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Today’s interview features Trader Mayne on the Words of Rizdom podcast in Toronto—a veteran who cut his teeth in early crypto, learned hard lessons after the 2013 crash, and refined his craft through FX, prop trading, and systematic risk management. He talks candidly about losing big, rebuilding with structure, and why social media’s highlight reels hide the grind: journaling, statistics, and thousands of chart hours. If you’ve ever wondered how a trader goes from luck to a real, scalable process, this conversation is the blueprint.
In this post, you’ll learn the core of Mayne’s strategy: think in R, make the system mechanical, and let stats—not emotions—drive decisions. We’ll unpack his shift from low-timeframe noise to higher-timeframe clarity, how he handles FOMO without nuking an account, and the exact trade management habit he recommends for beginners (set TP/SL and don’t touch it). You’ll also get his take on live vs. demo realities—fees, slippage, depth of market—and why treating trading like a business (not a lottery) is what separates long-term winners from the rest.
Trader Mayne Playbook & Strategy: How He Actually Trades
The Core Framework: Think in “R,” run a system, remove the noise
Here’s the backbone of how he operates: treat trading like a business, measure everything in “R” (risk units), and make execution mechanical. You’ll see fewer hero moments and more repeatable processes, which is exactly what lets results compound.
- Define 1R as a fixed % of equity per trade (e.g., 0.5%); never vary risk because a setup “feels” better.
- Only take trades where the minimum target is 2R; if you can’t see 2R cleanly, pass.
- Predefine entry, stop, and target before clicking; no mid-trade improvisation.
- Keep a watchlist of 3–6 instruments you understand; avoid chasing novelty coins/pairs.
- Log every trade with the date, setup tag, and R planned vs. R realized for later statistics.
Risk & Position Sizing: Make the math do the heavy lifting
His edge isn’t a secret indicator—it’s strict risk math. When your R stays constant and your targets are defined, the equity curve becomes a function of process, not adrenaline.
- Cap daily risk at 1.5R and weekly risk at 5R; if hit, stop trading until the next window.
- Size positions so a full stop = 1R regardless of instrument volatility.
- If spread/liquidity is poor, reduce size by 25–50% to account for potential slippage.
- Never widen stops to “save” a trade; exit at the planned stop and re-assess.
- When scaling the account, increase R slowly (e.g., from 0.25% → 0.5% after 40 trades and a positive expectancy).
Setup Selection: Higher-quality trades beat higher frequency
The goal is to filter noise and wait for clean, asymmetric shots. That means a clear bias, a level worth trading, and a trigger that gets you in without guessing.
- Establish bias from higher timeframes first (HTF structure up/down/sideways).
- Mark key levels: prior day/week high/low, HTF swing points, and consolidation breaks.
- Only trade 2–3 playbook setups (e.g., break-retest continuation, failed breakout reversal, range fade at extremes).
- Require confluence: HTF bias + clean level + trigger candle/structure. If any piece is missing, skip.
- Avoid entries within 5–10 minutes before/after major news on FX/crypto with strong macro sensitivity.
Entry Triggers & Orders: Keep it clean, predictable, and testable
Entries are about removing hesitation. You want a rulebook that gets you in the same way every time so your stats mean something.
- Use limit orders at the retest level when the structure is clean; use stop orders on momentum breaks when you want confirmation.
- If price misses your level by <0.1R and runs, do not chase; either wait for the next setup or the first pullback to structure.
- Place stops beyond invalidation, not at round numbers; avoid clustering with the crowd.
- If the spread widens into your entry, cancel and re-evaluate rather than forcing a fill.
- Maximum two attempts per setup per session; if both fail, that idea is done for the day.
Trade Management: Start simple, then layer “dynamic” rules later
The simplest way to let stats work for you is set-and-forget. Once your sample size proves an edge, you can add nuance like partials and break-even moves.
- Baseline mode (for new system): hard TP at 2R, hard SL at −1R. No partials, no break-even, no tinkering.
- After 100+ trades with positive expectancy, allow partial at +1R (50%) and move stop to break-even only after the partial is booked.
- For momentum setups, consider trailing to the last swing after +2R is reached to capture runners without giving back winners.
