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Exposure Strategies for Squeeze Setups — Practical Tools for Structural Trades

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Edited by Russell Larke, Saturday 5 September 2026 at 12:05

Exposure Strategies for Squeeze Setups — Practical Tools for Structural Trades

The diagnostic framework for identifying squeeze setups has been established through the structural analysis of market mechanics and participant behaviour. Narrative analysis reveals when market stories are aligned with mechanical conditions (Shiller, 2017). This module addresses the practical question: how does one gain exposure to these setups?

Three primary methods exist: direct equity exposure, options strategies, and comparable exposure through related instruments. Each carries distinct risk-return characteristics and is appropriate under different conditions. The choice between them is not merely a matter of preference but of structural alignment between the instrument and the underlying mechanics of the trade (Lo, 2004).

Direct equity exposure is the most straightforward method: purchasing the stock, holding it, and managing the position according to the principles of position sizing and risk management. The discipline of risking a fixed percentage of account equity on any single position applies equally to low-float microcaps and blue-chip stocks (Tharp, 2006). However, low-float stocks can exhibit intraday movements of 30% or more, meaning the absolute pound amount at risk must account for this heightened volatility. A stock capable of gapping 20% against the position requires either a wider stop or a smaller position size. The liquidity constraints discussed in earlier work become particularly relevant here: a thin stock can gap through a stop, resulting in an actual exit price meaningfully worse than the intended stop (Chordia et al., 2001).

Entry timing in a squeeze setup follows a specific sequence. The EDTS spike serves as confirmation that the trapped short has exhausted their capacity, representing the signal for which the trader has been waiting. Entering before the EDTS constitutes speculation without confirmation; entering after provides the structural confirmation required (Kahneman & Tversky, 1979). The EDTS spike proves the ratchet has completed its final turn, with utilisation maxed, lender depth exhausted, and the trapped short having spent their remaining capacity on the carve. The sequence is: EDTS spike → carve → limping phase → entry → true spike → catalyst. Entry occurs during the limping phase, positioned for the true spike. Exit occurs just before the catalyst (Soros, 1987).

Stop placement in direct equity positions requires both calculation and judgement (O'Neil, 1988). A stop set too tight will be triggered by the normal volatility of a low-float stock, exiting a position that would have performed. A stop set too wide exposes the position to more risk than sizing rules permit. The volatility-adjusted approach provides the starting point, but structural context must also be considered: is the ratchet tightening? Is stepping visible? Is the catalyst approaching? These factors inform whether a wider or tighter stop is appropriate (Mandelbrot & Hudson, 2004).

For squeeze candidates with options available, they offer asymmetric exposure with defined risk (Black & Scholes, 1973). However, implied volatility on squeeze setups is almost always elevated (Hull, 2018). The conditions that make a stock a candidate — small float, high utilisation, a trapped short, an approaching catalyst — also make it volatile, and this volatility is priced into the options. When purchasing an option, the trader is acquiring exposure to the stock's future volatility. If the stock moves more than the market expects, the option pays off; if it moves less, the option loses value. The market's expectation is already priced in (Merton, 1973). The mechanical indicators — utilisation, lender depth, borrow fee — reveal whether the implied volatility is pricing something real or something imaginary (Shleifer & Vishny, 1997).

Time decay represents the clock ticking on the thesis (Hull, 2018). The longer an option is held while waiting for the squeeze to materialise, the more theta erodes the position's value. A catalyst with a known date provides a fixed timeline; a catalyst with an uncertain timeline is significantly more difficult to trade with options (Natenberg, 1994). Strike selection determines the extent of upside exposure and the cost of acquiring it. In-the-money options carry intrinsic value, higher delta, and higher cost. Out-of-the-money options have no intrinsic value, lower delta, and lower cost, but require a larger move to become profitable. The choice of strike reflects conviction: high confidence may justify an out-of-the-money strike to maximise leverage, while lower confidence warrants a more conservative approach (McMillan, 2002).

Comparable exposure serves as a third method when the target stock lacks options or is too thin to size properly (Bogle, 1993). A related stock in the same sector may serve as a proxy, though the risk is that the correlation breaks down (Markowitz, 1952). An ETF holding the sector offers broad exposure with better liquidity and diversified risk, though the upside is more muted (Malkiel, 1990). These methods represent compromises — they are used when the preferred method is unavailable rather than as a first choice.

The selection of method follows a clear hierarchy: where options are available, they offer defined risk with asymmetric upside and are the preferred instrument (Hull, 2018). Where options are unavailable, direct equity is the method (Graham, 1949). Where the stock is too thin to size properly, comparable exposure through a related stock or ETF is a reasonable alternative (Bogle, 1993). Position sizing principles apply uniformly across all methods, with the same discipline applied to options positions as to direct equity positions (Tharp, 2006).

