Table of Contents
Samuel Leach sits down for a candid interview about how he really trades—why fundamentals drive his decisions, how he mixes FX and equities, and what it takes to build a career rather than chase setups. He’s a UK-based trader known for transparency and clear explanations, and here he lays out the path from serious education to practical execution, including the mindset required to survive drawdowns and avoid the casino vibe that traps many beginners.
In this piece, you’ll learn Samuel Leach’s strategy in plain English: start with macro and company fundamentals, align with bank outlooks, then use technicals only to fine-tune entries; target steady returns over flashy leverage; and manage risk with rules that survive real markets. You’ll also see how he evaluates value in stocks, why he used an ETF for the oil rebound, his take on prop-firm pitfalls, and a blueprint for developing skills that compound over years—not days.
Samuel Leach Playbook & Strategy: How He Actually Trades
Core Philosophy: Fundamentals First, Charts Second
Samuel Leach starts with “why” before “where.” He wants a real economic or business reason for every trade and uses charts mainly to time, not to justify. This keeps him from chasing noise and anchors decisions to drivers that actually move prices.
- Define the thesis in one sentence (e.g., “X profits expand if rates fall”); if you can’t, don’t trade.
- Identify the primary catalyst (earnings, policy shift, supply shock) and the specific metric it should impact.
- Require at least two independent fundamental confirmations (e.g., macro data + sector trend, central-bank guidance + valuation).
- Use technicals only to refine entry/exit after the thesis is set; never the other way around.
- If the fundamental reason changes materially, flatten—no “hope holds.”
Market Selection & Instruments: Pick the Cleanest Vehicle
He trades FX and equities, and he’s pragmatic about the instrument choice. If the thesis is macro or commodity-linked, he’ll consider liquid ETFs to simplify roll and carry; if it’s company-specific, he’ll use the stock.
- Choose the vehicle that minimizes frictions (fees, roll, borrow) for your thesis.
- If the driver is broad (e.g., oil supply/demand), prefer a widely traded ETF or index proxy.
- If the driver is idiosyncratic (single company earnings), trade the stock.
- When the carry/roll is complex, price it up front and size it smaller.
- Avoid thin, exotic products; liquidity is a risk-management tool.
Risk & Sizing: Survive First, Compound Second
Samuel treats risk as the product you’re selling to yourself—buy too much of it and the account goes out of business. He sizes by scenario, not vibes, and assigns capital where the thesis has the fewest unknowns.
- Cap per-trade risk (e.g., 0.5%–1.0% of equity) and never exceed a daily loss limit (e.g., 2R).
- Predefine invalidation: one factual condition that would disprove the thesis (not just a price).
- Convert the invalidation into a stop location and compute the position size backward from risk.
- Cut size when volatility spikes or catalyst timing is uncertain; scale only after proof of thesis.
- If your loss comes from thesis failure (not execution error), halt trading and reassess the framework.
Timing With Technicals: Entries That Respect the Thesis
He times with simple, robust tools—structure, levels, and momentum—after fundamentals say “go.” The goal is to reduce noise, not to predict the universe.
- Enter on a break-retest or pullback to an obvious level aligned with the thesis direction.
- Use ATR or recent range to place stops beyond “normal” shakeouts.
- Demand confirmation: volume pickup or momentum shift at the level before full size.
- If price action contradicts the thesis at key levels twice, step aside—information changed.
- Avoid “knife-catch” entries; let the market show it agrees first.
Thesis Examples: Common-Sense Macro in Practice
Samuel favors trades where common sense and data rhyme. One example: when global conditions normalize, demand for energy recovers—play the recovery with a clean vehicle instead of wrestling with futures rolls.
- Map the driver (e.g., reopening → energy demand) and the cleanest expression (e.g., broad energy ETF).
- Price the lag between the underlying and your product; don’t expect 1:1 tracking.
- Preplan partials: take a first scale where the recovery is “proved,” hold a runner for trend.
- Track storage, inventories, and policy headlines that directly touch the thesis.
- Exit if the real-world constraint that powered the idea no longer exists.
FX Approach: Align With Big Money and Policy
In currencies, he aligns with central-bank direction and major bank outlooks, then uses technicals for execution. The edge is being on the right side of policy for months, not minutes.
