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In this interview, Ali Crooks—founder of Trader Support Club, 20-year market veteran, and fund manager—sits down to talk shop on what truly sustains performance over decades. He’s traded his own capital, launched and manages a regulated fund, and coached traders in multiple rooms for over a decade, so he’s got receipts. The conversation cuts through the “oasis in the desert” myth and gets real about how experienced traders actually deal with changing markets, losing runs, and the psychology that keeps you in the game.
You’ll learn the strategy behind Ali’s process: why journaling and data ownership matter more than slick equity curves, how to size risk with realistic drawdown expectations, and how to avoid recency bias so you don’t abandon a good edge too soon. He explains when to trust a backtest, when to halve the profit and add 50% to drawdown in your expectations, and how to structure prop-firm or fund trading so it mirrors your own playbook—not the other way around. It’s practical, beginner-friendly guidance you can apply today to trade with more consistency and less stress.
Ali Crooks Playbook & Strategy: How He Actually Trades
Core Philosophy: Own Your Process, Not the Outcome
Ali Crooks builds around process control and realistic expectations rather than chasing perfect wins. He emphasizes “ownership” of your strategy—knowing exactly what it does in good, bad, and average conditions—so you can stay consistent through changing markets.
- Define your strategy in one paragraph: market(s), timeframe, pattern/trigger, risk per trade, target logic.
- Write three edges you exploit (e.g., momentum continuation after a pullback) and three market states to avoid (e.g., low-vol chop).
- For any new tactic, pre-commit to 50 trades of sample size before judging it.
- Measure success weekly by rule-following rate (% of trades executed exactly as planned), not P&L.
- Set “maximum decision points” per day (e.g., 3 A-setups only) to eliminate boredom trades.
Risk Sizing & Drawdown Math You Can Live With
He designs risk from the bottom up: first, the worst case you can tolerate, then the daily/weekly trade risk that fits inside it. That way, losing runs don’t force emotional rule-breaks.
- Choose a tolerable peak-to-trough drawdown (e.g., 10%) and reverse-engineer risk per trade so a statistically likely losing run (e.g., 8–12 trades) fits inside it.
- Start with 0.25–0.5R per trade until your rule-following rate is above 85% over 100 trades.
- Convert backtests into “realistic expectations”: halve projected profit and add 50% to max drawdown for planning.
- Cap daily loss to 1–2R and weekly loss to 3–5R; if hit, stop trading and switch to replay/journal work.
- Track rolling drawdown and equity high watermark; scale down by 50% risk when DD exceeds your “yellow zone,” scale back up only after recovering to within 1R of the watermark.
Trade Selection: One Clean Trigger, One Clean Exit
Ali favors simple, testable triggers over multi-indicator clutter. The goal is mechanical entries with discretion reserved for when not to trade.
- Define a single entry condition (e.g., break-retest of a session high with momentum) and a single invalidation (last swing low/high).
- Pre-tag sessions: “A-session” (full risk), “B-session” (half risk), “C-session” (no trading).
- Use bracket orders: entry + stop at invalidation + first target at 1R; trail remainder behind structure or session VWAP only after 1R banked.
- Enforce “two strikes rule”: if two valid trades fail in the same session, stand down; protect your emotional capital.
- Log a “skip reason” every time you avoid a marginal setup; celebrate good passes as much as good entries.
Journaling That Actually Changes Behavior
He pushes traders to journal both positives and negatives so they stop fixating on missed trades or losses only. The journal is a scoreboard for behavior, not a diary of feelings.
- For each trade, record: setup tag, R risked, R outcome, rule-score (0–1 for each rule), and a 1–2 line debrief.
- Add a “positive audit” line item: what you did well even on losers (e.g., perfect stop discipline).
- Review weekly: top three recurring mistakes and one micro-rule to patch each (e.g., “no entry within 2 minutes of major news”).
- Maintain a “kill list” of patterns/time windows that cost R over 50+ trades; stop taking them for 20 sessions and re-evaluate.
- Create a dashboard: win rate, avg R win/loss, expectancy (E), payoff ratio, average DD depth/duration, rule-adherence %.
Turning Backtests Into Real Trading Rules
Ali stresses converting data into behavior before going live. That means deliberately stress-testing the worst periods and making your rules robust to them.
- Segment test results by regime (trend/chop/volatility quartiles) and create “do not trade” filters for the bottom decile.
- Write a “worst month playbook”: what you’ll keep doing, stop doing, and reduce (risk) when metrics slump.
- Enforce a “30-trade probation” when changing anything: fixed risk, no discretionary overrides, full journal tagging.
- Only promote a variant to live risk after it survives a red month without a rule break.
- Keep a live-to-backtest slippage factor; if live E deviates >30% for 100 trades, pause and diagnose.
