Table of Contents
Albert Maté sits down on the Desire To Trade podcast from Montreal to unpack how he went from manual scalper to running an algorithmic shop, Albi Trading, aimed at consistently outpacing the S&P 500. He explains why he ditched seat-of-the-pants clicks for a rules-driven, intraday system that finishes each day flat—no overnight risk, less stress, more consistency. If you’re a retail trader who’s wrestled with emotions at the keyboard, Albert’s journey and philosophy land with uncommon clarity.
In this piece, you’ll learn Albert’s strategy pillars: build the system around risk first, trade only when the signal’s there, and keep emotions out with automation. We’ll cover his intraday, market-agnostic approach, why backtests must include out-of-sample data, and how he translates trader common sense—stops, trailing exits, patience—into code. Expect clear, beginner-friendly takeaways you can adapt, whether you’re clicking manually or moving toward a semi- or fully-automated process.
Albert Maté Playbook & Strategy: How He Actually Trades
North Star: Consistency First, Then Returns
This section sets the tone for how Albert thinks: protect the downside, standardize the process, and let the edge show up over a large sample. You’ll see rules that translate “be consistent” into daily behaviors you can actually follow.
- Define a single primary objective: positive expectancy with small drawdowns; write it at the top of your playbook.
- Cap daily downside with a hard stop (e.g., −1R or −2R on the day); stop trading when hit—no exceptions.
- Finish the day flat by design unless you have a specific, tested overnight edge; otherwise, carry zero positions after the close.
- Restrict your playbook to a narrow set of A+ setups; eliminate “meh” trades with a written checklist gate.
Instruments & Sessions: Where He Hunts
Albert narrows his universe to liquid markets and specific windows where signals are most reliable. Use these rules to avoid random noise and trade when your edge historically peaks.
- Trade only instruments with tight spreads and deep liquidity (e.g., index futures, top FX pairs); blacklist anything with frequent slippage spikes.
- Define exact sessions you’ll trade (e.g., first 120 minutes of London/NY overlap); outside those windows, do not trade.
- If volatility is outside your tested band (e.g., ATR > 2× normal), reduce size by half or skip until conditions normalize.
- Keep a live “no-go” list (events, instruments, times) and refresh it weekly.
Signal Framework: From Idea to Rule
He converts discretionary observations into coded rules that can be executed the same way every time. These bullets show you how to turn “I think” into “if/then”.
- Write each setup as Boolean logic: “IF (trend filter) AND (pullback condition) AND (trigger), THEN enter.”
- Require alignment across at least two timeframes (e.g., H1 bias + M5 trigger) before any entry is valid.
- Use a regime filter (risk-on/off) driven by objective measures (e.g., moving-average slope, breadth, or volatility state).
- Forbid “manual overrides” unless pre-specified in the playbook; log any override and its outcome.
Risk Sizing: Volatility-Adjusted, Always
Sizing flexes with volatility, ty so one trade can’t ruin the day. These rules make your risk budget stable across regimes.
- Target a fixed fraction of account risk per trade (e.g., 0.25R–0.5R); never exceed your daily loss cap.
- Size position via: position_size = (risk_per_trade) ÷ (stop_distance in ticks × tick_value).
- If spread/latency widens beyond a set threshold, reduce size by 50% or stand down.
- Scale risk down after two consecutive losses (−30% size) and restore only after two consecutive wins or a new session.
Entries: Only When Price Invites You In
Entries are precise and rules-based; no chasing. This keeps slippage low and R-multiples intact.
- Enton limit/stop orders at predefined trigger levels; no at-market clicks unless it’s part of the rule.
- Require fresh momentum after the trigger (e.g., one confirming bar closing in your direction) to avoid “knife-catch” fills.
- If price front-runs your level by more than X% of ATR, cancel the order—missed trades are cheaper than bad trades.
- One retry rule: if a valid signal fails to fill, allow exactly one re-queue; then it’s done.
Exits: Premeditated, Not Emotional
Albert treats exits as important as entries. Use these to remove “hope” and lock in mathematically sound outcomes.
- Hard stop goes where the setup is wrong (structure-based), not where the P&L feels uncomfortable.
- First scale at +1R or the first structure target; move stop to breakeven only after scale-out is complete.
- Trail the remainder with an objective method (e.g., last swing low/high or ATR-based) and never widen the stop.
- Time-stop any trade that hasn’t progressed by X minutes/bars; capital is finite—free it up.
