Table of Contents
Adrian Reid—founder of Enlightened Stock Trading and a 20+ year market veteran—sits down to unpack how he went from late-night chart hunts to clean, rules-based execution on daily/weekly timeframes. In this interview, Adrian explains why systematic trading clicked for him, how trend following became his core edge, and why position sizing, low leverage, and drawdown tolerance matter more than predictions. You’ll hear how he diversifies across markets and strategies, and why he now automates execution to reduce mistakes and free up mental bandwidth.
In this piece, you’ll learn Adrian’s practical blueprint: start with one robust, backtested strategy that fits your personality, risk a small fixed fraction per trade, and expand into complementary systems (long/short, trend/mean-reversion) to smooth the equity curve. He shows how to judge “good enough” vs. over-optimized, monitor edge without ego, and build a portfolio that survives the bad days so it can compound on the good ones—less prediction, more process, and zero tolerance for blow-ups.
Adrian Reid Playbook & Strategy: How He Actually Trades
Core Philosophy: Rules First, Predictions Last
Adrian Reid runs on rules. His edge comes from robust, tested systems that work across markets without needing to “feel” the next move. This section lays out the mindset that keeps his equity curve climbing and his risk contained.
- Trade written, testable rules; never discretion you can’t quantify.
- Favor simple logic that survives across symbols and years over complex filters that only work in-sample.
- Judge everything by R-multiples, drawdown, and long-run expectancy—not recent wins or losses.
- Accept uncertainty: your job is to execute the plan, not forecast.
- Keep multiple independent strategies so no single regime buries you.
Markets & Timeframes: Daily First, Broader Later
He prefers daily and weekly bars to cut noise and keep decisions clean. Liquidity and slippage matter; so do costs and borrowing availability. Here’s how to pick your hunting ground and cadence.
- Start with liquid equities or ETFs; add futures/crypto only after you prove process control.
- Use daily bars for entries/exits; check weekly trend to avoid trading against major flow.
- Screen out thin names (e.g., average daily dollar volume below your threshold, such as $2–5M).
- Precompute position size at the close; place orders near the next open using limit-or-stop workflows you can automate.
- Cap portfolio concentration by sector/theme to avoid hidden correlation spikes.
Strategy Archetypes: Trend + Mean Reversion Combo
Adrian mixes uncorrelated edges—a trend follower to ride expansions and a mean-reverter to harvest snaps. This pairing keeps the equity curve smoother than running one idea alone.
- Run a long-only trend-following system on broad universes (e.g., 52-week breakouts with ATR-based stops).
- Pair it with a mean-reversion system (e.g., RSI(2) or multi-day pullback with volatility filter) on high-liquidity names.
- Add a short side (breakdown or overbought mean reversion) only after long-side discipline is proven.
- Stagger holding periods (days for MR, weeks/months for trend) to reduce overlap.
- Require distinct entry/exit logic per system to keep correlations low.
Entries: Simple, Testable, Repeatable
Entries don’t have to be fancy; they have to be clear. Use momentum for trend and exhaustion for reversion, both wrapped in a volatility context.
- Trend: Buy a breakout (e.g., 100-day high) only when price > long-term MA and ATR is not spiking unusually.
- Mean reversion: Buy after a 2–4 day selloff if price above 200-day MA, RSI(2) < 10, and spread < X bps.
- Shorts (advanced): Sell breakdowns below recent swing lows with the larger trend down and borrow confirmed.
- Enforce minimum distance from earnings/major news if your testing shows event risk kills expectancy.
- Use “good-til-canceled” conditional orders, never market-chasing.
Exits: Trailing the Trend, Snapping Back the Snap
Exits are where systems get paid. For trends, trail and let it run; for reversion, take the snap and step aside.
- Trend: Initial stop at 2–3 ATR below entry; trail with a Chandelier stop or a long MA/ATR hybrid.
- Mean reversion: Profit target at prior swing/mean (e.g., 5–10 day high or VWAP band); time-stop at 5–8 bars if no snap.
