Table of Contents
This episode features Brian McAboy on the Desire To Trade podcast—an engineer-turned-trading coach known for Inside Out Trading and a no-nonsense “treat it like a business” approach. Host Etienne Crète digs into Brian’s background in quality management and how that shapes his practical framework for building a reliable trading operation, not just chasing setups.
In this piece, you’ll learn how to structure your trading like a real business: define roles you play (owner, manager, risk, QC), document a mechanical system before adding discretion, backtest to set expectations, and align strategy with your time, goals, and lifestyle. Brian McAboy shows why most traders struggle with “enough to be dangerous” knowledge, and offers a step-by-step, beginner-friendly way to add structure, reduce emotional decision-making, and improve consistency—so your results come from your process, not your mood.
Brian McAboy Playbook & Strategy: How He Actually Trades
Philosophy: Treat Trading Like a Real Business
A trading account is a small business with cash flow, inventory (open positions), and processes. Brian’s edge is consistency through structure, not prediction. This section shows how to run your trading-like operations, with roles, plans, and audits.
- Write a one-page business plan: mission, target markets, strategy types, risk limits, and weekly review cadence.
- Assign yourself roles for each session: Owner (objectives), Manager (process & KPIs), Trader (execution), Risk Officer (limits & kill-switch).
- Define three KPIs you’ll track weekly: expectancy (R/trade), process adherence (% of trades 100% rule-compliant), and error rate (# of rule breaks per 20 trades).
- Use checklists for every repeated task: market scan, setup validation, order entry, and post-trade journal.
- Commit to a fixed improvement cycle: identify one defect, design a countermeasure, test it for 20 trades, then standardize.
Market & Timeframe Fit
Brian emphasizes matching your lifestyle and attention budget to the market and timeframe. If you can’t execute a plan reliably, the edge is wasted. Set realistic constraints first; let those constraints pick the playbook.
- Choose a primary session you can attend 90% of the time (e.g., first 2 hours of NY open).
- Pick one instrument “home base” (e.g., ES, NQ, EURUSD) and one backup; master their rhythm before expanding.
- Select a timeframe that fits your decision speed (e.g., 15–60m for calm execution; 1–5m only if you can make and log decisions under 10s).
- Cap simultaneous positions to your monitoring capacity (e.g., max 2 at intraday, 4 swing).
- Set a daily screen-time limit and a hard stop time to avoid fatigue trading.
Define the Edge: Setup, Context, and Trigger
Edges are built from repeatable patterns with context plus a precise trigger. Brian separates “idea quality” from “entry precision” so you can measure each step.
- Document each setup: market condition, structure, volatility regime, and invalidation logic on one card.
- Require two context filters (e.g., higher-timeframe bias and volatility state) before any trigger is allowed.
- Specify one trigger per setup (e.g., break-retest + limit entry; or momentum break + stop order).
- Pre-write invalidation rules (structural break, time stop, or signal failure) that must exist before entry.
- Set a minimum “sample size” rule: do not edit the setup until you’ve logged 30 fully compliant trades.
Risk & Position Sizing
Risk is a budget, not a feeling. Brian standardizes risk in R (risk units) so performance is comparable across setups and markets.
- Risk a fixed fraction per trade (e.g., 0.25–0.5% of equity for new systems; max 1% once stable).
- Place stops where the setup is wrong, not where it “feels safe” (e.g., beyond structure or 1.5–2.5× ATR).
- Size positions from stop distance: position = (account × risk%) ÷ stop$.
- Cap daily risk: max 2R down for the day; hit it, you’re done.
- Reduce size by 50% during a drawdown of 5R until you print three green, rule-compliant trades.
Trade Management: Exits, Targets, and Adjustments
Management rules create most of the variance. Brian keeps them mechanical so you can isolate what actually adds value.
- Predefine primary exit: fixed R target (e.g., 2R) or structure-based take-profit; no in-trade invention.
- Use one trailing method per setup (e.g., last swing low/high or ATR multiple) and never mix mid-trade.
- Time stop: if price hasn’t moved at least +0.5R in your expected window (e.g., 2× average bar duration), exit at market.
- Move to break-even only after a structural confirmation (e.g., new higher low / lower high), not simply at +1R by habit.
- Never widen stops. If context changes, close and re-enter on a fresh signal.
Pre-Trade Checklist (2 Minutes)
Checklists cut errors. Brian wants proof that you followed the process before the risk goes on.
- Market context confirmed? (HTF bias, volatility regime, no major news inside next bar set)
- Set up a card to match the current condition? (yes/no only)
- Risk computed and within limits? (daily risk remaining ≥ 1R)
- Entry, stop, target typed into the ticket before clicking buy/sell.
