-1-2 that also aligns with a quarterly turning point is a fundamentally different proposition, one where the higher-timeframe superstructure supports the hold through daily resets and overnight volatility. You’re not fighting the reset. You’re positioned above it.

Where most traders start: Sara Strat Sniper’s foundation vids

When I first started swing trading with TheStrat, Sara Strat Sniper’s three-part swing trading series was where I cut my teeth. If you haven’t watched it, it should be your first stop before you do anything else.

Sara is one of the clearest practitioners in the TheStrat community, and her swing trading series cuts through the complexity of the methodology to give you a practical, repeatable framework. Her approach is built around a top-down drill-down: start with the monthly chart to identify the macro setup, step to the weekly for intermediate structure and use the daily for the specific entry.

She focuses particularly on RevStrat setups, where a higher-timeframe 2-down or inside bar is followed by a signal price is about to reverse. She is meticulous about one principle most beginners miss entirely: wait for corrective activity before entry.

What Sara means is that before you enter a swing trade to the upside, the stock needs to have shown a 2-down bar on the entry timeframe, a directional bar making a lower low. That 2-down bar gives you two things:

• A clear stop location, below the low of the corrective bar. Without corrective activity you have no natural stop level and without a stop level every sizing decision becomes a guess.

• A reference for trade validity. If price takes out that low after you enter, the trade is invalidated by price, not by your opinion.

In force: the concept that changes everything

Her other foundational rule is the one most traders underestimate until they’ve held a losing position for two weeks telling themselves it’s “just a pullback”. The actionable signals must remain in force.

When a TheStrat actionable signal triggers, say a break above the high of an inside bar (1-2 up) completing a 2-1-2 reversal, or a break above the high of a 2-down bar completing a 2d-2u reversal, that trigger level becomes your line in the sand. As long as price remains above it, the signal is in force. The trade is active. The scenario is playing out. Every corrective move back toward that level is noise within the context of a live, valid setup.

The moment price closes below that trigger level, the signal is no longer in force. Not struggling. Not in question. Gone.

The setup has been violated by price, and the response is mechanical, not emotional: you reduce, exit or wait for a new signal to form. This rule removes the most dangerous emotion in position management, which is hope.

Traders without a clear in-force rule hold losing positions because they believe the original thesis is still valid. They watch the stock drift lower and tell themselves it’s a pullback. The in-force concept eliminates all of that. The question is binary: is price above the trigger level, or isn’t it?

Sara marks her trigger levels visually, with every actionable signal having a corresponding level, and as long as price respects it the trade remains live. In practice for swing traders, this creates a clear daily review process.

There’s a secondary nuance worth understanding: in force applies at the timeframe of the signal. A monthly signal remains in force even whilst the daily timeframe goes through corrective activity, because the daily close is a subset of the monthly range.

A monthly reversal signal remains in force as long as price holds above the monthly trigger level, even if the daily and weekly show weakness.

This is precisely why starting from the higher timeframes matters so much. A quarterly or monthly signal in force provides structural support daily noise cannot invalidate. A daily signal in force provides no such protection when the weekly is turning against you.

Sara’s framework is the right foundation. Learn it. Internalise it. Build a set of rules around it, that suits your personality and the prevalent market conditions.

But here’s what it doesn’t address: which stocks to run through it in the first place.

The setup looks identical on the chart whether you’re in a stock with deep institutional support or one with thin, fragile buying interest. The stock selection layer is what changes the overnight risk profile.

The stock has to ‘earn’ the overnight hold

Not 'every stock deserves to be held overnight, regardless of how good the setup looks.

When you enter a day trade, the worst realistic outcome is a loss to your stop. The session closes, the risk ends, tomorrow is a new opportunity. When you enter a swing trade and hold overnight, you’re extending your exposure through a period where markets can reprice violently in response to events you cannot anticipate or manage.

The stock needs to justify that extended exposure.

It needs to demonstrate specific attributes that reduce the probability of catastrophic overnight moves and increase the probability of continued institutional support.

These are not TheStrat criteria. They are stock selection criteria.

Relative strength

Relative strength is the most important filter in the entire framework.

At its simplest, it measures how the stock is performing relative to a benchmark, usually the S&P 500 or the relevant sector index.

