Table of Contents
In this interview, Ceri Bryans sits down to share how she evolved from a manual day trader to a fully mechanical, rules-driven operator who lets her robot execute while she lives her life. She explains the catalyst—major life events—and why automation, simple time-based breakouts around the U.S. session, and strict backtesting now anchor her edge. You’ll hear how Ceri picks a pre-open candle, places both long and short orders, and removes discretion so results come from process, not predictions.
You’ll learn the exact mechanics behind Ceri’s approach: adding “padding” above/below the signal candle based on spread to filter fake breaks; choosing timeframes (start at H1, drop to M30 or M15) to balance range vs. target; and using fixed 3:1 targets alongside trailing-stop variants (e.g., 1:1 trail activated at 2:1, or a 1.5R ladder) to harvest extended moves. She also shows how to backtest honestly, track worst runs, and size risk to a personal “comfort zone” so a 40% win rate with 3R targets still compounds—plus why she filters by day of week, rotates markets, and scales via separate accounts to keep execution clean and stress low.
Ceri Bryans Playbook & Strategy: How She Actually Trades
Core Philosophy: rules beat predictions
Ceri Bryans trades like a builder: define the blueprint, then let the tools do the work. Her edge comes from repeating a simple, mechanical routine that removes hesitation and second-guessing.
- Trade a written, rules-only plan; no gut calls.
- Automate everything you can (entries, stops, exits, logging).
- Judges’ decisions by process quality, not single-trade outcomes.
- Review only weekly; don’t tinker midweek unless a hard rule breaks.
What she trades & when
Ceri focuses on liquid majors and index CFDs during the most active hours. Volatility is the fuel; consistency comes from fixed windows.
- Primary markets: 2–4 FX majors (e.g., EUR/USD, GBP/USD) plus 1–2 indices (e.g., US100, US500).
- Trade one defined session window per market (e.g., pre-US through first two hours).
- If the spread exceeds a preset cap at the session open, stand down.
- One setup per market per session; no “revenge” reruns.
The setup: time-boxed breakout
Instead of hunting patterns all day, Ceri defines a “signal candle” window and trades the breakout of that range. The window is chosen for consistent liquidity, not aesthetics.
- Choose a signal window (e.g., the 30–60 minutes before your session starts).
- Mark the high/low of that window; that’s your reference range.
- Skip if the range is too small/too large (set min/max ATR-based thresholds).
- No trade if the signal range overlaps major scheduled news in the next 10 minutes.
Entry mechanics: bracket with padding
Breakouts fail when the price barely tags a level. Ceri adds a small buffer to reduce “kiss and reverse” fakes and places both sides as OCO (one-cancels-other).
- Place a buy stop at high + padding, and a sell stop at low − padding.
- Padding = max(spread × K, tick_size × M); pick constants (e.g., K=2, M=4) and keep them fixed per market.
- Orders are OCO: fill one, cancel the other immediately.
- If not triggered within X minutes from session open (e.g., 30–45), cancel both and stand down.
Initial stop: structure, not hope
Stops are pre-committed before entry and sized to volatility. The goal is to survive routine noise while keeping R tight enough to compound.
- The initial stop goes just beyond the opposite side of the signal range (beyond the padding).
- If the range is unusually wide, cap stop distance at a max ATR multiple (e.g., 1.2× 14-ATR of the entry timeframe).
- If the stop would exceed your risk cap, reduce position size; if still over, skip the trade.
- Never move the stop away from risk; only reduce.
Targets & exits: fixed R with trail options
Ceri aims for asymmetric payouts first, then lets winners breathe via optional, rule-based trails. Pick one model and stick to it for a full review cycle.
- Default target = +3R fixed; take full or partial at target per your model.
- Optional trail model A: when price reaches +2R, move stop to +1R and trail behind the last two bar lows/highs.
- Optional trail model B: scale out 50% at +2R, run the rest with an ATR-based chandelier (e.g., 2.5× ATR).
- No discretionary early exits; only the prewritten exit rules apply.
Risk & position sizing: stay in the comfort zone
Edge dies when risk is too big to execute faithfully. Ceri sizes small enough to endure inevitable losing streaks without flinching.
- Fixed fractional risk per trade (e.g., 0.25%–0.5% of account).
