This article directly challenges “Smart Money Concepts” and the anecdotal success often used to support them.Before we go deeper I need be clear, This post is human written. I have spent many minutes formatting this manually. Multiple key lessons will register post-reading.Many trading frameworks fail on real market logic, and anecdotal winners do not rescue it because variance alone can produce impressive outliers, naturally. In this article I aim to:Show what SMC gets partly right, This article isn’t only to “expose” SMC, it is also for learning about the weaknesses of retail frameworks in a sober way to encourage personal improvements. This article is about substance. This post contains over 8 images to help make things click. Part 1: Introduction:Some say they trade ICT/SMC others say they “trade liquidity”.
A 2025 video transcript extract.
What is old, renamed and repackaged (revisted later)Order Blocks -> Supply and demand Sam Seiden 2006 Part 2: The Reality/Missing Context:The Primary Claim:Price movement is not dictated purely by “buy and sell pressure” The secondary claimsThe Liquidity Sweep Narrative:Stop losses do cluster and can lead to cascading and other consequences during price discovery. Correct. How is it wrong?Market maker algorithms manage risk they actively reduce their directional risk, actively pushing the price around increases it. Why is the liquidity hunting claim convincing to many?It borrows authority from a real, studied price phenomenon. The reality e.g., in research papers use phrases such as “adverse selection” which are unfamiliar to retail traders which reduces accessibility to the truth. Defining it: The result of adverse selection (P&L)The trader gets high volume filled at advantageous prices -> the market maker is filled on the opposite side of that position losing money -> The trader gets a better price artificially as a result from information asymmetry. What happens to the price: Other claims surrounding liquidity provision: “Price is delivered by an algorithm.” – verbatim Reality: A “central algorithm” does not exist. There are no studies and it is not cited in any journal. it is fictitious. It is not a real thing. There are many Investment banks, LPs, exchanges and Multilateral trading facilities which work both unilaterally and bilaterally to provide quotes to trade CFDs (FX especially). For futures, equities and other centralised markets, many firms are actively making markets by quoting prices. Below, I have provided clear statements that directly challenge and ultimately undermine the core foundations that “SMC” relies on.
These are verifiable market truths.
What would change my mind?If instruments (especially derivatives) were traded with one central dealer with no meaningful alternative exchanges/venues, then it could start to be believable with additional evidence. But in real markets, those conditions generally do not hold. Part 3: Common objections, answeredStatement: But what about X guy who made 100k using ICT?“Anything can work” Even breakeven systems with zero edge can make money due to variance. Anecdotal successes are a flawed measure for viability. Survivorship BiasICT/SMC is fundamentally baseless, so are many other retail frameworks. Sunk cost binds traders to work within flawed frameworks for years. Assertion 1“Liquidity grabs/order blocks/inducement patterns aren’t just buzzwords that ICT traders use; they tie back to things like order flow and institutional positioning, which are 100% real and observable dynamics in the market that are talked about in academic papers all the time.” Addressing Assertion 1:Yes, I get it, but you are trying to infer this from candlesticks; that’s where it’s pure narrative. You aren’t getting liquidity grab or institutional insight that has predictive value from candlesticks. People will teach you that story, but that doesn’t mean that it is factual. The initial ideas are old and are referred to as the “composite man” frameworks with similar ideas to ICT, e.g., Dow theory has been exposed since 1934, for example, by Alfred Cowles. Question: Isn’t ICT known to be a fraud?People tend to give emotional arguments against ICT and use his tainted reputation, but a common logical fallacy is “But his concepts work”, tied to supposed anecdotal successes paired with ad hoc reasoning. Image context/source: Dow Theory or what ICT calls a “Breaker block” Follow-up: I thought this was a well-known fact? The unfortunate part of all this is that I have interacted with over half a dozen ICT traders who have wasted more than 2 years trying to make it work. I know what it’s like to suffer, which makes this worth writing about. Challenge 1 (Straw-man)“You make the assertion that ICT doesn’t work.” I did not make an assertion that ICT doesn’t work; I said it is not viable because it conflicts with market microstructure realities. This post includes an equity curve simulation with strategies that have no edge (BE). The simulations display many profitable and many negative outcomes. People can make money from luck (variance) with ICT, but that alone does not provide a persistent edge. Challenge 2“This is how the market is actually run from day to day, and unfortunately some of it does line up with what michael huddleston teaches.” – Verbatim A man could have predicted a coin flip correctly e.g., 55% of the time yesterday but that is just chance that will average out to 50% with more flips, it is not a viable forecasting skill. In the same way, occasional correct descriptions of markets do not prove that a framework has pedagogical value. What matters is whether the approach is consistently insightful, not whether it happens to be right here and there or appear logical at X and Y angle but not Z. “You definitely wont get a $2M+ payout from a really lucky run with a breakeven strategy.” – Verbatim