Playbook Hygiene: Checklists & Pre-Flight
Before any order is live, he runs quick pre-flight checks. Adopt these to catch preventable mistakes.
- Pre-trade checklist: bias confirmed, volatility within band, economic calendar cleared, news risk assessed, platform latency checked.
- Reject trades that fail even one checklist item; “almost” doesn’t qualify.
- Confirm order parameters twice: instrument, direction, size, stop, target, and time-in-force.
- Screenshot the chart at entry and exit; store with notes for later review.
Automation & Execution: Reduce Variance
He uses automation to enforce rules and reduce reaction-time errors. Even if you’re semi-manual, these rules will tighten execution.
- Let code place and manage initial orders (entry, stop, target) to avoid fat-finger risk.
- Disable hotkeys that bypass risk checks; all manual actions must still route through risk guards.
- Use server-side OCO (one-cancels-other) so exits persist if your platform disconnects.
- Keep a “degraded mode” plan (e.g., internet down): broker phone number, fixed close-all command, and halt trading for the session.
Data Integrity & Testing: Trust, but Verify
He validates ideas across regimes with strict test hygiene. Follow this to prevent fooling yourself with pretty backtests.
- Split data into in-sample (design), out-of-sample (validation), and live-forward (confirmation).
- Ban indicator settings chosen by eye; optimize on in-sample only, then lock parameters before validation.
- Pass/fail rule: if the edge disappears in out-of-sample or forward, the setup is rejected or reworked.
- Measure robustness with simple stress tests (wider spreads, random missed fills, delayed entries).
Daily Routine: Open to Close
A stable routine compresses decision time and keeps energy focused. These bullets make your day predictable and productive.
- Pre-market (15–30 min): mark key levels, set alerts, load watchlist, confirm news, run platform diagnostics.
- During session: execute only playbook setups; log each trade immediately with reason code.
- Post-session (20–30 min): export fills, annotate charts, tag mistakes, compute R-multiple, and update stats dashboard.
- Hard stop for the day after the post-session review—no revenge trading in after-hours.
Drawdown Protocols: Survive to Compete
When the edge stumbles, Albert shifts to defense first. These rules keep the account—and your head—intact.
- At −3R on a rolling 5-day window, cut size by 50%; at −5R, pause trading for 24–48 hours and review.
- Identify the failure type (execution mistake vs. edge degradation) and write a corrective action before resuming size.
- Do not add new setups during a drawdown; reduce complexity until baseline performance returns.
- Resume normal size only after two green days and a review sign-off.
Metrics That Matter: Score What You Control
He tracks a few high-signal metrics to steer improvements. Copy these to avoid dashboard overload.
- Process KPIs: checklist compliance %, rule violations count, average trade review time.
- Performance KPIs: expectancy per trade (in R), win rate by setup, average adverse excursion, and profit factor.
- Execution KPIs: slippage vs. model, time-to-fill, cancelled/modified order ratio.
- Weekly action rule: if a KPI drifts beyond a threshold, schedule one focused fix for the coming week.
Scaling the Edge: From One Setup to a Portfolio
Growth comes from stacking uncorrelated edges, not doubling the size of one idea. Here’s how to scale without new headaches.
- Add new setups only after 3+ months of live-forward stability on the current ones.
- Correlation cap: avoid running multiple setups that fire on the same condition set; stagger regimes and timeframes.
- Keep aggregate daily risk constant; when adding setups, proportionally reduce per-setup risk.
- Quarterly cull: retire the bottom-performing setup and incubate one newcomer with micro-size.
Mindset & Energy: Trade Like a Pro, Not a Hero
Finally, the soft stuff that makes the hard rules work. Treat energy and attention as scarce resources.
- Sleep and session selection beat motivation; if you’re not rested, skip the first session.
- Ban PnL watching during open trades; watch structure and rules, not dollars.
- Define a shutdown ritual (log wins/losses, gratitude note, 10-minute walk) to reset your nervous system.
- Keep a “mistake cost” tally; when it exceeds a weekly cap, the next session is observation-only.
Size risk small, survive streaks, keep daily downside strictly capped
Albert Maté treats risk like oxygen—use too much and the session suffocates. He fixes a tiny per-trade risk and a hard daily stop so one dumb sequence can’t wreck the week. That cap turns a bad morning into a controlled bruise, not a spiral. By planning for losing streaks in advance, he removes the urge to “win it back” when emotions are loud.
