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In this interview, host Riz sits down with guest Usman Noah—a prop-funded day trader known for paring his playbook down to essentials—to talk candidly about how he built discipline, cut noise, and kept a paycheck mindset out of his trading. They dig into the reset that took him from distracted, overactive screens to a streamlined routine that actually compounds, and why that matters for anyone trying to make the jump from random wins to repeatable results.
You’ll learn exactly how Usman Noah frames risk per idea, times entries with simple, testable triggers, and limits himself to high-quality setups instead of chasing every candle. We’ll cover his session planning, trade journaling, and post-trade review loop, plus the mental models he uses to avoid overtrading and keep size proportional to edge—so newer traders can adopt a clear strategy fast and avoid the costly detours.
Usman Noah Playbook & Strategy: How He Actually Trades
The Core Setup He Hunts
This section spells out the bread-and-butter trade Noah waits for and why that patience pays. You’ll see the higher-timeframe map he uses, the intraday trigger he trusts, and the simple confluences that keep him out of mediocre ideas.
- Start top-down: mark weekly and daily swing highs/lows, trend direction, and obvious liquidity (equal highs/lows, prior day’s H/L, session H/L).
- Build a bias only at key levels; if price is mid-range, pass.
- Drop to 1H–4H for structure and 5–15m for execution; trigger = break-retest or sweep-reclaim of the level you mapped.
- Require 2–3 confluences max (e.g., level + shift in structure + momentum pop); avoid “indicator soup.”
- If the level breaks impulsively without a clean retest, skip the trade—don’t chase candles.
Risk + Trade Limits (The “One and Done” Discipline)
Here’s how Noah keeps losers survivable and winners meaningful. The rules hard-cap daily damage, stop revenge trades, and make each click matter.
- Risk a fixed fraction per idea (e.g., 0.25%–0.5% of account); never widen stops to “make it work.”
- One A-setup per session is the default; two only if the first was a clean BE/very small loss and the second is A+.
- Hard daily loss stop = 1R–1.5R. Hit it? Flat for the day—no exceptions.
- Move to breakeven only when the price leaves the level and prints a clear higher-low/lower-high on your entry timeframe.
- Scale partials at +1R and +2R; leave a runner to the next HTF level. If price returns to entry after +2R, close it.
Routine That Keeps Him Consistent
Noah treats the day like a sport: a short warm-up, a defined game window, and a clean shutdown. This removes “just one more look” decisions that drain P&L and energy.
- Pre-market (20–30 min): mark levels, write bias in one sentence, and pre-define invalidation.
- Trading window: focus on one or two sessions you actually perform in (e.g., London or New York); outside that window, the platform is closed.
- After two stop-outs or one sloppy execution, a mandatory 24-hour cooldown.
- Post-market (15 min): screenshot chart with annotations, log the plan vs. reality, and tag the setup quality (A/B/C).
Journal & Accountability (What Actually Improves the Edge)
Improvement comes from patterns you can see. Noah’s journaling is fast, objective, and tied to concrete behavior, not feelings.
- Track for every trade: setup tag, session, R taken, MAE/MFE, adherence (yes/no), and a one-line lesson.
- Weekly score: % of trades taken only at mapped HTF levels, % that followed the written trigger, and net R by setup tag.
- If adherence <80% for the week, the size drops by half next week. If adherence ≥90% for two straight weeks, consider a small size bump.
- Share a weekly recap with a trusted peer: 3 best charts, 3 worst, and one process tweak you’ll run next week.
Prop vs. Self-Funded: How He Structures Capital
He’s pragmatic about capital sources: use prop to prove the process and build a track record; keep self-funded accounts for flexibility and long-term compounding.
- Treat prop evaluations like real: same risk per idea, same number of trades, same hours. No “challenge mode.”
- Payout plan: withdraw to a reserve until 3–6 months of living costs are banked; only then add size.
- Keep rules identical across accounts so performance is comparable; if rules differ, you don’t have data—you have noise.
- When equity drawdown hits 3R from peak, reduce risk by 50% until you print two green weeks.
Execution Micro-Rules (So You Don’t Overthink in the Moment)
These are the small, mechanical habits that prevent hesitation, FOMO, and late entries. They’re simple on purpose.
- Type your entry, stop, target, and the trigger you’re waiting for before you place the order.
- Place the stop where the idea is objectively wrong (beyond the swing/level), not at a round number.
- If the candle that should break…doesn’t, cancel the order. “Almost” isn’t a trigger.
- No adding to losers, ever. Add only to winners on a structured pullback that respects your HTF level.
- If price hits target while you’re away, accept the exit—no retroactive “I would’ve held.”
Psychology Without the Woo-Woo
Noah’s mindset rules are just structure and environment—things you can control that make better decisions feel easier.
- Sleep, food, and gym come before charts; if you’re compromised, you don’t trade.
- Reduce screen time: if your bias is “wait for X at Y,” step away until Y is in play.
- After any outlier day (big win or loss), the next day, max risk = 0.25R per idea to avoid emotional spillover.
- Keep a “pre-trade checklist” on paper; if you can’t tick every box, you don’t click.