- Trade with the current policy bias (hawkish vs. dovish); fade only after a genuine policy pivot.
- Pair a strong currency vs. a weak one—don’t waste time on neutral vs. neutral.
- Anchor entries around data beats/misses that reinforce the policy path.
- Use time windows (first 30–60 minutes post-data) for liquidity and cleaner fills.
- If forward guidance shifts, flatten and rebuild the thesis before re-entry.
Stock Selection: Fundamentals You Can Explain to a Teenager
For equities, the story must tie to measurable business drivers—revenue mix, margins, cash flow, or a sector tailwind. If the only reason is “the chart looks good,” it’s a pass.
- Write the business driver in plain English (“unit economics improve as X scales”).
- Validate with at least two numbers (margin trend, revenue growth, debt schedule, or inventory turns).
- Demand a clear catalyst window (earnings, product launch, regulatory milestone).
- Avoid narrative stocks with no cash-flow path; they’re timing traps.
- Re-underwrite after each earnings; keep or cut based on facts, not attachment.
Trade Management: Systematic Upside, Mechanical Downside
Samuel keeps exiting, boring, and repetitive. He prefers partials into objective targets and lets the remainder prove if a larger trend exists.
- Pre-place a stop at invalidation; never “widen” to feel better.
- Set a first target at 1–1.5R for partials; move the stop to breakeven only after a genuine structure shift.
- Trail behind swing structure or ATR bands—avoid arbitrary round numbers.
- If the catalyst passes and the move doesn’t materialize, de-risk or exit; time decay is a cost.
- Log reason-taken, reason-exited, and what would have improved the sequence.
Prop-Firm & Capital Reality: Avoid Evaluation Traps
He’s blunt about chasing funded-account challenges without real risk control: targets and time limits can bend behavior. The goal is a durable process, not passing once.
- Don’t alter sizing to hit artificial challenge targets; design risk to survive real markets.
- If your rules change under time pressure, step back—you’re trading the rules, not the market.
- Replicate live conditions in practice (slippage, news spikes, spread widening).
- Prefer capital paths that reward consistency over short-term win-rates.
- Measure by expectancy and drawdown, not by “pass/fail” banners.
Routine & Review: Professional Habits, Not Hero Trades
Samuel treats trading like a job with calendars, checklists, and reviews. The edge compounds when you do the same smart thing many times.
- Pre-market: update macro calendar, mark high-impact events, and set “no-trade” windows around them.
- Build a daily watchlist of 3–5 names/pairs with the best thesis alignment; ignore the rest.
- Post-market: record trades with screenshot and 2-line thesis review; tag mistakes vs. bad luck.
- Weekly: grade yourself on rule adherence, not P&L; fix one process leak at a time.
- Monthly: archive dead theses and re-rank opportunity sets by fresh catalysts.
Education & Edge Building: Level Up the Right Way
He advocates structured learning that connects macro, instruments, and execution. The win is clarity—knowing exactly what you trade and why it should work.
- Master three pillars: driver identification (macro/micro), clean expression (instrument), and execution (timing).
- Specialize first (one sector or two FX pairs) before you diversify.
- Build a reusable checklist for each trade type (recovery, policy trend, earnings re-rate).
- Backtest for logic, then forward-test for behavior; only then add size.
- Keep a “kill list” of patterns you’re statistically bad at—and stop trading them.
Size Risk Like a Pro: Fixed R, Daily Loss Guardrails
Samuel Leach keeps position size boring on purpose: every trade risks a fixed R, not a feeling. He decides invalidation first, converts that distance into size, and refuses to stretch stops to “make it fit.” If the thesis is strong but volatility is high, the R stays the same, and the position just gets smaller. That way, one loser equals one R, not a random dent that wrecks the week.
He also runs hard daily loss guardrails, so a bad morning can’t become a bad month. Hit the limit—typically around two R—and he’s done for the day, no negotiation. The next session starts fresh, with focus on process metrics instead of revenge trades. This is how Samuel Leach compounds: small, repeatable risk on each idea, protected by rules that stop the bleeding early.
