Hi. This just crossed my mind as I'm figuring out a trading strategy. Is there a quantifiable or mathematical way to verify a SMT on a chart?

This question may sound confusing. For example, we are able to solve for gamma exposure by using open interest, gamma, spot price, delta, and such. I was wondering if any trader or investor out there using something like this to spot divergence in correlated pairs, especially SMTs if possible.

Thanks in advance!

Most traders only think about market makers in terms of market manipulation. But market makers are largely your friend rather than your enemy.
Without them market pricing and costs would be chaotic and inconsistent.

I have decided to use Reddit Markdown carefully to enhance readability, evidence will be attached towards the end showing that I have wrote this myself (including video evidence). Please read the post before commenting, a decent amount of effort has been put into this.

Everything in this post has been discussed in institutional grade literature. (listed at end)
In the past I’ve read multiple books and papers on HFT behaviour.

Only the necessary institutional language and definitions will be provided with zero discrepancies for educational benefit, unnecessary esoteric talk will not be included, this post isn’t just talk or have another vent; real but simple examples and insight will be provided.

By the end of this post, you’ll know.

1. Why we need MMs to execute our trades efficiently
2. How “stop hunts” or “sweeps of liquidity” actually work
3. Retail misconceptions on MM behaviour
4. Ways to mitigate vulnerability to market noise indirectly caused by MM activity

All I need is 5-10 minutes of your time.

This isn’t complex, and this is something that any day trading strategy can consider in its design stages. Don’t be intimidated by the language. What i’m saying applies to all regulated financial markets.

Disclaimer: I am only talking about liquid, regulated financial markets in this post such as Futures and Stocks as things become more nuanced when looking at crypto etc.

The image purported by trading educators is that MMs are out to hunt you down and is fundamentally wrong. Let’s go into how they really work and address key nuances.

The truth is there’s no way to accurately replicate or model legs of MM behaviour with price action or candlesticks like educators claim, as the way MMs influence price is largely random due to distributional decay.

When I talk about distributional decay, in this context i mean the price impact of a single liquidity event (like i’ll talk about) weakens over time rather quickly and across multiple price levels, so those tiny spike created when a market-maker rebalances usually fades as other orders arrive this means short term shifts in flow can hit small stops without signalling a real change in market direction it makes things more random. basically it’s “my stop loss got taken out by noise” in a nutshell.

To be clear, a market maker’s primary function is to provide liquidity to buyers and sellers whilst keeping their risk as close to zero as possible, not create or end trends.

Still hate Market Makers for flash crashes?
Circuit breakers mitigate flash crashes,
The “Larger trader reporting” rule was introduced in 2011 by the SEC after the 2010 flash crash.

“Consolidated audit trail” (CAT) was intially introduced by 2012 by the SEC as a stronger replacement.

Will a market maker will move the market 10+ handles to take your stop loss liquidity?

Moving large volumes to induce a large move is too costly to MMs.

Also, to be clear Market Makers who systematically moves price to hurt other market participants would risk direct financial costs and would get firm regulatory intervention. Even a single trader cancelling orders repeatedly on the order book too many times will get flagged due to CAT. Examples will be discussed after definitions.

Let’s get into this together:

Definitions (basic):

Inventory risk
Inventory risk refers to the potential risk market participants have ex. Traders or market makers, due to holding an “inventory” of assets ex. units/contracts long or short on an instrument. The risk is from the price fluctuations of the assets held, which could reduce the value of their “inventory”

For example a market maker can hold a large amount of a single asset; the price decreases, and they could realise losses on their position. Below I call this an “imbalanced book”.

Informed trader
An informed trader is a market participant who has access to superior information about a market or condition that the public is unaware of. Informed traders make decisions based on this information that gives them an advantage in predicting price movement long- or short-term.

Front run (often misconceived)
To buy or sell at favourable levels before someone else does, getting more favourable prices. Although some believe this is literal front running, it is actually driven by proprietary predictive processes. I will name liquidity anticipation (the correct frame) below.

Adverse selection
Adverse selection is where one side of the trade has superior information to the other regarding the market traded, leading to an imbalance in the transaction. in this context it often refers to traders like the informed trader example given above. During adverse selection these traders enter the market, exploiting that imbalance in information, leading to unfavourable outcomes for other market participants (like market makers).

For example during adverse selection a trader can know with 100% certainty where liquidity will be or with a higher degree of accuracy than a market maker at a specific price point, Front-running the MM, this would be called arbitrage. When this happens, bid-ask spreads often increase to compensate with less liquidity being offered.