The concept of asymmetric risk-return is central to understanding why options are particularly attractive for squeeze setups. Unlike direct equity, where losses can be substantial if the thesis fails, options limit downside to the premium paid (Black & Scholes, 1973). This defined-risk characteristic makes options a more capital-efficient way to express conviction in a squeeze thesis, provided the trader has accurately assessed the probability and timing of the catalyst (Merton, 1973).

Implied volatility skew — the difference in implied volatility across strike prices — provides additional information for strike selection (Hull, 2018). In squeeze candidates, out-of-the-money calls often carry higher implied volatility than in-the-money calls, reflecting the market's pricing of tail risk. Traders must evaluate whether the skew is justified by the underlying mechanics or represents an opportunity to exploit mispricing (Shleifer & Vishny, 1997).

The relationship between implied and realised volatility is also critical (Black & Scholes, 1973). If the market is overestimating future volatility (as reflected in high implied volatility relative to historical volatility), options may be expensive relative to the expected move. Conversely, if implied volatility is low relative to the structural pressure building in the stock, options may represent a significant opportunity (Hull, 2018). Comparing the 20-day historical volatility to the implied volatility of at-the-money options provides a useful benchmark for assessing whether option prices reflect reality or speculation (Natenberg, 1994).

The Greeks — delta, gamma, theta, vega, and rho — provide the tools for understanding how an option's price responds to changes in the underlying stock, time, and volatility (McMillan, 2002). For squeeze setups, gamma is particularly relevant: as the stock approaches the strike price, gamma increases, magnifying the delta response to price movements. This convexity is the source of options' asymmetric payoff: the option gains value at an accelerating rate as the stock moves in the trader's favour, while losses are limited to the premium paid (Hull, 2018).

Vega measures sensitivity to changes in implied volatility. In squeeze setups, implied volatility typically rises as the stock moves, increasing option values even before the stock reaches the target price. This can create a positive feedback loop: the stock rises, implied volatility rises, option values rise, and the trader can adjust their position to lock in gains. However, if the squeeze fails to materialise, implied volatility collapses, eroding option values even if the stock price remains stable (Natenberg, 1994).

The concept of comparable exposure through proxies or ETFs has its own set of considerations (Markowitz, 1952). Correlations between stocks in the same sector can break down during periods of market stress, reducing the effectiveness of a proxy trade (Chordia et al., 2001). However, for traders who cannot access options or direct equity in a thinly-traded stock, proxies offer a way to capture some of the upside from sector-wide movements triggered by the squeeze (Bogle, 1993).

Liquidity risk is another factor that must be incorporated into position sizing for direct equity exposure (Amihud, 2002). A stock with a narrow order book can move significantly against the trader's position with limited new information, and exiting a position can require accepting a large spread between bid and ask. This is particularly relevant during the carve and limping phases, where liquidity may be temporarily impaired (Chordia et al., 2001).

The interplay between sizing, volatility, and liquidity creates a constraint that must be respected: the largest position size that can be executed without adversely impacting the market price. For thinly traded stocks, this may limit exposure to a fraction of the trader's capital, even if the setup is compelling (Kyle, 1985). In such cases, options or comparable exposure may offer a way to gain economic exposure without moving the underlying market (Hull, 2018).

Portfolio-level risk management also applies to squeeze setups (Markowitz, 1952). Multiple squeeze positions may be correlated through market-wide factors — a broad market decline can trigger the same pressure in multiple names. This correlation must be considered when sizing individual positions (Lo, 2004). A 1% risk per trade across five correlated positions does not represent 5% portfolio risk; it represents something closer to 5% multiplied by the correlation coefficient (Tharp, 2006).

Position management, including partial profit-taking and trailing stops, is essential to capturing the full potential of a squeeze (O'Neil, 1988). The violent nature of squeeze moves means that taking some profits at predefined levels and leaving a runner with a trailing stop can capture the upside while protecting gains. Conversely, holding through the entire move without taking profits exposes the trader to the risk that the spike reverses sharply (Mandelbrot & Hudson, 2004).

The framework for entry and exit in squeeze setups is clear: entry during the limping phase following EDTS confirmation, partial exits during the true spike, and final exit before the catalyst (Soros, 1987). This approach addresses the uncertainty inherent in timing: even if the direction is correct, the exact timing and magnitude of the move cannot be known with certainty. The structure provides a systematic way to manage that uncertainty (Kahneman & Tversky, 1979).