A stock with high relative strength is outperforming: when the market goes up 1%, it goes up 2%. When the market pulls back 1%, it holds flat or pulls back less.

That persistent outperformance is not random. It’s evidence that institutional money is actively and repeatedly choosing this stock over alternatives. They keep buying the dips. They keep accumulating on weakness. That buying is what creates the relative strength profile. That buying is also what provides the support underneath your overnight position.

When a stock has been quietly outperforming for weeks or months before printing a high-volume gap up on earnings, the existing RS tells you institutional discovery was already underway. The earnings gap is the public event confirming what institutions already knew.

The follow-through is more reliable because the buyers aren’t new entrants chasing a headline. They’re existing holders with a thesis that’s just been validated. The same applies to news catalysts, too. A stock with high RS is far more likely to gap up on the slightest hint of good news. Look at NBIS from earlier this week. [13 Mar 2026]

Contrast this with a stock gapping up from a period of relative weakness. The gap may be large. The news may be genuinely positive. But without pre-existing institutional interest, follow-through is far less predictable. You’re hoping the catalyst converts passive observers into active buyers, rather than knowing active buyers were already present before the event.

RS is your first and most important overnight filter.

Strong earnings and strong sales: the specific numbers

Earnings growth: the numbers that matter

The baseline threshold used by serious growth-oriented swing traders is at least 20% earnings-per-share growth year-over-year. A stock growing EPS at 20% or above is demonstrating meaningful profitability acceleration catching institutional attention. But the specific number is less important than the pattern of that number across multiple quarters.

A stock growing EPS by 15% in Q1, 22% in Q2 and 35% in Q3 is more interesting than a stock growing by 30% for three consecutive quarters. Consistent growth is good. Accelerating growth means the business is entering a compounding phase, typically the phase immediately before institutional buyers move in at scale, because forward earnings estimates are about to be revised upward and analysts are about to upgrade their price targets. That combination creates a tailwind of forced institutional buying.

The specific benchmarks to use as hard filters:

• Minimum 20% EPS growth year-over-year for the most recent reported quarter

• Two or more consecutive quarters of 20%+ growth (one quarter can be a one-off; two establishes a pattern)

• Accelerating quarterly EPS growth, each quarter stronger than the last

• Annual EPS growth of 25% or more for the full fiscal year, confirming the quarterly strength is structural and not seasonal

Revenue and sales growth: the honest number

Sales growth is harder to engineer than earnings growth, which makes it the more honest signal of fundamental health. The thresholds:

• Minimum 20-25% revenue growth year-over-year for the most recent quarter

• Consistency across at least three quarters (three consecutive quarters of strong sales growth confirms the business model is genuinely scaling)

• Revenue acceleration: a business growing revenue at 20%, then 28%, then 37% quarter on quarter is entering a scaling phase

The most powerful qualification is when both metrics are accelerating simultaneously.

Earnings and revenue both accelerating, quarter after quarter, is the fundamental signature of the stocks producing the largest and most sustained swing trading moves.

The warning signs: what immediately disqualifies a stock

Equally important to knowing what you want is knowing what removes a stock from consideration regardless of how good the setup looks:

• Decelerating growth. A stock growing EPS 40% last quarter and 20% this quarter is still growing, but the rate is falling. Institutional buyers are forward-looking; decelerating growth means forward estimates may be revised downward and the buyers driving the previous move may be reducing, not adding. This is not a buy setup. It’s a warning.

• Earnings beats on declining revenue. When a company beats earnings estimates but misses on sales, the beat is almost certainly cost-cutting rather than genuine growth. Cost cuts are finite. Revenue is the engine. If the engine is slowing, the earnings beat is cosmetic.

• Guidance cuts alongside a beat. A company can beat the current quarter’s estimates and simultaneously lower guidance for the next quarter. The market almost always punishes this harshly, because institutional buyers are focused on forward earnings, not backward results. A guidance cut is the company itself telling you the next quarter is going to be worse than previously expected. That is not a stock you want to hold overnight.

• Shrinking margins. If revenue is growing but gross margin or operating margin is compressing, the business is growing less profitably. For the stocks you’re targeting in swing trading, margin compression is a red flag suggesting institutional support may be quietly reducing.

