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Scott Welsh is in the hot seat—yes, the former tennis pro turned systematic trader—walking through how he evolved from fundamentals and MACD tinkering to fully automated strategies. Recorded on a trading podcast, this conversation matters because Scott blends athletic discipline with hard-won market lessons: deep testing, simple rules, and the humility to size down after lucky streaks. He’s refreshingly candid about the “Mother’s Day Massacre,” why leverage seduces, and how moving to robots lets him love the research without babysitting every tick.
You’ll learn how Scott builds trust in a strategy (clear logic + long data), why simple trend and mean-revert rules beat filter-stacking, and how he combines multiple timeframes into a diversified, system-driven portfolio. He breaks down the real-world stuff traders skip—spreads, VPS uptime, demo-first deployment, and the psychology of staying with a system through losing streaks. If you want a practical blueprint for going from ideas to live, automated execution—and beating index-fund thinking—this interview is your map.
Scott Welsh Playbook & Strategy: How He Actually Trades
Core philosophy: simple systems, heavy evidence
Scott keeps the edge simple and the proof deep. He favors a few transparent rules over complex indicators and wants thousands of trades across years to trust a system.
- Define every entry, exit, and size rule so that two people would take the same trade.
- Prefer 2–4 rules per system; remove any rule that doesn’t add measurable expectancy.
- Require long test histories (10+ years or 1000+ trades on lower timeframes) before going live.
- Accept losing streaks as math; build systems that survive them rather than try to avoid them.
Markets & timeframes he actually uses
He picks liquid, widely followed symbols and sticks to timeframes that reduce noise but still give enough trades. Think major FX pairs, index CFDs/futures, and a few robust ETFs.
- Focus on EURUSD, GBPUSD, USDJPY, XAUUSD, and equity index products (e.g., ES/US100 or liquid ETF proxies).
- Primary timeframes: Daily and 60-minute; add 15-minute only if spreads are tight and history is deep.
- Avoid illiquid instruments and news-sensitive microcaps; the edge is rule repetition, not headlines.
Setup #1: breakout trend rider (daily)
This is his “let the big move do the work” template. It aims to catch expansions after compression using a channel or recent-high breakout with volatility-aware exits.
- Indicator scaffolding: Donchian High/Low(55) for entry, Donchian(20) for exit; ATR(14) for stops and sizing.
- Long entry: Buy stop at 55-day high; short entry: Sell stop at 55-day low (skip shorts on indices if your plan forbids).
- Initial stop: 2.5 × ATR(14) from entry.
- Trailing exit: Close on breach of 20-day low (for longs) / 20-day high (for shorts).
- Time exit: If neither stop nor trail is hit by 120 bars, close at market.
- Filter: Skip trades if today’s range < 0.6 × 20-day ATR (avoid ultra-quiet breakouts).
- Risk: 0.25%–0.5% of equity per trade; one position per market in this system.
Setup #2: mean-revert pullback (60-minute)
When a market trends on the higher timeframe but overextends intraday, this ruleset buys the dip/sells the pop back to average.
- Trend filter: Price above 200-SMA on the daily chart for longs; below for shorts.
- Long entry: On a 60-minute chart, wait for RSI(2) ≤ 10 and a close below a 20-EMA; place a buy stop 1 tick above the prior bar’s high.
- Short entry: RSI(2) ≥ 90 and a close above 20-EMA; sell stop 1 tick below prior bar’s low.
- Stop: 1.8 × ATR(14) (H1) from entry.
- Profit target: Midline reversion to 20-EMA or a fixed 1.2R, whichever hits first.
- Time stop: Exit after 10 bars if neither target nor stop hits.
- Risk: 0.2%–0.35% per position; allow up to two uncorrelated pairs simultaneously.
Risk & position sizing that survives real drawdowns
Scott treats risk like a thermostat—never off, always controlled. The idea is small, consistent heat, so the system can keep firing through cold streaks.
- Per-trade risk cap: 0.25%–0.5% of account; lower for intraday.
- Portfolio heat cap: Total open risk ≤ 2% across all systems.
- Volatility sizing: Position = (Account × Risk%) ÷ (Stop distance in price × Value per point).
