Z Reddit Mentions | ChartExchange

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\[38\] Mahmood, A., Zheng, Z., & Chen, Y. (2024). Zinc–bromine batteries: Challenges, prospective solutions, and future. *Advanced Science, 11*, 2305561. [https://doi.org/10.1002/advs.202305561](https://doi.org/10.1002/advs.202305561)
\[39\] Liu, S., Wu, J., et al. (2022). A high-energy efficiency static membrane-free zinc–bromine battery enabled by a high concentration hybrid electrolyte. *Sustainable Energy & Fuels, 6*, 1148–1155. [https://doi.org/10.1039/D1SE01749G](https://doi.org/10.1039/D1SE01749G)
\[40\] [https://www.eose.com/wp-content/uploads/2023/05/eos\_productsheet\_Z3\_050223.pdf](https://www.eose.com/wp-content/uploads/2023/05/eos_productsheet_Z3_050223.pdf)
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\[42\] Zheng, X., Luo, R., et al. (2024). A practical zinc-bromine pouch cell enabled by electrolyte dynamic stabilizer. *Materials Today, 80*, 353–364. [https://doi.org/10.1016/j.mattod.2024.09.015](https://doi.org/10.1016/j.mattod.2024.09.015)
\[43\] Ballas, E., Nimkar, A., et al. (2024). Self-discharge in flowless Zn-Br2 batteries and its mitigation. *Energy Storage Materials, 70*, 103461. [https://doi.org/10.1016/j.ensm.2024.103461](https://doi.org/10.1016/j.ensm.2024.103461)
\[44\] Xu, C., Lei, C., et al. (2024). Practical high-energy aqueous zinc-bromine static batteries enabled by synergistic exclusion-complexation chemistry. *Joule, 8*, 461–481. [https://doi.org/10.1016/j.joule.2023.12.023](https://doi.org/10.1016/j.joule.2023.12.023)
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\[46\] Trovò, A., Marini, G., et al. (2023). Redox flow batteries: A glance at safety and regulation issues. Electronics, 12, 1844. [https://doi.org/10.3390/electronics12081844](https://doi.org/10.3390/electronics12081844)
\[47\] Nambafu, G. S., Hollas, A., et al. (2026). Recent advancements in iron (Fe)-based redox flow batteries for stationary energy storage applications: Features and perspectives. ACS Applied Energy Materials, 9, 3–25. [https://doi.org/10.1021/acsaem.5c02643](https://doi.org/10.1021/acsaem.5c02643)
\[48\] He, C., Zhang, Y., et al. (2025). Aqueous iron-based redox flow batteries for large-scale energy storage. National Science Review, 12, nwaf218. [https://doi.org/10.1093/nsr/nwaf218](https://doi.org/10.1093/nsr/nwaf218)
\[49\] Jayathilake, B. S., Plichta, E. J., et al. (2018). Improvements to the coulombic efficiency of the iron electrode for an all-iron redox-flow battery. Journal of The Electrochemical Society, 165(9), A1630–A1638. [https://doi.org/10.1149/2.0451809jes]()
\[50\] Ju, Z., Zheng, T., et al. (2024). Interfacial chemistry in multivalent aqueous batteries: Fundamentals, challenges, and advances. Chemical Society Reviews, 53, 8980–9028. [https://doi.org/10.1039/d4cs00474d](https://doi.org/10.1039/d4cs00474d)
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This article directly challenges “Smart Money Concepts” and the anecdotal success often used to support them, though the same principles apply to any trading framework built on weak logic.
Multiple key lessons will register post-reading.
[REDDIT MARKDOWN](https://preview.redd.it/66wo30rqz7pg1.png?width=1454&format=png&auto=webp&s=4ec3520cb34d2b9e7e7375b6f391048b0eea9f34)
Before we go deeper I need be clear, This post is human written. Proof is attached at the end for reassurance.
This post isn't only to expose SMC but to learn about the weaknesses of retail frameworks to encourage improvements. Let us begin.
Some say they trade ICT/SMC others say they “trade liquidity”.
Different words, same framework.
# Where they are right:
Price movement is not dictated purely by “buy and sell pressure”.
# The Reality/Missing Context:
Price movement is also dictated by liquidity offered to participants relative to current buy and sell activity.