He also sizes to current volatility, so a wider stop doesn’t secretly balloon account risk. If spread or slippage widens beyond his thresholds, he cuts size or stands down entirely. And when the daily limit is hit, Albert stops trading—no exceptions, no “one last try.” That discipline keeps him solvent long enough for the edge to show up.
Trade mechanics over predictions: rules, checklists, time-stops beat gut feel
Albert Maté keeps forecasting to a minimum and execution to a maximum. He runs each setup through a pre-trade checklist so only A+ conditions make it to the order ticket. Entries are pre-defined, placed via limit or stop orders, and never chased at market. If the signal isn’t complete, he passes without second-guessing.
Once in, Albert manages the trade with time-stops and objective structure, not vibes. If the price hasn’t moved within a set number of bars, he exits and frees capital. He logs every action with a reason code to spot slippage, hesitation, or early exits. The goal is simple: let the rules do the heavy lifting while the predictions take a back seat.
Adjust position size to volatility; skip outlier days and slippage.
Albert Maté sizes trades to the market’s current mood, not yesterday’s. He ties position size to stop distance and an ATR-style volatility rea, so 0.5R risk stays 0.5R regardless of noise. When ranges expand, his size contracts automatically; when ranges compress, size scales up within strict caps. If spreads widen or books thin, he treats it as hidden volatility and dials down risk.
On outlier days—wild gaps, surprise news, disorderly tape—Albert Maté either halves in size or steps aside entirely. He also runs a slippage guard: if average fill deviates beyond a set threshold, the next orders go smaller or get cancelled. This keeps bad fills from compounding into outsized losses. The result is steady risk per trade across regimes and fewer “death by volatility” sessions.
Diversify by underlying, strategy, and timeframe; cap correlation and overlap.
Albert Maté spreads risk across uncorrelated ideas instead of doubling down on one hot setup. He mixes instruments (index futures, major FX, large-cap equities) so one market’s funk doesn’t drag everything down. Each strategy targets a different behavior—trend, mean reversion, breakout—so not all signals fire at once. Before going live, he checks historical co-movement to keep portfolio correlation low.
He also staggers timeframes to smooth the equity curve, pairing higher-timeframe bias with tactical intraday triggers. If two strategies keep triggering back-to-back on the same move, Albert Maté reduces one or parks it to avoid overlap. Daily risk stays constant across the whole book, so adding setups reduces per-setup size, not total discipline. Quarterly, he retires the weakest edge and incubates a new one with a micro-size to keep the roster fresh.
Prefer defined risk structures; automate entries, OCO exits, and reviews.
Albert Maté designs trades so the maximum loss is known the moment he clicks send. Stops are hard, placed with the order, and never widened once the price is moving. He uses OCO brackets so profit targets and stops live on the server, not in his head. Entries are automated to prevent late clicks and fat-finger-size errors. If latency or spread jumps at the trigger, the system reduces the size or cancels the order by rule.
Automation doesn’t replace judgment; it enforces it. Albert Maté reviews every session with tagged screenshots to see whether execution matched the playbook. Any rule violation triggers a small penalty on tomorrow’s size until discipline is clean again. The result is consistent risk, cleaner fills, and fewer “I knew better” moments.
Albert Maté’s core lesson is simple: build a rule set that protects capital first, and let consistency—not hero trades—compound the edge. He moved from manual clicks to an intraday, market-agnostic algorithm that finishes each session in cash, so there’s no overnight surprise wiping out a week’s work. Stops are preplanned, trailing exits prevent winners from turning into losers, and if conditions deteriorate—spreads widen, books thin—the right move is smaller size or no trade at all. That bias toward preservation gives him the psychological breathing room to execute cleanly.
The second lesson is that mechanics beat predictions. Albert Maté codifies his ideas into if/then rules, runs a tight pre-trade checklist, and lets entries and exits fire without last-second improvisation. Time-stops recycle stagnant capital, and every action is logged, so execution drift gets caught and corrected. He’d rather miss a move than chase one outside his plan, because the plan is what scales.
Finally, he treats trading like a business. The system is designed for liquid markets, repeatable windows, and stable operations that could serve clients as well as himself. Performance gets judged over large samples, not single days, and drawdowns trigger structured responses—cut size, review, and only then resume. If you copy nothing else from Albert Maté, copy this: define the risk, automate the discipline, close flat, and give your edge enough calm, repeatable reps to prove itself.
