Structural Diagnostic
Deployment Distortion (DDX)
“Is risk being deployed the way the model assumes?”
Deployment Distortion measures how far live risk deployment has bent efficiency away from the fixed benchmark frame. Positive distortion means live deployment is running lighter than the range median and mechanically flattering the numbers; negative distortion means heavier live risk is diluting them. Neutral means the live account is behaving like the benchmarked model.
The formula, derived
The gap between the range-median benchmark reading and the live-risk reading is classified in bands: ≥ +20% Positive Distortion, +5% to +20% Mild Positive, −5% to +5% Neutral / Aligned, −20% to −5% Mild Negative, below −20% Negative Distortion.
How MARS reads it
Technical explanation
MARS authorizes deployment through a deterministic chain: gate → tier cap → cycle pool → per-trade risk. Distortion accumulates when live behavior deviates — oversized singles, ignored smart-exposure adjustments, habitual overrides.
The Cycle Decision Log is the evidence source: every cycle records authorized versus effective deployment, making distortion measurable instead of anecdotal.
Persistent distortion invalidates benchmark comparison — the simulation modeled a system the trader is no longer actually running.
Interpretation bands
Strong. Neutral. Weak.
Strong
Live deployment matches authorization; overrides rare, logged, and justified.
Neutral
Occasional drift or override clustering — review cadence and tighten logging discipline.
Weak
Systematic deviation from authorized deployment — benchmark comparability is broken and governance is leaking.
Use cases
Where it earns its place
- ▸Weekly override-frequency and justification-quality review
- ▸Detecting tier overuse — living in T5–T7 when the gate expects baseline dwell
- ▸Auditing whether smart open-exposure sizing was respected in live execution
- ▸Explaining benchmark divergence caused by behavior rather than edge
Edge cases
Where it can mislead
- !Justified distortion: a logged, reasoned override during exceptional conditions is governance working — unlogged habitual override is distortion.
- !Compression periods: defensive gates shrink deployment legitimately; distortion measures deviation from authority, not smallness of size.
Example scenarios
The metric in the wild
The silent 30% drift
Per-trade risk crept from 0.50% to 0.65% over six weeks with no gate change. Equity grew, so nothing felt wrong — the distortion read exposes that the live system now runs 30% hotter than the one that was validated.
Monte Carlo connection
The benchmark simulated the authorized deployment rules. Distortion severs that link: the wider the distortion, the less the percentile bands describe the system actually being traded.
Monte Carlo Lab →Live benchmark comparison
Live-vs-benchmark deviation is first screened for deployment distortion before being attributed to edge — outperformance driven by overrides is governance leakage, not alpha.
7-Tier MC Benchmark →Go deeper
Operator briefs on this territory.
Deep dive — 01
Expectancy arithmetic: what a trade is worth before it happens.
The EV formula walked end to end — why per-trade worth beats realized P&L as the master metric.
Read the full brief →
Deep dive — 02
Reading the fan: Monte Carlo without self-deception.
What the percentile bands actually promise, why P10 governs sizing, and where simulation authority ends.
Read the full brief →
Deep dive — 03
MAE/MFE: what your trades did while you weren't looking.
Reading maximum adverse and favorable excursion to audit stops, targets, and exits with evidence.
Read the full brief →
DDX is calculated for you — automatically.
Every reading on this page is produced, tracked, and interpreted inside the MARS workbook ecosystem.