Table of Contents
Alejandro sits down for a candid New Year’s conversation about trading on the Desire To Trade podcast—why process beats prediction, how to reset cleanly for a new year, and what really moves the needle for a developing trader. He’s direct and beginner-friendly, walking through the habits that actually compound results: reviewing journals, spotting repeated mistakes, and turning those insights into a practical development plan that fits your lifestyle and risk tolerance.
In this piece, you’ll get the distilled playbook: how Alejandro builds a trading growth plan, the right way to use a journal, when to simplify versus optimize, and how to think about algos, crypto volatility, and scaling capital without blowing up your edge. You’ll see why higher timeframes can deliver “hands-off” consistency, how to set milestone-based capital increases, and how to keep strategies fresh with small, ongoing tweaks. The goal is simple: a strategy you can actually follow—smoother equity, clearer rules, and more time for life while still trading like a pro.
Alejandro Playbook & Strategy: How He Actually Trades
Yearly Reset: From Messy Results to a Clean Plan
A new year (or quarter) is Alejandro’s trigger to wipe the slate, review everything, and recommit to what works. The goal is simple: mine your own data for edge, cut the junk, and restart with clearer rules than last time.
- Block 2–3 hours to review last period’s P&L, win rate, avg R, max drawdown, and monthly equity curve.
- Tag your top three repeat mistakes (e.g., “late chase,” “no stop,” “overtrading after loss”) and write the fix beside each.
- Keep only two play setups that produced the majority of profits; archive the rest for now.
- Convert fixes into rules you’ll actually follow next week (not “someday”).
Development Plan: A Trader’s Roadmap You’ll Actually Use
Alejandro runs a living development plan—one page, not a novel. It sets a single learning focus per cycle and defines how you’ll test, measure, and ship changes without breaking the whole system.
- One theme per month (e.g., “news filter quality” or “stop placement on trending days”).
- One change at a time; no stacking tweaks. If it’s not measurable in 20–30 trades, it’s too big.
- Define success before you start (e.g., “reduce MAE by 15% with the same expectancy”).
- Schedule two checkpoints: mid-cycle quick audit, end-cycle keep/kill decision.
Timeframes & Structure: Hands-Off Bias, Clear On-Ramps
He favors higher timeframes for sanity and scalability, then uses a simple intraday on-ramp. The intent is fewer, better decisions—so the strategy fits real life and still compounds.
- Build bias on D1/H4; execute on H1/M15 only when higher-TF direction and conditions align.
- Pre-mark levels before the session (prior day high/low, session high/low, key swing).
- No ad-hoc trades outside your preplanned session window.
Entries & Exits: Define the Mechanics, Not the Prediction
Prediction is optional; mechanics are not. Alejandro’s execution is rule-driven: a small set of triggers, repeatable stops, and exits that don’t require heroics.
- Choose 1–2 entry triggers (e.g., break-retest at pre-marked level; momentum continuation after pullback).
- Place the initial stop where the setup is invalidated on the execution timeframe; don’t use arbitrary pip counts.
- First scale-out at +1R or structure target; trail only behind structure or a fixed multiple (e.g., last swing or 2×ATR stop).
- Max two attempts per idea per session; after 2 losses, stand down.
Risk & Sizing: Survive First, Scale Later
Alejandro treats risk like oxygen. The rules are boring by design—so you can trade longer and scale.
- Risk 0.25%–0.5% per trade starting; cap daily loss at 1% or 2R (whichever hits first).
- Weekly loss stop: pause after −3% or −6R; review before next session.
- One open position per instrument; two total correlated positions max.
- If trailing, never widen stops; only reduce or move to break-even per plan.
News & Events: Filter the Chaos
News is a volatility filter, not a gambling excuse. He bakes the calendar into rules so surprises don’t nuke an otherwise solid month.
- Flat or reduced size within 5 minutes before and 10 minutes after high-impact releases on the instrument’s currency/underlying.
- If in profit pre-news, scale down to a runner with locked risk or flatten—decide at entry.
- No fresh trades in the first 3 candles after a major release; let spread and emotion normalize.