- If price stalls within 0.2R of TP and prints a clear rejection against your direction, permit one discretionary exit per session based on a written rule.
- Never re-enter immediately after a full TP or SL; wait for a fresh setup to avoid revenge trading.
Live vs. Demo Reality: Costs, slippage, and depth matter
Performance on paper ignores frictions that hit your P&L live. Account for them up front so your “edge” doesn’t evaporate in execution.
- Assume slippage of 0.05–0.20R on breakouts and thin pairs; build that into expected R-returns.
- Track effective spread (quoted spread + hidden costs) and reduce the size of instruments with jumpy order books.
- Avoid market orders during volatility spikes unless your plan explicitly accounts for it.
- If fees materially reduce edge, favor fewer, higher-R trades over scalping.
- Log slippage per setup type; revise playbook if the worst-case slippage routinely crushes R.
The Daily Checklist: Structure beats motivation
A short, repeatable checklist keeps you from winging it. Run this before you place a single order.
- Mark HTF bias, overnight levels, and today’s key areas.
- Define two A-setups you’d take and two traps you’ll avoid.
- Pre-write entry, stop, target, and expected R multiple for each idea.
- Set alerts at levels; do not stare at the screen waiting.
- Confirm news windows; stand down if conditions conflict with your setup.
Journaling & Stats: Where the edge actually gets built
He journals relentlessly because the improvements come from numbers, not vibes. This is how you turn a strategy into a money printer.
- Record setup tag, market, R planned, R realized, MAE/MFE, screenshots before/after.
- Review weekly: win rate, average R, expectancy = (Win% × AvgWinR) − (Loss% × AvgLossR).
- Kill any setup with expectancy < 0 after 50–75 trades; double down on those with expectancy > 0.3R.
- Note behavioral mistakes (chased, widened stop, skipped TP) and add a rule to prevent recurrence.
- Once a quarter, prune the playbook to the top 2–3 performers and re-focus size there.
Scaling Up: Grow risk the slow way so it sticks
Big jumps in size break psychology. Scale in increments tied to data so your discipline survives the growth.
- Increase R by one notch (e.g., 0.25% → 0.4% → 0.5%) only after 40+ trades with positive expectancy and drawdown < 5R.
- Withdraw a portion of profits at milestones to de-risk psychologically while compounding the core.
- Run a two-tier model: main account trades only A-setups; a sandbox account tests new variations with half-R.
- If equity drops 8–10R from the high, cut R by 50% until the curve makes new highs.
- Keep one instrument constant during size-ups so you’re not stacking new variables with new size.
Psychology You Can Actually Use: Simple guardrails that work
You don’t need a bookshelf of mindset hacks—just a few guardrails that keep you inside the system when emotions spike.
- Two-loss rule: after −2R on the day, stop. After 4R on the week, review before resuming.
- FOMO antidote: if a move runs without you, log why you passed and set an alert for the next level; no impulse entries.
- Environment rule: trade only when you can be undistracted for the first 90 minutes of your session.
- Process goal: measure success by checklist adherence ≥ 90%, not daily P&L.
- Reset ritual: after a tilt day, do a chart-only SIM for one session to rebuild execution confidence.
Size Risk in R: Fixed Percent, Asymmetric Targets, No Hero Trades
John Gregory—better known as Trader Mayne—frames every decision around a single unit: R. He fixes R as a small, constant slice of equity, then hunts only for setups that can pay at least 2R so the math bails him out when win rate dips. The point isn’t to be right; it’s to make the winners larger than the losers by design. When the numbers run the show, there’s no room for “I feel great about this one” sizing errors.
In practice, Gregory picks the entry, the stop that defines 1R, and a target that clears 2R before clicking anything. If the chart can’t show that asymmetry cleanly, he passes and waits—no hero trades, no widening stops to “save” a loser. Position size flexes to the distance of the stop, so the loss is always the same R regardless of volatility. That simple discipline turns a string of small losses into tuition and lets a handful of planned winners do the heavy lifting.