There is not a sole liquidity provider or market maker for Futures (Direct Market Access) or FX/CFDs (Over The Counter)
Markets are auctions, there is no central algorithm that controls price.
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.
https://preview.redd.it/89th560vz7pg1.jpg?width=980&format=pjpg&auto=webp&s=10e95f797a496f658e8d1b60b91a1d6545ff91ae
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.
**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.
# But what about X guy who made 100k using ICT?
***“Anything can work”***
https://preview.redd.it/s7jt1g2xz7pg1.jpg?width=1080&format=pjpg&auto=webp&s=1534962266f4275bcafaf58b6badbbf8ec91d4bc
Even breakeven systems with zero edge can make money due to variance. Anecdotal successes are a flawed measure for viability.
# Survivorship Bias
ICT/SMC is fundamentally baseless, so are many other retail frameworks.
You can be profitable purposefully with logic based on research backing up your trades, or reach profitability coincidentally with hope in barely reproducible ways. You will always find someone on a “winning” path lacking any real edge if you look hard enough.
Traders should be aiming to use methods rooted in basis instead of relying on luck with SMC.
**Sunk cost binds traders to work within flawed frameworks for years.**
I have seen people waste years of their lives trying to make strategies with weak foundations work. The primary goal of the post is to save people's time. There are many other reasons I could list, such as alpha decay, but I wish to keep this post short and simple.
# This is your moment to take the craft seriously.
Some will read this post and feel anger, but it is your opportunity to pause, reflect, and turn that energy into growth. This is about you.
**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.**
Even after reading Trading and Exchanges: Market Microstructure for Practitioners by Larry Harris, followed by Market Microstructure Theory by Maureen O'Hara, your perception of price will change forever, and it will work as a strong filter when building your system.
**Copy and bookmark this to save your time.**
# 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.
This post exists to **prove** that the framework at its core is nonsense, so people cannot hide behind excuses.
https://preview.redd.it/caa0jo1408pg1.jpg?width=415&format=pjpg&auto=webp&s=8482e1c456eff61280499afcac33a0194b1a752a
**Image context/source: Dow Theory or what ICT calls a “Breaker block”**
This material is over a century old, yet it continues to deceive people to this day.
**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
**“Everything i said in my reply is fact based and 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.
***ICT’s flawed reasoning and incorrect assertions are no small mistakes. It collapses the entire framework.***
https://preview.redd.it/3jt3b1j808pg1.jpg?width=1280&format=pjpg&auto=webp&s=8ece56d13eed5fadc39247e49c8546047f142675
**“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.
Over 2 million ICT traders have existed (not including SMC educators and those taught the method by brokers, prop firms and other sources) with many more million iterations maybe even billions of iterations as many persist. It is highly probable that outliers like this would surface, that’s how statistics work.
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.
**See the breakeven simulation provided as the ICT/SMC framework and each path is a different ICT trader.**
To prove my point 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: $3,712,309.53
# Worst outcome in the simulation: $2.6368543372 (Blowup)
https://preview.redd.it/wbd9hfwc08pg1.jpg?width=1188&format=pjpg&auto=webp&s=a5b15b00f839a0a55c134eb13d3fce698aac35a5

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# Dumb Money Concepts and Stat Test Limitations
# This article directly challenges “Smart Money Concepts” and the anecdotal success often used to support them, though the same principles apply to any trading framework built on weak logic.
[](https://medium.com/@SentientTradingSociety?source=post_page---byline--110dcd4b67cf---------------------------------------)
[Sentient Trading Society](https://medium.com/@SentientTradingSociety?source=post_page---byline--110dcd4b67cf---------------------------------------)
15 min read
·
5 days ago
53
[](https://medium.com/plans?dimension=post_audio_button&postId=110dcd4b67cf&source=upgrade_membership---post_audio_button-----------------------------------------)
Multiple key lessons will register post-reading.
Some say they trade ICT/SMC others say they “trade liquidity”.
Different words, same framework.
# Where they are right:
Price movement is not dictated purely by “buy and sell pressure”.
# The Reality/Missing Context:
Price movement is also dictated by liquidity offered to participants relative to current buy and sell activity.
There is not a sole liquidity provider or market maker for Futures (Direct Market Access) or FX/CFDs (Over The Counter)
Markets are auctions, there is no central algorithm that controls price.
