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Finland-based Aatu Kokkila is an event-driven FX trader and Darwinex fund manager—best known for turning a video-game-honed pattern-recognition skillset into a structured approach to trading central bank decisions and data releases. In this interview, Aatu explains how he grew to manage multi-million-dollar capital on Darwinex, why he consolidated execution there, and how he keeps returns uncorrelated to equities while staying disciplined on drawdowns and expectations with investors.
You’ll learn Aatu Kokkila’s core playbook: trade after the news, not before; build scenario sheets for each event; read the official statement (not just headlines); and align fundamentals with real-time price action to decide between momentum or mean-reversion. He walks through risk sizing around volatility, why controlling drawdown is the key to attracting serious capital, and how post-event debriefs with his analyst compound edge over time. Expect clear, beginner-friendly takeaways you can use on the next CPI, NFP, or rate decision—plus a practical view on communication, capacity, and process that lets a strategy scale without losing its edge.
Aatu Kokkila Playbook & Strategy: How He Actually Trades
Core Edge: Trade after the event, not before
Event risk is messy; the edge shows up when the dust settles. Aatu waits for the first impulse to print, then trades the path that follows the information, not the guess before it. This keeps him out of random whipsaws and aligned with real money flow.
- Wait for the initial 1–3 minutes after the release; do not place orders before the print.
- Let the “event bar” close on your execution timeframe (M1–M5) before acting.
- Only trade if the spread normalizes to within 1.5× its pre-event average.
- Require a clean impulse: at least 1.0× daily ATR(14)/20 in the event leg or a 3× multiple of recent M5 range.
- Stand down if the first leg is directionless (overlap >60% of the bar’s range).
Pre-Event Playbook: Scenario sheets for every calendar risk
Aatu doesn’t improvise on macro days. He writes a one-page sheet: base case, surprise up, surprise down, and what each means for the relevant FX pairs. You’ll go faster and make fewer mistakes when the data hits.
- Build a template for each event (CPI, NFP, rate decision, PMI).
- For each, define: instruments to trade (2–4 tickers max), directional bias per surprise, and invalidation logic.
- Pre-mark levels: previous day high/low, session VWAP, nearest HTF swing, and options barriers if known.
- Decide beforehand which setup you’ll take: momentum continuation or mean-reversion fade.
- Pre-commit to skip if liquidity/volatility thresholds aren’t met (document the thresholds).
Execution: Momentum vs. mean-reversion — pick one per event
He keeps execution binary. If the number confirms the policy path, ride the impulse; if it contradicts positioning, fade the overshoot back to structure. The point is focus: one play, one set of rules.
- Momentum play: enter on the first pullback that holds above/below the event bar’s 50% line.
- Momentum confirmation: higher low/lower high + break of pullback high/low with volume uptick or tick-speed burst.
- Mean-reversion play: wait for a stop-run beyond the HTF level (H1/H4) and enter back inside the level on a close.
- Never mix modes mid-event: if you chose momentum, no fading that day (and vice versa).
- Maximum two attempts per event per pair; third try is a no-trade.
Risk & Volatility Sizing: Small risk, elastic size
Volatility expands on macro prints, so position size must contract. Aatu scales size to the event bar’s true range and caps damage fast. This is how he stays in business through unlucky outliers.
- Risk per trade: 0.25%–0.50% of account; lower on Tier-1 events (e.g., NFP, CPI, rate decisions).
- Stop location (momentum): beyond the event bar’s 61.8% retrace or 0.8–1.2× the pullback’s M5 ATR.
- Stop location (mean-reversion): beyond the stop-run extreme + spread buffer (0.5–1.0 pip majors; more for crosses).
- Position size = (risk $) ÷ (stop distance + spread + slippage buffer).
- Daily loss cap: 1.0–1.5R; weekly cap: 3–4R — hit it, stop trading calendar events until next week.
- Hard rule: cut size by 50% after two consecutive losing events.
News/Calendar Selection: Only trade your lane
Not all news is worth your attention. He focuses on liquid G10 pairs and top-tier releases where the reaction function is clear. Consistency in the calendar breeds data you can trust.
- Prioritize: CPI, jobs, central bank decisions, flash PMIs, retail sales in USD, EUR, GBP, CAD, AUD, NZD.
- Avoid exotics and pairs with known microstructure issues during news.
- Do not trade overlapping major releases; pick the highest-quality event per session.
- If the central bank is in a clearly hawkish/dovish cycle, it biases momentum in that cycle’s direction.
- Skip when the market is already pricing a shock (implied move far above average) and liquidity is thin.
Entry Triggers & Filters: Make the tape do the talking
Aatu uses a tight set of triggers that keep entries mechanical. You don’t need ten indicators — just structure, pace, and spreads.
- Structure filter: event bar breaks a pre-marked HTF level and holds the retest.
- Pace filter: tick-speed or volume surge on the break, then deceleration on the pullback.
- Spread filter: spread ≤ 1.5× baseline; if wider, wait.