- Journal pre-filled with a hypothesis and an invalidation statement.
Execution Ritual
Rituals reduce noise and make performance reproducible. Keep it boring—and fast.
- Use a single watchlist layout and hotkeys you practice daily.
- Enter only during your pre-defined entry windows (e.g., first 90 minutes; avoid the last 10 minutes of the session).
- Speak the order aloud: “Long ES, 0.5% risk, stop 20.25 points, target 2R.” Then click.
- Screenshot the chart at entry with stop/target drawn; attach to journal immediately.
- Hands off the mouse for the first 3 minutes after entry to prevent impulsive tinkering.
Post-Trade Review: Journal Fields That Matter
Brian treats journaling as a quality tool, not a diary. Log the fields that let you fix defects.
- Mandatory fields: setup name, context filters, trigger type, R risked, R result, MFE/MAE, rule-compliance flag (Y/N).
- If N, tag the defect type: setup selection, execution timing, sizing calc, management, or emotional deviation.
- Add a one-sentence “root cause” and one countermeasure you’ll test on the next opportunity.
- Paste the entry and exit screenshots; mark where the rules were or weren’t followed.
- Weekly, aggregate: win rate, average R, expectancy, and error rate per setup.
Metrics & Quality Control (QC)
What you measure, you can manage. Brian borrows from quality engineering to keep processes inside healthy ranges.
- Track expectancy by setup and by market; stop trading any bucket with expectancy < 0 after 30 trades until adjusted.
- Monitor process adherence: target ≥ 90% rule-compliant trades; anything lower triggers a size reduction.
- Use a simple control band for win rate (e.g., rolling 20-trade average with ± standard-error bands) to detect regime shifts.
- Maintain a “defect leaderboard” and tackle the top one each week with a specific countermeasure.
- Re-backtest or paper test after any rule change; do not push changes live until you log 20 compliant trials.
Drawdown Protocol
Drawdowns happen; the protocol keeps you solvent and sane. Brian’s approach is pre-committed and mechanical.
- At −3R in a rolling 5-day window: reduce risk by 50% and switch to A-setups only.
- At −6R total drawdown: pause live trading; run paper execution of the same setups for 20 trades.
- At −10R: conduct a full system audit—verify that context filters, stop placement, and targets still fit current volatility.
- Resume size only after two consecutive green weeks with ≥ 90% process adherence.
- If emotions spike (sleep loss, revenge urges), activate a 24-hour cooldown regardless of PnL.
System Evolution: Change Control
Your system evolves, but only on schedule and with data. This prevents thrashing.
- Make changes on a fixed cadence (e.g., monthly), never mid-week except for risk reductions.
- Add or remove one rule at a time; test 20–30 trades before judging.
- Keep a living “standard work” doc for each setup; update version numbers and change logs.
- Retire setups that underperform across two full market regimes.
- When adding a new market, clone an existing setup and adjust only filters/volatility parameters.
Psychology & Error-Proofing
Confidence comes from proof, not pep talks. Brian builds confidence by eliminating decision clutter.
- Limit discretionary overrides to two predefined cases (e.g., obvious news shock or platform failure) and log them.
- Use if-then scripts for common urges: “If I want to move my stop tighter, then I will write the reason and wait one bar.”
- Pre-commit to a maximum of one “re-entry attempt” per setup to avoid chase loops.
- Keep a small “wins archive” of textbook trades to review before sessions for pattern recall.
- End each day with a 5-minute debrief: one thing to keep, one thing to fix, one thing to try.
Tooling & Templates
Simple tools, rigorously used, beat complex stacks you won’t maintain.
- Chart templates: one clean template per setup with only essential indicators (e.g., structure, ATR, and a higher-timeframe overlay).
- Order templates: saved ticket with default risk %, bracket for stop/target, and notes field pre-filled.
- Journal template with required fields and automatic R calculations; tagged by setup and market.
- Weekly review board: KPI tiles (expectancy, adherence, error rate), defect leaderboard, and next week’s single improvement.
- Backup plan: secondary broker/platform and a phone-in order procedure written on your desk.
Treat Trading Like a Business: Process Discipline Over Predictions
Brian McAboy says the fastest way to steady results is to run your trading like a real business, not a guessing game. That means you, the “owner,” set objectives, the “manager” enforces processes, and the “trader” just executes the plan. Forecasting becomes secondary to following a documented routine—market scan, setup validation, trigger, risk, and exit—so outcomes come from the system, not your feelings. When you treat trades like work orders rather than lottery tickets, consistency finally has a chance.