You absolutely can with concentrated risk, it is only extremely improbable. I and many other traders have had consecutive profitable days exceeding 20R averages before, I know what the extremes of variability look like. Edges come and go. Edge decay. “Nobody is becoming a multi-millionaire from trading by pure luck” – Common Assertion. Challenge 3“Where is your data or research for why ICT doesn’t work?” Answer:I have provided a research paper for example, Part 4: The simulation, and what it does, and does not.To show why anecdotal winners prove very little, I will simulate 5 million iterations of a breakeven framework (2.5m traders with two models attempted on average with a $1000 starting balance) each trader averages a 1:3 RRR system with a winrate of 25% (breakeven) and a risk per trade of 2.5%. Monte Carlo Simulation Results:Best outcome: $3,712,309.53Worst outcome in the simulation: $2.6368543372 (Blowup)Visual: Monte Carlo Simulation Outputs My value selection reasoning:Some ICT traders may aim for modest 1:2 setups, while others aim for much high RRR positions, so I went with a ratio of 1:3. Some ICT traders risk extremely low amounts, while others risk extremely high amounts or trade with prop firms, which skew outcomes positively. So I chose $5,000 as the maximum risk per path, with a 1k sample. In plain terms, this assumes the ICT/SMC framework on average produces breakeven results, and each trader uses two models before giving up. The numbers chosen are generous, as there are more than 2.5M traders, but 2.5M is the highest I could go without speculation. The 5m simulation number caps the best performer by more than necessary the best “lucky” performance could easily be higher. Before we go deeper…With conservative breakeven framework assumptions the values are still noticeably high. A net losing framework would likely still have profitable traders if thousands to millions have tried it at different times. I could lower the sample and increase the iterations and number of “SMC” traders and still get similar values from simulating outcomes. There are definitely at least 10Ms of iterations of SMC strategies due to the popularity, but I do not want to inflate values through speculation. Remember that many “SMC” traders persist for years, and the simulation assumes that the average “SMC” trader gives up after two tries, which could easily be a lot higher. The best outcome of $3,712,309.53 was based on conservative assumptions. Monte Carlo Simulation: Additional Information:15 out of 5 million tries resulted in an outcome beyond 1 million USD in the simulation. There are less than 3 ICT/SMC traders with profits on regulated platforms or prop firms exceeding this number which suggests the framework might be less than BE (after costs are factored in). 139 paths exceeded 500k. 139/5,000,000 tries resulted in wealth beyond 500k that does not reflect what is shown publicly. Some will intuitively think The monte carlo simulation’s environment was configured to be similar in nature to coinflips. 1000 traders (a small sample) over 100 trades with independent 1:1 RRR, 50% win rate breakeven system provide a best outcome of 9,901.03 USD with a starting balance of $5000 assuming the risk is 2.5% per trade in this simulation. These traders use asymmetric RRR which increases the potential for positive skew in anomalous favourable outcomes. Anomalous profitable periods with higher ratios are more impactful than ones with lower ratios statistically. Most of these traders use ratios beyond 1:1 and some use ratios beyond 1:10, 1:3 is a conservative value in this case. The same inputs with independent 1:3 RRR, breakeven win rate systems provide a best outcome of 19,043.62. This is over double the positive skew when compared to a ratio of 1:1, even though both strategies have breakeven win rates. The higher the number of times the same type of coin is flipped (paths), and the more iterations (flips) are simulated, the higher the chance that anomalies (unusual results) start to appear. The Simulation’s Value and Limits.The simulations do not show whether specific observed winners are lucky or skilled, but they do show that anecdotal millionaire outcomes are highly compatible with variance (randomness) alone in a large population (2.5m+ traders) using a breakeven or weak framework. This is the problem. This is one example out of many nonsense discretionary frameworks. Part 5: Probability Theory and Statistics (Important)The Infinite Monkey Theorem suggests that if you have enough “monkeys” (traders) hitting keys (buying/selling) at random, one will eventually “type” a perfect equity curve.Why this is possible: What happens: The Illusion and Logic: In plain terms the higher the iterations the more probable an outlier will exist with enough tries large wins are guaranteed. This cuts both ways as a framework with no edge can be used to create profitable systems coincidentally with enough iterations, this means successful trading influencers can function as a false positive for a baseless framework. Anecdotal successes do not prove a method’s effectiveness. To add, another key problem which increases the skew for extreme positive and negative outcomes is discretion (noise added to strategy decision making). Think of SMC like fractional distillationYou have a range of temperatures where you can extract a substance (profitable, efficient strategies) instead of the specific temperature required. It’s only a loose guide. That’s similar to data snooping and the other data science flaws when applied. The point is, you might still get the substance you need from the distillation process, but a lot of excess time and energy is wasted because you don’t apply the correct amount of heat to get the desired substance, as the framework requires guesswork. Decent, unoriginal