Scaling With Proof, Not Hope
Growth is boring and data-driven. Noah scales when the process proves it, not when he “feels ready.”
- Only increase size after 50–100 trades with positive expectancy and ≥1.5 profit factor on A-setups.
- Size up in small increments (e.g., +25% risk) and hold it for at least 20 trades before another bump.
- Add instruments only when the primary market is consistently positive; new markets must follow the same rules.
- Quarterly, prune: drop the lowest-performing setup tag or session until results improve.
Size Risk First: Fixed R Per Idea, Never Widen Stops
Usman Noah treats risk as the product, not the trade. Before he even looks for entries, he locks a fixed R per idea—small enough to survive a cold streak, large enough to care. That pre-commitment kills the urge to nudge, stops “just a little,” which he considers a silent account killer. He measures invalidation at the chart level, then sizes the position to match that distance, not the other way around.
When price proves the idea wrong, he exits—no edits, no hope, no “it might bounce.” If volatility expands, he shrinks the size to keep R constant; if volatility contracts, he allows a larger position with the same risk. Partial profits are planned in R terms too, so he’s scaling based on math, not mood. The result is consistent drawdown control and a clean data trail that lets him scale only when the process—not emotion—says it’s time.
Trade Less, Win More: One A-Setup Per Session Only
Usman Noah trims his day down to a single, high-quality swing at the ball. He defines the A-setup before the session starts—location, trigger, and invalidation in one sentence—so there’s no mid-game improvisation. If it doesn’t appear, he does nothing, because “flat” is a valid position that protects capital and confidence. This removes FOMO and forces him to respect time-of-day edges instead of grazing for action.
When the A-setup prints, he executes cleanly and then closes the platform. No “second chance” trades unless the first was a scratch and the second is clearly superior. By limiting decision points, Usman Noah avoids cumulative error from boredom, fatigue, and micro-chop. Fewer trades mean clearer stats, faster feedback, and a process that’s easy to scale when the data says go.
Let Levels Lead: HTF Map, LTF Trigger, No Chasing
Usman Noah starts with a higher-timeframe map so the intraday noise can’t push him around. He marks weekly and daily swing highs/lows, prior day range, and obvious liquidity pools, then writes a one-line bias tied to those levels. Only when price is actually interacting with a mapped level does he care about entries; mid-range ideas are ignored by design. This keeps him selective, patient, and aligned with where larger players are likely active.
When the level is in play, he drops to a lower timeframe for a precise trigger—think clean break-and-retest or a quick sweep and reclaim that flips structure. He measures the stop beyond the invalidation point, sizes to that distance, and executes without chasing impulsive candles. If the trigger doesn’t form, he walks away rather than “making it work,” because no trigger means no trade. By letting levels lead and triggers confirm, Usman Noah avoids random clicks and keeps his edge tied to location, not prediction.
Journal To Improve: Tag Setups, Score Adherence, Adjust Size
Usman Noah treats his journal like a performance lab, not a diary. Every trade gets a setup tag, session label, R result, and a simple yes/no on whether he followed his written plan. That binary adherence score cuts through excuses and makes it obvious where leaks come from.
Each week, Usman Noah tallies the numbers: win rate by setup tag, profit factor on A-setups only, and adherence percentage. If adherence drops below his threshold, he reduces the size of the following week to remove pressure and rebuild discipline. If adherence is high and A-setups are profitable, he allows a measured size bump—never before. The journal decides the next move; his opinion doesn’t.
Scale With Proof: Raise Size After 50–100 Trades, Positive Expectancy
Usman Noah won’t touch size until the data says his edge is real. He waits for a sample of 50–100 trades on the same A-setup, then checks expectancy, profit factor, and drawdown depth. If the curve is positive and the process adherence is high, he nudges risk up in small steps—think 20–25% increases—not a leap. That way, execution habits don’t break under sudden pressure, and one bad week doesn’t erase months of progress.
When conditions wobble, Usman Noah dials back just as methodically. A 3R equity drawdown from peak means automatic risk reduction until two green weeks restore rhythm. He keeps instruments and sessions constant while sizing, so any change in results traces to size—not a hidden variable. By scaling only after proof and scaling back at the first sign of slippage, he compounds responsibly and protects the engine that makes compounding possible.
In the end, Usman Noah’s edge isn’t a magic indicator—it’s a set of non-negotiable behaviors. He fixes risk per idea, maps higher-timeframe levels, and waits for a simple lower-timeframe trigger instead of predicting. “One trade a day” isn’t a slogan; it’s how he keeps clean data, avoids boredom trades, and protects confidence. He used prop funding as a proving ground to build a year of trackable performance, then thought in terms of probability and process rather than hot takes and hindsight.
What really stands out is how deliberately he ties trading to real life. Usman Noah limits screen time, prioritizes routine, gym, sleep, and food choices, and imposes cool-off rules after emotional days. He reflects after each session—even when he’s not doing formal backtests—so leaks get identified and closed. Scale comes only after evidence: 50–100 trades of the same A-setup, positive expectancy, and adherence north of “good enough.” If the curve wobbles, he cuts size first, not corners. That’s the real “secret sauce”: simple rules, enforced daily, so results are a function of discipline—not luck.
