T Reddit Mentions | ChartExchange

# 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 know this sub gets flooded with low-effort AI posts, this isn’t one of them.**
**Proof is attached at the end for reassurance.**
[I have spent many minutes formatting this manually.](https://preview.redd.it/rvctvzjnampg1.png?width=1454&format=png&auto=webp&s=8dfe182a56e86484387cff7f4f7efcf531b9cdd2)
# 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,
Reveal what is old and renamed,
Show how the framework fails on real market logic,
Address the most common objections,
Show rigorously why anecdotal winners prove very little,
Present the simulations, their limitations, and the sound theory that supports my claims, then explain why flawed frameworks continue to survive and offer a coherent way to filter them out.
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.
For some, this may be the most important trading article they read.
Let us begin.
# Part 1: Introduction:
Some say they trade ICT/SMC others say they “trade liquidity”.
Different words, same framework.
**Where they are right:**
1. Price movement is not dictated purely by “buy and sell pressure”.
https://preview.redd.it/e9ju87l8fmpg1.png?width=808&format=png&auto=webp&s=1129086d62fca8f1612658582558341d507fb67c
A 2025 video transcript extract.
2. Stop losses do cluster and can lead to cascading and other consequences during price discovery.
Source: Stop-loss orders and price cascades in currency markets - Journal of International Money and Finance
# What is old, renamed and repackaged (revisted later)
**Order Blocks** \-> Supply and demand Sam Seiden 2006
**FVG** \-> Low volume node
Origin: J steidlmayer (Single prints, concept 1985 -> LVN popularised in 2000s with time series charts), -> Al brooks “micro gap” 2009–2012 OHLC formation.
**Breaker and mitigation blocks** \-> Dow theory extractions (1902)
“The algorithm/controlled narrative” -> The Wyckoff Composite man heuristic
And so on…
*This is verifiable information, feel research it post-reading.*
# Part 2: The Reality/Missing Context:
# The Primary Claim:
Price movement is not dictated purely by “buy and sell pressure”
**Reality:**
Price movement is dictated by liquidity offered to participants relative to current buy and sell activity. For example, prices can still move down if there aren’t enough buyers willing to support the price, even when the amount being bought and sold appears to be the same (e.g., 1100 units of buy volume, and 1000 units sell volume but price still goes down).
# The secondary claims
# The Liquidity Sweep Narrative:
Stop losses do cluster and can lead to cascading and other consequences during price discovery. Correct.
Market makers or “the algorithm” is reading candles and deliberately creating a wick to “sweep liquidity”. Nonsense.
# How is it wrong?
Market maker algorithms manage risk they actively reduce their directional risk, actively pushing the price around increases it.
Many reputable sources including show this in exceptional detail such as in Maureen O’ Hara’s work and peer reviewed submissions like Dealer behavior and trading systems in foreign exchange markets - Journal of Financial Economics
MMs would not only likely lose money by employing such strategies, but they would also face heavy fines due to the Consolidated Audit Trail logging market activity, visibility on Time and Sales, and the transparent limit order book.
# 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.
***For example, most traders have clicked off the article by now, that is apart of the misinformation advantage.***
**Defining it:**
Adverse selection is when a trader with better information than the algorithm takes advantage of it by buying or selling aggressively to take the liquidity it is providing at favourable prices. For example, a trader might believe that the price is lower than it should be and expect others to receive that information in the next couple of minutes, so they buy first in large volume to benefit.
# 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:**
The price jumps showing a one sided move as the market maker has reduced the amount of sell-side liquidity they are willing to offer (less available liquidity on the best ask and/or less limit order liquidity refreshes).
**Other claims surrounding liquidity provision:**
“I’m going to prove that these markets are absolutely controlled. And it’s through an algorithm” - Preserved tweet
“Price is delivered by an algorithm.” - verbatim
**Reality:**
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/pbjekvw6fmpg1.png?width=980&format=png&auto=webp&s=c529aa246024ee94bd559de98161ed448cd68f5b
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.**
1. 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.
2. Market makers generally seek to reduce directional risk, while directional traders want to take it on. For that reason, these algorithms are unlikely to move price across multiple ticks simply to “hunt liquidity”, since doing so would expose them to unnecessary directional risk. 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.