- Equity curve brake: If rolling drawdown reaches 1.5 × your historical median drawdown, cut size by 50% until a new equity high.
Portfolio construction: diversify the rules, not the hope
He combines a few uncorrelated systems across symbols and timeframes, so something is usually working. The goal isn’t perfection; it’s resilience.
- Run at least two systems (trend + mean-revert) across 4–8 symbols.
- Avoid doubling up on the same exposure (e.g., ES + NQ + SPY is mostly one bet).
- Stagger timeframes (Daily + 60-minute) to smooth equity swings.
- Rebalance symbol weights quarterly; drop any symbol that degrades system expectancy.
Entry execution & slippage control
Entries are preplanned; execution is about getting filled close to the plan and avoiding dumb slippage.
- Place stop/limit orders after session close (daily) or after the bar closes (intraday).
- No market orders unless exiting on a protective stop.
- For FX/CFDs, avoid entries during spreads’ nightly widening; shift orders 15–30 minutes after roll.
- If slippage > 0.25R on average in forward trades, widen stops modestly and reduce size to keep risk constant.
Automation workflow: from idea to live
He loves robots because they remove second-guessing. Automation enforces the rules and frees attention for research, not babysitting.
- Prototype in a platform that exports exact backtest trades; ensure bar-close logic matches live fills.
- Forward test on demo for 4–8 weeks; confirm slippage, spread, and uptime.
- Deploy on a VPS close to broker servers; monitor logs, rejected orders, and disconnects daily.
- Hard-stop killswitch: If the bot misses 2 consecutive exits, disable and investigate before re-enabling.
Testing & robustness checks you can repeat
Confidence comes from trying to break the system and failing. Small variations shouldn’t flip the edge.
- Parameter jitter: Shift lookbacks ±20%—expect similar equity shape and positive expectancy.
- Symbol roll: Test the same rules on related symbols; keep only if results rhyme, not just rhyme once.
- OOS discipline: Reserve the most recent 25% of data as untouched out-of-sample; require profitability there.
- Monte Carlo: Shuffle trade order 1000×; ensure max drawdown stays within your pain budget.
Daily, weekly, monthly routine
A steady cadence keeps the human out of the way and the machine accountable.
- Daily: Check VPS/broker connection, review open risk vs. heat cap, confirm orders placed for next bar/day.
- Weekly: Recalculate ATR-based stops/sizes, review system discrepancies, and export fills to a journal.
- Monthly/Quarterly: Rebalance symbols, refresh the walk-forward test, and scale size only after new equity highs.
Drawdown playbook & psychology
Scott plans for pain in advance so he can execute when it shows up. The rules protect the mindset, and the mindset protects the rules.
- Pre-commit to the max expected drawdown × 1.3 buffer; write it down before going live.
- If drawdown hits the buffer, halve size and keep trading the plan; never system-hop mid-drawdown.
- Celebrate rule-following, not P&L: tag every trade “A” (perfect) / “B” (minor slip) / “C” (rule break); target ≥ 95% “A”.
Scaling up without blowing up
Growth comes from more systems and small size bumps, not from swinging bigger on one setup.
- Add capital or raise risk by 0.05% increments only after a new 90-day equity high.
- Prefer adding a new uncorrelated symbol/system over doubling the size of an existing one.
- Cap single-market exposure at 30% of total open risk across all systems.
Practical checklist before each new system goes live
Final guardrails ensure you’re launching something sturdy, not shiny.
- 1000+ trades (intraday) or 10+ years (daily) of tests with stable expectancy.
- Walk-forward and Monte Carlo passed; max drawdown within your plan.
- Live demo fills match backtest assumptions within 10% slippage tolerance.
- Automation logs are clean for 4 consecutive weeks; contingency plan documented.
Size small, survive drawdowns, let math compound the edge.
Scott Welsh pounds one theme: trade tiny so your strategy lives long enough to work. Risk per trade stays small on purpose because staying in the game beats any single winner. When losers cluster—as they always do—the small bite keeps you calm and lets the math play out. Think fractional risk, consistent execution, and enough repetitions for expectancy to show up.
