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In this interview, Blayn Marshall— a funded trader at Darwinex who runs fully automated systems—breaks down how he went from stock-picking as a teen to managing a diversified, low-drawdown portfolio of algos. He explains why he tests one strategy at a time live, cross-checks results against backtests, and then assembles an uncorrelated mix using tools like Quant Analyzer, swapping out any system that hits a predefined max drawdown. You’ll also hear about his ongoing experiment to scale toward 100 live algorithms and why simplicity often beats complexity over the long run.
Read on to learn the exact playbook: how Blayn sizes risk as low as 0.05% per trade to stay calm through volatile news, the metrics he watches to decide when a strategy’s edge is eroding, and how he balances longs/shorts and styles (breakouts, trend-following) across multiple FX pairs. If you’re a newer trader, this is a beginner-friendly path to avoid strategy-hopping: start simple, validate live, diversify by uncorrelated edges, and grow only what actually performs—so your equity curve can keep pointing to the top-right without the sleepless nights.
Blayn Marshall Playbook & Strategy: How He Actually Trades
Core Thesis & Market Universe
Here’s the big picture of how Blayn approaches the game. He hunts for simple, testable edges and spreads them across multiple uncorrelated markets so no single idea can break the account. The goal is smooth, durable compounding—not home-run trades.
- Trade a defined basket (e.g., 12–24 liquid FX pairs, major indices, and/or metals); no microcaps or illiquid exotics.
- Ban discretionary “gut” trades; every entry/exit must be rule-based and backtested.
- Decide the primary regime per market weekly (trending, range, or mixed) and only run models suited to that regime.
- Maintain a max concurrent systems cap (e.g., 20–40 active strategies) with diversity by market, direction, and style.
Risk & Position Sizing
Risk comes first. Blayn’s sizing is deliberately small so that losing streaks stay survivable and psychology stays calm enough to execute the plan. He sizes by account risk, not by pip value or contract count.
- Risk per trade: 0.05%–0.25% of equity, depending on strategy quality and correlation to open risk.
- Platform-level guardrails: max daily loss = 0.8–1.2× average green day; max weekly loss = 2.5–3.0× average green day.
- Hard stop on total open risk: never exceed 1.0–1.5% across all open positions.
- Reduce size by 50% after any -3R day or -5R week; restore only after two green days or one green week.
- Recompute units from volatility (ATR), not fixed lots; target equalized volatility-scaled positions.
Entry Models That Actually Trigger
Entries are kept mechanical and few in number to reduce curve-fitting. Each model is independently testable and must show a positive expectancy with realistic slippage.
- Breakout Trend: Buy on N-day high + buffer (e.g., 20-day high + 0.1×ATR); sell the mirror for shorts.
- Pullback Continuation: In an uptrend (MA200 up, price above MA50), buy when price dips 1.0–1.5×ATR below a rising MA20 and prints a higher close.
- Mean Revert to VWAP (indices/majors only): Fade 2.0×ATR extensions from the session VWAP during non-news hours; scale in no more than 2 bullets.
- Session Break (FX): Enter with the London or NY open when the first 30-minute range breaks alongside the H4 trend filter.
- Always add a “news block”: no new entries inside 30–60 minutes around tier-1 events for the pair’s base currency.
Exit, Stops, and Timeouts
Exits determine your equity curve shape. Blayn uses asymmetric stops and simple “give-up” timers to avoid death-by-chop.
- Initial stop = 1.2–1.8×ATR from entry; trail only after +1R is reached.
- Take partials at +1R (reduce 30–50%); let the remainder ride to a 2.5–3.5R target or trailing stop.
- Time stop: if trade hasn’t reached +0.5R within 3–5 bars (model-dependent), flatten at market.
- Structure invalid: close if price closes beyond the prior swing against the trade by 0.2×ATR.
- Overnight FX: if swap is strongly negative and carry matters, consider EOD flattening unless in a strong trend.
Portfolio Construction & Correlation Control
The edge is the portfolio—many small, independent edges, not a single “holy grail” system. Keep correlations low so drawdowns don’t stack.
- Limit pair overlap: no more than 2 strategies simultaneously on any one symbol.
- Cap directional concentration: max 60% of open risk in the same broad USD or risk-on/risk-off bucket.
- Use rolling 60-day correlation of equity curves between strategies; if ρ > 0.65 for a month, bench one.
- Risk budget by style: trend (40–60%), mean-revert (20–40%), intraday/session (10–30%).
- Replace the worst decile strategy each month with a new or improved candidate; keep the top quartile untouched.
