The Macro Environment: A Systems Analysis of Market-Wide Structure and Its Interaction with Company-Specific Dynamics

Financial markets are commonly discussed as though individual securities operated within a vacuum, their price movements attributable solely to company-specific developments. This assumption, while convenient for analytical purposes, obscures a fundamental truth: every security trades within a broader environment that shapes, constrains, and sometimes overrides the dynamics of the individual asset. The macro environment—interest rates, economic data releases, sector-wide correlations, and aggregate risk appetite—constitutes the larger system within which the micro environment, the company-specific layer, is nested. Systems Thinking in Practice (STiP) offers a framework for understanding this relationship not as a hierarchy of competing explanations but as a structure of interacting layers, where the behaviour of the whole emerges from the coupling of its parts (Meadows, 2008). This article advances the argument that the macro environment functions as the systemic context that determines whether company-specific conditions can manifest as observable market behaviour. A micro setup may exhibit all the structural prerequisites for a significant price movement, yet fail to ignite if the macro environment withholds the necessary flows of capital and risk appetite. Conversely, a structurally weak setup may perform beyond expectation when the macro environment actively supplies the conditions for speculative activity. The interaction between these two layers is not additive but systemic: the macro environment does not merely add to or subtract from micro dynamics; it transforms their significance (Sterman, 2000).

This article examines the macro environment through a systems lens. It first establishes the conceptual basis for treating macro conditions as a distinct systemic layer, bounded yet permeable, and inherently coupled to the micro structures beneath it. It then analyses interest rates as a fundamental feedback mechanism within the financial system. The discussion proceeds to economic data releases as macro-level catalysts, followed by an examination of sector contagion as a manifestation of structural coupling. The role of aggregate sentiment is then explored as an emergent property of the system. Throughout, the emphasis remains on interconnection: how the macro environment shapes the conditions under which micro dynamics operate, and how micro behaviour, in aggregate, feeds back into the macro structure itself (Meadows, 2008).

The Macro Environment as a System Layer

Systems thinking recognises that complex phenomena are organised hierarchically, with each level of organisation both containing and being contained by other levels (Simon, 1957). The macro environment represents one such level in the structure of financial markets. It is bounded by what is market-wide rather than company-specific: interest rates set by central banks, economic indicators released on fixed schedules, sector correlations maintained by systematic trading strategies, and the aggregate sentiment that emerges from the interaction of millions of market participants. These components form a system with its own dynamics, irreducible to the behaviour of any single security (Meadows, 2008).

The boundary between the macro and micro layers is real but permeable. A change in interest rates originates in the macro layer—a decision by a central bank—but propagates downward, altering the cost of capital for individual companies, the attractiveness of risk for individual investors, and the valuation models applied to individual securities. Equally, the micro layer feeds upward: a sufficient number of company-specific failures can trigger sector-wide repricing, which can, in sufficient magnitude, influence macroeconomic indicators and, ultimately, central bank policy. The two layers are not separate systems but coupled subsystems within a larger whole, each influencing the other through defined channels of feedback (Sterman, 2000).

The permeability of this boundary carries practical implications. An analyst who focuses exclusively on the micro layer—float, short interest, utilisation, borrow fee—may construct a thesis that is internally coherent yet externally invalidated. The company-specific conditions may be accurately assessed, but the macro environment may be structured to prevent those conditions from producing the expected outcome. The systems thinker recognises that any analysis bounded too narrowly will miss the constraints imposed by the larger system within which the narrower system operates (Simon, 1957). This insight connects directly to the concept of reflexivity in financial markets: participants act on their understanding of the system, and their actions alter the system itself, creating a circular relationship between perception and reality that cannot be captured by linear analysis (Soros, 2008).

Interest Rates as a Fundamental Feedback Mechanism

Interest rates function within the financial system as a primary feedback mechanism. They represent the cost of money itself, and through this cost they regulate the flow of capital between risk categories. When rates rise, the return available from low-risk instruments increases. Capital that might otherwise have flowed into speculative, high-volatility securities now has a viable alternative destination offering comparable return with lower risk. The flow of capital into speculative markets diminishes. When rates fall, the return on safe instruments declines, and capital flows back toward risk in search of yield. This is a balancing feedback loop: rising rates dampen speculative activity, falling rates stimulate it (Meadows, 2008).