- Plan for the statistically worst losing run from your backtest; size so that the drawdown stays within your psychological limit.
- One position per market per session; no stacking on correlated pairs.
- If hitting your daily max loss (e.g., −1%), stop trading for the day.
Trade filters: simple knobs that matter
A couple of lightweight filters can meaningfully improve quality without turning discretionary. Use toggles you can code and verify.
- Day-of-week filter: if historical Monday performance is weak, disable Mondays until the next review.
- Spread filter: if live spread > threshold at order time, skip.
- News filter: skip trades if high-impact news (for that market) lands inside your initial holding window.
- Volatility filter: if 14-ATR is below a minimum, stand down; breakouts need energy.
Timeframes & charting: see the same thing every day
Ceri keeps the analysis tight so the robot and the human agree. Consistent timeframes prevent “chart shopping.”
- Define a primary execution timeframe (e.g., M30) and a backup (M15) for smaller ranges; do not mix within a session.
- Use the same broker feed/timezone for all calculations to avoid off-by-one candle issues.
- No indicators required beyond ATR and a session/time window marker.
Backtesting & validation: prove it first
The strategy is only as good as its track record. Ceri validates rules before they ever touch live risk and measures what truly matters.
- Backtest at least 3–5 years per market with out-of-sample months at the end.
- Record every trade’s entry/exit price, R result, MAE/MFE, and time-in-trade.
- Evaluate by expectancy (R), win rate, max consecutive losers, and monthly distribution.
- Only change one variable per iteration; re-validate for at least 100 trades before adopting.
Execution & discipline: let the machine do it
Once rules are proven, the job is mostly scheduling and monitoring. The fewer buttons you press, the fewer errors you make.
- Pre-place OCO orders before the session; step away during the noise.
- Use alerts/logging to confirm fills, not to micro-manage.
- If a platform error occurs, flatten and record it as a “technical scratch”—don’t re-enter outside rules.
- Review weekly: export trades, tag them (market, DOW, news, model), and decide any toggle changes for the coming week.
Portfolio construction: diversify by market and model
Ceri expands by adding uncorrelated markets and small variations of the same core idea. Diversification smooths the equity curve without complicating the day.
- Run the breakout on 3–5 instruments that don’t all move together; cap to 1–2 highly correlated at once.
- Separate accounts or sub-accounts per model to keep P&L diagnostics clean.
- Weekly risk cap across all models (e.g., −2%). If hit, pause all trading until the next review.
- Promote or bench markets each month based on 3- and 6-month rolling performance.
Daily checklist: fast and repeatable
Consistency wins. A tight checklist makes performance boring—in a good way.
- Before the session: check spreads, news calendar, ATR, and that your OCO templates are correct.
- During session: no chart dragging; let orders work; log anomalies only.
- After the session: export fills, tag results, and reset for tomorrow within 10 minutes.
- No post-market strategy edits; all tweaks happen at the weekly review.
Process Over Prediction: Build a Mechanical Breakout Routine
Ceri Bryans wins by treating trading like a checklist, not a guessing game. She defines a session window, marks the signal candle’s high and low, and lets pending orders do the heavy lifting. No opinions about direction—just an OCO bracket with small padding, so price must prove it wants to run. If the order doesn’t trigger within the window, she cancels and moves on without second-guessing.
The rest is rinse and repeat: fixed stops beyond the opposite side of the range, fixed targets in R, and a trail that only activates according to prewritten rules. Ceri Bryans reviews results weekly, not trade-by-trade, so one outcome never dictates the next decision. She sizes each position the same way, keeps correlated markets from clustering risk, and skips sessions where spreads or news break the plan. The result is a routine any trader can execute—simple, testable, and built to work without prediction.
Size Risk to Survive Losing Streaks and Keep Compounding
Ceri Bryans treats risk like a utility bill—predictable, capped, and never skipped. She risks a small, fixed fraction perturbed, so a cold streak dents the curve, not destroys it. Before going live, Ceri calculates the statistically ugly side of her backtest—the longest losing run—and sizes positions so that drawdown from that run stays inside her psychological comfort zone. If a setup requires too large a stop to meet that risk cap, she reduces the size or stands down.