3. 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. Any time and sales market feed proves this statement easily (order timestamps are distributed unevenly, T&S has natural variability).
2. Market microstructure basics, aggressive order flow (market orders) 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. Reputable peer-reviewed research on market-maker behaviour, including work on adverse selection and inventory management, support this reality.
3. 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
# 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, answered
# Statement: But what about X guy who made 100k using ICT?
***“Anything can work”***
https://preview.redd.it/mfkl4cryempg1.png?width=1080&format=png&auto=webp&s=b752c2e98765c83632b75bac01b97427e8c0b91c
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.
# 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/w1khbyudempg1.png?width=415&format=png&auto=webp&s=f4241d78534d94ea83a5b77504df466d24507214
**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
**“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.
***ICT’s flawed reasoning and incorrect assertions are no small mistakes. It collapses the entire framework.***
https://preview.redd.it/4sehnnecempg1.png?width=1280&format=png&auto=webp&s=8b85e11918f49a0910922825c1e2c70243f338fc
**“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.
Later in this article I will present a Monte Carlo Simulation paired with simplified breakdowns to aid these claims.
**“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 Economic**
***Verifiable statements have also been provided earlier in this article.***
# 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: $3,712,309.53
# Worst outcome in the simulation: $2.6368543372 (Blowup)
https://preview.redd.it/k60wsa5aempg1.png?width=1188&format=png&auto=webp&s=a13315d85600405aab1ea8ea71d972a6d48bb302
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.
Breakeven after costs is generous considering the named misalignments.
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/gjfru6j7empg1.png?width=1600&format=png&auto=webp&s=d50d1c1aec56da33497815ec2cd83a0463335e0c
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/uemb6d55empg1.png?width=1600&format=png&auto=webp&s=7aa57d60d49b2e00aa1f6abe5f7c0178475143e1
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.
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.
Let us revisit this with probability theory (statistics).
# 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:**
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. Anecdotal successes do not prove a method’s effectiveness.
**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).
https://preview.redd.it/2n2dz2w0empg1.png?width=1048&format=png&auto=webp&s=0a58077ab3ff6d18364190354f6bd39d2b7d071a
# 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/n4j6ksczdmpg1.png?width=640&format=png&auto=webp&s=cce63cd19d80377dfc4065003cbb8a709beab90c
>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/in8hnq9ydmpg1.png?width=640&format=png&auto=webp&s=7fe9a7c772a775460a608def6ce1698efe9aff4c
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 frameworks
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.
https://preview.redd.it/7pkniedwdmpg1.png?width=1200&format=png&auto=webp&s=cb9795d2ad3fc94b81987487775827336c567290
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
https://preview.redd.it/qk4sqrsudmpg1.png?width=1027&format=png&auto=webp&s=b2b734128012b9c8b78ac90f444298fd6db4572a
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
no surprise as they are underfitted strategies.
Example:
**Core Returns - Direct copy and paste from OP showing market underperformance**
* Total Return: **2.47%**
* CAGR: **5.52%**
* Profit Factor: **1.07**
* Win Rate: **68.61%** (94 Wins / 43 Losses)
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.
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.
# 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**
“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?”.**
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.
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.
# TLDR
**If you are struggling, visit the original valid material without the fluff.**
Do not waste your time with SMC, if you want to use the techniques visit the original material without the illusive, noisy framework.
**Read real market literature**
Use the new knowledge to filter out nonsense that holds you back in trading. It will take hours but you will save many days in guru watch time, save you money, and it forces you to improve your deductive reasoning abilities. These benefits are universal.
# My final statement.
Meaningful trading outcomes are bound to **logical structures or luck.**
Which one will you pick?
https://preview.redd.it/tsqis99dfmpg1.png?width=1080&format=png&auto=webp&s=6636b3ab002aa0d0213f843c40e2d57cffe2b5bc
**Thanks for reading**

# 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 know this sub gets flooded with low-effort AI posts, this isn’t one of them.**
**Proof is attached at the end for reassurance.**
[I have spent many minutes formatting this manually.](https://preview.redd.it/upbi0o598mpg1.png?width=1454&format=png&auto=webp&s=e346b076c721959ee9031885e81778b30ef162d0)
# 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,
Reveal what is old and renamed,
Show how the framework fails on real market logic,
Address the most common objections,
Show rigorously why anecdotal winners prove very little,
Present the simulations, their limitations, and the sound theory that supports my claims, then explain why flawed frameworks continue to survive and offer a coherent way to filter them out.