Scott Welsh also treats drawdowns as a cost of doing business, not a crisis. He pre-decides the max pain he’ll accept, then trades through it with reduced size instead of changing the system. That discipline lets compounding do its thing when the next streak of winners arrives. Keep risk steady, cuts automatically, and your edge gets the time it needs to compound.
Allocate by volatility so each position pulls equal weight.
Scott Welsh keeps sizing fair by letting volatility call the shots. Instead of buying the same lot size everywhere, he scales positions so each trade risks roughly the same slice of equity. That was a quiet pair that doesn’t bloat your exposure, and a wild market doesn’t hijack your P&L. He likes simple measures—think ATR or recent range—to translate noise into distance for stops and size.
In practice, Scott Welsh picks a fixed risk percent, divides by the stop distance, and lets the math decide the quantity. When volatility expands, size shrinks; when it contracts, size grows—always within a hard cap on total portfolio heat. He also watches correlations so two “different” trades don’t secretly double the same risk. Re-sizing on schedule keeps contributions balanced and helps the equity curve reflect skill, not randomness.
Diversify by system, symbol, and timeframe to smooth equity.
Scott Welsh spreads risk across edges, not just tickers. He runs a trend setup alongside a mean-reversion idea, so one can win while the other rests. He rotates across symbols that don’t all move together, like major FX pairs and equity indices, to reduce the chance of one theme dragging everything down. Mixing daily and 60-minute charts adds another layer, because signals fire at different rhythms.
Scott Welsh keeps caps per symbol and per strategy, so nothing dominates the book. If two markets are highly correlated, he treats them like one bet and trims size. He reviews contributions regularly and cuts what isn’t pulling its weight. The result is a steadier equity curve where consistency comes from design, not luck.
Trade mechanical rules, not predictions—execute consistency over conviction
Scott Welsh builds rule-sets so clear that two traders would take the same trade. He doesn’t try to guess news, pivots, or narratives; he lets entries, exits, and size fire from prewritten conditions. When the signal prints, he executes—no downgrading a valid trade because of a hunch. The goal is to remove heat-of-the-moment judgment and keep the edge reproducible.
Scott Welsh also measures behavior, not bravado. He tracks whether he followed the rules and grades each trade “A” or not, because execution quality drives outcomes more than opinions. If a setup underperforms, he checks logic and stats instead of rewriting rules mid-drawdown. Consistency turns small edges into durable results, while prediction turns strong edges into occasional luck.
Prefer defined risk, automate exits, and cap total portfolio heat.
Scott Welsh favors defined risk because it keeps the downside visible and enforceable. Every position has a pre-set stop or payout structure, so the worst case is known before entry. He programs exits to trigger without debate, removing the “maybe it bounces” stall that expands losses. When volatility jumps, his stop distance adjusts, but the capital at risk stays constant.
Scott Welsh also limits portfolio heat so multiple good ideas don’t become one big bet. He caps total open risk at a fixed percentage and refuses new signals once the cap is hit. If a drawdown breaches his pain line, size ratchets down automatically until the equity curve recovers. Defined risk, automated exits, and a hard heat ceiling make the account antifragile—small cuts, quick resets, and steady compounding.
Scott Welsh leaves you with a clear blueprint: keep rules simple, size small, and let repetition reveal the edge. He leans on mechanical systems—mostly a blend of trend-following and mean reversion—then spreads them across symbols and timeframes so something is usually working. The tennis-coach mindset shows up everywhere: pre-commit to risk, accept losing streaks as math, and grade execution so discipline stays front and center. Automation isn’t a gimmick for him; it’s the guardrail that removes second-guessing, enforces exits, and frees him to do the one thing that actually scales results—test, refine, and add robust systems over time.
Equally important are the unglamorous details Scott Welsh refuses to skip. He sizes by volatility so each position contributes fairly, caps total portfolio heat so multiple “good” ideas don’t morph into one oversized bet, and cuts size automatically when the equity curve hits a pain threshold. He forward-tests before going live, runs on reliable infrastructure, and watches slippage, spreads, and correlations like a hawk. The net effect is a trading business that survives the rough patches, compounds through persistence, and stays calm because the worst case is known in advance.