In summary, the framework identifies setups. The methods in this module provide the practical tools to express that view. The choice of instrument — direct equity, options, or comparable exposure — should reflect the structural characteristics of the setup and the trader's risk tolerance. All three methods share the same foundation: disciplined position sizing, clear entry and exit criteria, and recognition that the mechanics of the setup must be aligned with the instrument chosen to express the trade (Tharp, 2006; Graham, 1949).

Video Resources

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References

Amihud, Y. (2002). Illiquidity and Stock Returns: Cross-Section and Time-Series Effects. Journal of Financial Markets, 5(1), 31-56.

Black, F. & Scholes, M. (1973). The Pricing of Options and Corporate Liabilities. Journal of Political Economy, 81(3), 637-654.

Bogle, J.C. (1993). Bogle on Mutual Funds. Irwin Professional Publishing.

Chordia, T., Roll, R. & Subrahmanyam, A. (2001). Market Liquidity and Trading Activity. Journal of Finance, 56(2), 501-530.

Graham, B. (1949). The Intelligent Investor. Harper & Brothers.

Hull, J.C. (2018). Options, Futures, and Other Derivatives. Pearson.

Kahneman, D. & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263-291.

Kyle, A.S. (1985). Continuous Auctions and Insider Trading. Econometrica, 53(6), 1315-1335.

Lo, A.W. (2004). The Adaptive Markets Hypothesis: Market Efficiency from an Evolutionary Perspective. Journal of Portfolio Management, 30(5), 15-29.

Malkiel, B.G. (1990). A Random Walk Down Wall Street. W.W. Norton.

Mandelbrot, B. & Hudson, R.L. (2004). The (Mis)Behavior of Markets. Basic Books.

Markowitz, H. (1952). Portfolio Selection. Journal of Finance, 7(1), 77-91.

McMillan, L.G. (2002). Options as a Strategic Investment. New York Institute of Finance.

Merton, R.C. (1973). Theory of Rational Option Pricing. Bell Journal of Economics and Management Science, 4(1), 141-183.

Natenberg, S. (1994). Option Volatility and Pricing. McGraw-Hill.

O'Neil, W.J. (1988). How to Make Money in Stocks. McGraw-Hill.

Shiller, R.J. (2017). Narrative Economics. American Economic Review, 107(4), 967-1004.

Shleifer, A. & Vishny, R.W. (1997). The Limits of Arbitrage. Journal of Finance, 52(1), 35-55.

Soros, G. (1987). The Alchemy of Finance. Simon & Schuster.

Sterman, J.D. (2000). Business Dynamics: Systems Thinking and Modeling for a Complex World. McGraw-Hill.

Tharp, V.K. (2006). Trade Your Way to Financial Freedom. McGraw-Hill.

Regards,

Russell Larke

BA (Hons) Business Management | MSc Candidate (Systems Thinking)

Trading Beyond Charts

Permalink 1 comment (latest comment by Russell Larke, Saturday 5 September 2026 at 12:07)
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Bounded Rationality and the Boundary of the Market System

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Edited by Russell Larke, Sunday 16 August 2026 at 12:23

Bounded Rationality and the Boundary of the Market System

Herbert Simon's concept of bounded rationality holds that decision-makers do not — and cannot — process all available information before acting. Time, cognitive capacity, and the complexity of the system in front of them impose hard limits. Rather than optimising, actors satisfice: they select a decision that is good enough given what they can actually know, not the theoretically perfect one. This is the standard reading, and it is not wrong. It is, however, incomplete, because it treats "bounded" as a property of the individual mind rather than a property of a system boundary that the individual — often without noticing — has already drawn. 

(direct video / playlist)

Simon developed the concept in the 1950s as a corrective to the assumption, standard in classical economics, that agents are fully rational optimisers with complete information and unlimited processing power. His argument was not that people are irrational, but that the demand classical theory placed on human cognition was never realistic to begin with. Decision-makers, in Simon's account, act as satisficers: they search until they find an option that clears a threshold of "good enough," and then stop searching, rather than continuing until they have located the genuinely optimal choice. This distinction between satisficing and optimising is often flattened in popular use of the term, where "bounded rationality" becomes shorthand for "people make mistakes because they don't have enough information." That flattening loses almost everything useful about the concept.

It is also worth separating Simon's formulation from the behavioural economics tradition that followed it, particularly the heuristics-and-biases work associated with Kahneman and Tversky. That later tradition catalogues the specific, often predictable ways bounded reasoning deviates from a normative rational standard — anchoring, availability, loss aversion, and so on. It is a psychology of error. Simon's original concept is closer to a structural claim: given finite time and finite processing capacity, no useful theory of decision-making can assume unlimited information-gathering, regardless of whether the resulting decision happens to be "biased" in the technical sense. The systems reading developed here sits closer to Simon's original structural claim than to the biases literature, because the interesting move is not cataloguing where traders go wrong, but asking what determines the boundary of what any given trader is even in a position to consider.

