Table of Contents
In this interview, Martyn Tinsley sits down to unpack how he went from manual chart-watching to running robust algorithmic systems—plus why that shift finally made his trading consistent. He trades on Darwinex and runs “Trade Like a Machine,” but what really stands out is his obsession with turning rules into code and then managing risk at the portfolio level, not just per trade. If you’re a newer trader wondering how pros keep emotions out of decisions and handle drawdowns like an adult, Martyn’s playbook is a masterclass.
You’ll learn why Martyn ditched manual discretion for algos, how he adapts rules to shifting market regimes (mean reversion vs. momentum), and the simple mindset shift that took him from cutting winners early to letting systems do the work. He also breaks down portfolio construction using correlations and an efficient-frontier approach to smooth the equity curve—vital if you ever want outside capital. By the end, you’ll know how to size positions across multiple strategies, reduce drawdowns when markets move in lockstep, and build a trader strategy that scales beyond your own account.
Martyn Tinsley Playbook & Strategy: How He Actually Trades
Core philosophy: rules first, code second
Martyn builds rules that make sense to a human first, then turns them into code so they can run the same way every time. The goal is not prediction; it’s consistent execution and portfolio-level risk control.
- Write rules in plain English before you code them; if you can’t explain a rule in one sentence, you can’t automate it.
- Every rule must define: market universe, setup, entry trigger, exit logic, and risk per trade.
- No discretionary overrides during live trading—edits happen only between test cycles, never mid-flight.
- Track performance by strategy and by portfolio; decisions are made at the portfolio level.
Market regime detection: mean reversion vs. momentum
Markets flip between mean-reverting and momentum phases. Martyn’s systems detect the current state and adapt position-taking so you’re not fighting the tape.
- Classify regime daily using objective stats (e.g., ratio of trend strength to noise, or a moving-average slope filter + z-score of returns).
- Only enable mean-reversion entries when the regime flag is “MR”; only enable breakout/trend entries when the regime flag is “MOM”.
- If the regime signal is mixed or flat, trade half-size or stand down.
- Recheck the regime at a fixed cadence (e.g., end of day) and never intra-bar to avoid whipsaw.
Entry logic: simple, testable triggers
Entries are deliberately simple so they generalize across markets. Complexity moves to the portfolio layer, not the entry.
- For MR systems: buy when price closes ≥1.5 standard deviations below a short-term mean with a bullish close; reverse for shorts.
- For MOM systems: enter on the break of a recent high/low (e.g., 20-day), but only when the higher-timeframe trend agrees.
- Require confirmation from a volatility filter (e.g., current ATR within a defined band) to avoid entries during abnormal spikes.
- Time-of-day guardrails for intraday: only trade in defined windows; no new positions in the last N minutes of the session.
Exit logic: pre-declared and enforced by code
Exits are where discipline pays. Martyn uses rule-based exits so the system never “hopes.”
- Always place a protective stop at entry; no widening.
- Profit exits favor “let winners run”: use a trailing stop (ATR-based or structure-based) instead of fixed targets.
- Add a time-stop (e.g., close the position after N bars if neither stop nor trail hit) to clear dead money.
- One active exit dominates: if the trailing stop is closer than the initial stop, trail rules take control.
Risk per position: small and consistent
He caps trade risk tightly so multiple trades can run without stressing the account. This also keeps investors comfortable.
- Risk a fixed fraction of equity per position (e.g., 0.25%–0.50% of account).
- Calculate position size from stop distance: size = (risk per trade) ÷ (stop width in ticks or ATR).
- Round sizes down to the nearest lot/contract to avoid overfills.
- If slippage/commission exceeds X% of average trade expectancy, reduce frequency or widen signals.
Portfolio construction: correlation is the hidden risk
Most pain comes when multiple strategies move together. Martyn treats the portfolio as the product, not the individual trade.
- Maintain a rolling correlation table across strategies/markets (e.g., 90 trading days).
- If |ρ| > 0.6 between two active strategies, treat them as one “cluster” for risk caps.
- Stagger entries across clusters; never open two highly correlated trades in the same bar.
- Allocate more weight to strategies with higher risk-adjusted returns and lower average correlation to the book.
Exposure caps: guardrails that prevent cluster hits
Before any order goes live, it must pass portfolio exposure checks. These guardrails prevent a string of “perfectly valid” trades from stacking into one big drawdown.