Instrument Choice: FX Core, Crypto via Regulated Rails
Alejandro prefers the depth and stability of FX, while expressing crypto views through more regulated products when possible. The aim: keep operational risk low while capturing volatility.
- Focus on 3–5 liquid majors/crosses (e.g., EURUSD, GBPUSD, USDJPY, XAUUSD); know their session personalities.
- For crypto exposure, prefer listed futures/ETFs where available; avoid over-reliance on exotic venues.
- If an instrument’s spread/latency degrades your edge, drop it—no loyalty.
Journal That Pays: Turn Notes into Edge
His journal is a diagnostic tool, not a diary. If it doesn’t change behavior, it’s noise.
- Log each trade with: setup name, R risked, R result, MAE/MFE in R, and one behavior tag.
- Weekly: sort by setup; cut the bottom 20% by expectancy; double down on the top producer.
- Snapshot three screens per winner/loser and annotate what you’ll do differently next time.
Algo & System Thinking: Strategy vs. Execution
Alejandro separates strategy development from execution. You can automate parts, but only if the logic is stable and auditable.
- When “improving” a system, isolate one variable (e.g., news filter, time-of-day) and test on fresh out-of-sample data.
- Lock code/params for a full cycle; no mid-week tweaking after a few losses.
- Keep a change log with date, what changed, why, and expected metric shift.
Simplify Your Stack: Fewer Tools, Fewer Errors
Most traders drown in tools. Alejandro prunes to the minimum needed to express the edge.
- Pick one charting platform, one execution venue, one journal—make them talk to each other.
- Limit indicators to 1–2 that serve a purpose (e.g., ATR for stops, session marker for context).
- Quarterly “tool audit”: if a tool didn’t drive a measurable improvement, remove it.
Scaling Capital: Step-Ladder, Not Elevator
You earn size by proving consistency. He scales via milestones, so a bad week doesn’t erase months.
- Level up size after +10R net or three winning weeks with <1.5× max drawdown of the period.
- Step down one tier after −5R in a week or −8R from equity high; rebuild for two weeks before re-scaling.
- Withdraw a fixed % of new equity highs to reduce psychological pressure.
Session Routine: Start, Execute, Close
Routines make the strategy durable. Alejandro guards the open and the closed so each day ends clean.
- Pre-market (15–20 min): mark levels, confirm bias, read calendar, write the day’s one focus.
- During session: execute only pre-defined setups; after two losses, switch to observer mode.
- Post-market (10 min): journal, screenshot, tag behavior, schedule next test or tweak.
Size Risk First: Small, Consistent R Drives Long-Term Survival
Alejandro puts risk sizing before everything else because the account only grows when the downside is capped. He treats each trade as a fixed-R bet—small enough to survive a losing streak, consistent enough to compound when the edge shows up. By keeping R steady, he can compare setups apples-to-apples and resist the urge to “double up” after a winner. That steadiness is what keeps emotions quiet and execution clean.
He also designs his day around risk stops, not profit goals, so the worst case is known before the session begins. Alejandro reviews drawdowns in R, not dollars, which makes scale changes far easier and less emotional. When volatility spikes, he adjusts position size to keep the same R at risk, protecting the equity curve without abandoning the strategy. Over time, this discipline turns randomness into a manageable cost and lets the edge do the heavy lifting.
Trade Mechanics Over Prediction: Define Entries, Stops, and Exits
Alejandro keeps the focus on what he can control: execution mechanics. He predefines the exact trigger that makes a setup valid, so he’s not guessing once the price is moving. For entries, that means one or two clear signals, like a break-retest at a pre-marked level or a momentum continuation after a clean pullback. If neither condition exists, Alejandro doesn’t invent a narrative; he simply waits. The edge lives in repeating the same checklist, not calling the next headline.
He treats the stop as a truth test—placed where the setup idea is objectively wrong. Alejandro sets the target before clicking buy or sell, typically at a structure level or fixed R multiple, and he trails only behind structure, so he can’t widen risk mid-trade. He limits himself to two attempts per idea per session to avoid turning a concept into a spiral. The result is a calm loop: valid trigger, defined risk, preplanned exit. Prediction is optional; mechanics are mandatory.
