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This interview features Evan Medeiros—systems trader, developer, and founder of The Trade Risk—walking through how a poker-obsessed computer science grad became a disciplined market operator. Recorded as a casual podcast conversation, it spotlights why Evan matters to retail traders: he’s blended real-world coding, statistical thinking, and years of trial-and-error into practical rules anyone can adopt. From early account blowups to building robust swing-trading processes, Evan explains how documenting, sizing, and simplifying turned him from a discretionary gunslinger into a methodical, rules-driven trader.
In this piece, you’ll learn how Evan Medeiros designs strategies from first principles, evolves them into systems, and uses tools like position-size calculators, scanners, and daily P&L streams to keep emotions out. We’ll cover his trend vs. mean-reversion framework, how to decide between hybrid and fully systematic approaches, why tracking your own “HUD-style” stats beats chart-hopping, and how a small portfolio of uncorrelated strategies can smooth drawdowns. Expect clear takeaways on risk, time-efficiency, and the mindset shifts that turn randomness into a repeatable process.
Evan Medeiros Playbook & Strategy: How He Actually Trades
Philosophy: rules over opinions
Evan Medeiros approaches markets like a systems engineer: define the edge, codify the rules, then execute without drama. The aim is to turn randomness into a repeatable process and keep discretion for maintenance, not for on-the-fly decisions.
- Write each strategy as a one-page spec: objective, universe, timeframe, signals, sizing, exits, risk limits.
- Backtest for directionality only (is there an edge?) and forward-test live in tiny size before scaling.
- If a rule isn’t written, it doesn’t exist; no ad-hoc trades, ever.
- Success metric hierarchy: risk-adjusted return → drawdown depth/duration → time spent.
Market universe and tools
He trades liquid U.S. equities and ETFs to keep slippage and borrow headaches low. Scanners and alerts do the heavy lifting, so daily execution is fast and boring.
- Universe: top ~1,500 U.S. stocks by volume + core ETFs (SPY/QQQ/IWM/sector/volatility).
- Liquidity rule: average daily dollar volume ≥ $20M; skip low-float names.
- Corporate events filter: no new positions within 5 trading days of earnings, splits, or known catalysts.
- Pre-screen with daily bars; intraday only for execution, not for signal generation.
Regime filter: when to press and when to chill
Evan uses a simple market health overlay so strategies only run when conditions historically favor them. This cuts whipsaws and keeps capital for higher-quality windows.
- “Risk-on” if SPY is above its 200-day SMA and breadth (e.g., % of S&P stocks > 200-day) > 50%.
- “Caution” if one is true and the other false; reduce gross exposure caps by 50%.
- “Risk-off” if both are false; new trend entries paused, mean-reversion sizes cut by 50% and stops tightened.
- Recalculate regime after the close; never intraday.
Strategy 1: daily trend-following swings
Core idea: buy strength that’s breaking out of consolidation with the broader tape healthy. Trades last days to weeks, letting winners work with mechanical trails.
- Setup: 20-day consolidation or base; breakout on the highest close in 20 days with volume ≥ 1.5× 20-day average.
- Entry: stop order a few ticks above breakout level; if not triggered within 3 days, cancel.
- Initial stop: 2× ATR(14) below entry or below base low, whichever is farther.
- Trail: ratchet to 2× ATR(14) below the highest close; no moving stops down.
- Profit add: one add only at +1R using the same 2× ATR stop; total risk on adds never exceeds initial R.
Strategy 2: mean-reversion snaps in strong trends
He buys sharp pullbacks within established uptrends and sells the snapback toward the short-term mean. Short hold times keep exposure low.
- Trend qualifier: 50-day SMA > 200-day SMA and price above the 50-day.
- Signal: close below the lower Bollinger Band(20,2) with RSI(2) < 5.
- Entry: limit at prior day’s close or market on close if still below the band.
- Exit: scale out 50% at the 10-day SMA; exit the remainder at 20-day SMA or 5 trading days, whichever comes first.
- Fail-safe stop: 3× ATR(14) or break of 50-day SMA on a closing basis.
Position sizing and risk caps
Evan’s sizing is volatility-aware and portfolio-aware. He caps total heat first, then individual position risk, so a string of losers can’t crater the account.
- Define 1R = 0.5% of equity per trade (adjust to taste).
- Position size = (1R) ÷ (stop distance in $); round down to nearest 5 shares.
- Gross exposure cap: 60% in “caution” and 100% in “risk-on”; 20% or less in “risk-off.”
- Correlation cap: max 2 positions in the same sector or highly correlated theme.