- Max concurrent positions: set a portfolio-wide cap (e.g., 8).
- Max cluster exposure: total open risk across correlated positions ≤ 1.5× single-trade risk.
- Max market exposure: per asset class (FX, indices, metals, etc.) ≤ a defined fraction of equity (e.g., 3%).
- Block new trades if total open risk ≥ a portfolio ceiling (e.g., 3% of equity).
Drawdown control: automatic de-risking
Martyn plans for drawdowns in advance. When the line is crossed, the system itself takes the wheel.
- Implement a portfolio “circuit breaker”: if peak-to-trough drawdown ≥ D1 (e.g., 6%), cut per-trade risk by 50%.
- If drawdown ≥ D2 (e.g., 10%), halt new entries; only manage exits until equity recovers to D1.
- After recovery to within 1% of equity highs, gradually restore risk (e.g., +25% per week) rather than flipping back all at once.
- Log the date, cause (e.g., correlation spike, regime shift), and which guardrail fired to inform weight updates.
Strategy weights: update with data, not feelings
Weights drift as markets change. Martyn refreshes them on a schedule using recent performance and correlations.
- Recalculate strategy weights monthly using rolling Sharpe (or MAR) and correlation penalties.
- Cap turnover so weights don’t swing wildly (e.g., max ±20% change per rebalance).
- Hard floor for any live strategy (e.g., 5% weight) to preserve diversification unless it’s disabled.
- Disable a strategy if its live drawdown exceeds its historical 95th percentile by a set multiple.
Build–test–deploy cycle: keep the factory clean.
He separates research from live trading. New ideas must pass objective tests before touching capital.
- Require out-of-sample validation and a Monte Carlo shuffle of trade sequences; reject systems that collapse under randomness.
- Minimum trade count and market breadth thresholds before going live (e.g., >500 trades across ≥3 markets).
- Enforce a cool-down period between demo and production (e.g., 4 weeks) to catch operational glitches.
- Version everything; only one live version per strategy at a time.
Execution discipline: zero heroics
Great systems fail without clean execution. Martyn keeps routing simple and transparent.
- Use limit-or-market-with-protection orders according to the strategy’s slippage tolerance; predefine which is allowed.
- No manual entries; if an order fails, it’s logged and the opportunity is skipped—no chasing.
- Sync clocks and verify data integrity at session start; if the data feed drops, all new entries are blocked.
- Daily post-close checklist: reconcile fills, slippage, and exposure caps; update regime and correlation tables.
Investor-ready risk profile: smooth and scalable
Because he’s investor-facing, the equity curve matters almost as much as raw return. The portfolio is designed to be held.
- Target consistent risk-adjusted returns; avoid strategies that add return but raise portfolio correlation.
- Use smaller per-trade risk with more independent strategies to smooth the curve.
- Publish pre-agreed risk limits (max drawdown, max open risk, cluster caps) and adhere to them mechanically.
- If correlation across the book spikes (e.g., macro shock), automatically scale down gross exposure until normality returns.
Size Risk First: Fixed Fraction, ATR Stops, No Exceptions
Martyn Tinsley starts with risk, not entries. Before any signal fires, he decides exactly how much of the account is on the line—think 0.25% to 0.50%—and sizes the position from that number backward. That fixed fraction turns every trade into the same business bet, so one outlier doesn’t wreck the month. With Martyn, the discipline is mechanical: position size = (risk per trade) ÷ (stop distance), and the stop distance is defined, not guessed.
He anchors that stop to volatility using ATR, so the market decides where the line in the sand is—not his feelings. If the ATR is wide, the position shrinks; if volatility contracts, size can grow, but only within the fixed-fraction cap. No widening stops, no “I’ll give it one more tick,” and no chasing fills that blow through slippage limits. By standardizing risk and using ATR-based exits, Martyn Tinsley keeps losers small, lets winners expand naturally, and keeps the equity curve boring—in the best possible way.
Trade the Regime: Switch Between Mean Reversion and Momentum
Martyn Tinsley doesn’t marry a setup—he marries the market’s current state. When his regime filter says conditions are choppy and reverting, he enables mean-reversion entries and dials back trend plays. When momentum takes over, he flips the switch: breakout logic comes online, and fade setups are benched. The point is simple—trade what the market is doing now, not what you wish it would do.