Discretionary vs. Algo: Same Rules, Different Wrapping
Whether discretionary or automated, Ali wants the same clarity: inputs, outputs, boundaries. Automation removes impulse; discretion filters context.
- If discretionary, pre-define context filters (session, news, ADR, HTF bias) and forbid mid-trade changes.
- If automated, require human “go/no-go” windows (e.g., no trades 10 minutes pre/post tier-1 news).
- For both, build a sandbox account or paper instance for experiments—never mix with the core system.
- Version every change (v1.1, v1.2) and journal performance by version to avoid “strategy drift.”
- If you can’t describe the edge to a new trader in 60 seconds, it’s not deployable.
Daily Routine: Prepare, Execute, Decompress
Consistency comes from ritual. Ali structures the day so decisions happen inside guardrails, not moods.
- Pre-market (15–20 min): mark levels, bias note (bull/bear/neutral), A-setups, risk plan, news windows.
- During session: only act on pre-planned A-setups; timer-based check-ins every 30–60 minutes to prevent overtrading.
- Post-market (10–15 min): journal trades, screenshot best/worst, update metrics, write one improvement for tomorrow.
- Two green flags to continue (focus, rule-score ≥0.8), two red flags to stop (tilt signs, rule-score ≤0.5).
- Weekly reset: clean charts/layouts, refresh watchlist, archive screenshots into a “playbook” folder.
Scaling & Capital Multipliers Without Breaking the Edge
He treats scaling as a privilege earned by stable behavior and stable metrics. Size only rises when the process proves it can carry it.
- Increase size by +25% after 100 trades with expectancy ≥0.3R and rule-adherence ≥85%; drop by −50% after any breach of weekly max loss.
- Diversify by timeframe or market only if the correlation of drawdowns is low; add one instrument at a time with a 50-trade probation.
- Use “risk tranches”: core risk for A-setups, half-risk for B-setups, zero for C.
- Withdraw a portion of profits on schedule to reduce psychological pressure and anchor progress.
- Never change risk mid-drawdown; adjust only at the weekly review.
Prop, Personal, or Fund: Align Rules With Mandate
Ali makes the trading plan fit the capital mandate, not the other way around. Targets, DD limits, and payout schedules change behavior—plan for that upfront.
- Write separate risk profiles for personal, prop, and investor capital: different daily loss caps, trailing rules, and allowed tactics.
- For prop rules that force tight daily stops, favor high-quality, fewer trades; avoid mean-reversion flurries near the cutoff.
- For investor funds, publish an expectations sheet: return bands, typical DDs, worst-case DD, and average recovery time.
- Sync reporting cadence with your journal cycle (weekly/monthly) so messaging matches actual variance.
- If the mandate conflicts with your edge’s natural rhythm, reduce allocation or decline the mandate.
Psychology: Pre-Commitments Beat Willpower
Ali’s edge compounds because he designs environments where the right choice is the easy choice. Reduce in-session decisions; increase pre-session commitments.
- Write a “tilt protocol”: mandatory 20-minute break, then one checklist quiz before any new trade.
- Limit screen time when conditions are poor; schedule alternative work (backtest review, screenshot tagging) to prevent boredom trades.
- Use “if-then” rules: “If I see three micro-flags against the setup, then I pass regardless of FOMO.”
- Score each session on composure (1–5); if ≤2 for two sessions, reduce size by half for the next five.
- End the week with a “win without P&L” note: list three process wins to keep confidence grounded in behavior.
Maintenance: Keep the Edge Fresh
Edges erode unless you maintain them. Ali sets routine checkups so the system stays simple, testable, and responsive.
- Quarterly: re-test the core setup on fresh data; confirm that payoff ratio and win rate remain within tolerance bands.
- Monthly: prune indicators/rules that add complexity without measurable lift; simplify before you add.
- Weekly: one deliberate practice block (replay 10 trades at 2× speed, tag entries/exits, grade).
- Archive playbook examples (both winners and losers) and refresh the “A-plus montage” you reviewed before the session.
- Keep a “stop trading” checklist (fatigue, external stress, tech issues); a missed day is cheaper than a broken month.
Size Risk Backwards From Tolerable Drawdown, Not Desired Profit
Ali Crooks starts with the number that actually keeps you in the chair: the drawdown you can stomach without breaking your rules. From that ceiling, he reverse-engineers position size so a normal losing run fits inside your limits with room to breathe. It’s the opposite of “how much can I make?”—it’s “how much heat can I carry and still execute cleanly?” When you size from tolerance, your plan survives the inevitable cold streaks.
Ali Crooks also ties daily and weekly limits to that same tolerance, so you never stack a bad session into a ruined week. He’d rather trade smaller and last longer than swing for headlines and blow the edge during variance. Once the math fits, you judge progress by consistency of execution, not the loudness of single-day P&L. That’s how you earn the right to scale: your drawdown stays livable, your head stays clear, and your rules stay intact.