- Never move stops farther away; tighten only per rule (e.g., ATR contraction).
- Scale out only if tests show higher expectancy than all-out exits; otherwise, keep it all-or-nothing.
- Hard exit on earnings day if your tests flag a negative expectancy.
Position Sizing: Volatility Normalized, Fractional Risk
Sizing is the throttle on risk. Adrian normalizes by volatility, so every trade risks about the same fraction of equity.
- Risk per trade: 0.25%–0.75% of equity (pick one number; keep it).
- Position size = (Risk $) / (Stop distance in $). Use ATR-based stops to maintain consistency.
- Minimum position size threshold—skip trades that would be too tiny after costs.
- Round shares/contracts to respect lot sizes and slippage realities.
- Recalc size weekly as equity changes to avoid over- or under-sizing.
Portfolio Heat & Exposure: Don’t Cook the Account
Great strategies still blow up if all your risk fires at once. Cap total “heat” and diversify entries over time and symbols.
- Max open risk (“portfolio heat”) ≤ 5% of equity across all positions.
- Cap correlated exposure: e.g., no more than 2 positions in the same sector/theme per system.
- Limit net leverage; avoid pyramiding across highly correlated names.
- Set daily/weekly new-trade quotas to avoid overtrading during volatility spikes.
- Pause new entries if drawdown hits a preset line (e.g., 1× monthly expected drawdown).
Quality Filters: Liquidity, Gaps, and Clean Data
Filters keep you out of landmines. Apply them consistently before orders go live.
- Exclude stocks with average bid-ask spread above your max (e.g., 30–50 bps).
- Require minimum price (e.g., > $5) to reduce microstructure noise.
- Avoid newly listed tickers until N trading days of data accumulate (e.g., 90 sessions).
- For shorts, confirm borrow availability and fee caps before signals trigger.
- Skip symbols with abnormal corporate actions until data stabilizes.
Execution & Automation: Fewer Touches, Fewer Errors
The more manual clicks, the more mistakes. Automate scanning, sizing, and order placement with end-of-day logic.
- Generate signals after the close; place next-session orders via API or broker basket.
- Use limit-on-open (or stop-on-quote for breakouts) to control slippage and avoid chasing.
- Log every order with the rule that triggered it; include size, stop, target, and rationale code.
- Schedule checks twice per day: pre-open (orders) and after close (updates).
- Implement fail-safes: max order count per day and a “kill switch” if slippage exceeds thresholds.
Risk & Drawdown Controls: Live to Compound
Adrian treats risk like oxygen—silent but essential. He predefines pain points and reduces exposure before the market forces him.
- Set a hard monthly loss limit (e.g., 2–4%); hit it and cut system position sizes by 50% for the rest of the month.
- Tiered drawdown plan: at −8%, freeze new MR trades; at −12%, halve trend sizes; at −15%, trade trend only.
- News shock protocol: if gap risk jumps (e.g., VIX > threshold), widen stops only if tests support; otherwise, reduce size.
- Always keep emergency cash to meet margin and avoid forced liquidation.
- Review heat maps weekly to catch creeping correlation.
Monitoring the Edge: Math Over Mood
Edges drift. Measure with data, not feelings, and decide whether to keep, tweak, or retire a system.
- Track rolling expectancy, win rate, average R, and payoff ratio by regime (volatility buckets).
- Use out-of-sample paper tracking for any tweak for at least 100 signals before going live.
- Run periodic Monte Carlo on recent trades to estimate risk-of-ruin and expected drawdowns.
- If metrics breach control limits (e.g., expectancy < 0 for 200+ trades), pause and review.
- Maintain a changelog so you can tie performance shifts to code changes, not superstition.
Building Your First System: From Zero to Execution
Start small and boring, then layer complexity only when your basics are profitable and stable.
- Pick one market (e.g., US equities) and one archetype (trend or MR) that matches your temperament.
- Code five simple entry ideas; backtest with realistic costs; keep the strongest one.