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.
Press enter or click to view image in full size
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.
**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.
# But what about X guy who made 100k using ICT?
***“Anything can work”***
Press enter or click to view image in full size
Even breakeven systems with zero edge can make money due to variance. Anecdotal successes are a flawed measure for viability.
# Survivorship Bias
ICT/SMC is fundamentally baseless, so are many other retail frameworks.
You can be profitable purposefully with logic based on research backing up your trades, or reach profitability coincidentally with hope in barely reproducible ways. You will always find someone on a “winning” path lacking any real edge if you look hard enough.
Traders should be aiming to use methods rooted in basis instead of relying on luck with SMC.
**Sunk cost binds traders to work within flawed frameworks for years.**
I have seen people waste years of their lives trying to make strategies with weak foundations work. The primary goal of the post is to save people's time. There are many other reasons I could list, such as alpha decay, but I wish to keep this post short and simple.
# This is your moment to take the craft seriously.
Some will read this post and feel anger, but it is your opportunity to pause, reflect, and turn that energy into growth. This is about you.
**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.**
Even after reading Trading and Exchanges: Market Microstructure for Practitioners by Larry Harris, followed by Market Microstructure Theory by Maureen O'Hara, your perception of price will change forever, and it will work as a strong filter when building your system.
**Copy and bookmark this to save your time.**
# 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.
This post exists to **prove** that the framework at its core is nonsense, so people cannot hide behind excuses.
**Image context/source: Dow Theory or what ICT calls a “Breaker block”**
This material is over a century old, yet it continues to deceive people to this day.
**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
**“Everything i said in my reply is fact based and 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.
***ICT’s flawed reasoning and incorrect assertions are no small mistakes. It collapses the entire framework.***
Press enter or click to view image in full size
**“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.
Over 2 million ICT traders have existed (not including SMC educators and those taught the method by brokers, prop firms and other sources) with many more million iterations maybe even billions of iterations as many persist. It is highly probable that outliers like this would surface, that’s how statistics work.
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.
**See the breakeven simulation provided as the ICT/SMC framework and each path is a different ICT trader.**
To prove my point 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: $3,712,309.53
# Worst outcome in the simulation: $2.6368543372 (Blowup)
https://preview.redd.it/tuelj7yh08pg1.jpg?width=1188&format=pjpg&auto=webp&s=7d5d2db3dc4d7e7f107cf4f23324e10ef607f79b
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 move on...
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**
“What about coinflip logic instead? 50/50.”
**The monte carlo simulation’s environment was configured to be similar in nature to coinflips.**
A 25% winrate with a ratio of 1:3 (BE) is equal to a 1:1 ratio with a 50% ratio (BE). In the simulation the average value is breakeven.
But what changes it is the values diverge on anomalous paths (there are millions of tries), that is the point of the simulation.
https://preview.redd.it/784kmcbm08pg1.jpg?width=1400&format=pjpg&auto=webp&s=6802403736e4b82f37a8562419728def7292076a
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.
https://preview.redd.it/sc71545s08pg1.jpg?width=1400&format=pjpg&auto=webp&s=5767d7bf485bf1b3b65b1ba75473bca2d96c6295
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 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:**
A massive volume of independent actions (on each path).
**What happens:**
A “millionaire trader expert” is produced not because they understood the market, but because the statistical space it self (they are one of millions) was large enough to contain their profitable sequence.
**The Illusion and Logic:**
To the average trader the “millionaire monkey” looks like a genius. But this reminds us that the outcome is a function of sample size itself (Over 2.5m traders) rather than the monkey’s intent or skill. The law of large numbers averages the average outcome close to +0 across all paths and the monkey is one of the extreme values in the distribution (Extreme Value Theory).
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.
**This is why anecdotal evidence is not a suitable measure for viability.**
**To add, another key problem which increases the skew for extreme positive and negative outcomes is discretion (noise added to strategy decision making).**
The more choices a system allows, the easier it is to accidentally find patterns that are just randomness. This has the ability to make winrates fluctuate in ways that cannot be measured resulting in extreme ceilings for positive statistical outliers in trading. A trader’s discretion can add noise to a breakeven system’s positive result adding immeasurable positive (pulling returns higher) or negative drag (pulling returns lower).