Trade the Why, Time the When: Fundamentals First, Technicals Second
Samuel Leach starts every trade with a plain-English reason the market should move—policy shift, earnings re-rate, or a clear supply-demand imbalance. He writes that “why” down first, so the idea can’t drift into chart-chasing. Then he identifies the catalyst window and the metric that should confirm it, like guidance revisions or a macro print. If the fundamental driver weakens, he scraps the trade—no amount of candlesticks can rescue a broken story.
Only after the “why” is solid does Samuel Leach use charts to time entries and exits. He’ll wait for a pullback or break-retest into a clean level, set a stop beyond recent range, and size the position from risk, not hope. Momentum or volume needs to agree before he presses size; otherwise, he keeps it light or passes entirely. This separation—fundamentals for direction, technicals for execution—keeps him aligned with real drivers while avoiding knife-catching noise.
Volatility Sets Your Size: ATR Stops, Dynamic Position Scaling
Samuel Leach treats volatility as the thermostat for risk—hotter markets mean smaller size, cooler markets allow a touch more. He converts the recent range into an ATR value and places stops just beyond normal noise, typically a fixed multiple rather than a guess. Position size is then calculated backward from the stop distance, so each trade risks the same R. This way, a wild day can’t quietly multiply its risk just because the candle got bigger.
When volatility expands, Samuel Leach narrows exposure by cutting size, not by dragging stops closer to price. If volatility contracts and the thesis are intact, he allows slightly larger size while keeping the same R per trade. He scales in only after price confirms with structure, never before, and cuts back quickly if momentum fades. The rule is simple: ATR defines the stop, stop defines the size, and size protects the account when markets roar.
Diversify by Driver, Instrument, and Duration—Not Just Tickers
Samuel Leach spreads risk across reasons, not just names, so one broken theme can’t sink the book. He organizes the watchlist by drivers like policy direction, sector earnings cycles, and commodity supply shocks, then makes sure no single driver owns the risk budget. If two stocks move for the same reason, he treats them as one exposure and sizes the basket accordingly. He’ll also mix instruments—stock, FX, or a clean ETF proxy—so execution frictions and liquidity don’t all correlate at the worst time.
Time matters too, and Samuel Leach staggers holding periods to smooth equity-curve noise. A catalyst trade might live for days, while a trend-following core position rides for weeks, and a tactical hedge offsets event risk overnight. If volatility rises or correlations spike, he cuts overlapping trades and favors the cleanest expression of the surviving thesis. The result is a portfolio that breathes: diversified by driver, instrument, and duration, resilient when narratives change, and focused enough to still compound.
Defined Exits, Mechanical Management: Partial Takes, Trail Winners, Kill Hope
Samuel Leach plans exits before clicking buy, so execution becomes follow-through, not fresh decision-making under stress. He takes partial profits at objective levels—prior structure, measured move, or 1–1.5R—so the trade pays him while information unfolds. Once paid, he shifts from offense to defense, tightening risk behind swing structure rather than random round numbers. If price action invalidates the thesis, he’s out on the stop without delay—no averaging, no “it’ll come back.”
After the first scale, Samuel Leach lets the remainder prove whether a bigger trend exists. He trails behind higher lows in uptrends or uses an ATR-based step so the stop moves only when structure does. If momentum stalls after the catalyst window, he reduces to a token runner or exits entirely to free risk budget. The mantra is simple: define exits when calm, manage them mechanically when it counts, and kill hope the moment facts change.
Samuel Leach’s core message lands cleanly: build ideas from real drivers, then use charts to execute. He pushes traders to understand the economic “why,” align with policy and business realities, and only then pick entries with simple, robust timing. He’s adamant that a chart pattern without a fundamental thesis is flimsy, and that retail traders don’t see the same liquidity picture as banks—so copying “bank flow” from a retail platform is a dead end. The fix is education and a 360° view of markets: learn how economies and companies actually work, then translate that into clear, testable trade plans.
He’s equally blunt about professionalizing the craft: get qualified, respect risk, and be realistic about capital, costs, and lifestyle pressure. Leach highlights structured training that leads to employable skills and recognizes that many “funded” models distort behavior, so they build a process that would hold up in a real desk environment. His own stress-testing of traders—long sessions, event-driven all-nighters, and live-account challenges—underscores the point: success is process discipline under pressure, not highlight-reel setups. If you treat trading like a job, diversify by true drivers, size via volatility, and manage exits mechanically, you give yourself a chance to compound for years—not days.