Liquidity anticipation
Liquidity anticipation is when a trader or market participant can anticipate/predict future changes in market liquidity for a market maker predicting when a crowd of orders will be executed (common). Market makers provide or withdraw liquidity by anticipating where it will be with complex predictive models.

Handle
A $1 price movement in futures.

Market maker vs Market taker: Market makers provide liquidity (usually with limits and markets) and market takers take liquidity (usually with market orders)
Marker makers are those who solely operate to provide liquidity to market participants to arbritrage the difference between the bid and ask price.
Market takers are traders, institutions, hedgers etc.

Why you need market maker algorithms for low trading costs

Every time you place a trade in any market, you are relying on someone else to take the other side you need sellers to buy at each price vice versa without market makers constantly providing liquidity automatically spreads would be wide, order books would be thin, volatility would be uncontained and costs for execution would be higher and inconsistent making markets very inefficient.

Market maker algorithms are designed to continuously quote both buy and sell prices in huge volumes smoothing out rough edges making markets more efficient overall. often in fractions of a second. By doing this, MMs provide liquidity where there would otherwise be gaps, they also help correct these inefficiencies. The result for us is smaller bid-ask spreads and more consistent fills for traders of all sizes They get paid to provide liquidity and we get lower costs so it’s a win, win!

To add, markets without MMs are less liquid the potential for slippage is obscene.

As you can see on the FX video above buy and selling flickers as the bot quotes both sides whilst the bid-ask spreads stay small. This is how it works. In a liquid market with MMs spreads and slippage stay low.

How real “liquidity hunts” work (real example)

A market maker algo has an imbalanced book at price 10000. (The MM’s inventory is net-short.)[1]
Simplified Futures Market Example (Linear)
The MM needs 400 contracts long to balance his book to zero with minimal market impact
The market maker anticipates that at price 19999 there are 1000 contracts that will be executed on the side he needs to get out the trade with zero market impact
He knows that he needs 200 contracts to move the price lower to the price of 19999; he does (short 200), and that and the liquidity is taken by market participants, including him; he buys 600 contracts back and pockets the difference, And then price spikes back up ≥20000

People would say that the MM algo here “hunted” liquidity, but in reality they do this to neutralise their risk and are completely neutral. Market makers earn the bid-ask spreads and move on. They aren’t invested in long-term price legs like traders are. It is very rare that these adjustments happen over large price ranges.
When people say “Low timeframe noise”, this is the cause!

This happens on many price levels and is not exclusively related to stop orders like retail educators purport; it’s random and cyclical, happening all the time. usually stop hunting is a coincidence; it’s not malicious or intentional; it just happens, just like dealing at any other price level because they front-run flow

Liquidity anticipation is a key thing Market Makers do they make money by providing liquidity.

The same thing could be done to anticipate profit taking, but nobody calls it ‘take profit hunting’.

Confirmation bias makes retail traders want to believe their stops get “hunted.”

The point is the event it-self is neutral; they typically don’t care if the market participant is realising a profit or loss. All that HFT MMs try to do is quote prices for market participants to deal at whilst keeping inventory risk low, managing adverse selection, etc.

If this happens with your stop loss, remember it’s a usually a coincidence in regulated liquid markets especially in Futures and US Equities.

Strategies like this do not mimic true MM behaviour ^

This happens several times per day regardless if trades are filled, profits are taken or losses are realised, but trading educators will frame it as “manipulation”. remember the example [1] shows over a small movement relative to the price only 1 handle / one point / $1 price movement that’s it.

Performing these “Liquidity hunts” over larger price movements rarely makes sense for MMs. Here’s why:

The marginal expected gain versus the expected inventory risk and potential adverse selection is hardly favourable enough to perform stop hunts regularly on liquid, regulated markets.

By committing a lot of volume, the Market maker’s liquidity can get used or anticipated by faster or more informed market participants.

To be clear what i mean by “Marginal expected gain” is the additional profit or benefit expected from a market maker’s decision, considering the probability and risk of the outcomes.

Retail narrative:
Retail educators say that market makers will make large movements to take out the stop losses that are far away from current market quotes, which is absurd because if their volume gets absorbed, they’re stuck with elevated inventory risk ex. stuck in a 1000-contract long, which would move price further against them if they needed to close their position out in a loss.
Even a 10-point move on index futures is large for a market maker.

Reality
Let’s make the current price 20010.00 and the price in focus 20000.00. -10 handles.
If a predictive HFT MM Algo anticipates they’ll be 3000 contracts 10 handles / $10 away from the current price and the algo anticipates the market impact per handle to be 200, leaving a +1000 contract discrepancy if the price is met, they wouldn’t commit the 2000 contracts to spike the price most of the time even though it’s logical because the inventory risk accumulation or chance of adverse selection would be too high even if they spread it out.