Validation Before Real Money
Ideas earn their way into live trading. The pipeline is: research → out-of-sample test → paper trade → tiny live capital → full deployment.
- Require walk-forward tests across at least 3 regimes (trend, range, high-volatility).
- Minimum stats to graduate: PF ≥ 1.20, Win% ≥ 38% (or higher for mean-revert), Max DD ≤ 10× avg trade.
- Monte Carlo resample to get a 95% worst-case drawdown; size so this worst case is still < your weekly loss cap.
- Paper trade 4–8 weeks, then trade at 25–33% of the target size for the first month of live.
- Any live/backtest deviation > 0.4R per trade sustained for 20 trades sends the system back to the lab.
Drawdown Protocols & Kill-Switches
You survive by cutting heat fast. Pre-commit rules remove emotion when a cold streak hits.
- Single-system stop: pause a strategy if it hits 8R rolling or 1.2× its backtested 95% drawdown.
- Portfolio stop: if equity hits -6% from peak, reduce all sizes by 50% and freeze new strategy deployments.
- Cooldown: after a -2% day or -3% week, run only the top-two systems by expectancy for the next session/week.
- Reactivation only after a +1.5% recovery from the cycle low or 10 sessions of flat PnL without fresh lows.
Daily Routine & Execution Hygiene
Consistency beats intensity. Blayn keeps a short, repeatable checklist so the machine runs the same way every day.
- Pre-market (30–45 min): check economic calendar, spreads, platform status, and ATR shifts; disable models that violate news or volatility limits.
- During session: execute only valid signals; no manual overrides except for platform failures or news blackouts.
- EOD: export trades, tag by strategy and setup, update equity-curve dashboards, and log top 3 anomalies.
- Weekly: rebalance risk budgets, re-estimate correlations, and swap out any underperformers.
Funding/Prop Guardrails (If Applicable)
If you trade funded accounts, treat the rulebook as part of your edge. Structure your day so that account rules can’t be broken by surprise.
- Bake in “max daily loss – 10% buffer” at the platform level so you flatten before any breach.
- Disable all mean-revert models during spread widening windows (rollovers, session handoffs).
- Set an equity alert at -70% of daily max loss; if hit, enable “recovery mode”: only high-expectancy trend models, half size.
- Never scale up mid-day to “get it back”; size changes only pre-session according to plan.
Data, Slippage, and Practical Frictions
Edges die in the gap between the spreadsheet and real fills. Budget for friction before you trade live.
- Assume slippage = 0.2–0.4×ATR per 100 trades for volatile pairs; widen stops/targets to keep net R intact.
- Block trading during news spreads > 1.5× average; auto-cancel resting orders during those windows.
- Re-optimize parameters no more than quarterly; prefer simple rules that survive small parameter drifts.
- Keep a “failed pattern” tag; if a setup fails the same way 10–15 times, design a stop-gap filter or retire it.
Upgrades & Scaling Path
Growth is methodical: add strategies and size only when the portfolio proves it can carry the weight.
- Add at most 1–2 new systems per month; each must be low-correlated with what’s live.
- Scale size by equity milestones (e.g., +5%, +10% from baseline), not by recent wins; never increase size after a drawdown.
- Periodically flip direction tests (long-only vs short-only) to ensure the edge isn’t one-sided regime luck.
- Keep a sunset list of candidates to replace; upgrades are swaps, not open-ended bloat.
Personal Rules That Keep the Edge Sharp
Process disciplines turn good ideas into durable returns. These rules protect focus and prevent random tinkering.
- “Two-knob rule”: you may adjust at most two parameters per quarter across the whole portfolio.
- No trading after <6 hours of sleep or during illness; if violated, run only passive trend models at half size.
- If you manually intervene once, log the why and create an automation or rule so you never need to do it again.
- Celebrate flat days where rules were followed; process P&L> daily P&L.
Start Tiny: Risk 0.1%–0.25% So Drawdowns Stay Manageable.
Blayn Marshall keeps his risk per trade microscopic on purpose, and that restraint is what lets him scale without drama. At 0.1%–0.25% risk, even a nasty losing streak translates into a dent, not a crater, which keeps you calm enough to follow rules. The point, as Blayn frames it, isn’t to “win big” today—it’s to compound steadily by surviving every regime the market throws at you.
He also ties sizing to actual volatility so that one spicy pair can’t hijack the entire day. With tiny, volatility-scaled positions, Blayn Marshall can run multiple uncorrelated systems without correlations turning a bad hour into a bad month. This small-risk posture makes every decision cleaner: stops are respected, recovery is faster, and you never feel forced to “make it back” by breaking your plan.