This mechanism is not new. Keynes (1936) identified the rate of interest as the price that equilibrates the desire to hold wealth in liquid form with the available supply of liquidity. When liquidity preference shifts, the rate of interest adjusts, and with it the entire structure of asset prices. The insight remains relevant: the interest rate is not merely a technical variable but a systemic regulator of the relationship between liquidity, risk, and asset valuation. The systems perspective extends this understanding by recognising that the rate of interest operates within a network of feedback loops, influencing and being influenced by inflation, employment, growth, and the expectations of market participants themselves (Sterman, 2000).

The mechanism operates with characteristic systemic properties: delay, nonlinearity, and threshold effects. The impact of a rate change does not manifest immediately; it propagates through the system over time as borrowing costs adjust, investment decisions are made, and portfolios are rebalanced. The relationship between rate changes and market behaviour is nonlinear: a small change near a critical threshold—where the risk-reward calculus for a significant number of participants shifts—can produce a disproportionately large effect. And the system exhibits thresholds: below a certain rate level, speculative capital flows freely; above it, the flow diminishes rapidly (Sterman, 2000). Minsky (1986) described a related dynamic in his financial instability hypothesis: stability itself breeds instability, as prolonged periods of low rates and stable conditions encourage the accumulation of speculative positions that eventually become unsustainable. The structure of the system generates the conditions for its own transformation.

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The relevance of interest rates to the micro layer is particularly acute for speculative, low-float securities. These instruments depend, to an unusually high degree, on the continuous influx of risk-tolerant capital. They do not attract capital on the basis of fundamental value; they attract capital because they offer the possibility of rapid, outsized returns. When rates rise and safe alternatives become more attractive, the capital that sustains speculative setups is the first to retreat. When rates fall, it is the first to return. The consequence is that the same micro structure—identical float, identical short interest, identical catalyst—will behave differently under different interest rate regimes. The structure determines the potential; the macro environment determines whether that potential is realised (Simon, 1957).

Economic Data as Macro-Level Catalysts

Economic data releases function in the macro layer as catalysts in the same systemic sense that company-specific events function in the micro layer. A catalyst is a scheduled point at which new information enters the system, forcing a revaluation and potentially triggering a state transition (Sterman, 2000). In the micro layer, the catalyst is company-specific: an earnings date, a regulatory decision, a court ruling. In the macro layer, the catalyst is market-wide: an inflation print, an employment report, a growth figure. The mechanism is structurally identical; only the scale differs.

The systemic property of a catalyst is its capacity to compress time. Before a catalyst, the system exists in a state of unresolved tension. Participants hold positions based on expectations of what the catalyst will reveal. When the catalyst arrives, the tension is released—either in the direction anticipated or in the opposite direction. The critical point for the interaction between macro and micro layers is that macro catalysts release tension across the entire system simultaneously. A surprising inflation print does not merely revalue one sector or one type of security; it revalues every security in the market, because it changes the expected trajectory of interest rates, which changes the discount rate applied to all future cash flows, which changes the relative attractiveness of every asset class (Meadows, 2008).

The behavioural dimension of this revaluation is significant. Prospect theory demonstrates that market participants do not respond to new information symmetrically; losses are weighted more heavily than equivalent gains (Kahneman and Tversky, 1979). A negative surprise in an economic data release can therefore trigger a disproportionate response, as participants rush to avoid losses rather than pursue gains. The result is that macro catalysts often produce market-wide movements that exceed what a purely rational revaluation would suggest. The system overreacts, and the overreaction itself becomes a structural feature of the environment within which micro setups must operate (Shefrin and Statman, 1985).

The consequence for company-specific setups is that a macro catalyst landing on the same day as a micro catalyst can overwhelm it. The capital flows triggered by the macro event are orders of magnitude larger than those triggered by the micro event. The attention of market participants is consumed by the macro revaluation. A genuinely sound micro thesis can go unnoticed, not because it is flawed but because the system's processing capacity is occupied elsewhere. This is not a failure of the micro analysis; it is a property of the system's hierarchical structure (Simon, 1957).