Ceri Bryans also spreads exposure across uncorrelated markets to avoid stacking the same risk twice. She uses daily and weekly loss limits to force a clean stop when noise turns into damage. Because the R-value is fixed, wins build in chunks while losses remain uniform, allowing the math to work as intended. Kept small enough to execute without fear, her risk engine turns an average month into a survivable one—and a good month into steady compounding.
Use Volatility Windows and Padding to Filter Fake Breaks
Ceri Bryans leans on the market’s natural rhythm instead of chasing every tick. She defines a volatility window—typically the pre-session build and first burst of the U.S. hours—then builds her breakout around that consistent energy. By bracketing the signal candle’s range only during this window, she forces trades to happen when spreads are tighter and follow-through is statistically better. If the range is abnormally small or abnormally large for that hour, Ceri stands down instead of bending the rules to fit the day.
The padding is her secret sauce against “tap-and-snap” failures. Ceri Bryans places entries a small distance beyond the range, using a fixed buffer tied to live spread or tick size, so the price must convincingly clear the level. That little gap filters out random pokes that would otherwise tag a stop and reverse. Combined with a strict time-out—cancel if not triggered within the window—this keeps her entries clean, her stops honest, and her focus on moves that she actually wants to run.
Fixed 3R Targets with Rule-Based Trails for Bigger Winners
Ceri Bryans starts with a clear destination: a fixed 3R take-profit that locks in asymmetric payoff. By committing to that number before the trade, she avoids the urge to snatch small wins or “let it ride” without rules. If price reaches +2R, she can switch on a predefined trail—never discretionary—to harvest a runner while protecting open equity. The key is choosing one exit model per review cycle and sticking to it long enough to get meaningful data.
When the trail is active, Ceri Bryans moves the stop in measured steps, such as behind recent swing structure or an ATR-based distance, and never widens it. She may scale a portion at +2R while letting the remainder follow the trail toward +4R or more, but only according to the written plan. If momentum stalls or the trail is tagged, she accepts the exit without post-mortem tinkering. Over time, the fixed 3R foundation supplies reliable chunks, and the rule-based trail turns the handful of outliers into account-moving winners.
Diversify by Market and Model, Not Redundant Signals
Ceri Bryans spreads her bets across instruments that don’t all move for the same reasons. She’ll run the breakout on a few FX majors and one or two indices, but avoids stacking highly correlated pairs in the same session. The idea is to let independent edges express themselves while keeping total risk capped. If two charts are essentially the same trade in disguise, Ceri chooses one and skips the duplicate.
She treats models the same way—one core breakout, plus a clearly different exit variant or timeframe, instead of minor tweaks pretending to be diversity. Ceri Bryans reviews rolling performance to bench underperforming markets and promote those behaving well, without chasing last week’s hero. Separate accounts or tracking buckets keep P&L clean so she can tell which model truly earns its keep. Over time, this mix of uncorrelated markets and distinct rule sets smooths the equity curve and reduces the chance that a single theme drags down the whole book.
Ceri Bryans’ edge is radical simplicity: define a session window, bracket both sides of a pre-open signal candle, and let price choose the direction. She removes prediction entirely—no discretionary bias, no mid-trade tinkering—so each order either triggers and plays out or quietly expires. That mindset pairs with a comfort-first math profile: she’s happy with roughly a 40% win rate so long as targets are 3R, because it trains her expectations and lets the statistics—not her emotions—carry the day.
Mechanically, Ceri frames the move around consistent volatility: start on the H1 before the U.S. or London open, then only step down to M30 or M15 if the hourly doesn’t test well. Entries sit beyond the range with “padding” sized to the market’s spread—think a two-point spread implying about five points of buffer, or at least ~2× spread—so price must prove intent instead of nicking the level and snapping back. Exits are prewritten: fixed 3R take-profit plus optional, rule-based trails that ratchet risk after +2R, or a 1.5R/3R/4.5R ladder when that variant outperforms.
Discipline shows up in the filters and portfolio design: she disables weak days (e.g., Mondays) when the data says so, runs multiple markets and models in separate accounts for clean tracking, and sizes every trade to stay calm through inevitable losing runs. As markets produce profits, she redeploys the original stake into new markets—compounding breadth while letting winners keep working—then monitors live stats against backtests to ensure alignment. The result is a boring-by-design process that compounds steadily: simple time-boxed breakouts, smart padding, fixed-R math, and unemotional toggles that keep her on the right side of volatility.