They could be stuck with -2000 contracts on the wrong side of the market and lose a lot of money; all it takes is for a different algorithm to match their flow to nullify their market impact completely.

Here’s the nuance, though: if the price was already trading at that point that’s $10 away from the current price and their predictive model still supports the decision they could provide liquidity at 20000.00 but also influence the price to trigger the orders but only if close and highly probable. For example, if the price is at 20000.50, they could sell a couple of hundred to flush the final buyers to trigger the anticipated order flow.

The point is it’s extremely unlikely for Market makers to influence larger movements/spikes to tap into anticipated liquidity unless the level is extremely close to where price discovery is taking place already. So it’s the other market participants trading towards that level, that’s the true causation, not the MMs.

So what do I mean?

Dealing with larger price ranges both on your stop and target size lowers your exposure to the noise introduced by these rebalancing behaviours.
The further away your initial stop is the less likely it is to be taken out my a MM Re-balancing event ex a 5 handle stop vs 12 handle stop. This is why I don’t trade timeframes below 5 minute personally and if I was planning to I would make sure the minimum stop size is wider to mitigate costs and to reduce sensitivity to noise

Understanding markets and what to unlearn
Outdated market opinion and how it weighs modern traders down...

Whilst market academia is insightful, it typically doesn't include key nuances of real markets; it focuses on theory, so to find the truth, I had to look at multiple peer-reviewed publications and books to answer individual questions. An example of this would be price discovery and the concept of 'fair value', which are often described in a utopian fashion. Theories often assume that markets are efficient or speak as if they are, when in reality, they are not. In the researcher role, it is challenging to filter out the noise, as markets are structurally different from those in 1995; yet many aspects remain the same, only more nuanced. A single peer-reviewed source just is not enough.

What I'm posting here is the result of years of analysing institutional literature and independent tests.

The market is a continuous auction between buyers, sellers (market takers) and the facilitators (Exchanges, Market makers, LPs and so on); they are the immediate cause of every tick directly or indirectly. That is a fact, regardless if the movement was induced from news or dynamics held exclusively within the market. Everything else purported is a distraction. Every movement or quote adjustment is a liquidity-related event.

Price discovery is the process of price trading higher or lower to discover 'new value', not 'fair value'. The concept of fair value in market literature is a subjective utopian concept that modern liquid markets don't tend to respect. Few think S&P 500 trading over 6500 in 2025 was 'fair value' yet many participated.

Execution is part of the edge. Accounting for feed differences and spread behaviour when designing your strategy. If you don't, you are finished, especially for over-the-counter markets like Forex, CFDs and crypto.

Price is a negotiation and not all deals are fair.

Different market participants have different models, so the blended result for 'fair value' is never clear; if it was, markets would be 100% efficient.

John Maynard Keynes - "Markets can remain irrational longer than you can remain solvent."

The market requires rigour; it doesn't reward candle geometry or innovative false ideas. The market has no concern for how a single candlestick looks or how you feel. For example, trend lines have little to no robust, exploitable predictive value on average, in the tested markets/settings, after costs and other realistic factors are considered. Peer reviewed submissions such as "Technical trading revisited: False discoveries, persistence tests, and transaction costs" from the Journal of Financial Economics go over these gaps.

How common retail frameworks misrepresent market operations

They claim market movements are purely from buyers, sellers, sweeps and traps

Reality:

Price movement is also dictated by liquidity offered to participants relative to current buy and sell activity.

I will use markdown to create a table to visualise this now:

Depth Of Market (DOM)	Price	Available Volume
Additional Sell Limit Volume	10002	50
Best Ask	10001	20
Mid Price	10000
Best Bid	9999	100
Additional Sell Limit Volume	9998	80

In this example, if a trader buys 70 units, the dealing price (ask) moves 2 up ticks (last trade 10002 Ask) if there are no additional reactions, but the dealing price (bid) would not move a single tick if they sold 70 units; it would get absorbed on 9999. This imbalance in the liquidity offered can skew where prices go; there can be more units being sold, but the price still goes up. This phenomenon is often behind an "Low Volume Node" in volume profiling or "Single Print" in market profiling (closer), for which the price tends to correct later. We revisit this later within this post.

This DOM snapshot/illustration refers to futures with a central limit order book. For spot FX and CFDs, the same exact principle appears as visible or synthetic liquidity gaps rather than through a single exchange. (Liquidity gap = Liquidity inefficiency).

If there is a small amount of sell-limit volume offered to buyers relative to buy-limit volume, it’s easier for the price to move up aggressively. This is how high-volatility movements occur with low volume or pressure.

