ICT's #1 Student Exposes The TRUTH About ICT - Ali Khan
Ali Khan pulls apart ICT's algorithmic price‑action framework, exposing the psychology, risk‑management and education pitfalls that shape modern traders.
1h 27m·Guest Ali Khan·Host RZ·
ICT as Algorithmic Price Action, Not a Fixed Strategy
ICT does not hand out a single, canned trading system. It teaches the underlying inter-bank price-delivery algorithm that drives market moves, allowing traders to build countless model variations. Understanding this algorithmic foundation is the only way to generate adaptable, high-probability setups.
ICT is not a single strategy but an algorithmic framework for price action.
- The community often looks for a “ICT strategy,” but the reality is a conceptual toolbox that describes how the inter‑bank algorithm books price.
- Because the algorithm is deterministic, traders can construct millions of bespoke models that fit their style.
- This flexibility explains why no two ICT students trade the exact same setup, yet they all reference the same underlying price‑action logic.
- The misconception that ICT offers a one‑size‑fits‑all strategy fuels the proliferation of low‑quality copy‑cat courses.
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00:00:31“people think that there's an ICT strategy there's no strategy with ICT this is a concept it's how the algorithms Book price in the market” — RZ
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00:00:45“there literally millions of models that you can make with ICT Concepts it's the underpinnings of how the markets actually book” — RZ
The interbank price delivery algorithm underpins market pricing.
- Michael’s “inter‑bank price delivery algorithm” (IPDA) was created by banks to automate price delivery.
- The algorithm sets the price of money, the banks’ commodity, and thus determines where liquidity pools form.
- Because the algorithm operates at the inter‑bank level, retail price action is a reflection of its deterministic rules.
- Recognizing the IPDA lets traders anticipate where the market will likely move, rather than chasing random patterns.
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00:01:01“the algorithm is known as the interbank price delivery algorithm now essentially this algorithm was created to automate the delivery of price and it was created by the Banks” — RZ
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00:33:33“the algorithm is called the interbank price delivery algorithm” — RZ
ICT concepts enable millions of model variations, giving traders flexibility.
- Because the algorithmic framework is open‑ended, any trader can combine order‑blocks, fair‑value gaps, liquidity pools, etc., into a custom model.
- The banks’ ability to set money’s price means the algorithm can be expressed in countless ways, from simple breakout models to multi‑time‑frame structures.
- This explains why you’ll see “millions of models” built on the same ICT concepts—each reflects a different interpretation of the same price‑booking logic.
- The flexibility is a strength, but only when the trader truly understands the algorithmic underpinnings.
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00:01:08“the Banks money is their commodity for example right they can set the price of that money” — RZ
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00:01:14“back in the day there was actual real market makers what we've been told and the pressing buttons and their setting price” — RZ
Psychology and Risk Management Over Study Volume
Spending endless hours on theory does not make a better trader. The decisive edge comes from mastering psychology and tailoring risk-management to one's personal tolerance. Over-studying without mental discipline creates false confidence and costly mistakes.
Psychology outweighs study volume in trading success.
- Academics assume that more concepts equal better performance, but ICT’s own experience shows that psychology and risk‑management dominate.
- The transcript repeatedly notes that “the more concepts we know… that does not work” because the market is driven by human and algorithmic behavior, not textbook knowledge.
- Traders who ignore the mental side end up over‑trading, mis‑reading price, and burning capital.
- Therefore, the first investment for any trader should be in self‑awareness, not in more video courses.
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00:00:00“academics who com to ICT what we naturally do is we study more and we think the more that we study and the more Concepts we know the better we're going to be as traders that does not work and it doesn't apply to the way ICT Concepts works because there is a huge amount of psychology and understanding risk management and stuff that's involved” — RZ
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00:00:13“that does not work and it doesn't apply to the way ICT Concepts works because there is a huge amount of psychology” — RZ
Risk management must be personalized to individual risk tolerance.
- The conversation stresses that risk tolerance is “personal to your risk appetite,” meaning a one‑size‑fits‑all rule will fail.
- Different traders have wildly different comfort zones; a 10 % drawdown for one may be catastrophic for another.
- Personalizing stop‑loss placement, position size, and partial‑exit strategy aligns risk with the trader’s psychology.
- This personalization also explains why round‑table discussions reveal huge belief divergences on risk‑management.