techniques, but a lot of noise during the application. Weather that noise positively or negatively impacts to Trader is unquantifiable on a case by case basis. Costs will do most of the damage. You can have Supply and Demand with Sam Seiden on Windows XP (in 2006) or you can have “Order Blocks” paired with a high-variance framework in the mid 2010s. Take two. Same idea, same narrative, different name. Many of the ideas are weak, but VERY few take advantage of actual short-term market inefficiencies. Unfortunately, SMC shares the same structural weaknesses as many retail systems: heavy discretion in most applications, limited first-party testing, and heightened potential exposure to alpha decay due to the technique’s widespread use. All of this, paired with flawed logic, makes it unappealing. Part 6: Why logic matters more than isolated backtests for retail trading frameworksA statistical test that isolates one technical component often misses the way a multi-component framework creates edge through interaction effects with its other parts, such as entry timing, confluence, filters, risk management and so on. Image: Volume Profile – Low Volume Node or “FVG”? A result which shows no edge after costs, i.e., null, shows that a specific part, e.g., an FVG, may have very little signal, people have tested this, and poor testing outcomes are the result of probing in isolation. It will be underfitted as seen with profit factors close to 1.0 as seen in the post. Defining underfitting in trading:Underfitting vs Good Fits When a strategy is underfitted it means a model or strategy is too simple to capture the real structure of the market. The complexity is too low. At STS, we aim to design strategies that are aligned with a market’s behaviour but not overadjusted or forced to work; this leads to a “good fit” scenario. Posts showing poor results when testing “FVGs”, as expected.Users such as user vaanam-dev have tested them and poor results were output such as
Out of many tests performed across multiple assets general return efficiency and sharpe ratios were consistently low after trading costs (especially). Surprisingly, an “FVG” can appear to signal inefficient price movement when defined mechanically. In reality, there is no genuine “gap in fair value”; the limitation lies in the framework itself rather than in the formation. Tests like the ones I have linked isolate the formation rather than disprove the process. Part 7: Accepting or rejecting the framework itself is far more important.Why?Because identifying poor logic saves the time and money many traders commit to flawed methodology. If the combinations and decision noise from interpretation is materially infinite only the rationale can be attacked.¹ If I backtest a specific model that a trading influencer pushes, people will rely on subjective excuses such as “it is being applied incorrectly” when poor results materialise. There is no objective way to use SMC, it is a framework that depends on how the person who uses it decides to use it. So it is only worth attacking it from the roots; otherwise, the debate lacks logically grounded substance and will never end. The point of the evidence I’ve submitted is to end the circular nature of these debates. The framework itself unfalsifiable but the logic itself is not so I have refuted what is possible to save you time [1]. A direct quote from the creator of SMC:“What other Trading Theory is this consistent, predictable, streamlined and so precise?” – verbatim.If a framework can always be rescued by reinterpretation, then the logic is not robust. In the world of precision, variability in judgement is the enemy. Why do people believe in it?SMC imitates depth without actually having depth. This is why it survives amongst retail traders while serious traders, especially quants, laugh at it. It sounds sophisticated, gives people labels to attach to common price movements, and makes people feel like random or ordinary market phenomena are secretly coordinated. This a seductive combination to those who do not have the market microstructure knowledge to filter it out. A false breakout sounds technical and boring while a “liquidity sweep” sounds profound to many. That is the dress up. Some will state To save time and money, it is good to prioritise “is this framework logical” versus “what do people think” or “what does my backtest say?”. A backtest is just one interpretation or opinion; the root is its entire foundation. If there is no root, there is no plant. Hopefully it’s clicked for you now. The primary lesson behind this article is that sometimes you can’t take down methodology with tests; a lot of the time, you have to work backwards and undo the knots flawed reasoning has tied to break free. If a trading framework is unfalsifiable, as most naturally are, you must probe its logic instead, to avoid wasting time applying it. Logically grounded and tested trading strategies are required for an increased probability of success in financial markets. You may be dealing with some of the same issues in your own framework. If that seems possible, it is absolutely worth doing some focused research and manual reviews to fill the blanks or to justify discarding it entirely. Part 8: This is your moment to take the craft seriously.Some will think I am extreme, others may read this and feel anger, but it is your opportunity to pause, reflect, and turn that energy into growth. If you are struggling and have seen what has surfaced, I gently urge you to detach from common methodologies and engage in real market literature and research. TLDRIf you are struggling, visit the original valid material without the fluff. Read real market literature My final statement.Meaningful trading outcomes are bound to logical structures or luck. Thanks for reading submitted by /u/SentientRon to r/Forexstrategy |