• Single-quarter anomalies. A massive earnings beat including a one-time asset sale, a tax benefit or an accounting reclassification hasn’t demonstrated business quality. Strip out the one-time items and check whether the underlying growth holds. If it doesn’t, the headline beat is misleading.

The rule: if any of these warning signs are present, the stock fails the earnings and sales filter regardless of how compelling the setup looks on the chart.

TheStrat will generate signals in broken fundamental stories. Your job is not to take them, or position size taking this into consideration.

Gap ups and unfilled gap ups

When a stock gaps up on earnings or a significant catalyst, it’s the market repricing the stock’s value in a single event. The gap represents a collective decision the stock is worth more than where it closed the night before, by enough to open at a significantly higher level without any trades occurring in between.

Every pullback toward the gap level is met with buyers. The gap is not just a price event; it’s a structural support level, and the fact it remains unfilled tells you sellers cannot push price back to where it was before. The buyers are simply too strong.

These unfilled gaps become reference points for everything that follows, stair steps of institutional support climbing up the chart, each one representing a repricing event where institutions committed capital and have not been willing to retreat from since.

Post-earnings drift

When a stock continues grinding higher for days or weeks after an earnings event, the market is still processing the news. Not all institutional buyers enter on earnings day. Large funds with significant capital to deploy take days or weeks to build a position without moving the stock against themselves. The drift is their ongoing accumulation, spread across multiple sessions.

The thesis is intact and the accumulation is continuing. For swing trading purposes, this is a stock where overnight holds are supported by an ongoing institutional buying programme, arguably the best protection you can have against adverse overnight moves.

Volume as a signal, not a background metric

Now to the controversial bit.

Brian Shannon says “only price pays.” Rob Smith didn’t care much for volume either.

However, I feel ‘Unusual volume’ deserves its own section because it operates differently to the attributes above.

But before we get into what to look for, there’s an important caution: volume can be ‘dirty’. Webby has made this point with particular clarity, and it’s worth internalising before you build any volume-based approach.

What “dirty” means is that the raw volume number you see on any chart is a composite of multiple different types of activity, most of which have nothing to do with the directional conviction of the buyers and sellers you’re trying to track. Raw volume includes:

• Dark pool prints, large institutional trades executed off-exchange and reported with a delay

• High-frequency trading activity, where algorithmic systems generate enormous share volumes through rapid back-and-forth trades that cancel each other out directionally but inflate the total

• Options-related delta hedging by market makers, ETF rebalancing flows and program trades buying or selling baskets of stocks for macro positioning reasons

None of these tell you anything about a specific, directional institutional bet on the stock.

This is precisely why Ameet Rai’s tiered framework is so valuable in the context of Webby’s warning. Rather than looking at raw volume in isolation, Rai’s three tiers provide historical context that filters out routine noise.

A stock printing its highest volume ever is doing so across the full population of days in its trading history, the vast majority of which included the same HFT activity, dark pool prints and mechanical flows. If this day is still the highest-volume day in the stock’s history despite all of that normal noise, the signal rising above it is genuinely extraordinary.

The three tiers:

• Highest volume ever

• Highest volume since IPO

• Highest volume in a year

Highest volume ever. When a stock prints more volume than at any point in its entire trading history, the supply/demand balance has shifted at a structural level. The dirty baseline was the same on every other day; what happened today was categorically different. ASTS ran +800% in 95 days following this trigger.

Highest volume since IPO. Particularly powerful for stocks still establishing their institutional base; the first moment in the stock’s public life where peak institutional discovery has occurred simultaneously across the most buyers.

Highest volume in a year. A significant rotation signal; a new category of buyer is entering the name. HIMS ran +280% in 134 days from this trigger.

When a stock prints one of these signatures alongside strong fundamentals and price structure, that combination is the trigger to move it to the top of your watchlist.

Two supporting volume behaviours that strengthen the read:

• A strong closing range on the gap or high-volume day, buyers controlling the full session, not just the opening rush

• Volume dry-ups on subsequent pullbacks, where the post-event consolidation arrives on significantly lower volume, signalling an absence of sellers rather than a presence of buyers. The stock is being held, not distributed.

That distinction matters enormously for overnight holds.

The stage check

Before looking for a specific entry, run one structural sanity check: is the stock in the right phase?