Brian McAboy also pushes measurable accountability: define KPIs, track rule adherence, and audit mistakes like a quality engineer. If your rules aren’t written, they aren’t real, and you’ll improvise under stress. Process discipline beats prediction because it eliminates decision clutter and makes each error visible and fixable. The payoff is boring—but repeatable—profits built on procedure, not bold calls.
Size Risk in R, Adjust Position to Volatility and Structure
Brian McAboy frames every trade in R—one unit of predefined risk—so results are comparable across markets and setups. You first place the stop where the setup is invalidated by structure, then let the stop distance dictate position size. If volatility expands, the same R buys fewer contracts; if volatility contracts, your size scales up, but the risk in dollars stays constant. This keeps emotions out of sizing and protects the account from “feeling-based” overexposure.
Brian McAboy also anchors the day with a hard loss cap to stop tilt, then reduces size during drawdowns to lower pressure and error rates. Targets are planned up front (e.g., 2R or a structural level) so you’re not negotiating mid-trade. No widening stops—if the premise breaks, you’re out and ready for the next clean signal. With R as the language, you know exactly what you risk, why you sized that way, and how the edge performs across regimes.
Codify Setup, Context, and Trigger With Prewritten Invalidation Rules
Brian McAboy insists every setup lives on a single page: what it is, the market condition it needs, and the exact trigger. Context comes first—higher-timeframe bias and volatility state—so you’re not forcing entries in the wrong environment. Only after context is green do you allow the trigger, which could be a break-retest, momentum continuation, or a pullback to structure. If any ingredient is missing, Brian says it’s not your setup today—stand down.
Equally important, Brian McAboy wants the invalidation written before you risk a cent: the specific structural break, time stop, or signal failure that means “I’m wrong.” The trigger is a door; the invalidation is the fire exit. Your order ticket should already contain entry, stop, and target, so there’s zero mid-trade invention. Log the trade with a rule-compliance checkbox to keep you honest and to make post-trade reviews actually fixable.
Diversify by Market, Strategy, and Timeframe to Smooth Equity Curve
Brian McAboy pushes diversification as an operating principle, not an afterthought. You don’t just trade more symbols—you split risk across uncorrelated buckets: instruments (indices, FX, commodities), strategy types (trend, mean reversion, breakout), and holding durations (intra-day, swing, position). This spreads your dependency, so one regime shift can’t nuke the whole book. When one edge cools off, another often heats up, flattening drawdowns and keeping you in business. Brian McAboy frames it simply: multiple small engines pull the train better than one big engine that occasionally stalls.
To implement, Brian McAboy suggests fixed risk budgets per bucket and hard caps on exposure overlap. If ES trend and NQ trend fire together, you may fill only one to avoid hidden correlation; the mean-reversion pair in FX can be your second fill. Review each bucket’s expectancy monthly and reallocate toward what’s working without chasing last week’s heat. Keep the sizing language in R across all buckets so your performance reads as one coherent system, not a patchwork of guesses.
Define Exits and Drawdown Protocols; Never Widen Stops, Ever
Brian McAboy keeps exits mechanical because that’s where traders leak the most P&L. Targets are chosen before entry—fixed R, structure level, or a single trailing method—and they don’t change mid-trade. If price stalls past a pre-set time window, he’s out and looking for the next clean setup. Most importantly, Brian McAboy forbids widening stops; if the premise breaks, the trade is over.
During rough patches, Brian McAboy switches from ego to protocol. Hit the daily loss cap and you shut it down; breach the larger drawdown threshold and you cut size, restrict to best setups, or go paper until stats recover. Reviews focus on rule adherence and defect types, not excuses, so the fix is specific and testable. The goal is survival first, consistency second, and only then growth—because exits and drawdown rules are the guardrails that keep you on the road.
In the end, Brian McAboy’s message is simple and ruthless: run your trading like a business, or it will run you. Put the roles on paper, write the procedures, and measure the work. Define the setup, the context that makes it valid, the single trigger that gets you in, and the exact condition that kicks you out—before you ever click buy. Speak in R so risk is the same language across ES, EURUSD, and crude; let volatility and structure set your size, not your mood. Keep exits mechanical and drawdown rules pre-committed, because discipline at the edges is what keeps small leaks from sinking the boat.
From there, smooth the ride: diversify by instrument, strategy type, and timeframe so you’re not hostage to one regime. Track KPIs that matter—expectancy, adherence, and error rate—and treat defects like quality issues with root causes and countermeasures. Use a checklist to enter clean screenshots to review, and a weekly cadence to standardize improvements. When the numbers dip, scale risk down and narrow to A-setups until the stats recover. Do this consistently and, as Brian McAboy frames it, your performance stops depending on prediction and starts depending on process—the only edge you control every single day.