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.
For some, this may be the most important trading article they read.
Let us begin.
# Part 1: Introduction:
Some say they trade ICT/SMC others say they “trade liquidity”.
Different words, same framework.
**Where they are right:**
1. Price movement is not dictated purely by “buy and sell pressure”.
https://preview.redd.it/0ziq7ccx7mpg1.png?width=808&format=png&auto=webp&s=f07f5a8f02d13b53c7a786d79bb1eab8b80de0bb
A 2025 video transcript extract.
2. Stop losses do cluster and can lead to cascading and other consequences during price discovery.
Source: Stop-loss orders and price cascades in currency markets - Journal of International Money and Finance
# What is old, renamed and repackaged (revisted later)
**Order Blocks** \-> Supply and demand Sam Seiden 2006
**FVG** \-> Low volume node
Origin: J steidlmayer (Single prints, concept 1985 -> LVN popularised in 2000s with time series charts), -> Al brooks “micro gap” 2009–2012 OHLC formation.
**Breaker and mitigation blocks** \-> Dow theory extractions (1902)
“The algorithm/controlled narrative” -> The Wyckoff Composite man heuristic
And so on…
*This is verifiable information, feel research it post-reading.*
# Part 2: The Reality/Missing Context:
# The Primary Claim:
Price movement is not dictated purely by “buy and sell pressure”
**Reality:**
Price movement is dictated by liquidity offered to participants relative to current buy and sell activity. For example, prices can still move down if there aren’t enough buyers willing to support the price, even when the amount being bought and sold appears to be the same (e.g., 1100 units of buy volume, and 1000 units sell volume but price still goes down).
# The secondary claims
# The Liquidity Sweep Narrative:
Stop losses do cluster and can lead to cascading and other consequences during price discovery. Correct.
Market makers or “the algorithm” is reading candles and deliberately creating a wick to “sweep liquidity”. Nonsense.
# How is it wrong?
Market maker algorithms manage risk they actively reduce their directional risk, actively pushing the price around increases it.
Many reputable sources including show this in exceptional detail such as in Maureen O’ Hara’s work and peer reviewed submissions like Dealer behavior and trading systems in foreign exchange markets - Journal of Financial Economics
MMs would not only likely lose money by employing such strategies, but they would also face heavy fines due to the Consolidated Audit Trail logging market activity, visibility on Time and Sales, and the transparent limit order book.
# 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.
***For example, most traders have clicked off the article by now, that is apart of the misinformation advantage.***
**Defining it:**
Adverse selection is when a trader with better information than the algorithm takes advantage of it by buying or selling aggressively to take the liquidity it is providing at favourable prices. For example, a trader might believe that the price is lower than it should be and expect others to receive that information in the next couple of minutes, so they buy first in large volume to benefit.
# 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:**
The price jumps showing a one sided move as the market maker has reduced the amount of sell-side liquidity they are willing to offer (less available liquidity on the best ask and/or less limit order liquidity refreshes).
**Other claims surrounding liquidity provision:**
“I’m going to prove that these markets are absolutely controlled. And it’s through an algorithm” - Preserved tweet
“Price is delivered by an algorithm.” - verbatim
**Reality:**
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/tgai8f2v7mpg1.png?width=980&format=png&auto=webp&s=02e95acfc6d1df64d79f0af75c2822b03c1611c6
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.**
1. 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.
2. Market makers generally seek to reduce directional risk, while directional traders want to take it on. For that reason, these algorithms are unlikely to move price across multiple ticks simply to “hunt liquidity”, since doing so would expose them to unnecessary directional risk. 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.
3. 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. Any time and sales market feed proves this statement easily (order timestamps are distributed unevenly, T&S has natural variability).
2. Market microstructure basics, aggressive order flow (market orders) 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. Reputable peer-reviewed research on market-maker behaviour, including work on adverse selection and inventory management, support this reality.