- Hard daily risk stop: if cumulative open + closed P&L is −1.5R, no new orders until tomorrow.
Order execution and slippage control
He treats execution like a manufacturing step—simple, repeatable, and resistant to noise. No chasing candles; the plan triggers the order, not the trader.
- Default order types: stop for breakouts, limits for pullbacks; avoid market orders except MOC in mean-reversion.
- If spread > 0.25% of price, use a midpoint-to-limit bracket and give it one minute.
- Never “improve” entry after signal; either it triggers or it doesn’t.
- Skip trades if the opening gap exceeds 1× ATR beyond the planned trigger.
Trade management playbook
Once in, the job is defending R and harvesting expectancy. Evan’s rules prioritize speed to break-even and consistency over perfection.
- Move stop to break-even at +0.75R in trend strategy; at first scale in mean-reversion.
- No averaging down, ever.
- If a valid exit signal triggers but the price is extended intraday, still execute at the close—no exceptions.
- If a stock closes below the 200-day during any long position, reduce the position by 50% that day.
Portfolio construction across strategies
Multiple uncorrelated edges beat one “perfect” system. He splits capital between trend and mean-reversion, so drawdowns are shallower and recoveries faster.
- Base split: 60% trend, 40% mean-reversion in risk-on; invert to 40/60 in caution.
- Max concurrent positions: 10 trend + 10 mean-reversion; if full, rank by signal quality score (breakout quality or stretch from mean).
- Hold cash when there’s no signal; cash is a position, not a failure.
Weekly and daily routine
Consistency comes from checklists. Evan batches thinking after an hour, so the live session is just clicking the plan.
- Weekend: refresh universe, recalc regime, rebuild watchlists, run scans, update stats, archive screenshots.
- Daily pre-close (last hour): review alerts, validate signals, stage orders, recheck earnings calendar.
- Post-close: log fills, update stops, tag trades with setup codes, and write a 2-line narrative for each.
Journal, metrics, and continuous improvement
He treats data like a poker HUD. Stats drive tweaks; feelings don’t. Reviews focus on edges by setup, not on single trade outcomes.
- Track by setup: win rate, average win/loss, profit factor, expectancy, average days in trade, max adverse excursion.
- Flag operator errors (late entry, skipped signal, early exit). Aim for a <5% operator error rate monthly.
- Quarterly: prune one underperforming rule and test one new micro-rule; never change more than one variable at a time.
- If a strategy’s profit factor < 1.1 over 100 trades, pause it and re-evaluate.
Drawdown and recovery rules
The worst time to improvise is during pain. Evan pre-commits to throttle risk and protect headspace so he can survive to the next opportunity set.
- At −5R rolling, cut per-trade R by 50% and halve gross exposure caps.
- At −10R rolling, pause the weaker strategy; trade only the stronger performer for 20 trades.
- No new rules, no new markets during a drawdown; reduce complexity until the equity curve stabilizes.
- Mandatory no-trade day after any single-day loss ≥ −3R; review logs, charts, and checklists before resuming.
Tech stack and automation (keep it boring)
Let software find patterns and ping you; your job is to say yes/no. Automation shrinks decision time and slashes error rates.
- Use scans/alerts to detect only pre-defined signals; disable “discovery” scans during live hours.
- Pre-built order templates for each setup with standard stops and share rounding.
- Daily backup: export trades, logs, and screenshots; keep a rolling 90-day archive.
- Quarterly disaster drill: simulate platform outage; rehearse phone orders and secondary broker failover.
Size Risk First: Volatility-Based Positions That Survive Losing Streaks
Evan Medeiros starts by deciding risk, not direction. He treats “R” as sacred—fixing a small, consistent percent of equity per trade and then letting volatility determine share count. If a setup needs a wider stop, the position simply shrinks; if the stop is tight, it grows, but the account heat stays constant. This turns choppy weeks into paper cuts instead of amputations, and it keeps confidence intact when a strategy’s edge is temporarily cold.
Medeiros also ties risk to regime: when the market is fragile, his total exposure dial turns down before pain forces it. He never averages down, because that breaks the math of controlled R, and he moves stops only in the direction of risk reduction. By standardizing R and letting ATR or recent range define distance to the stop, he removes ego from sizing and replaces it with a clean formula. That’s how Evan Medeiros makes losing streaks survivable and winning streaks scalable without changing the core strategy midstream.