Sector Contagion as Structural Coupling

Sector contagion represents a particularly clear example of structural coupling within the financial system. The phenomenon occurs when a price movement in one security propagates to other securities that share structural similarities, regardless of whether those securities share the underlying cause of the movement. The mechanism is systemic: a significant portion of trading volume today is executed by algorithmic strategies that operate on baskets of correlated securities. These strategies do not evaluate each security on its own merits; they respond to signals that apply to the basket as a whole. When a signal triggers—a negative earnings report from a major company, a regulatory setback, a profit warning—the strategy sells the entire basket, by code, within minutes (Sterman, 2000).

The systemic property at work here is correlation. Securities are coupled to one another through the strategies that trade them. The coupling is not based on fundamental similarity; it is based on statistical association—beta, sector membership, factor exposure. The consequence is that a company with no connection to the triggering event can experience significant price movement simply because it is correlated with the company that did. The movement is not a verdict on the company's own thesis; it is a structural effect of the system's organisation (Meadows, 2008).

This phenomenon illustrates a deeper systems principle: the behaviour of a system cannot be fully explained by examining its components in isolation. A company-specific analysis that finds no negative development in the company's own filings, no change in its float, no deterioration in its borrow conditions, may still observe a sharp price decline. The explanation lies not in the company but in the company's position within the larger structure—its correlation with other securities, its membership in traded baskets, its exposure to systematic strategies. The systems thinker recognises that position within a structure is itself a property of the component, as real as any property that inheres in the component alone (Simon, 1957).

Schelling (1978) demonstrated that aggregate patterns can emerge from the interaction of individual decisions even when no individual intends the aggregate outcome. The same principle applies to sector contagion: no single algorithmic strategy intends to move an entire sector, but the simultaneous operation of many such strategies, each responding to the same signal, produces a sector-wide movement that no individual strategy would have generated alone. The behaviour of the whole emerges from the interaction of the parts, and the resulting pattern cannot be attributed to any single cause (Meadows, 2008).

Sentiment as an Emergent Property

Aggregate market sentiment—the collective disposition of market participants toward risk—functions as a stock in the systems sense. It is an accumulation, built up over time through the flow of individual decisions, and it exhibits inertia. Sentiment does not shift instantly; it changes gradually, through a process of accumulation and erosion. The systems perspective recognises that sentiment is not merely a reflection of market conditions; it is a causal factor in its own right. A market in a state of fear behaves differently from a market in a state of greed, even if the underlying fundamentals are identical. Fear reduces the flow of speculative capital. Greed increases it. The same micro setup—identical float, identical short interest, identical catalyst—will attract different levels of buying interest depending on the mood of the macro environment (Sterman, 2000).

The feedback relationship between sentiment and price action is inherently reinforcing. Rising prices generate optimism, which attracts capital, which pushes prices higher. Falling prices generate pessimism, which repels capital, which pushes prices lower. This is a reinforcing feedback loop, and it operates at the level of the entire market, not just individual securities. Shiller (2000) described this dynamic as irrational exuberance: the process by which rising prices generate expectations of further rises, which attract new buyers, which push prices higher still, creating a feedback loop that carries markets far beyond any fundamental justification before the loop eventually reverses. The reversal, when it comes, is equally self-reinforcing on the downside (Meadows, 2008).

Kindleberger (1978) documented this pattern across centuries of financial history: the cycle of mania, panic, and crash is not an anomaly but a recurring structural feature of financial systems. The systems perspective explains why: the structure of the system—the feedback loops between prices, expectations, and capital flows—generates cyclical behaviour as an inherent property, not as a response to external shocks. A micro setup that would have triggered a squeeze in a greed-dominated environment may fail to ignite in a fear-dominated environment, not because the setup is weaker but because the sentiment loop is running in the opposite direction, starving the setup of the fuel it needs to ignite (Sterman, 2000).

The Coupled System

The macro environment and the micro environment are not separate systems. They are two levels of a single coupled system, each influencing the other through defined channels of feedback. The micro layer provides the structural conditions for individual securities: the float, the short interest, the lending market, the catalyst schedule. The macro layer provides the environmental conditions for the market as a whole: the interest rate regime, the economic calendar, the sector correlations, the aggregate sentiment. Neither layer can be understood in isolation. The micro layer determines which securities are structurally primed; the macro layer determines whether the conditions exist for that priming to matter (Meadows, 2008).