Minervini’s trend template, where the 50-day moving average sits above the 150-day, which sits above the 200-day and price is trading near 52-week highs well above all three, confirms a Stage 2 uptrend.

This is where institutional accumulation is most reliably ongoing rather than sporadic. The stacked EMA structure matters for overnight swing traders specifically because it tells you the trend is self-reinforcing: each pullback to the rising moving averages is a buying opportunity for institutions building positions over months. Those buyers don’t disappear because of a bad overnight tape. They add.

This is just a filter, not the full methodology. Confirm it and move on. If the EMAs are not stacked, be significantly more cautious about overnight exposure regardless of how compelling the setup looks.

Sectors, themes and rotations

A technically perfect setup in a sector losing institutional favour operates in headwinds not visible on the stock’s individual chart. The setup triggers, you enter, but the sector rotation quietly reducing institutional exposure means your overnight hold is fighting a current moving against you at the macro level, even when the individual stock’s setup is valid.

Conversely, a setup in a stock receiving active institutional rotation, where money is flowing in and the macro narrative is supporting continued buying, has a tailwind strengthening every aspect of the overnight risk profile.

The same setup, in the right sector context, is a fundamentally different trade.

Before committing overnight capital, identify the sector and ask whether it’s currently gaining or losing relative strength versus the broader market. Identify the theme: is there a macro narrative driving institutional interest in this area? Understand the rotation: where is money moving into, and where is it moving out of?

Trading TheStrat signals in leading sectors and themes means swimming with the current rather than across it. In the current market cycle look at Photonics and Oil names.

Where TheStrat takes over: precision entry in a qualified stock

By the time you’ve worked through every filter above, you know a great deal about the stock you’re looking at. You know its fundamental health. You know its institutional interest level. You know where the volume events have occurred and what they signal. You know the macro context.

Now, and only now, do you apply TheStrat’s precision entry tools.

Broadening formations are TheStrat’s flagship concept for understanding a stock’s macro price structure. A broadening formation, where a stock makes higher highs and lower lows over time, tells you price is expanding in both directions as the market works through a period of discovery. Within that expansion, TheStrat gives you the framework to identify where price is likely to find resistance at the top and support at the bottom. For swing traders, identifying where a stock sits within its broadening formation tells you both the direction of the likely next move and the approximate magnitude of the opportunity.

Undercuts and rallies represent one of TheStrat’s most powerful and under-appreciated capabilities for swing trading. The term is widely used in the Minervini and IBD communities, but TheStrat gives it a more precise vocabulary, and that precision makes it more actionable.

In plain language, an undercut and rally occurs when price briefly takes out a significant prior low, sweeping the stops of traders positioned above it, and then reverses sharply, often with significant force. The brief breach triggers stops on weak longs, traps short sellers who entered expecting continuation, and then leaves both groups on the wrong side as the reversal takes hold.

In TheStrat language, this is a scenario 3 (outside bar) forming at a key level, or one of the RevStrats (inside bar reversals), or the completion of a 2-down to 2-up reversal sequence or a 2dG bar.

When price takes out the low of a prior significant bar, completing a 2-down move, then immediately reverses to take out the high of that same bar, the resulting candle is a scenario 3. It’s made both a lower low and a higher high relative to the preceding bar.

• The undercut is the sweep that creates the lower half of the outside bar

• The rally is the reclaim back through the prior range, creating the upper half

• The outside bar is the visual record of both events within a single candle. However, this can also take the form as a compound 3-scenario, starting with a 2-down green bar.

Entry mechanics: Identify the key level in advance: the low of a prior weekly inside bar, a significant daily swing low, a broadening formation boundary. Then:

1. Price approaches with a 2-down bar

2. Price briefly breaches the level, stops are triggered

3. Price immediately reverses and reclaims

4. The daily bar closes back above the key level with a strong DCR (more on DCR later in the article)

Your entry is on the reclaim, with your stop below the low of the 2dG bar.

Bear in mind, this is an aggressive entry and you’ll be breaking the in-force rule. However a similar concept applies: if price returns to that reclaim level and closes below it, the signal is gone.

This setup works because it simultaneously removes two categories of wrong-positioned market participants. The weak longs who had stops below the level are now flat. The short sellers who entered on the break are now trapped as the reversal occurs above their entry. Both groups will need to buy as the rally develops, adding fuel to the move precisely when you’re entering long.