- Add exits and stops; validate on multiple symbol sets and time windows.
- Trade tiny for 100–200 signals live; only scale when live results resemble tests.
- Once stable, add the complementary archetype to reduce equity-curve noise.
Checklists: Daily, Weekly, Monthly
Consistency beats brilliance. Checklists keep you consistent when markets are loud or you’re tired.
- Daily (post-close): update data, run scans, size positions, queue orders, export trade plan.
- Daily (pre-open): confirm orders, news filters, borrow status; cancel anything that violates rules.
- Weekly: rebalance watchlists, refresh quality filters, review sector/strategy concentration.
- Monthly: audit slippage vs. assumptions; adjust liquidity thresholds; run drawdown drill.
- Quarterly: full system review, Monte Carlo update, and—as needed—small, isolated tweaks.
Psychology & Process Discipline: Make Boredom a Feature
Systematic trading is intentionally dull. That’s by design—so your behavior doesn’t ruin your math.
- Never override signals because of a headline or hunch.
- Keep P&L out of sight during market hours; focus on checklists and execution.
- If you feel the need to “do more,” write it down as a hypothesis and test it—don’t touch live code.
- Celebrate rule-following days, not green days.
- Build a weekly “missed rules” report and fix the process, not the trades.
Scaling & Diversification: Add Edges, Not Noise
As capital grows, widen your base thoughtfully. More symbols and more strategies only help if they’re truly different.
- Add international markets or ETFs after verifying cost/slippage assumptions.
- Introduce a short book with separate sizing/heat caps to protect in bear phases.
- Mix holding periods (intraweek vs. multi-month) to spread timing risk.
- Keep per-strategy max capital allocation (e.g., 35–40%) to avoid single-edge dependency.
- Re-optimize rarely; prefer parameter ranges that work “good enough” across time.
Recordkeeping & Post-Trade Review: Turn Data Into Upgrades
Your trade log is the roadmap to better rules. Treat it like an asset.
- Log signal ID, entry/exit timestamps, size, stop, target, slippage, and rule variants.
- Tag trades by regime (volatility, trend slope) to see when edges sing or slump.
- Review losers monthly: identify which rules allowed avoidable pain; fix the rule, not the memory.
- Archive decommissioned ideas with reasons; avoid resurrecting them without fresh validation.
- Publish a one-page “state of systems” to yourself each month with clear keep/tweak/kill decisions.
Size Risk First: Fractional Positioning Beats Perfect Entries Every Time
Adrian Reid is crystal clear: risk comes before everything else, including your favorite entry signal. He sizes each trade as a fixed fraction of equity, so a single loser can’t wreck the month. That keeps him emotionally steady and mathematically consistent, regardless of whether the next setup is a home run or a dud. When your risk is standardized, you stop obsessing over perfect entries and start compounding steadily.
He also normalizes by volatility, so each position carries similar dollar risk even when prices whip around. Small, repeatable risk per trade means your edge can express itself over hundreds of signals without blow-ups. Adrian Reid treats sizing as the throttle: if conditions get rough, he dials down risk rather than chasing bigger gains. Do that long enough and your winners add up while your losers stay tiny.
Trade the Rules, Not Your Gut: Mechanics Over Prediction Daily
Adrian Reid treats the market like a factory floor—signals come in, trades go out, and feelings never touch the assembly line. He runs prewritten rules for entries, exits, and sizing, then follows a checklist so nothing gets “interpreted” mid-trade. If a setup doesn’t meet the rule, it’s a pass, even if the chart looks tempting. Adrian Reid would rather miss a flashy move than contaminate a system that compounds over hundreds of trades.
He measures success by rule adherence, not by any single P&L print. Forecasts, opinions, and headlines are background noise once the rules fire. When performance dips, he audits logic and execution instead of guessing the market’s next twist. The result is consistency—same process on green days and red days—because mechanics beat prediction when you scale, automate, and keep your hands off the wheel.