The coupling operates in both directions. Macro conditions constrain micro behaviour: a hostile macro environment can prevent a primed micro setup from realising its potential. Micro behaviour constitutes macro conditions: the aggregate of individual trading decisions produces the sentiment, the correlations, and the price patterns that characterise the macro environment. The relationship is recursive, not hierarchical. The whole constrains the parts; the parts produce the whole. This is the essence of systems thinking: the recognition that behaviour emerges from the interaction of components, and that the resulting behaviour then feeds back to constrain the components that produced it (Sterman, 2000).

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The implications of this perspective are significant for anyone attempting to understand or predict market behaviour. An analysis that focuses exclusively on the micro layer will produce accurate descriptions of company-specific conditions but will miss the environmental factors that determine whether those conditions produce outcomes. An analysis that focuses exclusively on the macro layer will produce accurate descriptions of market-wide conditions but will miss the structural variations between individual securities that determine which ones respond most strongly to environmental changes. Only an analysis that treats both layers as part of a single coupled system can capture the full dynamics of market behaviour (Simon, 1957).

Conclusion

The macro environment constitutes the systemic context within which company-specific dynamics operate. It is not a separate domain of analysis but a structural layer in the same coupled system that includes the micro environment. Interest rates function as a fundamental feedback mechanism, regulating the flow of capital into speculative activity. Economic data releases serve as macro-level catalysts, forcing market-wide revaluations that can overwhelm company-specific developments. Sector contagion illustrates the structural coupling that exists between securities, whereby price movements propagate through correlation regardless of fundamental merit. Aggregate sentiment operates as an emergent property of the system, a stock with inertia that constrains the very behaviour that produces it.

The systems perspective reveals that the macro and micro layers are not competing explanations for market behaviour but complementary descriptions of a single integrated structure. The micro layer determines potential; the macro layer determines realisation. The micro layer identifies which securities are structurally primed; the macro layer identifies whether the environment will supply the fuel needed to ignite that priming. Neither layer alone is sufficient. The behaviour of the market as a whole emerges from the interaction of both, and any analysis that treats them as separate will miss the feedback loops that connect them. The macro environment is not the background to the micro; it is the system within which the micro operates, and the two can only be understood together (Meadows, 2008).

Regards,

Russell Larke

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

Trading Beyond Charts

References

Kahneman, D. and Tversky, A. (1979) 'Prospect theory: an analysis of decision under risk', Econometrica, 47(2), pp. 263–291.

Keynes, J.M. (1936) The General Theory of Employment, Interest and Money. London: Macmillan.

Kindleberger, C.P. (1978) Manias, Panics, and Crashes: A History of Financial Crises. New York: Basic Books.

Meadows, D.H. (2008) Thinking in Systems: A Primer. White River Junction, VT: Chelsea Green Publishing.

Minsky, H.P. (1986) Stabilizing an Unstable Economy. New Haven: Yale University Press.

Schelling, T.C. (1978) Micromotives and Macrobehavior. New York: W.W. Norton.

Shefrin, H. and Statman, M. (1985) 'The disposition to sell winners too early and ride losers too long: theory and evidence', The Journal of Finance, 40(3), pp. 777–790.

Shiller, R.J. (2000) Irrational Exuberance. Princeton: Princeton University Press.

Simon, H.A. (1957) Models of Man: Social and Rational. New York: John Wiley & Sons.

Soros, G. (2008) The New Paradigm for Financial Markets: The Credit Crisis of 2008 and What It Means. New York: PublicAffairs.

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

The Church of the Chart: Technical Analysis as Epistemic Cult (why technical analysis fails)

By Russell Larke —

Introduction

There is a particular moment familiar to anyone who has spent serious time in retail trading communities. Someone posts a chart. A line has been drawn — sometimes several lines, forming a triangle, a wedge, a "cup and handle," a head-and-shoulders formation rendered with the confidence of a geometry proof. Beneath it, a caption: "Textbook setup. Target $X. Can't make this stuff up." Dozens of replies follow, most of them variations on agreement, a few emoji rockets, the occasional dissenter quietly ignored or ratioed into silence. 