Mechanics Over Opinions: Execute Written Rules, Ignore Market Narratives
Evan Medeiros treats the plan as the trader and himself as the operator. He writes rules in plain language, pre-programs alerts, and shows up to click “yes” or “no,” not to reinvent the thesis. News, tweets, and guru calls are background noise; if the signal is there, he takes it, and if it isn’t, he doesn’t. That separation protects him from the emotional whipsaw that comes from trying to predict the next headline.
Medeiros also grades himself on rule fidelity, not P&L per trade. A win taken outside the plan is logged as a mistake, and a loss taken inside the plan is logged as a success, because only repeatable mechanics compound. He batches decisions after the close, stages orders, and lets stops and targets do the heavy lifting intraday. When uncertainty spikes, he narrows choices further—fewer setups, smaller R—so execution stays crisp. The result is a calm, factory-like workflow where edges play out over a series, not a single opinionated bet.
Diversify by Strategy, Duration, and Underlying to Smooth Drawdowns
Evan Medeiros doesn’t rely on one “perfect” setup; he runs a small stable of uncorrelated edges. He pairs daily trend swings with short-hold mean-reversion, so one thrives when the other struggles. Durations are staggered—some trades aim for days to weeks, others for a quick snapback—so exits don’t cluster in the same session. Underlyings are spread across sectors and core ETFs to cut single-theme risk, with limits on how many positions can point at the same narrative. Evan Medeiros caps gross exposure by regime and rotates capital to the strategy that’s currently carrying the edge.
He avoids hidden correlation by tagging trades and reviewing where pain concentrates—if losses stack in one sector or style, he throttles that lane. Position adds are limited and never synchronized across similar names to prevent equity-curve “echoes.” Cash is used deliberately when signals are scarce; it’s not idle, it’s risk control. By diversifying across strategy type, time-in-trade, and underlying, Evan Medeiros flattens the equity curve and shortens recovery time after inevitable cold streaks.
Trend and Mean-Reversion: When to Press, When to Pause
Evan Medeiros runs two lanes and lets the market decide which gets the gas. When the market is healthy—index above long-term trend and breadth broadening—he presses trend setups, allowing winners to trail with wider room and adding once at +1R. In choppy or risk-off tape, he dials down trend risk and shifts attention to quick mean-reversion snaps, aiming for the 10–20 day averages and getting flat fast. If neither lane has clean signals, Medeiros parks in cash and waits; forcing trades is a hidden drawdown.
He never mixes the playbooks mid-trade: a trend entry is managed with trend rules, and a mean-reversion entry is managed with snapback rules—no switching hats after the fill. Evan Medeiros times effort, not predictions; he increases position count and hold times only when conditions statistically favor that lane. When volatility spikes and ranges widen, he reduces size but keeps structure, letting ATR do the talking. Press the lane with wind at your back, pause the one facing a headwind, and let the scoreboard over 50–100 trades confirm the allocation.
Defined Risk Exits, No Averaging Down, Journal Every Decision
Evan Medeiros hard-codes exits so he never “decides” under stress. Every trade is opened with a predefined stop—often ATR-based or just below the structure that invalidates the setup—and he only moves it in the direction of less risk. He refuses to average down because it converts a controlled bet into a drifting liability, and it muddies the stats that tell him whether the edge still works. When price hits the stop, he’s out—no negotiation, no “just one more candle.”
After each trade, Evan Medeiros writes a short journal entry that captures setup tag, R risked, execution notes, and any operator errors. That running log becomes his truth serum, revealing which rules deliver expectancy and which habits leak P&L. He reviews the journal weekly to spot patterns—late entries, stretched adds, skipped signals—and sets one micro-fix to improve next week. Clear exits protect capital; the journal protects the process, ensuring the next decision is sharper than the last.
In the end, Evan Medeiros shows that “edge” is really a set of habits you can repeat on command. He sizes first and lets volatility do the math, so any single trade is just one unit of risk, never a bet-the-farm moment. He separates thinking from doing: build the rules after hours, then execute them like a pro during market hours without narrating every tick. With a simple regime filter guiding when to press and when to chill, he funnels capital into the lane—trend or mean-reversion—that actually has the wind at its back.
What stands out is how deliberately boring he’s made success. Evan Medeiros diversifies by strategy, duration, and underlying to flatten the equity curve, caps correlation so one theme can’t take him down, and refuses to average down because it breaks the math. Exits are defined on entry, stops only move tighter, and journal notes turn wins and losses into usable stats. The message for retail traders is clear: write the plan, size by volatility, respect your stops, review your own data, and let a handful of uncorrelated, rule-driven systems carry you through noisy weeks and into the next compounding stretch.
