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In this interview, Siam Kidd—trend trader, entrepreneur, and author—breaks down how he went from blowing early accounts to running largely automated, machine-learning-driven trading. He explains why he migrated from scalping to higher timeframes, how trailing moving averages and scaling into established moves became his edge, and how building businesses removed the pressure to “trade to pay rent,” letting his account compound.
You’ll learn Siam Kidd’s practical playbook: trend trading on 4H/D1 with mechanical rules, tight risk (think fractions of a percent per position), trailing stops via moving averages, and aggressive scale-ins when momentum confirms. He also shares the stats mindset (expectancy, variance, AAEs/AFEs), meticulous logging, and why letting bots execute a positive-expectancy system beats human second-guessing. Finally, he lays out how business profits can fund a trading account so you focus on process over pressure—vital for any trader serious about consistency and growth.
Siam Kidd Playbook & Strategy: How He Actually Trades
Core philosophy
Siam Kidd keeps it simple: ride established trends, define risk to the penny, and let rules—not emotions—do the heavy lifting. The goal is consistency through mechanical decisions, so variance doesn’t knock you off plan.
- Trade with trend on 4H/D1; stand down in chop identified by compressed ATR and whipsaw around your moving averages.
- Prioritize mechanical rules over opinions; if a rule is not written, it doesn’t exist.
- Protect capital first; returns are a by-product of strict risk and position sizing.
Market selection & timeframes
He focuses on markets that trend cleanly and offer enough liquidity to scale in and out without slippage drama. Timeframes are chosen to reduce noise and decision fatigue.
- Primary charts: Daily for bias and 4H for timing; Weekly for context only.
- Tradable universe: liquid FX majors/crosses, top-volume indices, and the most liquid commodities/crypto pairs.
- Skip anything with erratic wicks or news-driven whipsaw over the last 90 days.
Trend filter & structure
A clear, objective trend filter keeps you on the right side of the move. Structure rules prevent impulsive counter-trend trades.
- Trend is “up” when price is above the 200 EMA on D1 and the 20 EMA is above the 50 EMA; trend is “down” when the opposite holds.
- Only take longs in uptrends and shorts in downtrends; no counter-trend entries.
- If D1 and 4H trends disagree, wait—no trade until both align.
Set up criteria (pre-entry)
Siam waits for alignment, a pullback, and a momentum re-engagement. This cut-and-dry recipe avoids FOMO and late entries.
- In an uptrend: wait for a 4H pullback to the 20–50 EMA zone plus an RSI(14) reset into 40–55.
- Require a higher-low (HL) on 4H with a bullish close back above the 20 EMA.
- In a downtrend: mirror the rules—pullback to 20–50 EMA, RSI(14) into 45–60, then lower-high (LH) and bearish close below 20 EMA.
Entry triggers
Entries are binary: either the market proves momentum is back, or you stand down. Triggers are chosen to be repeatable and backtest-friendly.
- Place stop orders one tick beyond the signal candle: buy stop above the HL confirmation bar; sell stop below the LH bar.
- Entry must occur within the next two 4H candles; otherwise, cancel and re-assess.
- No entries within 30 minutes before or after tier-1 macro releases on the instrument’s home currency or primary driver.
Initial stop & position sizing
Risk is planned before entry, so nothing surprises you after you’re in. Stops scale with volatility; size shrinks automatically when markets are wild.
- Initial stop: 1.5× ATR(14) on 4H from the entry price, tucked beyond the swing HL/LH.
- Per-trade risk: 0.25%–0.40% of account; default to 0.30%.
- Portfolio heat cap: maximum 2.0% aggregate open risk across all positions.
Scaling in (pyramiding rules)
When the trend moves, he presses the edge—responsibly. Ads are systematic and never widen risk irresponsibly.
- First add at +1.0× ATR(14) from initial entry; second add at +2.0× ATR(14); maximum three adds.
- Each add risks 50% of the initial unit’s risk (e.g., 0.15% if the initial was 0.30%).
- Move the combined stop to keep total open risk ≤ original per-trade risk after each add.
Trade management & trailing exits
Exits are where most traders leak P&L. Siam’s logic is simple: trail with structure and ATR so winners can breathe without giving back the farm.
- Convert to a trailing stop once the price closes beyond +1.0× ATR from entry.
- Trail with a Chandelier Exit(ATR 3.0, period 22) on 4H or with the 20 EMA on D1—whichever is farther from price (gives the trade room).