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Having spent over a decade trading microcap short squeeze setups, I have watched this ritual play out thousands of times. What strikes me now, after several years of formal systems thinking study, is not that chartism is wrong in some narrow technical sense — reasonable people can debate the marginal informational content of price patterns — but that the culture built around it exhibits nearly every structural feature of a belief system organised for social cohesion rather than truth-seeking. This piece sets out that argument: that chartism, as practised in most retail trading communities, functions less as an analytical method and more as a closed epistemic system with the sociological signature of a cult.

Defining the Object of Critique

It is worth being precise about what is being criticised, because "technical analysis" is a broad church (the metaphor is deliberate) covering everything from statistically grounded volatility modelling to the reading of tea leaves in candlestick shadows. My concern here is not with quantitative price-action research conducted with proper statistical controls, out-of-sample testing, and falsifiable hypotheses. That is legitimate empirical work, however modest its findings tend to be.

My concern is with chartism as a popular practice: the belief that visually identified patterns in historical price data carry reliable predictive power, taught and transmitted through informal apprenticeship, defended through post-hoc rationalisation, and organised socially around charismatic figures whose track records are curated rather than audited. This is the version of technical analysis that dominates retail trading Discords, Stocktwits threads, and YouTube "trading education" channels — and it is this version that exhibits cult-like structure.

The Five Marks of Epistemic Closure

Sociologists and psychologists who study high-control groups tend to converge on a handful of structural features: sacred or unfalsifiable doctrine, charismatic authority, in-group language, suppression of dissent, and the conflation of belief with identity. Chartism, as a retail culture, reproduces all five.

Unfalsifiable doctrine. A chart pattern that "fails" is rarely treated as evidence against the framework. It is instead relabelled — a failed breakout becomes a "bull trap," a failed support level becomes a "fakeout" that confirms an even larger pattern one timeframe up. The theory absorbs disconfirming evidence by generating new post-hoc categories rather than revising itself. This is precisely the structure Karl Popper identified as the marker of pseudoscience: a system flexible enough to explain any outcome explains nothing at all.

Charismatic authority. Trading communities orbit gurus whose authority rests on curated screenshots of winning trades rather than audited, continuous performance records. Survivorship and selection bias are not bugs in this system; they are the engine of it. The guru's confident narration of "what the chart is telling us" substitutes for demonstrated statistical edge, and followers extend epistemic trust on the basis of charisma and consistency of delivery rather than verified outcomes.

In-group language. Chartism has its own liturgy — "textbook," "clean setup," "the chart doesn't lie," "let the chart do the talking." This language performs a social function distinct from its analytical content: it signals membership, filters outsiders, and pre-empts scrutiny by wrapping claims in a vocabulary that sounds technical while resisting operational definition. Ask ten chartists to define precisely, in falsifiable terms, what constitutes a "clean" pattern versus a "messy" one, and you will get ten different answers, none of which can be tested in advance of the outcome.

Suppression of dissent. Critics of a given call are frequently treated not as offering useful counter-evidence but as displaying bad faith, jealousy, or a failure to "understand price action." This is a familiar move in closed belief systems: disagreement is reframed as a character flaw in the disagreer rather than a claim requiring engagement.

Fusion of belief and identity. Perhaps most tellingly, traders who lose money on a chart-based call often do not update their model of the market; they update their model of themselves, concluding they "read the chart wrong" rather than that the chart carried no real signal. The failure is internalised as a discipline problem rather than externalised as a framework problem. This is a hallmark of identity fusion with a belief system: contrary evidence is metabolised as personal inadequacy rather than as information about the theory's validity.

The Cognitive Substrate: Why This Particular Fallacy Is So Sticky

None of this would take hold if it did not exploit real and well-documented features of human cognition. Three deserve particular attention.

Apophenia and the pattern-recognition trap. Human perception is tuned to detect patterns, including in genuinely random or near-random sequences — a trait with obvious evolutionary utility (better to falsely see a predator in the rustling grass than fail to see a real one) that becomes a liability when applied to noisy financial time series. Price charts, especially on short timeframes and in illiquid microcap names, generate an abundance of visually compelling shapes purely as a function of volatility and sampling. The human eye does not distinguish a pattern with genuine predictive structure from a pattern that is simply what noise looks like when you stare at it long enough.

Confirmation bias and selective memory. Traders who believe in chart patterns notice and remember the instances where the pattern "worked" far more vividly than the instances where it did not, particularly because winning trades are more emotionally salient and more likely to be shared publicly. Over time this produces a subjectively overwhelming sense of validation that has little to do with the pattern's actual base rate of success.