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00:38:41“risk management is very personal it's personal to your risk appetite” — RZ
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00:38:44“personal to your risk appetite isn't it” — RZ
Taking partials reduces risk and improves portfolio efficiency.
- Partial exits at logical levels lock in profit while leaving upside potential, preventing the all‑or‑nothing scenario.
- The transcript shows Ali noting that “taking partials at logical levels” is a core habit of ICT traders.
- By scaling out, a trader avoids the “huge inefficiency” of holding a full position that later reverses.
- Over time, systematic partial‑taking yields a smoother equity curve and higher risk‑adjusted returns.
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00:23:31“taking partials at logical levels and pay the trader” — Ali Khan
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00:40:30“taking partials before the trade goes 80% up is better than holding full volume” — Ali Khan
Personalized Trading Style Based on Individual Traits
ADHD, impulsivity, and personal risk appetite shape how a trader should design a system. Matching the trading window, model complexity, and position-sizing to one's own neuro-profile yields consistency, while ignoring these traits leads to burnout and poor performance.
ADHD requires short, focused trading sessions.
- Ali admits he can only sustain two hours of market focus due to ADHD, which forces him to be highly selective.
- He discovered that constantly checking the chart every two minutes destroyed his swing‑trading attempts.
- By limiting his session length, he can concentrate on high‑quality setups and avoid decision fatigue.
- This approach mirrors the broader principle: design your trading schedule around your natural attention span.
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00:11:20“I realized okay swing trading wasn't working for me because I'm pulling up my phone every two minutes” — Ali Khan
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00:53:00“I limit my trading to two hours due to ADHD” — Ali Khan
Individual risk tolerance leads to divergent trading approaches.
- The round‑table discussion highlighted that “risk tolerance how you are as an individual” drives wildly different strategies.
- Some traders chase high‑risk, high‑reward setups, while others prefer low‑volatility, high‑probability models.
- Recognizing this personal variance prevents the false belief that there is a single “right” way to trade ICT concepts.
- Tailoring model complexity and position size to one’s comfort zone improves longevity.
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00:10:31“risk tolerance how you are as an individual when it comes to the psychology element so therefore there's going to be huge deviations just within the population” — RZ
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00:10:33“therefore there's going to be huge deviations just within the population” — RZ
Build a system that matches your attention span and personal constraints.
- Ali created a personal “A+ checklist” to keep his analysis within a two‑hour window.
- The checklist forces him to focus on a handful of confluences rather than endless data points.
- By aligning his workflow with his neuro‑profile, he reduces impulsive over‑trading and improves decision quality.
- This disciplined system replaces the chaotic habit of constantly opening new charts and chasing every signal.
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00:42:00“I think you need to build a system that works with your attention span” — Ali Khan
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00:55:30“I have a checklist of confluences and criteria” — Ali Khan
Focused Model Building vs Information Overload
Starting with a single, well-defined model prevents the paralysis that comes from trying to absorb every ICT concept. Incremental refinement and a disciplined "start-small" mindset produce a robust, adaptable trading system.
Start small to avoid information overload.
- Ali advises “start small and then you're building a business model around that,” emphasizing a single core concept first.
- By focusing on one model (e.g., fair‑value gaps), a trader can master its nuances before expanding.
- This approach counters the common habit of trying to learn all ICT concepts at once, which leads to analysis paralysis.
- A narrow focus also makes journaling and performance tracking far more manageable.
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00:13:01“start small and then you're building a business model around that” — Ali Khan
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00:14:12“I was trying to add absolutely everything that I learned into one trading model and it was just an overload of information” — Ali Khan
Overloading a model leads to confusion and poor execution.
- When a trader piles every ICT concept into a single model, the resulting “overload of information” blurs decision criteria.
- Ali recounts that his early attempts to combine order‑blocks, gaps, liquidity pools, and macro data produced indecisive entries.
- The lack of a clear hierarchy caused missed trades and unnecessary risk.
- Simplifying the model restores clarity, allowing the trader to act decisively when the algorithmic narrative aligns.
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00:14:21“it was just an overload of information” — Ali Khan
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00:14:45“it was just an overload of information” — Ali Khan
Iterative refinement based on time‑of‑day patterns improves model quality.
- Ali discovered a consistent “turning point around this time of day,” using it to prune his model.
- Recognizing that the algorithm moves differently during specific sessions (London, New York) lets him adjust entry criteria.
- Each refinement cycle removes low‑probability signals, sharpening the model’s edge.