Diversify by Strategy, Market, and Holding Time to Smooth Drawdowns
Adrian Reid doesn’t rely on a single edge; he stacks independent ones so the portfolio isn’t hostage to one market regime. He runs trend and mean-reversion side by side, across different universes, with distinct entry and exit logic. When one style stalls, the other often carries the load, keeping the equity curve steadier and the psychology manageable. Adrian Reid also spreads bets across sectors and instruments, so a shock in one corner doesn’t sink the whole ship.
Duration is part of his diversification, too: short-hold swing trades live next to multi-week trend positions, so profits and losses accrue on different clocks. He avoids overlap by capping exposure per theme and by staggering entries, which reduces correlation spikes. The aim isn’t maximum return in any single month—it’s resilient compounding over many months. That’s why Adrian Reid builds portfolios like a relay team, not a solo sprinter.
Use Volatility to Set Stops, Targets, and Position Sizing Consistently
Adrian Reid builds every trade around volatility, so risk stays comparable from one symbol to the next. He uses an ATR-based stop so the distance adapts to current movement, then sizes the position by dividing fixed account risk by that stop distance. Targets flex with volatility too—aiming for the mean or prior swing when running mean reversion, and trailing dynamically when riding trends. This way, a calm market doesn’t get over-sized, and a wild one doesn’t sneak in hidden leverage.
When volatility spikes, Adrian Reid lowers per-trade risk or widens stops only if his rules support it, prioritizing survival over bravado. He refuses to move stops farther away after entry; the only direction a stop moves is tighter, and only per the rule. Time stops keep him from lingering when volatility dries up and the setup stops paying. Consistent volatility normalization makes his results less dependent on luck and more on repeatable math. Over hundreds of trades, that discipline turns noise into a steady edge.
Control Portfolio Heat: Caps on Correlation, Exposure, and Leverage
Adrian Reid keeps a hard lid on “portfolio heat,” the sum of risk across all open positions. He caps total open risk, limits concentration by sector or theme, and staggers entries so correlated names don’t light up at once. If conditions get rough, Adrian Reid dials back new positions and trims sizes before the drawdown snowballs. The goal isn’t to be fully invested—it’s to keep dry powder and avoid compounding mistakes.
He also controls leverage with strict rules, so a good strategy can’t turn dangerous just because markets speed up. Correlation checks are baked in: if two trades rhyme too closely, he takes the better one and bins the rest. When a predefined loss threshold hits, Adrian Reid reduces exposure automatically rather than negotiating with the market. By enforcing these caps, he protects the equity curve so his edges can work longer—and compound cleaner—over time.
Adrian Reid’s message is simple and powerful: build written, testable rules and let them run the show. He’s candid about starting with too much discretion and not enough structure, then finding his footing through backtesting, clear entry/exit criteria, and small, repeatable risk per trade. Trend following became a backbone because it’s robust across markets, but he pairs it with other edges so he’s not hostage to a single regime. Drawdowns are expected, not feared—he treats them as the price of long-term expectancy, managed by strict stops, time limits, and the willingness to sit in cash when markets turn.
He sizes positions as a fixed fraction of equity and normalizes by volatility so every trade carries similar dollar risk, whether the chart is calm or choppy. That sizing discipline is matched with portfolio controls: caps on total open risk, limits on correlated exposure, and staggered entries to avoid everything lighting up at once. Adrian Reid spreads his systems across assets and durations—daily and weekly rhythms, trend and mean reversion, stocks and crypto—so profits and losses land on different clocks. Increasingly, he automates the whole workflow to cut errors, keep slippage honest, and focus on monitoring the edge instead of predicting the next headline.
Underneath it all is process pride: he takes satisfaction from following the plan and watching the account compound, not from nailing a flashy call. If a rule breaks, he fixes the rule—he doesn’t rewrite the narrative. That mindset—mechanics over prediction, diversified edges, volatility-aware sizing, and preplanned drawdown responses—is the real “secret sauce.” Adrian Reid shows that when you respect risk first and let tested rules do the heavy lifting, consistency stops being a dream and starts being your default.