You are buying as the previously wrong-positioned participants are forced to buy alongside you.

Reclaim levels extend this logic to missed entries or stop-outs. If you were stopped out of a position, or missed an initial entry, TheStrat gives you a mechanical re-entry: wait for price to reclaim the level invalidating the original trade, confirm the reclaim holds on a closing basis and re-enter with the same trade logic. You’re not deciding whether to re-engage based on how you feel about the stock. You’re following a rules-based process identifying when the original thesis has been re-validated by price.

Here’s a deep-dive on ‘Reclaims’, if you want to learn more .

Every swing trade has to start as a good day trade

Here is a rule sounding simple but fundamentally changed how I manage swing positions:

Think about what this means. You’ve done all the work. You’ve screened for relative strength, checked the volume signature, confirmed the earnings quality, identified the sector rotation. You’ve applied TheStrat’s entry precision and found a qualified setup. You enter. And then the stock goes nowhere. Or it makes a small move and gives it back. Or it closes red.

The setup was real. The stock qualifications were genuine. But the market, on this particular day, is not confirming the trade.

What do you do? The answer requires understanding three specific metrics: DCR, WCR and MCR.

DCR (daily closing range) measures where the stock’s closing price sits as a percentage of the full range of that day’s high and low.

If a stock trades between $50 and $56 and closes at $55, the DCR is approximately 83%. A DCR above 50% means the stock closed in the upper half of its daily range — buyers controlled the session. A DCR of 75% or above is a genuine strength signal: buyers weren’t just present, they were dominant. A DCR below 50% means sellers had the final say. The stock may have moved higher intraday but gave back gains and closed weak. That’s a warning, not a confirmation.

WCR (weekly closing range) applies the same principle to the full week’s range. If a stock’s weekly high is $56, its low is $48, and it closes Friday at $50, the WCR is 25% — deeply in the lower half. A stock closing in the bottom quarter of its weekly range is building a bearish weekly candle regardless of how the daily picture looks.

For swing traders, the WCR is perhaps the single most actionable number at the end of each week. It tells you the quality of the weekly candle you’re sitting inside.

MCR (monthly closing range) extends the view further, measuring where price sits within the month’s full high-to-low range. A stock closing in the upper half of its monthly range is in a position of institutional strength at the timeframe driving the largest sustained moves. A stock closing in the lower half is showing selling pressure that may not be visible on the daily or weekly chart.

The decision rule at the end of the entry day:

• Strong DCR (above 75%) and supported WCR. Carry the full position. The stock proved itself on day one. Buyers were in control. The overnight hold is backed by entry-day confirmation.

• Moderate DCR (50-75%). Carry a reduced position, perhaps half or two-thirds. The stock made progress but didn’t dominate the session. Keep skin in the game, but protect capital against an overnight move not continuing the thesis.

• Weak DCR (below 50%). Reduce to a very small core, or exit entirely.

The discipline here is real and it’s difficult. You’ve done the work. You believe in the stock. You’ve identified what looks like a perfect setup. And then the stock closes flat or slightly red, and the decision rule tells you to reduce.

The temptation to override the rule, to tell yourself tomorrow will be different, the setup is still intact, you’re being too cautious, is exactly the emotional pattern the rule is designed to prevent. The stocks closing strong on day one tend to continue. The stocks closing weak on day one tend to test patience further before resolving, if they resolve at all.

This principle connects directly back to TheStrat’s foundational philosophy. Rob Smith built the entire methodology on responding to what price is actually doing, not what you think it should do. A weak DCR on entry day is price telling you something. Carrying a full position in response to a weak DCR is trading your opinion rather than the price, which is exactly the pattern TheStrat is designed to eliminate.

Sizing the overnight hold

By the time you’ve worked through every layer of this framework — stock qualification, volume signature, sector context, TheStrat entry precision and DCR assessment on entry day — you should have a clear, evidence-based conviction level. That conviction should directly inform how you size the position.

Overnight exposure changes everything

The foundational principle: overnight exposure demands smaller initial size than intraday exposure.

A well-placed stop below a TheStrat pattern’s invalidation level is precise and effective for an intraday trade. For an overnight hold, that same stop can be bypassed entirely by a gap opening below it. You’re stopped out at a price you never saw, at a loss exceeding your planned risk.