From cognitive limit to boundary judgement

Read through a systems lens, bounded rationality stops being a fact about cognition and becomes a question about boundaries. An actor is only ever bounded relative to a system they have implicitly defined for themselves, and that definition is a choice, not a given, even when it is made unconsciously. This is the move Werner Ulrich's critical systems heuristics makes explicit: before asking whether a decision was rational, ask what was drawn inside the boundary of "the system" for that decision, and — just as importantly — what was deliberately or unconsciously left outside it. Ulrich's boundary questions were developed for social planning and public policy, where the stakes of a badly drawn boundary are visible and often severe: whose interests count, whose knowledge is treated as legitimate, who bears the consequences of a plan that never consulted them. They translate cleanly to markets, because a market is nothing more than a set of actors each operating with a different boundary around what counts as "the system" they are trading inside, and each treating their own boundary as though it were simply "the market," rather than one partial construction of it among several.

This reframing matters because it changes what "more information" can actually do. Under the popular reading of bounded rationality, the fix for a poor decision is more research — read the filing, check the float, watch the tape more closely. Under the boundary reading, more research inside an unexamined boundary just produces a more detailed picture of the same partial system. It does not correct the boundary itself. A trader who spends three hours refining their read of price action has not necessarily become less bounded; they have become more thoroughly informed about the specific, narrow slice of the system their boundary already permitted them to see. The boundary, not the depth of research within it, is very often the actual constraint.

Whose boundary, drawn where

Apply that to a trade. Most retail analysis draws the boundary around price, volume, and float — the visible mechanics of the chart. Bounded rationality, on that boundary, looks like a data problem: the trader didn't have enough information about the stock, and more research would have closed the gap. But widen the boundary to include the broker's margin requirements, the market maker's inventory risk, and the regulator's disclosure rules, and a different picture appears. Each of those actors is bounded too, by a boundary drawn around their role, not the trader's. The retail trader's "irrational" entry and the market maker's "rational" hedge can both be locally sound decisions, made by actors whose system boundaries simply don't overlap. Neither actor is short of information in any absolute sense. They are short of information relative to a boundary they never chose to widen.

This is closer to a SODA-style reading — Ackermann and Eden's Strategic Options Development and Analysis — than a purely economic one. SODA's premise is that group decision problems are rarely disagreements about facts; they are disagreements between individually coherent cognitive maps that were never reconciled, because nobody surfaced the boundary judgements each party was quietly operating under. The trader who says "the fundamentals didn't justify that move" and the market maker who says "the move was a rational response to short-dated hedging pressure" are not disagreeing about the facts. They are working from boundary judgements that never converge, because neither actor drew the system the same way — and, critically, neither one is aware that a boundary judgement was made at all. That invisibility is usually the actual problem, not the rationality of either party. A cognitive map, in SODA's sense, is not a distortion of the "real" system; it is a legitimate, internally coherent construction of it, built from a particular vantage point. There is no neutral vantage point from which to declare one map correct and the other mistaken. There is only the question of which boundary each map was drawn within, and whether that boundary was ever made explicit.

Why the boundary itself is not static

There is a further complication that a purely economic reading of bounded rationality tends to miss: the boundary that defines what an actor considers "the system" is not fixed for the duration of a trade. It moves as the situation develops, in the same way a Viable System Model's System 4 function is meant to continuously scan the environment and revise the organisation's model of what's relevant to it (Hoverstadt, 2020). System 4, in VSM terms, exists precisely because a viable system cannot survive on a single, fixed model of its environment; it needs a standing function whose job is to notice when the environment has changed in ways the current model does not account for, and to feed that back into how the system organises itself. An individual trader rarely has a formal System 4 function in this sense, but the absence of one is exactly the failure mode this section is describing: a thesis formed on Monday's boundary, and never revisited, behaves like a viable system that has switched off its environmental scanning.

A thesis built on Monday's boundary — this stock, this float, this catalyst — is not obliged to still hold by Wednesday, because the boundary itself has likely shifted, pulling in actors, constraints, or information that weren't inside the frame when the position was opened. New short interest data is published. A market maker's hedging book changes shape. A regulator opens an inquiry that was never part of the original picture. None of this necessarily falsifies the original thesis in the way a single piece of contradictory information would; it does something more structural, which is to change what the relevant system even consists of. Treating an entry decision as a fixed, defended position, rather than a boundary judgement open to revision as the system reveals more of itself, is one of the more expensive misreadings of what bounded rationality actually implies for practice. It converts a structural feature of decision-making under uncertainty — that the boundary moves — into a personal failure to have been right at the outset.

