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In this interview, I sit down in Hong Kong with Alejandro Perez, a full-time algorithmic trader and coach at Desire To Trade. He explains how he manages portfolios of automated strategies without losing touch with the charts, why “normal” drawdowns are part of the game, and how having the courage to ride them is often the difference between stagnation and the next big equity pop.
You’ll learn Alejandro Perez’s trader strategy stack—how he pairs a Bollinger Band Reversal approach with a trend-following system to balance signals—plus his rules for excluding underperforming symbols, handling statistically normal multi-month drawdowns, and choosing timeframes (why H1 has been his sweet spot and when he experiments with lower). He also walks through practical capital scaling—starting live small, then growing with profits and selective funding programs—so newer traders can copy the process without over-managing or second-guessing the data.
Alejandro Perez Playbook & Strategy: How He Actually Trades
Core Framework: Two Systems, One Accountable Process
Alejandro runs a simple, rules-first process: a mean-reversion engine for frequent opportunities and a trend-following engine for the big pushes. The goal is steady expectancy with diversified edges and zero discretion creep.
- Define two independent systems: Bollinger Reversal (BR) and Trend Follow (TF).
- Track each system’s stats separately (win rate, avg R, MAR, max DD) and the combined equity curve.
- If a discretionary call isn’t written in the rules, it doesn’t happen.
- All rules live in one place; push updates only on weekends after review, never intraday.
Markets, Sessions, and Timeframes
He focuses on liquid FX pairs and a small watchlist of indices to keep slippage and spread noise predictable. Execution is mostly on the H1 chart to balance signal quality with sample size.
- Primary universe: 10–15 liquid FX pairs + 1–2 indices (e.g., DAX/US100) if spreads are acceptable in your jurisdiction.
- Session focus: London through early New York; no new entries in the last 2 hours of New York unless a pending order is already set.
- Default timeframe: H1 for signals; use M15 only for refinement (never to invent trades).
- Hardly exclude exotics or pairs with average spread > 1.5× your baseline major.
Mean-Reversion Engine (Bollinger Reversal)
This engine catches snap-backs after overextension. The key is separating “stretched” from “broken,” then scaling out into the reversion.
- Use Bollinger Bands 20, 2.0 on H1; confirm stretch with RSI(14) > 70 for shorts or < 30 for longs.
- The candle must close outside the outer band; the next candle forms a re-entry back inside to arm a pending order in the reversal direction.
- Entry: stop order 1–2 pips beyond the re-entry candle; invalidate if price makes a fresh extreme beyond the signal candle’s wick.
- Initial stop: beyond signal extreme + spread + 0.1×ATR(14).
- Take profit: stage into two targets—T1 at mid-band, T2 at opposite inner band or 0.75×ATR(14), whichever comes first.
- If price tags mid-band and stalls for 3 consecutive candles, trail to break-even and let T2 work.
Trend-Following Engine (Continuation Pullbacks)
This engine rides established moves with brisk pullbacks and momentum re-acceleration. It protects you from overtrading chop that punishes mean-reversion.
- Trend filter: 200-EMA slope + price above/below 200-EMA, confirm with ADX(14) > 20.
- Setup: 2–5 candle pullback to the 20-EMA; look for a bull/bear engulfing or pin in trend direction.
- Entry: stop order above/below the trigger candle high/low; order cancels after 3 candles if unfilled.
- Initial stop: below/above pullback swing or 1.0×ATR(14), whichever’s wider.
- Take profit: risk multiple based—scale at +1R, trail remainder using 20-EMA close; exit on an opposite close beyond 20-EMA or loss of ADX trend (falls < 18).
Risk, Sizing, and Correlation
Sizing is volatility-aware and correlation-aware. He avoids stacking similar bets that turn one idea into three losses.
- Per-trade risk: 0.25–0.5% for BR, 0.5–0.75% for TF; cap total open risk ≤ 2.0%.
- If two pairs share ≥ 0.7 rolling 30-day correlation, treat them as one position—split risk, don’t double it.
- ATR position sizing: lot size set so SL distance × pip value equals your per-trade risk.
- If daily news risk is elevated (tier-1 events affecting more than one pair you trade), halve per-trade risk for that session.
Symbol Inclusion & Probation Rules
Not every symbol deserves a seat forever. Keep those that pay; sideline those that don’t.
- Maintain symbol scorecards (win%, PF, expectancy, avg DD length) by system and timeframe.
- Put a symbol on probation if: PF < 1.1 over the last 100 trades or max adverse excursion > 1.5× book average.
- While on probation, trade half size; remove if two consecutive monthly reviews fail the threshold.