The stop was technically correct. The gap made it irrelevant.

Sizing for gap risk

The solution isn’t to avoid stops. It’s to size the position so a worst-case gap loss — not just your nominal stop — still falls within your acceptable per-trade risk parameters.

If you’d typically risk 1% of capital on an intraday trade, ask yourself: what happens if this stock gaps down 8% overnight? If that 8% loss takes you beyond your acceptable total loss% of your capital, you’re oversized for overnight exposure.

Building the position

A practical starting point for most swing trades is 0.25-0.75% of total capital at risk on the initial position, with the understanding this is an entry rather than a full intended position.

As the stock proves itself over the first day or two — printing strong DCRs, maintaining WCR strength, continuing to show the relative strength that qualified it — you add at natural consolidation points. Each addition uses the corrective activity on shorter timeframes as both the entry timing and the stop location for that tranche, in exactly the way Sara Strat Sniper’s framework describes.

This pyramiding approach, entering conservatively and adding as the trade proves itself, is the most risk-efficient structure for swing trading with overnight exposure. Your largest position size is achieved at the point where you have the most evidence the trade is working, rather than at the point where you have the most conviction it should work. There is a significant difference between those two things.

Let the evidence determine size

The number of attribute boxes ticked during qualification should directly inform sizing:

• Multiple HV signatures and unfilled gap ups, strong RS in a leading sector, accelerating earnings and sales and a clean Stage 2 structure justifies the higher end of your overnight risk allocation

• Passing most filters but not all, has only earned the lower end of the risk allocation.

Patience is the real edge

Most stocks you look at on any given day won’t pass every filter in this framework. Not even close.

On any given trading day, thousands of stocks present technically valid TheStrat setups across the key timeframes. A fraction of those occur in stocks with strong relative strength. A smaller fraction of that fraction occur alongside the specific volume signatures signalling genuine institutional conviction. A smaller fraction still tick every box: RS, accelerating earnings and sales, no fundamental warning signs, unfilled gap structure, Stage 2 confirmation, leading sector context and strong historical volume tier — whilst also presenting a precise TheStrat entry with multi-framework support.

Those fully qualified trades are rare. They might appear few times a week. Sometimes less. In choppy, rotating or macro-uncertain markets, they might not appear for several weeks running. Like the environment we’re currently in. [Mar 2026]

That rarity isn’t a flaw in the framework. It’s the framework doing its job.

Every filter is doing work

Every filter exists to reduce the population of trades qualifying for your overnight capital. The more filters a trade passes, the more evidence you have that the overnight risk profile is manageable.

By the time you’ve found a trade passing every screen, you’re looking at a stock where:

• Institutional buyers are present and active

• The fundamental engine supports continued institutional interest

• Volume history confirms extraordinary conviction

• The macro context is supportive

• TheStrat’s entry precision aligns with structural support levels

• The entry day itself has confirmed through DCR that buyers showed up when it mattered

That’s a completely different risk profile to a stock passing two or three filters and looking like a decent setup.

Missing trades is part of the process

Sometimes the right move is to watch a setup play out without taking it, because the qualifying criteria weren’t fully met. Missing a trade that works is far less damaging than taking one that wasn’t fully qualified and paying the price overnight.

The hardest part of this framework to internalise isn’t the analysis. It’s the waiting.

The cost of selectivity

Every filter you apply is a trade you might not take. Every time you skip a trade because the volume signature wasn’t significant enough, the DCR closed weak, or the sector context was ambiguous — there will be occasions where that trade moves without you. That’s the cost of selectivity. It’s a real cost and it’s worth paying. It’s like insurance.

The traders consistently extracting returns from swing trading over long periods aren’t the ones trading the most setups, or with the most sophisticated entry criteria, or even those with the deepest understanding of any single methodology. They’re the traders who’ve developed the discipline to distinguish between a trade they want to take and a trade that has earned the right to be taken — and who are willing to sit on their hands through every session where nothing has fully qualified.

The edge in swing trading isn’t in the entry. It’s in the selectivity preceding it.

If you found this useful, the next article in this series will cover building a practical scanning workflow taking you from market open to watchlist to qualified setup in under thirty minutes. Subscribe to make sure you don’t miss it.