- Over time, this disciplined iteration yields a lean, high‑probability system.
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00:15:23“there is always a turning point around this time of day” — Ali Khan
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00:16:30“the algorithm is basically moving” — Ali Khan
Time-Frame Hierarchy and Market Narrative Alignment
Higher-time-frame structures dictate the market narrative, while lower-time-frame price action provides execution signals. Aligning fair-value gaps and liquidity pools with specific times of day maximizes edge.
Align fair‑value gaps with specific times of day for higher accuracy.
- Ali stresses looking for fair‑value gaps that appear at a “specific time of day,” because the algorithm’s liquidity‑seeking behavior is time‑dependent.
- When a gap coincides with a known liquidity‑drain window (e.g., London close), the probability of a clean retest rises dramatically.
- This temporal filter weeds out false gaps that would otherwise be filled quickly.
- Traders who respect the time‑of‑day filter report tighter stop‑loss placement and higher win rates.
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00:13:14“you’re looking for the fair value Gap at a specific time of day” — Ali Khan
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00:45:18“you blend all of that you're blending time first because like I know that there's time in the day for the price to run” — Ali Khan
Higher time frames set market narrative; lower frames provide execution points.
- The higher‑time‑frame order flow (e.g., weekly, daily) defines the overarching bias—bullish or bearish.
- Lower‑time‑frame confluences (5‑min, 1‑min) are only taken when they align with that higher‑frame narrative.
- Ali notes that “the higher time frame order flow is bearish,” which then guides his liquidity‑pool hunts on lower frames.
- Ignoring this hierarchy leads to trades that fight the algorithmic bias, increasing loss probability.
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00:45:45“the higher time frame order flow is bearish” — Ali Khan
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00:45:01“there's obviously a hierarchy within that yes is the primary for example do you believe in Market structure or does IC use Market structure yeah quite a bit” — Ali Khan
Daily and weekly charts serve as a roadmap for trade planning.
- Ali treats the weekly and daily time frames as the “road map” that guides where to look for high‑probability setups.
- By first identifying the macro bias on these higher frames, he narrows the universe of possible entry zones on lower frames.
- This top‑down approach reduces the number of false signals and aligns trade execution with the algorithm’s longer‑term intent.
- Traders who skip this step often chase low‑time‑frame noise that the algorithm will quickly invalidate.
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00:48:31“if you're going to have any levels on your charts look at the weekly and the daily cuz it's just that's your road map” — Ali Khan
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00:48:30“algorithmic price action across multiple time frames” — Ali Khan
Economic Calendar as a Trade Filter
High-impact news events act as a smoke screen for the algorithmic price-delivery process. Anticipating these events lets traders size positions, adjust stops, or stay out entirely, turning a potential volatility trap into an edge.
Economic calendar events act as smoke screens for algorithmic price moves.
- Ali describes the calendar as “the smoke screen” that the algorithm uses to hide its true intent.
- When a high‑impact release is scheduled, the market’s price‑booking algorithm temporarily masks liquidity, creating deceptive patterns.
- Recognizing this allows traders to avoid being misled by false breakouts that are merely news‑driven.
- Ignoring the calendar often results in entering on a price move that will reverse once the algorithm re‑establishes equilibrium.
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00:48:40“the economic calendar is the smoke screen so the news that comes out they're all smoke screen for the algorithm to do” — Ali Khan
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00:49:02“if I can determine where the high impact news events are likely to occur during the week I can that's going to give me a huge amount of insight to say okay whether I want to hold for this position or whether I want to close this position” — Ali Khan
Anticipating high‑impact news helps determine position sizing and trade decisions.
- By forecasting the timing of major releases, Ali can decide whether to stay flat, reduce exposure, or add a small position.
- The calendar provides a “filter” that turns a chaotic news‑driven environment into a predictable risk window.
- This proactive stance replaces reactive panic‑selling and improves the risk‑reward profile of each trade.
- Even if the news is ignored, the algorithm will later create a liquidity‑drain that the trader can exploit.
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00:49:02“if I can determine where the high impact news events are likely to occur during the week I can that's going to give me a huge amount of insight to say okay whether I want to hold for this position or whether I want to close this position” — Ali Khan
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00:49:12“I don't even read my tweets” — Ali Khan
Ignoring the economic calendar can lead to missed opportunities and surprise moves.
- Traders who skip the calendar often “missed out on that” when a news‑driven swing reverses.