3. 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
# 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, answered
# Statement: But what about X guy who made 100k using ICT?
***“Anything can work”***
https://preview.redd.it/g9zbmpps7mpg1.png?width=1080&format=png&auto=webp&s=26dd2cd4e742c1f25725d499f6d4983283269933
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.
# 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/ohk9lt3r7mpg1.png?width=415&format=png&auto=webp&s=afe67d6aa47ed902c7993b202b7c9a03022c3bbf
**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
**“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.
***ICT’s flawed reasoning and incorrect assertions are no small mistakes. It collapses the entire framework.***
https://preview.redd.it/aroajojp7mpg1.png?width=1280&format=png&auto=webp&s=017299417008f114539a19006b3a53ddb3d52a1f
**“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.
Later in this article I will present a Monte Carlo Simulation paired with simplified breakdowns to aid these claims.
**“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 Economic**
***Verifiable statements have also been provided earlier in this article.***
# 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: $3,712,309.53
# Worst outcome in the simulation: $2.6368543372 (Blowup)
[Visual: Monte Carlo Simulation Outputs](https://preview.redd.it/meqy72rl7mpg1.png?width=1188&format=png&auto=webp&s=0ba41520c571d23060a8c367798361864add2478)
# 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.
Breakeven after costs is generous considering the named misalignments.
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/tznsccbj7mpg1.png?width=1600&format=png&auto=webp&s=9c6967013afbe40cca2cf531c761b603ff1f9c66
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/havatswh7mpg1.png?width=1600&format=png&auto=webp&s=174869104265ccbe6b612077e6f543b664ef68c5
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.
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.
Let us revisit this with probability theory (statistics).
# 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:**
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. Anecdotal successes do not prove a method’s effectiveness.
**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).
https://preview.redd.it/wew3pk6d7mpg1.png?width=1048&format=png&auto=webp&s=4dfa3f2d95baa887bffcceb45bd529a97b7852aa
# 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/aq67qwlb7mpg1.png?width=640&format=png&auto=webp&s=fa7dd1f48ff0226a67078bddda3016428921ba91
>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/0ibxwl5a7mpg1.png?width=640&format=png&auto=webp&s=812cca4a8cdcfe94820cc79c2e1041422cbf5031
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 frameworks
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.
https://preview.redd.it/wj47t1187mpg1.png?width=1200&format=png&auto=webp&s=471d01b7918b340d84644517d64cfaf651a5d496
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
https://preview.redd.it/0uzc2u917mpg1.png?width=1027&format=png&auto=webp&s=44d4d1bec354e90b8aea4bddb9350159272d6e4e
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 to beat the market. As traders it is better to aim to design strategies that are aligned with a market’s behaviour but not overadjusted or forced to work; this leads to a desirable “good fit” scenario.
# Posts showing poor results when testing “FVGs”, as expected.
**Core Returns - Direct copy and paste from OP showing market underperformance**
* Total Return: **2.47%**
* CAGR: **5.52%**
* Profit Factor: **1.07**
* Win Rate: **68.61%** (94 Wins / 43 Losses)
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.
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.
# 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**
“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?”.**
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.
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.
# TLDR
**If you are struggling, visit the original valid material without the fluff.**
Do not waste your time with SMC, if you want to use the techniques visit the original material without the illusive, noisy framework.
**Read real market literature**
Use the new knowledge to filter out nonsense that holds you back in trading. It will take hours but you will save many days in guru watch time, save you money, and it forces you to improve your deductive reasoning abilities. These benefits are universal.
https://preview.redd.it/g19u14fg9mpg1.png?width=1798&format=png&auto=webp&s=50080e4e35a7596d051aa679d91fc7ca80d66830
# My final statement.
Meaningful trading outcomes are bound to **logical structures** **or luck.**
Which one will you pick?
**Thanks for reading**

document.getElementById("xhNqWp").onerror = function(){this.style.display='none';}; .dark-mode .chDGN0{ color: white; background-color: #333333; border: 2px solid white; }.light-mode .chDGN0{ color: #333333; background-color: white; border: 2px solid #333333; }TAT&T Inc.stock NYSE

T
AT&T Inc.
stock NYSE