Structural Diagnostic
Risk Deployment Quality Score (RDQ)
“Was the risk budget spent on the right exposure?”
RDQS grades the quality of risk deployment itself — separate from whether the trades won. It rewards deployment that matches authorization, monetizes efficiently at live risk, and operates under low structural pressure. A profitable stretch can still print a weak RDQS when the deployment behind it drifted from what the gates and tiers actually authorized.
The formula, derived
A weighted composite of four scored inputs: the risk score (25%), the deployment-gap score (35% — the heaviest weight, because the gap between authorized and actual deployment is the strongest quality signal), the live RAPF score (25%), and inverted stability pressure (15%) so calm structure raises the score.
How MARS reads it
Technical explanation
Draws on Throttle Control tier records, smart open-exposure adjustments, and Branch Risk Engine quality context inside CP3.
Open trades consume capacity: RDQ penalizes stacking fresh risk as if floating exposure were free — the professional concept most retail sizing ignores.
High tiers are not inherently bad; unearned high tiers are. RDQ reads tier usage against gate state, daily EV status, and structural permission.
Interpretation bands
Strong. Neutral. Weak.
Strong
Tier usage earned, open exposure respected, allocation follows branch quality — the budget is spent like a professional desk.
Neutral
Broadly sound with episodes of stacking or timidity — refine cycle discipline.
Weak
Risk spent without regard to capacity, condition, or quality — the deployment layer is the leak.
Use cases
Where it earns its place
- ▸Monthly tier-usage review — overuse of high tiers or underuse of productive baseline tiers
- ▸Auditing smart-capacity behavior when carryover trades exist
- ▸Grading whether branch allocation followed branch quality
- ▸Explaining RAER weakness: inefficiency often traces to deployment quality
Edge cases
Where it can mislead
- !Defensive underdeployment: chronic timidity in Growth gate wastes edge just as overdeployment burns it — RDQ reads both directions.
- !Correlated stacking: four concurrent trades in one currency theme is one big trade wearing four costumes.
Example scenarios
The metric in the wild
Capacity ignored
Two carryover trades hold 0.9% active risk, yet the next cycle deploys a full fresh pool. Total exposure silently exceeds the authorized envelope — RDQ flags the stacking before drawdown teaches the lesson.
Monte Carlo connection
The benchmark models disciplined deployment: expected tier dwell, exposure compression, and smart-capacity behavior. Live RDQ divergence explains why live paths leave the envelope even when trade-level edge matches.
Monte Carlo Lab →Live benchmark comparison
Benchmark tier-usage and exposure tables are the reference; live RDQ is the compliance read against them.
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 →
RDQ is calculated for you — automatically.
Every reading on this page is produced, tracked, and interpreted inside the MARS workbook ecosystem.