Trade the Mechanics, Not Predictions: System Rules Beat Fancy Narratives
John Gregory (Trader Mayne) strips out the guesswork by hard-coding what to do before price moves. He builds a checklist—bias, level, trigger, entry, stop, target—and executes it the same way whether he’s confident or not. The story about “why” a market should move is optional; the rules for what he’ll do if it does move are mandatory. That shift from forecasting to following instructions keeps him from chasing headlines or tweeting into trades.
Gregory’s edge lives in repeatable mechanics: wait for the level, take the trigger, place the stop at invalidation, set a minimum 2R target, and let the data decide if the setup stays in the playbook. If conditions change mid-trade, he doesn’t rewrite the plan—he exits per the stop or books per the target. Over a large sample, those small, boring decisions compound better than any clever prediction. The market can be unpredictable; his behavior isn’t.
Let Volatility Set Position Size: ATR, Liquidity, and Slippage Adjustments
John Gregory (Trader Mayne) lets the market’s mood dictate how big he swings. When ATR expands or the level is further from invalidation, he shrinks position size so a full stop still equals −1R. In quieter conditions with tighter stops, he can size up—same −1R risk, just different units. This turns volatility from a threat into a sizing input.
Gregory also accounts for liquidity and slippage before placing orders. On thin books or during news windows, he cuts size 25–50% and prefers limit orders at structure instead of chasing with market orders. If the spread balloons into the trigger, he cancels and waits rather than forcing a fill that nukes the R-math. The rule is simple: volatility, depth, and execution costs must fit the plan—or he passes.
Diversify by Underlying, Strategy, and Duration to Smooth the Equity Curve
John Gregory (Trader Mayne) doesn’t let one market or one idea decide his month. He spreads risk across a small basket of underlyings he knows—majors, a couple of crypto leaders, maybe one index—so a single theme can’t sink the ship. He also diversifies by playbook: continuation breaks, failed breakout reversals, and range fades each get their own slot. The point is simple: when one style cools off, another can carry the load.
Duration is its own hedge, so Gregory mixes intraday shots with swing holds that live on the higher timeframes. He caps total exposure per “bucket” (underlying/strategy/duration) and avoids stacking correlated trades—no triple-long on highly related pairs. If two setups rhyme, he splits R between them or picks the cleaner one. That way, the equity curve benefits from different drivers instead of riding the same wave twice.
Define Risk Upfront: Stops, Profit Targets, and Strict Max Daily Drawdown
John Gregory (Trader Mayne) hard-codes the risk before he even thinks about the reward. Every trade gets a written stop at structural invalidation, a target that pays at least 2R, and a position size that makes the full stop exactly 1R. He also sets a firm max daily drawdown in R—hit it and he’s done, no negotiating with the market. That precommitment removes mid-trade bargaining and protects the equity curve from emotional detours.
Gregory treats exceptions as strategy bugs, not lucky breaks. If price comes within a hair of his target and flips, he eats it and logs the outcome, then adjusts only if the pattern repeats across a meaningful sample. No widening stops, no moving targets closer to “just book something,” and no adding size to losers. The mission is consistent: define the loss, define the win, cap the day’s damage, and let the stats work.
John Gregory (Trader Mayne) leaves you with a simple, durable message: trade a written system and let math—not moods—drive the outcome. His playbook starts with R-based thinking, where every idea is pre-sized so a full stop equals 1R and a clean target pays 2R or more. That single unit turns chaos into statistics; it’s why he won’t widen a stop, chase a missed entry, or “feel” his way into a bigger size. He builds bias from structure, waits for price to come to his level, and only fires when a trigger lines up—then he measures the result the same way every time.
Across the interview, Gregory hammers the frictions many traders ignore: slippage, spreads, thin books, and news volatility. He scales size down when liquidity thins or ATR expands, favors limit orders at structure, and caps daily/weekly loss in R so one bad session can’t unravel the month. He diversifies by a small set of underlyings, a handful of defined setups, and mixed holding periods, avoiding correlated stack-ups that make drawdowns snowball. Most importantly, he journals obsessively—sets up tags, MAE/MFE, planned vs. realized R—and prunes any tactic that doesn’t show positive expectancy over a real sample. The takeaway is refreshingly unglamorous: write the rules, enforce them with discipline, and let the stats compound while everyone else argues about predictions.