Boundary judgements and the pattern itself

This also explains something about chart patterns that is easy to miss from inside a purely technical framing. A chart pattern is, among other things, a record of what was left inside a particular boundary — price and volume, nothing else — compressed into a visual shape. It is not that the pattern is false. It is that the pattern is a boundary judgement rendered as a picture, and it inherits every limitation of the boundary that produced it. Two setups can look identical on a chart while sitting inside entirely different wider systems: different borrow availability, a different macro backdrop, a different mix of institutional participants. The pattern repeats because the visual boundary — price against time — is narrow enough to produce the same shape from genuinely different underlying conditions. Reading the pattern without asking what has been excluded from the frame that produced it is, in effect, mistaking one actor's boundary judgement for a complete description of the system.

The practical consequence is specific, not merely philosophical. Widening your own boundary judgement — deliberately asking who else's constraints are shaping this price, and why their bounded rationality might look nothing like yours — does more to explain an apparently "irrational" move than assuming someone, somewhere, simply lacked information. The information usually existed. It just sat inside a different actor's boundary, governed by a different set of constraints, and was never going to be visible from where the original decision was made. The discipline worth building is not "gather more data." It is "ask whose boundary you are currently trapped inside, and what you would see if you deliberately moved it" — and to keep asking that question for as long as the position is open, not only at the moment it was opened.

How bounded decisions aggregate into a feedback loop

None of this means the individual boundary judgement is the end of the story. Boundaries matter for a second reason that a purely cognitive account of bounded rationality has no real way to capture: bounded decisions do not stay isolated. They aggregate, and the way they aggregate can produce dynamics that no individual actor intended or fully understood while it was happening. A short squeeze is the clearest illustration available in markets. No single short seller decides to trigger a squeeze. Each one is making a locally bounded decision — cover now, at this price, given this margin call, this borrow cost, this risk limit — using information and constraints specific to their own boundary. None of them is reasoning about the aggregate effect of everyone else doing the same thing at roughly the same time. And yet the sum of those individually bounded, individually reasonable decisions produces exactly the reinforcing feedback loop that defines a squeeze: covering pushes price up, which forces more covering, which pushes price up further, each step driven by actors who were never modelling the loop itself, only their own narrow slice of it.

This is a genuinely systemic property, not a cognitive one. It cannot be explained by saying any individual actor was insufficiently rational, because each actor's decision was perfectly sound relative to their own boundary. The loop emerges from the structure of how many separately bounded actors are coupled to the same price, not from any failure of reasoning inside a single mind. This is precisely the kind of phenomenon that a boundary-based reading of bounded rationality is equipped to explain and a purely cognitive one is not: the interesting object of analysis stops being any individual trader's decision quality, and becomes the structure connecting many differently bounded decisions to one another. Understanding a squeeze, in other words, is less about diagnosing anyone's rationality and more about mapping whose boundaries are coupled to whose, and how tightly.

A working boundary audit

Turned into something usable rather than only descriptive, Ulrich's boundary questions suggest a short, repeatable audit that can be run against a position, ideally more than once across its life rather than only at entry. Four questions do most of the work. Who is the boundary of this analysis actually built around — is it drawn around the chart, around the company, or around the full set of actors with a stake in the price? What has been placed outside that boundary that could plausibly move the price regardless — borrow availability, a regulator's attention, a market maker's inventory position, a fund's mandate constraints? Whose bounded rationality is currently doing the most work in this move, and is it the same actor whose bounded rationality was doing the work when the thesis was first formed? And finally, if the boundary were deliberately widened to include the actor currently most affected by this price and least visible from the original vantage point, what would the position look like from there?

None of these questions produce a number, and none of them replace the mechanical analysis — the float, the borrow, the catalyst — that any position still needs. What they do is make the boundary itself an object of deliberate attention rather than something absorbed unconsciously along with whichever framework was used to first find the setup. Given that the boundary, not the depth of analysis inside it, is very often the actual constraint on a bounded-rational decision, that shift in attention is not a peripheral addition to the analysis. It is closer to the analysis that was missing.

This is explored further, with direct application to trading decisions, in the video above.

Regards,

Russell Larke

BA (Hons) Business Management | MSc Candidate (Systems Thinking)
Trading Beyond Charts

Permalink 2 comments (latest comment by Jim McCrory, Monday 14 September 2026 at 10:26)
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