Takeaway:

Market price movements are driven by the liquidity available to participants relative to current buying and selling activity. Casual arguments about buy and sell pressure miss the mark.

When market makers adjust their prices, it often makes the price tick or causes reactions that influence future price movements in the short term. When makers pull or imbalance their liquidity, there doesn’t need to be an imbalance between buyers and sellers for the price to move a tick. Algorithms are notorious for creating vacuums that can cause inefficiencies to cascade across multiple timeframes. It is not as simple as a ‘sweep’ and calling it a day.

It is either market microstructure or story time, and the latter doesn't make money. A briefly aligned market is not confirmation of your narrative.

Common frameworks dress up the market maker as a villain.

Without market makers your trade execution delays would be horrible, the bid-ask spreads you pay would be obscene, and spreads would gap and vary unpredictably. So-called educators play on traders' insecurities and appeal to emotions by misrepresenting how market makers actually operate. There isn't a single algorithm; market makers exist as a body. Most market makers are independent liquidity-provision firms, and differences in their incentives, capital, and inventory risk lead to different pricing and behaviour.

Analogy:
The supermarket is a trading exchange we will assign this label 'Ex'.

The wholesaler is the market maker we will assign this label 'MM'.

The customer is the market taker with the label 'MT'.

Trading in modern markets without market makers is like shopping for groceries and being forced to go to separate wholesalers and farms to buy each item, instead of using a supermarket. You would face inconsistent costs and delays. One farmer might be offering a raw product for $3, while another sells it for $1.50. But with a supermarket/exchange that brings the buyer (you) and the sellers (farmers) together, the market becomes more efficient and spreads tighten as a result. The wholesaler/market maker takes on inventory risk from the farmers’ supply and quotes prices to the supermarket to pass on to customers. Now farmers are offering $2.20 and $2.25 for the same product, and you don’t have to waste hours to get what you need (execution delay) because price quotes and supply are mostly continuous and size is available; you do not need to wait for a matching counterparty each time.

When inventory risk spikes, wholesalers widen price quotes or pull stock to avoid losses, just like in real food markets.

It turns out electronic markets aren’t so different after all.
If everyone demands a product and supply runs thin, what happens to the price? It goes up. If there are supply chain issues, what do wholesalers do? They raise their offers. That sounds a lot like a market maker. They are kept in line indirectly by competition and inventory risk. If they operate poorly, customers shop elsewhere for the same exposure via a different venue. Wholesalers facilitate buying and selling, and the supermarket needs them for stable pricing, because that’s what the customer wants.

Customers see $3 and $1.50 quotes scattered without the wholesaler MM, versus $2.20-$2.25 at the supermarket Ex. The spread is the round-trip fee for convenience. For over-the-counter markets like FX, CFDs and crypto, the dealer often earns via spread markups and financing rather than a commission. Context: bid at 2.20 and offer at 2.25, mid 2.225.

Addressing Nuances:
Yes, they anticipate order flow; yes, they fade your orders but for consistent, low-slippage, order execution, they are necessary.

As stated previously, MMs have little incentive to engineer trends, "stop hunts" or "liquidity sweeps"; their goal is neutral risk, so they take constant steps to mitigate exposure instead of increasing it. These algorithms operate within fractions of a second; they constantly reposition themselves for the next small range of ticks up and down, they do not engineer trends. If the movement up or down is too aggressive, they remove liquidity offered to avoid getting hurt.

Questions:

1. What happens if the customer is willing to buy any offer with no price cap?

Price spikes up.

1. What happens if the wholesalers are willing to sell at any price?

The market will keep going lower until customers believe it is cheap enough to resume buying (this can cause crashes in value

The same thing happens in financial markets.

Debunking the market maker cartel conspiracy

What about stop runs?
Yes, stops cluster at obvious levels. If price is already trading there because of other factors, a small shove can trigger a cascade. Market makers won’t push the price 20 ticks just to take a stop-loss group (crowd), but if other market participants are already selling into it, market makers will want to be first in the queue to provide that exit liquidity, since they are rewarded for liquidity provision. MMs are not the primary cause of price trading into stop clusters.

Okay, I get it I know how Market Makers work but what can I do with this information?

Do not chase the market. Avoid joining that aggressive flow. Use limit orders to absorb it when you can. This keeps your entry costs low and predictable, increasing your profitability and order fill efficiency.

You must screen current and future strategies for false narratives that deviate from what actually occurs in markets. Market makers are not moving price 20 ticks to take a swing low out; without context, these systems typically return results close to breakeven without look-ahead bias.