# 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 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.
But since many traders use SMC, the potential for anomalous outlier performance is far greater, contributing to the illusion of efficiency.
As our article states: **“the same principles apply to any trading framework built on weak logic.”**
Unfortunately many traders are interested in gurus instead of reading real market literature.
**“Nobody is becoming a multi-millionaire from trading by pure luck” — Common Assertion.**
Variance, not luck.
# Challenge 3
“Where is your data or research for why ICT doesn’t work?”
# Answer:
I have provided a research paper for example,
**The High-Frequency Trading Arms Race: Frequent Batch Auctions as a Market Design Response, The Quarterly Journal of Economics**
The statements I have provided are easy to prove, additional context is provided towards the end.
https://preview.redd.it/15g6s5o518pg1.jpg?width=1048&format=pjpg&auto=webp&s=2a5193d2c1af7df37e474a0fe6dfd5ac7a116701
# Think of SMC like fractional distillation
You 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.
If you want to know how prices really work look at market literature (books) and ***peer reviewed*** papers talking about **liquidity provision, price discovery** and **market auctions** for the truth.
https://preview.redd.it/17j1o11p18pg1.jpg?width=640&format=pjpg&auto=webp&s=672096cbff8a22f6ea0736f852e604f1d125db9e
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.
https://preview.redd.it/7ukzfrls18pg1.jpg?width=640&format=pjpg&auto=webp&s=e2d49e7d7dd2c9fb9aa97cc60d7bacc424324664
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.
# Why no backtesting data or other statistical tests?
A 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”?](https://preview.redd.it/s0ap97p328pg1.jpg?width=1200&format=pjpg&auto=webp&s=4718974d5c9e494b9a7ff5e56f821d0978ccd86f)

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](https://preview.redd.it/lw8nf7s728pg1.jpg?width=1027&format=pjpg&auto=webp&s=69e6bb0225b3618020d2e763e7c901800606e921)
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.
r/Trading/comments/1qqdce8/fair\_value\_gap\_backtest\_part\_2/

r/Trading/comments/1qmr3na/i\_backtested\_fair\_value\_gaps\_heres\_what\_i\_found/
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.
In our work, we see this as a local “time series inefficiency”, where buyers or sellers exceed the liquidity provided within a given time slot (a single bar), with a lack of immediate reversion, which can be caused by adverse selection and other microstructural effects. But coincidences are not enough to beat financial markets.
Tests like the ones I have linked isolate the formation rather than disprove the process. 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 an trading influencer pushes, people will rely on subjective excuses such as “it’s being applied incorrectly” when poor results materialise.
# Why does this problem exist?
Because 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.
**Below, I have provided clear statements that directly challenge and ultimately undermine the core foundations that “SMC” relies on.**
1. There is not a sole liquidity provider or market maker for Futures (Direct Market Access) or FX/CFDs (Over The Counter)
2. An algorithmic ‘delivery mechanism’ would imply stable timing patterns, but order arrivals and limit order queue priority at microsecond scales are largely random because how markets discover new value constantly changes.
3. Firms entertaining a deterministic pull to liquidity would suffer a lethal amount of fading because of the predictability. For an institution, funding an operation like this would be equivalent to donating money directly to faster firms. This would be arbitraged, swiftly eroding any edge in the process.
4. If a universal algorithm was responsible for price movements, identical markets across venues would print the same path, yet persistent cross-venue divergences and lead-lag relationships exist, creating price discrepancies which HFT algorithms, funny enough, close. ES-SPY price dislocations are a well-documented example.
**These are verifiable market truths.**
1. 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).
2. Any time and sales market feed proves this statement easily (times orders come in).
3. Market microstructure basics, aggressive order flow (market takers) meets passive (limit orders) when aggressive order flow is larger than passive. The bid or offer prices move in response unless other passive (limit orders) step in.
4. In this peer-reviewed submission, the repricing behaviour is shown repeatedly from page 4 and is proven throughout: A visual from The High-Frequency Trading Arms Race: Frequent Batch Auctions as a Market Design Response, The Quarterly Journal of Economics
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.
**Some will state**
“You can say this with majority of retail strategies, not just ict”
That is the point.
**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?”.**
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.
# My final statement.
Meaningful trading outcomes are bound to **logical structures** **or luck.**
Which one will you pick?
Thanks for reading