- Add a new symbol only after 50+ paper trades or A/B micro-size with 0.1% risk for one month.
Execution Hygiene: Orders, Slippage, and Spreads
Execution consistency compounds the edge. Small frictions add up—control what you can.
- Enter via stop/limit orders where defined; avoid market clicks unless managing exits.
- If spread > 2× its 30-day median at entry, skip the trade.
- Use server-side stops; never rely on mental stops.
- Record slippage on every filled stop order; if average slippage > 0.3R on a symbol, review or cut it.
Management: Trailing, Break-Even, and Partials
Simple, mechanical management prevents “fiddle losses.” Commit to it before the trade exists.
- Move to break-even at +0.8R (TF) and mid-band touch (BR).
- For TF, trail on 20-EMA close only—no intrabar nudges.
- For BR, if price tags mid-band and then closes back outside the band against you, exit the remainder.
- Never widen stops; if volatility spikes, reduce size next entries, not current stops.
Drawdown Playbook
Drawdowns are baked into both engines. The trick is pre-declaring how you’ll behave when they show up.
- Define normal DD as the worst historical system DD × 1.2 buffer; anything within that is business as usual.
- If combined equity drops > normal DD, halve risk across both systems until recovery to prior equity high × 0.9.
- Suspend only the system whose equity falls below its 10-month low and fails a walk-forward sanity check (PF < 1.0 over the last 200 trades).
- Keep trading the other engine to preserve opportunity flow.
News & Event Handling
You don’t need to predict news—just don’t stand in front of it unprotected.
- No new BR entries within 30 minutes pre/post tier-1 events (CPI, NFP, rates) on affected currencies.
- For TF positions in profit > +1R, reduce size by 50% ahead of the release; leave stop as is.
- If a surprise gap hits SL with slippage, log it separately as “event slippage” for future sizing adjustments.
Automation, Monitoring, and Overrides
Automate the repetitive; supervise the risky. Keep overrides rare and rule-bound.
- Automate signal detection, order placement, and log capture; manual tasks: weekly review, parameter changes, and symbol probation decisions.
- Human override allowed only to: cancel a pending order if spread spikes > 3× median, or flatten during platform outage risk.
- Every override must be journaled with a timestamp, screenshot, and reason code; three overrides in a month trigger a process review.
Scaling Capital and External Funding
Growth is methodical: increase size when the data says so, not when you “feel ready.”
- Internal scale-up: raise per-trade risk by +0.1% after a new equity high + 3R buffer, then hold for 20 trades of stable metrics.
- External funding: run the same rules; adapt only the daily loss cap to match the funder’s policy.
- If the daily drawdown limit is tight, halve BR sizing and prioritize TF signals to reduce churn.
Weekly Review & Maintenance
The edge is in the maintenance. A tight feedback loop keeps the system healthy.
- Every weekend, export trades by system and symbol; update PF, expectancy, DD, win% by weekday/session.
- If PF deterioration > 15% versus the 6-month baseline for a system, simulate parameter sanity (BB width 1.8–2.2, EMA 18–22, ADX 12–18) without curve-fitting.
- Rotate a 1-month test window where BR or TF trades H4 for diversification; keep it at half size until PF > 1.15 over 60 trades.
- Archive old rule versions with dates; only change one parameter set per week, max.
Personal Operating Rules (Behavior & Focus)
Discipline is a skill. Codify it like any other edge.
- Trade windows only: London + early New York; outside those, the platform is read-only.
- Max 2 rule checks per trade; if you need a third, the trade isn’t clean.
- If you break a rule, reduce next session’s risk by 50% and write a 3-sentence root cause.
- Daily cap: 3 new BR attempts and 2 TF attempts; stop when you hit the cap or +2R net, whichever comes first.
Size Risk Like a Pro: Fixed R, Correlation Caps, Daily Limits
Alejandro Perez keeps it brutally simple: every trade risks a fixed R, so the math stays consistent when emotions don’t. He calibrates that R to account for size and volatility, then refuses to bump it mid-streak—green or red. If two positions are 0.7+ correlated, he splits the allocation so combined exposure still equals one R, not two. That way, one macro theme can’t blindside the whole book.
He also hard-codes guardrails: a daily loss cap (e.g., 2–3R), a max open risk cap, and no revenge trades after the cap hits. When price expands and ATR widens, Alejandro Perez lets size contract automatically so defined risk remains constant in dollars. If slippage or spreads push the effective risk above planned R, he cancels and waits rather than forcing a fill. The result is a position-sizing spine that protects the account first and lets edge compound second.