- Hard exit on opposite 4H swing break against position (LL after a long’s HL, HH after a short’s LH).
Partial profits & re-risking rules
Banking partials reduces variance and makes it easier to sit through the rest of the move. Re-risking is controlled and pre-planned.
- Take 1/3 off at +1.5× initial risk (R); move stop to breakeven on remainder.
- Take the second 1/3 at +3R; convert to tighter trail (EMA20 on 4H).
- Hold the final 1/3 for D1 structure break or trailing stop hit—no discretionary overrides.
Bot execution & automation
He favors automation to eradicate second-guessing. Rules become code; code runs the plan without mood swings.
- Encode entry, stop, adds, and trails exactly; no “manual tweaks” permitted.
- Bot checks signals on closed candles only—no intrabar peeking.
- If a rule cannot be coded unambiguously, remove it from the strategy.
News & event filter
A simple guardrail around major news keeps you out of known minefields. The goal is fewer dumb losses, not predicting events.
- Flat or reduced size around tier-1 events (CPI, NFP, central bank rate decisions) on related instruments.
- No new positions from T-0:30 to T+0:30 around the release; existing positions trail only, no adds.
- If spread spikes beyond 2× average during the window, pause all entries for that instrument until spreads normalize.
Journaling & metrics
You can’t improve what you don’t measure. Siam tracks expectancy, variance, and execution errors relentlessly.
- Log every trade with screenshots of setup, entry, stop, adds, and exit rationale.
- Track metrics: win rate, average win/loss, expectancy per trade, average adverse excursion (AAE), average favorable excursion (AFE), and time in trade.
- Tag mistakes (late entry, broken rule, wrong size) and reduce the next cycle’s risk by 20% if the error rate >10% over the last 30 trades.
Risk controls & drawdown plan
Pre-agreed brakes keep a bad week from becoming a bad quarter. The system gets the benefit of the doubt, but risk dials down when necessary.
- Weekly loss limit: −1.5% realized; hit it and stop trading until next week’s open.
- Rolling drawdown cap: −6%; at −3% reduce per-trade risk by 50% until equity recovers.
- Correlation check: if two instruments show >0.7 correlation over 60 days, treat them as one position for heat limits.
Weekend & rollover handling
Gaps and thin liquidity can wreck careful risk management. These rules handle the boring—but crucial—edges.
- Close or half-size 4H positions before the weekend if the trailing stop would be >2.5× ATR away.
- No fresh positions in the final 4H candle of Friday.
- For instruments with chronic weekend gaps, use only D1 signals and a smaller size (−30% risk).
Routine & checklists
Consistency comes from a repeatable daily and weekly cadence. Here’s the simple loop that powers the process.
- Daily: D1 scan for trend alignment → 4H scan for pullback/HL-LH → stage orders → set alerts → update journal.
- Weekly: review metrics, tag errors, remove lowest-quality markets from the universe, and re-opt heat limits.
- Monthly: re-parameter check (EMA/ATR sanity), but only change after 100-trade sample or if slippage/spread regime has clearly shifted.
Building capital alongside trading
Trading does not need to carry the burden of paying bills early on. Using external cash flow keeps psychology clean and risk small.
- Fund the account regularly with fixed monthly deposits; never increase trade risk just because the balance grew.
- Route business/professional income to the trading account, but cap annual trading risk at a fixed percent of net worth.
- Keep lifestyle separate: withdrawals are scheduled (e.g., quarterly) and limited to a pre-set share of closed profits after new equity highs.
Size every trade small; compound through volatility-adjusted, rules-based risk
Siam Kidd is ruthless about keeping each bet tiny so he can survive variance and let math do the heavy lifting. He sizes by volatility, not vibes—if ATR is high, his position shrinks; if ATR is calm, size edges up but never beyond a tight cap. The result is a stable risk-per-trade that protects equity while still giving winners room to run. He also caps portfolio heat, so multiple open positions don’t secretly add up to one oversized gamble.
Kidd’s twist is to make this sizing fully rule-driven so there’s no “I feel good today” creep. He’ll predefine a base risk (think fractions of a percent) and let the stop distance dictated by volatility set the lot size automatically. As equity grows, he scales risk in tiny increments only after sustained performance, avoiding sudden jumps that spike drawdowns. This steady, volatility-adjusted approach compounds quietly in the background while he focuses on execution, not prediction.