- Time filter: if no valid trigger in 15–30 minutes after the print, pass.
- Corroboration: DXY or relevant rates move in the same direction on momentum plays.
Trade Management: Let winners work, cut losers fast
He treats post-news trades as campaigns with pre-defined add and scale-out points. The trade either behaves or it doesn’t; there’s no babysitting.
- First scale-out at +1R; move stop to breakeven only after a clean higher low/lower high forms.
- Trail behind the last confirmed M5 swing or a 10–20 EMA channel that the price respects during the impulse.
- Add size one time only on momentum continuation after a textbook pullback that holds structure.
- If momentum stalls (two failed pushes) or spreads widen again, take the rest and stand down.
- Never convert an intraday news trade into a swing; flatten by the end of the session.
Mean-Reversion Specifics: Fading the overshoot
When the data contradicts positioning, the first knee-jerk reaction can overshoot. Aatu fades only when the market shows acceptance back inside value, not before.
- Require a stop-run beyond the HTF level, followed by a close back inside the level.
- Enter on the first micro pullback after acceptance, not at the extremes.
- Target the event bar midpoint/VWAP first; leave a runner to the opposite side of the event range.
- Invalidate if price re-accepts beyond the extreme; no “give it room” on news fades.
Instruments & Sessions: Keep the roster tight
He doesn’t scatter attention across twenty charts. Two to four instruments per event, max, with clear session windows.
- Default pairs: EURUSD, GBPUSD, USDJPY, AUDUSD, USDCAD; add EURJPY/GBPJPY only with caution on JPY volatility.
- For US events, trade the London close into the NY session; for European events, EU morning only.
- Do not trade Asia for G10 macro unless it’s a BOJ/JPY-specific event.
- If your spread/latency is poor on a symbol, strike it from the roster.
Tech & Execution Hygiene: Reduce friction, reduce mistakes
Speed matters, but stability matters more. Aatu keeps a minimal stack and practices the routine until it’s boring.
- One-click entry/exit with preset risk; hotkeys for market-in, half-out, flat-all.
- Pre-load tickets with reduced size on Tier-1 days to avoid fat-finger oversizing.
- Keep a dedicated “news layout”: economic calendar, rates/US10Y, DXY, and 5–15m charts only.
- Screenshot pre-event levels and post-event outcomes for every trade.
Data & Review: Turn events into an edge that compounds
The playbook gets sharper when you track it. He measures how each event behaved and which trigger really paid, month after month.
- Log: event type, surprise vs. consensus, first-leg magnitude, spreads, chosen mode (momentum/fade), result in R.
- Tag trades by event family (CPI/NFP/CB), pair, and session for cohort analysis.
- Review monthly: keep the top two event families and drop the bottom one.
- Tighten rules where your losers cluster (e.g., widen spread filter, shorten time-to-trigger window).
Psychology & Drawdown Control: Protect the fund first
Aatu’s edge survives because he protects it during cold streaks. The goal is to stay solvent, confident, and selective.
- Pre-define pain: daily, weekly, and strategy-specific loss limits — obey them automatically.
- After a 4R drawdown in this strategy, switch to half-risk for the next five trades.
- No revenge trades after missed moves; if the trigger didn’t print, you did the right thing by not entering.
- Use a checklist before each event; if any item fails (sleep, focus, platform, spreads), skip the session.
Scalability & Capacity: Keep returns uncorrelated and scalable
The method works because it avoids equity beta and focuses on discrete catalysts. That makes it attractive and scalable — if you keep slippage and liquidity in mind.
- Size to average depth: do not exceed a size that would move the market on your entry timeframe.
- Cap the number of simultaneous pairs to avoid hidden correlation during USD-centric shocks.
- Track impact cost per trade; if it rises, cut size or switch to the most liquid pair for that event.
- Standardize the process so it’s delegable (scenario sheets, triggers, management rules written out).
Trade After the Print: Let Price Confirm the Event’s Story
Aatu Kokkila waits for the number to hit, the spreads to calm, and the first impulse to finish before committing risk. He treats the event bar like an X-ray of intent: once it closes, he asks whether the price is accepting the new information or rejecting it. By trading after the print, Aatu avoids the coin-flip slippage of guessing the surprise and instead rides the path institutions reveal in the first clean leg. This shift from prediction to confirmation immediately tightens execution and reduces the random losses that come from front-running headlines.
In practice, Aatu Kokkila wants the market to show its hand—normalized spreads, a decisive break of a pre-marked level, and a pullback that holds. If momentum is real, the retest won’t fully retrace; if it’s a fake, the close back inside structure tells him to stand down or flip to a fade later. The discipline is simple but strict: no entry before the event bar closes, no trade if liquidity is still messy, and no second-guessing once invalidation prints. The result is fewer trades with a higher signal, aligned to the actual flow instead of the forecast.
