Fixed-fractional position sizing is the rule of risking a constant fraction of current account equity on every trade — the canonical retail "1% rule" (or 2%). You decide in advance, say, that no single trade may lose more than 1% of the account; the number of shares you buy then falls out mechanically once you know your stop distance. Its core tension: it is the simplest, most robust, and most widely recommended sizing rule, yet because the fraction is fixed it deliberately ignores how much edge any individual trade has — making it safe and survivable but not growth-optimal in the way edge-weighted rules (Kelly, optimal-f) aim to be.

How it's calculated

Two quantities define the trade:

• Risk budget (dollars) = Equity × Risk%. With $50,000 and a 1% rule, the budget is $500 — the most you'll let this trade cost you.
• Per-share risk = Entry − Stop (for a long; Stop − Entry for a short) — the dollars lost per share if the stop is hit. This equals one "R" in R-multiple terms.

Shares then come from dividing one by the other:

Shares = (Equity × Risk%) / (Entry − Stop)

Equivalently, in the general systematic form (Adaptrade, Vince), N = f × Equity / |Trade Risk per unit|, where f is the fixed fraction and Trade Risk is the per-unit dollar loss at the stop. The two are the same rule — the first just makes per-share risk the stop distance explicitly.

Critical distinction — % risked vs % allocated. Risk% (1%) is a fraction of equity you can lose, measured on the stop, not the fraction of capital deployed. A tight stop lets the position be a large share of the account while still risking only 1%; a wide stop forces a small position for the same 1%. Confusing "risk 1%" with "put 1% of capital in" is the single most common beginner error here — they are unrelated numbers.

Worked example

• Equity = $50,000; Risk% = 1% → risk budget = $500
• Entry = $40.00; Stop = $37.50 → per-share risk = $2.50 (one R)
• Shares = $500 / $2.50 = 200 shares
• Position value = 200 × $40 = $8,000 → ~16% of capital deployed, but only 1% at risk.

If the same setup had a tighter stop at $39.00 (per-share risk $1.00), shares = $500 / $1.00 = 500 shares, a $20,000 position (40% of capital) — still exactly 1% risked. Same risk budget, very different position size: the stop distance, not a capital percentage, drives the share count.

How it auto-scales

Because the budget is a fraction of current equity, position size grows as the account grows and shrinks in drawdowns — automatically, with no discretionary intervention. After a run-up to $60,000 the 1% budget becomes $600; after a drawdown to $40,000 it falls to $400. This compounding-with-a-brake property is the method's defining virtue: it presses size into winning periods and throttles it during losing streaks, so a string of losses costs progressively fewer dollars (geometric decay rather than linear), which is what protects against ruin. The trade-off is the "drawdown asymmetry" of all percent-of-equity rules: after a 50% loss you must gain 100% to recover, and a shrinking base risks fewer dollars just when conditions may be improving.

Fixed-ratio (Ryan Jones) — the variant

Ryan Jones proposed fixed-ratio sizing in The Trading Game as an alternative aimed at fixing what he saw as fixed-fractional's two failings: too aggressive on large accounts and too timid on small ones. It increases size by a "delta" — the dollars of accumulated profit needed to add one contract/unit. Number of units:

N = 0.5 × [ (1 + 8 × P/delta)^0.5 + 1 ]

where P is accumulated closed-trade profit and delta is the chosen profit-per-unit step (Adaptrade). Crucially, neither per-trade risk nor current equity enters the formula — only accumulated profit and delta. This makes it more aggressive when an account is small (faster contract growth early) and progressively more conservative as the account grows, the mirror image of fixed-fractional. It is a futures/contract-oriented method; it does not naturally map to fractional-share equity sizing, and on large accounts the unit increments become uneconomically slow.

Strengths & limitations

Strengths. Trivial to compute in real time; needs no estimate of win-rate or payoff; auto-scales; mathematically can't bankrupt the account in one trade if the stop holds; the most-recommended retail default precisely because it is robust to not knowing your edge.

Limitations — be honest.

• Not growth-optimal. A flat fraction ignores edge variation across trades. Where Kelly/optimal-f weight size by the trade's actual edge, fixed-fractional treats every setup identically. Too small under-uses a real edge (slow compounding); too large courts ruin, because position size and drawdown depth both rise faster than the f you pick. Optimal-f and full-Kelly are theoretically growth-maximizing but produce drawdowns most traders cannot stomach (Vince/Adaptrade) — which is exactly why fixed retail fractions are pinned low (1–2%).
• The stop-fill assumption. The entire "I only risk 1%" claim depends on actually exiting at your stop. Overnight gaps, halts, and illiquid opens can fill far below the stop — so on a gap-down the realized loss can be several R, breaking the 1% guarantee. For swing trades held over nights/weekends and through earnings, this gap risk is the binding caveat, not a footnote.
• Choosing f is unsolved by the method itself. The rule tells you how to apply a fraction, not which fraction is right; 1–2% is convention/folklore, validated more by survivorship than by proof.

System relevance

This is the default, directly-computable sizing rule for the Augustus trade-setup agent: given a setup's entry and a stop distance (from an ATR Stop or structural level), share count is one division — (Equity × Risk%) / (Entry − Stop). It pairs naturally with sibling nodes: R-Multiples (per-share risk is 1R, so fixed-fractional makes every trade's loss ≈ a fixed −1R in dollars), Volatility-Based / ATR Stops (which set the stop distance the formula needs), Risk of Ruin (which quantifies why low fractions survive), and Kelly / optimal-f (the edge-weighted alternatives this rule deliberately simplifies away from). Augustus should treat the 1% budget as a pre-gap figure and flag any setup whose overnight/earnings-gap exposure could blow through the stop, since that is where the fixed-fraction assumption silently fails.

Sources

• Adaptrade Software — Fixed Fractional Position Sizing (N = f × Equity / |Trade Risk|; optimal-f / Vince note) — adaptrade.com/Articles/article-ffps.htm
• Adaptrade Software — Fixed Ratio Position Sizing (Ryan Jones delta formula N = 0.5×[(1+8P/delta)^0.5+1]) — adaptrade.com/Articles/article-frps.htm
• Ryan Jones, The Trading Game — origin of the fixed-ratio method (via Adaptrade, DayTrading.com, TradingBlox forum)
• Ralph Vince — optimal f as a Kelly extension; drawdown-tolerance critique (via Adaptrade)
• TTM Markets — Fixed Fractional vs Fixed Ratio Position Sizing — tttmarkets.com/articles/fixed-fractional-vs-fixed-ratio-position-sizing/
• QuantifiedStrategies — Fixed Fractional Position Sizing: Definition & Examples
• TradeZella — Position Size Calculator (worked 1%-rule example: $1,000 / ($50−$48) = 500 shares)