Trade the dominant trend; skip chop until timeframes fully align
Siam Kidd builds from the top down: identify the dominant trend first, then only look for entries in that direction. If daily is up but 4H is messy, he stands down until structure and momentum line up again. This filter removes most false starts because he isn’t trying to force a reversal or scalp noise. By waiting for alignment, Kidd trades fewer setups but at a much higher quality.
When trend and timing agree, he wants a simple, visual confirmation—pullback, higher low, and regain of momentum for longs; the mirror image for shorts. If those ingredients aren’t present, he passes and preserves mental capital for the next clean drive. The edge isn’t in predicting turns; it’s in repeatedly hitching a ride once the move proves itself. That patience is what keeps Siam Kidd on the right side of the market while everyone else fights chop.
Pyramid into strength with ATR milestones; never widen initial risk.
Siam Kidd only adds when the market proves him right, and he defines “proof” with ATR-based milestones. After the first leg moves one ATR in his favor, he adds a half-unit and pulls the combined stop so total open risk never exceeds the original. If price reaches the next ATR milestone, he adds again—still smaller than the first—while re-crunching size so an adverse swing can’t nuke the stack. The rule is ironclad: adds reduce risk-of-ruin, not increase it.
He refuses to widen the initial stop under any circumstance; the market can earn more size, but it can’t negotiate new risk. Siam Kidd treats pyramiding like a conveyor belt: new units step on as momentum strengthens, and the stop ratchets to protect equity as the position grows. If momentum fades or structure breaks, he exits the newest add first, then lets the trail handle the core. That way, the biggest size is only on during the strongest conditions, which is exactly when it pays to be bold—systematically, not emotionally.
Diversify by market, strategy, and duration; cap total portfolio heat
Siam Kidd spreads risk across uncorrelated markets so one theme can’t sink the month. He mixes FX majors with indices, commodities, and occasionally crypto, but sizes everything off the same risk framework so no single instrument can dominate the equity curve. He also diversifies by strategy—core trend-following on higher timeframes, with occasional tactical add-ons—so returns don’t rely on one playbook working all the time.
Duration matters too: Kidd staggers holds from multi-day swings to multi-week trends, which smooths P&L and reduces whipsaw stress. Over all of it sits a hard portfolio heat cap; if open risk reaches that ceiling, he stops adding, trims exposure, or rotates into less-correlated names. The goal is a portfolio that wins by many small, independent edges rather than one oversized bet. That’s how Siam Kidd keeps drawdowns controlled while still letting winners do the heavy lifting.
Trust mechanics over predictions; journal metrics, and enforce process discipline
Siam Kidd treats forecasting like a distraction and execution like the job. He runs a written checklist for setup, entry, stop, and add rules, so every decision has a binary yes/no. After each trade, he logs screenshots, R multiple, ATR regime, and any execution errors, then reviews weekly to tighten the screws. The aim isn’t to be right—it’s to be consistent enough that expectancy shows up despite variance.
Discipline is enforced with pre-committed penalties and brakes. If Siam Kidd breaks a rule, the next cycle’s risk auto-reduces, and trading pauses entirely after a defined drawdown or error-rate threshold. He also uses closed-candle signals and bot execution to eliminate mid-bar tinkering and “just this once” overrides. By trusting mechanics and measuring everything, he turns a fallible human into a reliable process that compounds over time.
Siam Kidd’s story is a blueprint for turning painful trial-and-error into a rule-based edge. He’s candid about the messy start—blowing early accounts, swinging from big wins to brutal losses, and realizing that trying to “pay the bills” from a small account was a recipe for self-sabotage. That hard-won honesty feeds his core lesson: build a simple, positively skewed system and then execute it flawlessly, on higher timeframes, with small, volatility-adjusted risk and strict portfolio heat limits. The aim isn’t to predict; it’s to ride trends with low hit rates but meaningful R-multiples, letting trailing exits and pyramids do the compounding while your psychology stays out of the way.
Kidd also reframes the money problem that crushes most traders: fund the account from outside income so you never have to force trades. He argues that building a business can deliver better “R-multiples” than any single trade, and that cash flow, rainy-day reserves, and liquidity discipline keep you in the game when conditions turn. In practice, that means keeping operational buffers liquid, resisting the itch to gamble with emergency funds, and—when appropriate—parking idle cash in a strategy you can withdraw from quickly. Paired with mechanical entries, ATR-based sizing, and closed-candle automation, the result is a calm process that survives variance and compounds over years, not weeks. That’s the real takeaway from Siam Kidd: professionalize everything—capital, rules, and review—and markets become a probability machine you can actually manage.