He keeps the detection rules objective so they’re easy to code and repeat. Think moving-average slope plus a volatility/dispersion check to decide if price is stretching or trending. If signals conflict, Martyn Tinsley cuts size or stands down to avoid death by chop. This way, his edge isn’t a single pattern—it’s a routing rule that sends capital to the strategy that fits the regime today.
Diversify Smart: Uncorrelated Strategies Across Markets, Timeframes, and Durations
Martyn Tinsley builds his edge at the portfolio level, not by obsessing over a single “killer” setup. He mixes strategies that behave differently—mean reversion, breakout, trend-following, and volatility filters—so that something sensible works in most conditions. Then he spreads those systems across asset classes and symbols to avoid a single macro theme knocking everything at once. The result is fewer emotional swings and a steadier equity curve that keeps you trading the plan.
He also diversifies by timeframe and holding period, so trades don’t all live and die on the same micro-noise. A short-duration mean-reversion system can pair with a multi-day momentum model, and their return streams won’t move in lockstep. Martyn Tinsley watches rolling correlations and treats highly correlated strategies as one cluster, capping their combined exposure. He sizes up the strategies that add return without raising correlation and trims those that ride the same wave. That’s how he turns multiple small, independent edges into one resilient book.
Let Winners Run: Trailing Exits, Time Stops, Zero Heroics
Martyn Tinsley is ruthless about exits because that’s where emotions sneak in. He uses trailing stops—ATR or structure-based—so profit-taking adapts to volatility and trend strength instead of guessing tops. Fixed targets are rare; the goal is to capture the middle of the move and let the market kick you out when momentum fades. This keeps the system honest and prevents the classic “took profits too early” regret.
There’s also a time stop: if the price goes nowhere for a predefined number of bars, Martyn Tinsley closes it and frees capital. No widening stops, no “one more candle,” no adding to losers for a better average. If slippage or spreads spike, the system respects the rules and logs the anomaly rather than improvising. The net effect is simple—small losses, occasional big wins, and an equity curve that reflects discipline, not drama.
Portfolio First: Exposure Caps, Cluster Control, Automatic Drawdown De-Risking
Martyn Tinsley treats the portfolio as the product, so every trade must pass exposure checks before it goes live. He caps total open risk, limits how many positions can be active, and groups correlated trades into clusters so a single theme can’t torpedo the account. If two strategies move together, they share a risk budget; if correlation spikes, new orders from that cluster get blocked. This forces breadth and keeps the equity curve stable even when markets get one-directional.
He also bakes in circuit breakers that cut risk automatically during drawdowns. At a predefined loss threshold, per-trade risk gets halved; at a deeper threshold, new entries pause, and only exits are managed. As equity recovers, risk ramps back up in steps rather than flipping to full size overnight. With these rules, Martyn Tinsley prevents small setbacks from snowballing and ensures the portfolio survives to compound another day.
In the end, Martyn Tinsley’s edge isn’t a single indicator or clever entry—it’s the factory that surrounds his trading. He moved from manual discretion to algorithmic rules because code executes without ego, then built those rules around fixed-fraction risk, ATR-defined stops, and exits that trail strength instead of guessing tops. He treats regime as a first-class decision: mean reversion when markets are choppy, momentum when they’re directional, and half-size or flat when the signal is mixed. The common thread is process discipline—change rules between test cycles, never mid-trade; let the playbook, not emotions, decide.
Zoom out, and the real power shows up at the portfolio level. Martyn diversifies across markets, strategy types, and holding periods so return streams don’t move in lockstep, then watches rolling correlations and caps exposure by cluster to prevent one macro theme from sinking the ship. Guardrails kick in automatically: total-open-risk ceilings, max positions, and circuit breakers that cut risk or pause entries during drawdowns, only ramping back up as equity heals. That mindset extends to “always improving”—adding dynamic regime toggles, refining weights with live data, and keeping the research–deploy pipeline clean so new ideas earn their way into production.
For traders, the lessons are simple and tough: size risk first, code the rules you actually follow, route capital to the strategy that fits today’s market, and make the portfolio—not the last trade—the product. Do that with the kind of boring consistency Martyn Tinsley champions, and you give yourself a chance to survive long enough for edges to compound.