Volatility-Based Allocation: Scale Up Only When Conditions Favor You
Alejandro ties position size to the market’s current volatility, so his R stays consistent through calm and chaos. When ATR or realized range expands, he dials down units to keep the same monetary risk per trade; when conditions compress, he can increase units without raising risk. He doesn’t “feel” his way into size—he lets a volatility metric define it and only scales up after the system shows clean execution and stable drawdowns. That keeps him in the game when markets whip and lets him press a proven edge when price is behaving.
He also gates any size increase behind regime checks: trending structure, clean levels, and spreads behaving. If those slip—or slippage jumps—Alejandro immediately steps back to baseline. He reviews weekly MAE/MFE to confirm that larger ranges are still yielding tradable follow-through, not random spikes. By scaling with volatility instead of emotion, Alejandro avoids the classic trap of getting big right before the chop and small right before the trend.
Diversify By Underlying, Strategy, and Duration To Smooth Equity
Alejandro spreads risk across a few liquid underlyings so one asset’s mood swings don’t dictate his month. He pairs a primary play (e.g., EURUSD or XAUUSD) with two or three alternates that behave differently across sessions. Then he diversifies by strategy type—one breakout, one pullback, one mean-revert—so he isn’t relying on a single market condition to show up. This mix reduces equity curve whiplash and lets winners cover for temporary underperformance elsewhere.
He also staggers duration: a higher-timeframe swing idea can quietly work while a shorter intraday setup handles day-to-day opportunity. Alejandro limits correlation by capping exposure when instruments move off the same driver, and he won’t run two highly similar trades at full size. Each strategy gets its own rules, journal tags, and keep/kill metrics, so he can cut only what’s failing without nuking the whole book. Over time, this structure turns randomness into smaller bumps—and lets the math of multiple edges do the heavy lifting.
Process Discipline: Journal, Review, and Iterate With Simple Rules
Alejandro treats process like a daily warm-up, not a chore you do when things go wrong. He journals every trade the same way—setup name, risk in R, result in R, MAE/MFE, and one behavior tag—so patterns jump off the page. Each week, he ranks setups by expectancy and trims the bottom performer, even if it had one flashy win. The goal isn’t perfect notes; it’s building a feedback loop that makes next week’s execution simpler.
Then he runs a quick retro: three screenshots per winner and loser, a sentence on what to keep, and a sentence on what to fix. Alejandro allows one rule change per cycle, tests it for 20–30 trades, and locks it for the full period—no midweek tinkering after a bad day. He uses checklists at the open and close to keep the routine tight: mark levels, confirm news, set risk; later, record outcomes, tag mistakes, and plan the next micro-test. If a change doesn’t reduce drawdown or improve consistency, it’s rolled back without debate. Over time, this small, boring discipline compounds into smoother execution and an equity curve that tells a cleaner story.
Alejandro’s core message is brutally practical: build a growth plan, then let the plan run your year. He reviews the past period in hard metrics—expectancy, drawdown, MAE/MFE—and turns repeat mistakes into one-line rules he can actually follow next week. Capital only scales when two things are true: you’re profitable and you’re compliant with your own rules. Without compliance, a bigger size just accelerates losses. He keeps risk expressed in R so sizing stays consistent across regimes, and he adjusts units to volatility rather than “feel,” which preserves the equity curve when ranges expand or spreads widen.
Mechanically, Alejandro favors higher timeframes to stay hands-off and sane, using simple, pre-marked levels and one or two entry conditions instead of chasing narratives. Stops sit where the idea is objectively wrong; exits are set before entry and trailed only behind structure. He limits attempts per idea, respects news windows, and journals the same way every day, so patterns are impossible to ignore. Even in crypto, he treats trend participation like a process—build early, add toward break-even, and avoid the euphoria stage that usually precedes the air pocket. Above all, he simplifies: fewer tools, fewer setups, fewer decisions. The result is a strategy you can live with—steady R, clearer feedback loops, and size that steps up only after the system proves it deserves it.