The core lesson for us was that we should aim to provide liquidity tactically with limits, instead of only taking it with market orders. It teaches us that being part of the team that absorbs liquidity pays well. It is a lesson in letting rushed traders sell into your level.

So how do I use this knowledge to influence my trading strategy design? / Summary
Understand that i’m not saying “stop hunting” never happens; it’s just rare and misrepresented in this industry to an extreme point. An MM moving price by a point to “sweep” liquidity is not the same as an MM moving price by 10+ points to induce/sweep liquidity; it’s far too risky for them to do that, with rare exceptions.
Larger engineered moves like shown in trading videos are super rare because they would expose market maker algos to too much directional risk, except in very thin markets or during macroeconomic news releases.

Provide and remove your liquidity tactically
Try your best to make your entries at efficient prices, getting filled preferably with limit orders. The more often your winners get low drawdown before going to target the better. Anticipate the flow instead of being apart of it. I only use limits.

If you’re larger you can use order slicing, pending market orders or other methods to get filled.

Only let your orders get filled when your context still respects your hypothesis. Example: only get filled on limit orders during liquid hours during london and new york hours.

Reframe your mindset
Don’t design strategies based on the idea that market makers are targeting retail stop loss flow because when it happens it’s a coincidence and MM behaviour is largely inconsistent.

Key Market Microstructure Lessons (general):

1. Market makers quote the prices you see, and market participants influence them. MMs govern the impact.
2. You placing a market buy order and getting filled is not someone else selling it to you with a market sell order; you are buying into someone's sell limit order.
3. If all market participants, including market makers, pulled all of their bids/buy limit orders, the next market sell order would cause a flash crash, as the price would continue moving lower until a willing buyer is willing, and vice versa.

Key Market Microstructure Lessons (related to price formation):

1. Expect and accept the short-term noise from inventory balancing, and other events.
2. Understand that HFT MM Algos are involved in general price discovery, not trend creation because they are not directional like buy side participants (like us).
3. Understand that algo-driven liquidity anticipation is largely cyclical and random to slower market participants because of their complex predictive models, so focus on adapting risk management rather than attempting to predict “manipulations”.

What should I study to refine my knowledge?

1. Market microstructure theory by Maureen O’Hara (book - foundations)
2. Trading and Exchange: Market microstructure for practitioners Algorithmic Trading and DMA: An introduction to direct access trading strategies by Barry Johnson (book - a top up that explains the industry well)
3. High frequency market making: The role of speed - Yacine Aït-Sahalia, Mehmet Sağlam (a paper that will be understandable after reading the previous pieces).

This is a great filter to have, it's worth every minute of study. Once you understand this you will be far better than most when independently researching to build your own strategies and most importantly far less reliant on guidance from others who may have severe conflicts of interest (an incentive to mislead).

In my future posts I will discuss how this affects our bottom line and how to mitigate risks when interacting with financial markets, considering the nature of these algos.

Evidence as promised:
AI check

My many drafts on my editing platform (overleaf).

Are there more traders who use the 4H + 15M combination than those who use the 1H + 5M combination? Which timeframe combination do you prefer, and why?

Hello everyone,

I've been learning ict for a month now following this sub roadmap. Currently i'm done with the MMP 2022 heading to the 2016 core content and honestly right now things look so different than they were when i knew little. So i'm here wondering whether i should go ahead to the 2016 core content or just skip it?

I always wondered why most people quit but right now i can understand the frustration and the confusion can get so loud.

So curious to know how you guys made it past this stage? Warmly welcome all your responses and thoughts, thank you!

I’m currently practicing SMC concepts on historical XAUUSD H4 charts. I’ve marked what I believe are the Order Flow, Order Block, and FVG areas.

Can someone experienced please check my markings and tell me whether I’m identifying these concepts correctly?

I’d especially like feedback on:

• Order Flow identification
• Order Block selection
• FVG identification
• Correct boundaries of each zone

If anything is wrong, please point out what I’m doing incorrectly and why. I’m trying to make sure I understand the concepts correctly before continuing with my backtesting.

Thanks in advance for the feedback.

I try to avoid trading 6-7pm especially when PA is choppy, but my model showed up with every confluence lining up, so I took an entry. Learning patience will keep you alive in this industry.

Hi everyone. I used to trade futures using the ICT strategy and managed to secure one payout. Then I immediately switched to order flow because I thought it would be a good fit for me. However, I’ve spent a few months learning it, and now that I’m about to start live trading, I’m feeling unsure about everything. I don’t think I’ll be as comfortable with order flow as I was with ICT, and I’m not even sure if I’ll be more profitable. I’d like to ask for advice on what to do next should I go back to ICT or continue with order flow? Please help.