The narrative fallacy. Human cognition strongly prefers causal stories to statistical distributions. A chart pattern offers a satisfying story — accumulation, then breakout, then markup — that feels far more cognitively comfortable than the more accurate but less satisfying description of price as a stochastic process shaped by liquidity, order flow, and reflexive crowd behaviour. We are, as a species, bad at sitting with "this was largely noise," and chartism offers an endless supply of stories that relieve that discomfort.

A Systems View: Confusing Events for Structure

This is where my background in systems thinking, and specifically work applying Viable System Model and SODA methodology to short squeeze dynamics, becomes directly relevant to the critique rather than merely adjacent to it.

Donella Meadows' iceberg model distinguishes four levels at which a system can be understood: events (what just happened), patterns of behaviour (trends over time), underlying structures (the feedback loops, stocks, and flows generating those trends), and mental models (the beliefs that allow those structures to persist). Chartism operates almost exclusively at the level of events and, at best, superficial pattern description. It reads the shape of the iceberg's tip and infers intention from it, without ever asking what submerged structure — what accumulation of stock, what reinforcing or balancing feedback loop, what liquidity constraint — is actually producing the visible shape.

This is not a trivial distinction. In my own work modelling short squeeze mechanics, the dynamics that actually govern price behaviour are structural: a reinforcing loop between short-covering and buying pressure that depletes available borrow and drives cost-to-borrow higher, a balancing loop of profit-taking that caps the advance, and a second reinforcing loop as late entrants chase momentum near exhaustion. These loops interact to produce recognisable macro-phases — a concept I have formalised elsewhere as the Larke Cycle — but critically, the phases are the output of the structure, not a pattern read off a chart in isolation. A dead-cat bounce, in this framing, is not a shape to be pattern-matched; it is the observable signature of a specific loop configuration (short-covering exhaustion meeting renewed selling pressure) that can, in principle, be reasoned about causally.

Chartism inverts this relationship. It treats the shape as primary and dispenses with the underlying structure entirely, which is precisely why it produces an unfalsifiable and infinitely flexible framework: without reference to structure, any shape can be reinterpreted to fit any outcome after the fact. Systems thinking's insistence on locating behaviour in structure is not merely a more rigorous analytical habit — it is the discipline that chartism, by its nature, cannot supply, because engaging with structure would surface the far more limited and conditional nature of what price shape alone can tell you.

The Tragedy of the Commons as Social Mechanism

There is also a social systems dimension worth naming. Retail trading education is increasingly monetised through courses, signal groups, and subscription communities, creating a structural incentive for content creators to produce confident, shareable, pattern-based calls rather than epistemically humble, structurally grounded analysis. Confidence sells; hedged uncertainty does not. This generates something close to a tragedy of the commons at the level of trading discourse itself: each individual guru is incentivised to defect toward simplified, confident chartist content, degrading the shared informational commons of the community even as it serves each defector's individual growth. The aggregate effect is a marketplace of ideas that systematically selects for confident-sounding pattern recitation over structurally grounded, appropriately uncertain analysis — not because the former is more accurate, but because it is more legible, more shareable, and more emotionally satisfying to a follower base seeking certainty in an inherently uncertain domain.

Conclusion: Toward Structural Literacy

None of this is an argument that price history is uninformative, or that visual inspection of price action has zero value as one input among many. It is an argument that the cultural apparatus built around chart reading in retail trading communities has drifted from analysis into something closer to doctrine — sustained not by predictive accuracy but by the same social and cognitive mechanisms that sustain any closed belief system: unfalsifiable reframing, charismatic authority, in-group signalling, suppression of dissent, and identity fusion.

The corrective, I would argue, is not simply "better technical analysis" but a shift in explanatory level: away from reading isolated shapes and toward reasoning about the feedback structures — liquidity, borrow availability, crowd behaviour, reflexivity — that actually generate those shapes. This is a harder discipline. It does not offer the same instant, shareable gratification as a chart with a triangle drawn on it. But it has the considerable advantage of being falsifiable, structurally grounded, and honest about the limits of what can be known in advance about a genuinely uncertain system. That, and not another indicator or another pattern name, is what trading education actually needs.

Regards,

Russell Larke

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