Volatility-Sized Positions: Elastic Risk That Shrinks on Macro Days
Aatu Kokkila sizes positions to the market’s mood, not his own conviction. On data days, he cuts risk because spreads expand and the event bar can dwarf normal ranges. He measures stop distance with the event bar and recent ATR, then lets that distance dictate size so the dollar risk stays constant. If spreads haven’t normalized, he assumes larger slippage and reduces the size again. This keeps Aatu trading the same risk per idea even when the tape is twice as wild.
In practice, Aatu Kokkila calculates position size as risk dollars divided by (stop distance + spread + slippage buffer). For Tier-1 releases, he halves his typical per-trade risk and caps daily loss so one bad print can’t spiral. After two consecutive losing events, he automatically halves the size for the next sequence to protect psychology and equity. Hard stops are non-negotiable, no averaging down, and no turning an intraday news idea into a swing when volatility fades.
Pick One Mode: Momentum or Mean Reversion, Never Both
Aatu Kokkila commits to a single playbook per event because mixed logic creates hesitation and sloppy exits. If the print confirms the policy path and fuels clean continuation, he runs momentum: buy strength after a controlled pullback, or sell weakness the same way. If the number contradicts positioning and triggers a stop-run beyond a key level, he chooses mean reversion: wait for re-acceptance back inside structure, then fade toward the event range midpoint. The key is binary thinking—once Aatu labels the environment, every signal and management rule aligns with that label.
Switching modes mid-trade is off-limits for Aatu Kokkila because it disguises a losing trade as a “different idea.” Momentum entries get momentum exits and add-on rules; mean-reversion entries get tight invalidations and conservative targets. This prevents revenge trading, removes overfitting in real time, and makes post-trade review honest because each event falls into one bucket. By guarding mode purity, he trades fewer, higher-quality setups and turns chaotic news spikes into structured, repeatable campaigns.
Pre-Event Scenario Sheets: Levels, Triggers, Invalidation Before Numbers Hit
Aatu Kokkila goes into every major print with a one-pager that makes decisions obvious under pressure. He pre-selects the two to four pairs most likely to move, marks HTF levels, and writes a base case plus surprise-up and surprise-down paths. Each path has a preferred mode (momentum or fade), the first trigger he wants to see, and the exact invalidation that kills the idea. By scripting the session this way, Aatu turns chaos into a checklist and removes the urge to improvise when the tape speeds up.
The sheet isn’t theory for Aatu Kokkila; it’s an execution map. He notes spread/volatility thresholds that must normalize, the time window when he’ll accept entries, and what constitutes acceptance versus rejection at key levels. If the market doesn’t print his trigger, he stands down—no “what ifs,” no FOMO. After the event, he annotates the sheet with outcomes to refine the next one, so each release trains the playbook instead of the ego.
Process Over Prediction: Logs, Loss Caps, Weekly Review Discipline
Aatu Kokkila treats the process as the product. He logs every event with the setup label, spread conditions, stop size, and R result so patterns emerge beyond gut feel. Loss caps are pre-committed—daily and weekly—so the worst outcome is known in advance, not negotiated mid-drawdown. When the cap is hit, Aatu stops, reviews, and protects mental capital the same way he protects financial capital.
Each week, Aatu Kokkila filters the log by event family and mode to see what’s really paying. Winners get clearer rules; losers get tightened filters or are dropped entirely for the next month. He rehearses the checklist before the next macro print and runs a brief post-mortem after, turning isolated trades into a measurable system. That cycle—log, cap, review—keeps results steady even when the news cycle gets noisy.
In the end, Aatu Kokkila’s edge is simple to say and hard to imitate: let the news land, then trade what the market proves. He won’t guess the surprise—he waits for spreads to normalize, the event bar to close, and price to accept or reject the fresh information. That’s where his binary logic kicks in: either ride momentum after a controlled pullback or fade the overshoot only after re-acceptance back inside structure. The calendar isn’t a roulette wheel; it’s a schedule of repeatable behaviors if you show up with levels, triggers, and invalidation already written down. This “post-print, rule-first” mindset strips out most of the randomness that kills retail accounts.
Risk is treated like a product with strict specifications, not a vibe. Position size flexes with volatility, so the dollars at risk stay constant even when the tape doubles in range. Stops are placed beyond structure plus a spread/slippage buffer, daily and weekly loss caps are non-negotiable, and size is cut after streaks to protect both equity and psychology. He never flips modes mid-trade, never averages down in a news spike, and never turns an intraday catalyst play into a swing because he missed the exit. The result is fewer trades, cleaner R multiples, and a P&L that survives the outliers.
Finally, Aatu turns every event into data that sharpens the next one. He logs the setup label, surprise context, first-leg magnitude, spreads, and result; then he reviews by event family to keep the best and cull the rest. That feedback loop—scenario sheet → disciplined execution → structured review—builds a playbook that scales, stays attractive to outside capital, and doesn’t depend on equity beta. If you copy only three things, make them these: trade after the print, size to volatility, and run a weekly review with hard caps. Do that, and your process will start looking a lot more like Aatu’s—and your results will stop looking like a coin flip.