- The algorithm will later correct the price, leaving the uninformed trader on the wrong side of a rapid move.
- Ali cites personal experience where a missed news cue caused a full‑volume loss that could have been avoided with a simple filter.
- The lesson: treat the calendar as a mandatory step before any high‑probability setup.
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00:41:01“you missed out on that” — Ali Khan
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00:41:05“that's a huge inefficiency in your portfolio because you missed out on that” — Ali Khan
Partial Position Taking and Risk Management
Scaling out at logical algorithmic levels protects capital, reduces opportunity cost, and aligns with the market's natural liquidity cycles. Partial exits are a core component of ICT's risk-management philosophy.
Partial exits at logical levels protect capital and improve risk/reward.
- Ali emphasizes “taking partials at logical levels” as a disciplined way to lock in profit while keeping a tail for further upside.
- Logical levels are derived from the algorithm’s expected reversal zones (e.g., after liquidity is taken).
- This method prevents the all‑or‑nothing scenario that can wipe out a trader’s equity on a single reversal.
- Consistent partial‑taking yields a smoother equity curve and higher Sharpe ratio.
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00:23:31“taking partials at logical levels and pay the trader” — Ali Khan
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00:40:30“taking partials before the trade goes 80% up is better than holding full volume” — Ali Khan
Logical levels are defined by algorithmic expectations and market structure.
- The transcript mentions a “hierarchy” and “logical levels” that the algorithm respects when price reverts.
- By identifying these levels (e.g., after a fair‑value gap is filled), a trader can set partial‑exit targets that align with the algorithm’s next move.
- This reduces the chance of being caught on the wrong side of a sudden liquidity‑drain.
- The practice also creates a systematic, repeatable exit plan rather than ad‑hoc decisions.
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00:45:01“there's obviously a hierarchy within that yes is the primary for example do you believe in Market structure or does IC use Market structure yeah quite a bit” — Ali Khan
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00:45:07“logical levels” — Ali Khan
Partial taking reduces opportunity cost compared to holding full volume.
- Holding a full position until a reversal can lock up capital that could be used elsewhere.
- Ali notes that “you missed out on that” when a trade reverses after 80 % profit, highlighting the inefficiency of full‑volume exposure.
- By scaling out, the trader frees capital to chase the next high‑probability setup, improving overall portfolio turnover.
- The net effect is higher cumulative returns without increasing risk.
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00:41:01“you missed out on that” — Ali Khan
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00:41:05“that's a huge inefficiency in your portfolio because you missed out on that” — Ali Khan
Misinformation and Watered-Down ICT Courses
A flood of low-quality ICT-based courses mis-identifies key concepts like breakers, leading to poor trade outcomes. The ICT Bible was created to cut through the noise and present the core framework in a concise, accurate format.
Many courses water down ICT concepts, leading to confusion.
- The host admits “there's a lot of misinformation out there as well when it comes to ICT concepts.”
- Ali adds that “people are taking his concepts and watering them down,” which creates a market of half‑baked teachings.
- Watered‑down material often omits the algorithmic narrative, leaving students with isolated patterns that fail in live markets.
- This proliferation of low‑quality content erodes trust in the ICT brand and harms new traders.
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00:05:06“there's a lot of misinformation out there as well when it comes to ICT concepts” — RZ
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00:24:14“people are taking his concepts and watering them down” — Ali Khan
Misidentifying breakers leads to faulty trades.
- A TikTok video with 600 k views incorrectly labeled a candle as a “breaker,” illustrating how easy it is to spread wrong technical definitions.
- Ali points out that “he's teaching the wrong candle,” meaning traders built positions on a mis‑identified structure.
- Such errors cause traders to enter at unfavorable price levels, increasing loss probability.
- The incident underscores the need for rigorous validation of any ICT‑derived pattern before live deployment.
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00:24:17“someone had like 600,000 views speaking about a breaker and he completely the wrong candle is being identified as a breaker” — Ali Khan
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00:24:20“he's teaching the wrong candle” — Ali Khan
The ICT Bible consolidates five years of knowledge to combat misinformation.
- Ali created the free “ICT Bible” ebook to distill five years of mentorship into an accessible format.
- The Bible “basically make the concepts easier in a format where people can understand it,” acting as a single source of truth.
- By providing a curated, vetted collection of the core algorithmic concepts, it counters the flood of inaccurate copy‑cat courses.