Let Volatility Size You: ATR-Based Units, Not Fixed Lots
Markets don’t move in equal steps, so Blayn Marshall sizes positions with Average True Range instead of fixed lots. When ATR expands, his position size contracts to keep the same risk; when ATR contracts, size increases to avoid underutilizing risk. This keeps the money at risk constant even when the price distance to the stop changes. It’s a simple switch that makes chaotic periods less dangerous and quiet periods more productive.
Blayn Marshall also recalculates units per trade so each setup risks a fixed fraction of equity, not a fixed number of pips. Stops and targets are expressed in ATR multiples, which keeps the R-multiple math consistent across pairs and days. That consistency prevents one volatile symbol from dominating the P&L and helps portfolio-level risk stay predictable. The result: smoother equity, cleaner psychology, and zero temptation to “favorite” a market just because the lot size looks bigger.
Diversify by Underlying, Strategy, and Duration to Smooth Equity
Blayn Marshall doesn’t bet the farm on one clever setup; he spreads risk across symbols, styles, and timeframes so no single wobble wrecks the week. That means EURUSD trend plus XAUUSD mean-revert plus indices session breakouts, not three variations of the same idea. He also balances longs and shorts and keeps a lid on USD exposure, so a dollar surge doesn’t sink everything at once. The effect is a calmer equity curve where losers are muffled by winners working in different places.
To keep diversification real, Blayn Marshall watches the correlation between systems and cuts overlap fast. He caps the number of active strategies per instrument, rotates out underperformers, and forces new additions to fill a portfolio “gap” rather than duplicate a current edge. Durations are staggered—some intraday, some swing—so PnL arrives from multiple cycles instead of one timing bet. Over time, this mix turns randomness into reliability, and reliability is what compounds.
Trade Rules, Not Opinions: Mechanics Over Prediction Every Session
Blayn Marshall builds his day around executable rules, not forecasts, so he never needs to “feel” the market to get paid. Each setup has a pre-defined trigger, stop, and exit logic; if the signal prints, he takes it—if it doesn’t, he passes without second-guessing. He uses a short checklist before each session (volatility, spreads, news window) to disable any model that’s out of bounds. By removing prediction, he removes hesitation, which is where most errors creep in.
When trades are live, Blayn Marshall follows the script: timeouts if momentum never shows, partials at preset R-multiples, and a trailing rule that only activates after +1R. If price action violates structure or news volatility expands beyond limits, he flattens because the rule says so, not because he “thinks” something. Post-session, he tags outcomes by setup so he can verify the rules still work in live conditions. The rules make the decisions; his job is to execute cleanly and review honestly.
Win with Boring Process: Checklists, Correlation Caps, Weekly Rebalancing
Blayn Marshall wins by repeating the same simple routines until they’re automatic. He runs a pre-session checklist—spreads, ATR shifts, news windows, platform status—so no “unknowns” sneak into live risk. During the session, he enforces correlation caps and total-open-risk limits so one hot theme can’t swamp the portfolio. At the close, he tags trades by setup and logs anomalies, letting data—not mood—drive the next day’s tweaks.
Each week, Blayn Marshall rebalances risk budgets, benchmarks overlapping systems, and promotes the highest-expectancy ideas. He retires underperformers without ceremony and only adds a new model if it lowers correlation or fills a missing style/duration. Parameters change sparingly; the process changes only when evidence is overwhelming. It’s the unglamorous discipline—checklists, caps, and scheduled reviews—that keeps the equity curve climbing while everyone else chases the next shiny thing.
Blayn Marshall’s edge boils down to building simple, testable systems and wiring them into a low-correlation portfolio that earns the right to scale. He stitched together dozens of strategies, pressure-testing them as a portfolio (not as isolated “holy grails”) and leaning on objective tools to keep overlap in check, all while proving the track record on a professional funding platform. The result is a rules-first operation where portfolio design—not any single signal—does the heavy lifting.
Risk is deliberately tiny, so volatility can punch without knocking the plan off course. When a system hits a defined drawdown line, it’s benched to a “back-burner” until it either re-proves itself or gets retired; meanwhile, the rest of the stable keeps working so the equity curve can keep nudging top-right. That small sizing—down to 0.05% on some models—and the willingness to rotate out cold strategies are what make the whole machine resilient across loud and quiet regimes.
From here, the plan is simple: scale thoughtfully and keep building differentiated portfolios, including pushing the envelope with a 100-algo experiment just to see how far disciplined process can go. That mindset—steady scaling, constant system creation, and portfolio diversity as a core principle—signals a trader focused on staying in the game for a very long time.