Let Volatility Decide: ATR-Based Entries, Dynamic Stops, Adaptive Position Sizing
Alejandro Perez lets ATR do the talking, so his trade size and stop distance scale with the market’s mood. When ATR expands, he tightens position size and gives the stop more room; when ATR contracts, he scales size up modestly and tightens the stop. Entries are only taken if the setup’s distance to stop is within a pre-defined ATR multiple, so no chasing stretched candles. This keeps risk constant in dollars while respecting the instrument’s current noise level.
He manages exits with volatility-aware rules: initial stops at 1.0–1.2× ATR, then trails using an ATR-based channel or a moving average that only updates on candle close. If ATR spikes mid-trade, Alejandro Perez won’t widen the stop; he reduces exposure on the next signal instead. He also blocks new trades when ATR jumps beyond a regime threshold that historically degrades expectancy. The net effect is simple—volatility sets the boundaries, Alejandro executes inside them, and sizing adapts automatically without guesswork.
Diversify Smart: Underlyings, Strategies, Durations—Not Just More GBPUSD Trades
Alejandro Perez diversifies like an operator, not a collector. Instead of stacking three GBPUSD trades that move together, he spreads risk across uncorrelated underlyings—majors, select indices, and a metal only if spread and behavior fit his rules. He runs two distinct edges in parallel (mean-reversion and trend-following), so one thrives when the other cools off. Time diversification matters too: H1 for primary signals, with a controlled H4 pilot to smooth equity swings without doubling exposure.
He caps “theme concentration” so no single macro story can sink the day. If two symbols show a 0.7+ rolling correlation, Alejandro Perez treats them as one position and splits the size accordingly. He staggers hold times—quick partials on the reversion engine, let-runs on the trend system—to avoid synchronized exits. The outcome is a portfolio that earns from different behaviors at different speeds, rather than one bet repeated five ways.
Rules Over Predictions: Mechanically Execute Setups, Journal Deviations, Reduce Discretion Risk
Alejandro Perez treats forecasts as noise and rules as revenue. He pre-defines entry, stop, size, and management before the session starts, then follows the checklist in order—no skipping steps, no “gut feel” overrides. Every trade ticket includes a reason code tied to the rule set, so he can audit whether he traded the plan or traded his mood. If a setup is 80% right but fails one rule, Alejandro Perez passes; missing a trade hurts less than breaking the framework.
He journals deviations in plain language within minutes of closing the trade and assigns a penalty for rule breaks—reduced size next session until three error-free trades. To avoid in-the-moment improvisation, he limits reviews to candle close and bans lower-timeframe peeking that tempts early exits. His process allows just two checks per signal; needing a third means the setup isn’t clean and gets skipped. Over time, the mechanical execution compounds expectancy, while the journal turns mistakes into enforceable, measurable improvements.
Protect the Downside: Defined Risk, News Filters, Drawdown Protocols Before Scaling
Alejandro Perez builds defense into the plan so the account can survive long enough to compound. He trades with hard stops on every position, pre-sized so a single loss never exceeds his fixed R, and he refuses to widen stops after entry. Before major releases like CPI or NFP, he blocks new mean-reversion entries and trims trend runners to reduce gap risk. If spreads blow out or slippage turns a 1R risk into more, Alejandro Perez cancels the order—no exceptions.
He treats drawdowns as operational, not emotional, with thresholds that trigger automatic adjustments. When equity hits his “normal DD” band, he keeps trading the plan; if it breaches the buffer, he halves risk across systems until recovery metrics clear. System-specific equity slipping below a multi-month low moves that engine to probation while the other continues to trade. Only after the account reclaims prior equity highs does he scale size—never during a drawdown bounce—so growth rides verified edge, not hope.
Alejandro Perez’s core lesson is deceptively simple: codify two complementary edges, size with fixed R, and ride statistically normal drawdowns without flinching. He pairs a Bollinger-style reversal engine with a trend-pullback engine so one harvests mean reversion while the other catches continuation—then runs both primarily on the H1 chart to balance sample size and signal quality. The emphasis is on letting volatility set the boundaries (ATR-aware stops and sizing), capping correlation so one macro theme can’t wreck the book, and refusing to widen stops or chase stretched candles. When the data says a phase is flat, he keeps executing; when the payoff window arrives, he’s in position because he never shuts the system off.
Equally important is Alejandro Perez’s operating discipline around automation and oversight. Algorithms do the heavy lifting—signal detection, order placement, logging—while he supervises with clear weekend rules for updates, symbol probation, and risk caps. He tests fast, goes live small on stable code, and only scales after real equity progress, often blending personal capital with selective funding programs that fit his risk framework. The takeaway: write rules you can follow on your worst day, trust the portfolio math over gut feel, and let time plus consistency turn boring execution into compounding results.
