Core Metric 04 / 05
Risk-Adjusted Efficiency Ratio (RAER)
“Is risk being converted efficiently?”
RAER is the capital-conversion metric: how much net PnL was produced for each dollar of risk deployed. A weekly RAER of 0.20 means every $1.00 of committed risk generated $0.20 of net return. It is brutally practical — it tells you whether deployed risk is actually becoming account progress, at the branch, composite, and portfolio level.
The formula, derived
RAER is already risk-adjusted by construction: the denominator is the actual dollars of risk committed, so no range-median baseline enters the formula. Its mathematical value is independent of the risk deployment ladder — the rails qualify the interpretation, never the calculation.
How MARS reads it
Technical explanation
The class ladder: below 0.15 Critical (extremely unstable), 0.15–0.25 Weak (noisy/unreliable), 0.25–0.35 Stable (acceptable), 0.35–0.50 Strong (consistent), 0.50+ Elite (exceptional efficiency). The quick dashboard anchor compresses this to three zones: under 0.20 noisy edge, 0.20–0.35 normal operating zone, above 0.35 elite consistency.
The never-average rule is doctrine: cumulative RAER is cumulative PnL over cumulative risk, and composite RAER (Normal + Trend, All Blended) is recomputed from composite totals — a $50-risk week and a $2,000-risk week must not carry equal weight. Averaging weekly or branch ratios is flagged as a formula failure in the SDE standard.
Three companion rails strengthen the interpretation without touching the math. The Average Risk Context Rail (3%–8%) asks how much deployment pressure produced the reading — 0.40 at 3% risk is strong-under-conservative-throttle; 0.40 at 8% is credible only if stability holds. The EV Companion Rail asks whether modeled edge supports realized efficiency (strong RAER with weak EV is treated as provisional — likely favorable variance). The Hit-Rate Companion Rail asks how repeatable the efficiency is — fat-tail dependence versus broad reliability.
A truly favorable profile is RAER Strong or Elite with risk appropriate for gate and branch, EV healthy, hit rate compatible with the branch role, and RAPF aligned. The same raw 0.42 without those confirmations deserves a fraction of the confidence — that layered read is what the SDE runs before RAER enters throttle permissions.
Interpretation bands
Strong. Neutral. Weak.
Strong
High return per unit of risk, stable across windows, confirmed by RAPF alignment.
Neutral
Acceptable conversion with drift or instability — efficient weeks mixed with wasteful ones.
Weak
Risk deployed without adequate return — the budget is being spent on exposure that does not pay.
Use cases
Where it earns its place
- ▸Auditing whether the risk budget produces enough return per unit spent
- ▸Identifying branches that only perform through heavy exposure
- ▸Detecting wasted risk on low-quality trades before it compounds
- ▸Structural permission input for throttle tier decisions
- ▸Cleanest single lens for comparing live efficiency against simulation assumptions
Edge cases
Where it can mislead
- !Efficiency without monetization: high RAER with weak RAPF means clean risk that is not converting — usually an exit or capture problem, not an entry problem.
- !Small-sample spikes: one elite week can spike RAER; rolling windows and stability tracking prevent overreaction.
- !Compression masking: in defensive gates, deployed risk shrinks — RAER must be read against gate state or low absolute returns get misdiagnosed as inefficiency.
Example scenarios
The metric in the wild
The 20% illusion
Account A returns 20% deploying triple the risk budget of Account B’s 12%. RAER exposes B as the structurally superior machine — the one that survives scaling.
Under-monetization flag
RAER reads Strong while RAPF reads Weak. The contradiction state routes review toward capture efficiency and giveback in the MAE/MFE Lab — the edge is real but leaking at the exit.
Monte Carlo connection
The simulation assumes a modeled risk-conversion efficiency. Live RAER below benchmark means the trader deploys risk but extracts less than modeled; above benchmark with controlled drawdown is one of the cleanest alpha signatures MARS recognizes.
Monte Carlo Lab →Live benchmark comparison
Benchmark comparison adjusts the conversation away from raw returns — live RAER against modeled conversion answers whether outperformance is skill or exposure.
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 →
RAER is calculated for you — automatically.
Every reading on this page is produced, tracked, and interpreted inside the MARS workbook ecosystem.