- Users report that the Bible “absolutely clicked” for them, accelerating their learning curve dramatically.
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00:24:45“the ICT Bible is just to basically make the concepts easier in a format where people can understand it” — Ali Khan
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00:24:48“the ICT Bible is a free ebook that you can get from my Twitter and website” — Ali Khan
Mentorship and Primary-Source Learning
Real-time commentary from Michael (ICT) provides market narrative insight that cannot be captured in static videos. The ICT Bible condenses this mentorship into a portable guide, allowing students to bypass second-hand interpretations.
Real‑time commentary from Michael provides market narrative insight.
- During weekly live sessions, Michael explains “why the market’s likely to go there,” delivering a narrative that links price action to the underlying algorithm.
- Ali notes that hearing Michael’s voice while watching the chart creates a mental model that “most of the time” proves accurate.
- This live narrative bridges the gap between raw price data and the algorithmic intent, a benefit unavailable in pre‑recorded tutorials.
- Students who attend these sessions develop a deeper, intuitive feel for market structure.
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00:17:06“when every week he was doing his commentary and he's saying okay the Market's likely to go here it was the underlying Narrative of why the Market's likely to go there” — Ali Khan
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00:17:08“and then when you're looking at charts in real time you've got his voice in your head” — Ali Khan
The ICT Bible distills mentorship content into an accessible format.
- Ali’s free ebook compiles the core algorithmic concepts from Michael’s years‑long mentorship.
- By “making the concepts easier,” it removes the need for learners to sift through hours of video.
- The Bible’s concise structure lets traders focus on the narrative, order‑flow, and liquidity principles without the overhead of full‑length sessions.
- This democratizes access to high‑quality ICT education, especially for those who cannot attend live mentorship.
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00:24:48“the ICT Bible is a free ebook that you can get from my Twitter and website” — Ali Khan
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00:24:57“the ICT Bible was just to basically make the concepts easier” — Ali Khan
Learning directly from the source avoids reliance on second‑hand interpretations.
- Ali stresses that “learning from the source” prevents the distortion that occurs when concepts are filtered through multiple layers of commentary.
- He cites the “Chinese whispers” effect where each reinterpretation adds errors, leading to faulty models.
- By consuming the original mentorship (or the distilled Bible), traders retain the algorithmic nuance.
- This approach reduces the time spent correcting misinformation and improves trade execution consistency.
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00:27:06“there's a lot of Chinese whispers along the way that they are just so deviated” — Ali Khan
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00:24:14“people are taking his concepts and watering them down” — Ali Khan
Community Collaboration and Reducing Toxicity
The ICT community is fragmented by ego and competition. Removing personal drama and focusing on shared algorithmic understanding can accelerate collective progress and improve industry standards.
Removing toxicity fosters collective progress.
- Both hosts agree that the ICT space suffers from “toxicity” and ego‑driven drama.
- Ali urges “we need to take the toxicity out of this community,” encouraging a supportive environment.
- When traders collaborate rather than compete, knowledge spreads faster and the overall skill level rises.
- A healthier community also attracts new talent, ensuring the longevity of ICT concepts.
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01:26:33“we need to take the toxicity out of this community” — Ali Khan
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01:26:30“we should all just try to help each other because we're all pursuing the same thing” — Ali Khan
Collaboration and shared mentorship improve industry standards.
- The hosts highlight that joint podcasts, dinners, and mentorships have already raised the bar for ICT education.
- Ali notes that “collaboration improves industry,” implying that shared resources reduce duplicated effort.
- By pooling insights, traders can develop more robust models and avoid reinventing the wheel.
- This cooperative mindset also curtails the spread of low‑quality copy‑cat courses.
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01:26:37“collaboration improves industry” — Ali Khan
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01:26:30“we should all just try to help each other because we're all pursuing the same thing” — Ali Khan
Putting personalities aside allows focus on algorithmic understanding.
- Ali argues that Michael’s “polarizing” personality distracts from the core teaching of the inter‑bank algorithm.
- By “putting Michael’s personality aside,” traders can concentrate on the narrative and price‑booking mechanics.
- This mindset reduces the “fraud” chatter that stems from ego rather than substance.
- A neutral, algorithm‑first approach yields clearer analysis and better trade outcomes.
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01:21:15“put Michael's personality aside because yes he's a polarizing and that's why a lot of people give him a lot of stick” — Ali Khan
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01:21:17“people call him a fraud because of his ego” — Ali Khan
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