Stop loss

Definition

A stop loss closes a position when loss reaches a predefined threshold — a price level, a percentage, an ATR multiple, or a thesis-invalidation point. Its two distinct jobs are often conflated: capping single-position damage (risk control) and improving return by exiting downtrends early (a timing claim). The first is arithmetic; the second is an empirical claim that is true in some return regimes and false in others.

How it works / structure

• Placement forms (engine-executable): fixed % from entry; volatility-scaled (k × atr_14_pct — adapts to the instrument’s noise floor); structural (beyond the level that invalidates the thesis — the platform’s preferred logic: the stop IS the falsifier’s price expression); time-compound (widening/tightening by holding period).
• Sizing coupling: stop distance × position size = risk per trade; the stop is one half of risk-fixed-fractional — set distance by thesis, size by budget, never the reverse.
• Order mechanics: stop-market guarantees exit, not price; stop-limit guarantees price, not exit (ms-bid-ask-spread); gaps execute stops far beyond their trigger — the stop bounds intent, not outcomes (ms-sessions-auctions).
• Trailing variant: ratchets with favorable movement, converting an initial risk cap into a profit-protection rule — the exit engine of trend systems (strategy-futures-trend-following).

When it applies

Positions with unbounded or large loss potential (shorts, futures, concentrated equity) as non-negotiable risk plumbing; trend/breakout systems where the stop doubles as the signal- failure exit. Poorly matched to mean-reversion entries (the entry logic buys weakness — see strategy-mean-reversion’s stop paradox) and to short-premium structures where the underlying’s stop maps nonlinearly to the option’s P&L.

Risk profile & failure modes

• Whipsaw cost: stops inside the instrument’s noise band convert volatility into a steady bleed of small losses; Kaminski-Lo (2014) show stop-loss value depends on return autocorrelation — stops help in momentum regimes and hurt in mean-reverting ones, the paper’s central result.
• Gap-through: overnight gaps and halts execute far beyond the level; stops do not bound gap risk — only sizing does.
• Stop clustering: obvious levels (round numbers, swing lows) concentrate resting stops; sweeps through them are microstructure, not conspiracy, but the cost is real (strategy-breakout false-break economics).
• Discipline theater: stops moved when threatened are not stops; the engine treats a moved stop as a new position requiring a new thesis (bias-loss-aversion is the mover).

Evidence & limits

Order mechanics are SEC/exchange-documented. Kaminski-Lo (2014) formalized when stop rules add or subtract expected return — regime-dependent, not universal. The platform’s stance: the risk-capping job is mandatory where loss is unbounded (sizing arithmetic, not empirics); the return-improvement job is a per-strategy replay question, never assumed.

Falsifiable-thesis examples

Illustrations only, not signals:

• “A 2-ATR initial stop on strategy S improves its replay Sharpe vs no stop this quarter” — falsified by the paired replay.
• “X will not close below the thesis-invalidation level L while the position is open” — falsified by a close below L (and the position exits by rule).

Cross-references

• The other half of sizing: risk-fixed-fractional; scale-aware placement: indicator-atr
• Exit siblings: mgmt-profit-target, mgmt-time-based-exit
• Mechanics and their gaps: ms-bid-ask-spread, ms-sessions-auctions
• The regime dependence: strategy-momentum vs strategy-mean-reversion