Turn Backtests Into Live Rules: Halve Profits, Inflate Drawdowns
Ali Crooks treats a backtest like a weather forecast, not a guarantee. He translates the numbers into behavior by cutting headline returns in half and padding drawdowns by about fifty percent. That simple adjustment turns a pretty curve into realistic expectations you can actually trade. When the live tape underperforms the spreadsheet, you’re calm because Ali already priced that slippage into the plan.
From there, Ali Crooks locks the conversion into rules: fixed risk per trade, pre-defined sessions, and a mandatory sample size before judging results. He grades himself on rule adherence while the first 50–100 live trades accumulate, not on early P&L noise. If the life expectancy deviates too far for too long, he pauses to diagnose execution, costs, or regime—not to chase new indicators. That’s how a backtest becomes a durable playbook instead of another abandoned idea.
Trade Fewer A-Setups, Diversify By Timeframe, Underlying, and Session
Ali Crooks narrows his focus to a tiny menu of A-setups and ignores everything else, because concentration sharpens execution and slashes decision fatigue. He grades each day’s conditions and only deploys size when the tape matches his best-defined pattern. That means fewer trades, cleaner entries, and a smoother equity line built on repeatable behavior rather than impulse. When your best setup isn’t present, you stand down—that’s a profitable decision, not a missed opportunity.
To keep performance steady across different tapes, Ali Crooks diversifies the same edge by timeframe, underlying, and session instead of collecting random strategies. The idea is to keep the mechanics constant while letting market context vary: one instrument trends in London, another ranges in New York, and a higher timeframe filters the chop. He limits correlation by tracking where drawdowns overlap and staggering risk across non-overlapping clocks. This way, the portfolio breathes—fewer, higher-quality trades, spread across structured windows, with variance dampened by intentional diversification.
Mechanics Over Prediction: One Clean Trigger, One Clean Exit
Ali Crooks strips out guesswork by defining a single, testable entry condition and an equally clear invalidation point. He wants the chart to answer “in or out” without a debate, so there’s no room for mid-trade storytelling. One clean trigger reduces hesitation and revenge entries because the setup is either present or it isn’t. With fewer levers to pull, execution becomes faster, calmer, and more consistent.
On exits, Ali Crooks pairs a first target that banks a fixed multiple—often 1R—with a rules-based trailing method that only engages after risk is paid. He never widens stops once the trade is live; invalidation is sacred, not a suggestion. If two valid trades fail in a session, he stands down to protect emotional capital rather than hunting for a “make-back” setup. The result is a mechanical loop—enter, defend, pay yourself, trail—where every action is scripted, repeatable, and immune to prediction fantasies.
Process Discipline Scorecard: Journal Adherence, Stop Tilt, Scale Only Earned
Ali Crooks keeps a scoreboard for behavior, not just P&L, so he always knows whether he’s trading the plan or trading his mood. He tracks a rule-adherence percentage per session and treats it like the primary KPI; if adherence dips, size drops automatically until discipline recovers. The journal isn’t a diary—it’s a performance console with setup tags, R risked, R outcome, and a quick note on what was done well, even on losers. By grading execution this tightly, he removes the wiggle room that usually lets bad habits creep back in.
When tilt shows up, Ali Crooks has a pre-committed protocol: stop trading, take a short reset, and only resume after passing a simple checklist. He scales only when the numbers earn it—expectancy positive, drawdown contained, and rule adherence consistently high over a meaningful sample, not just a hot week. If correlation or stress rises, he deliberately scales down first and diagnoses later, protecting the edge before defending the ego. The scorecard turns consistency into a habit, and the habit is what turns a good strategy into a durable career.
In the end, Ali Crooks’ edge isn’t a single indicator or magical filter—it’s the architecture around his decisions. He starts by sizing risk from a drawdown he can actually live with, then reverse-engineers daily and weekly limits so a normal cold streak doesn’t break his psychology. From there, he converts data into behavior: backtest outputs become live rules by halving projected profits, inflating drawdowns, and committing to a clean sample before judging anything. The result is a strategy that survives variance because it expects variance, and a trader who stays composed. After all, the plan already priced it in.
Ali Crooks then narrows the battlefield to a few A-setups, diversifies them by timeframe, underlying, and session to smooth the ride, and executes with mechanics that leave no room for mid-trade stories: one clear trigger, one sacred invalidation, pay risk, then trail. He measures himself on process—journaled rule adherence, not headline P&L—and scales only when the numbers earn it. Whether trading personal, prop, or fund capital, the mandate shapes the rules, not the other way around. If conditions degrade or a tilt appears, he steps down first and diagnoses later. That’s the real “secret sauce”: a simple, testable playbook protected by pre-commitments, where discipline compounds just as reliably as capital.
