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Montex Alpha Rail System
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Simulation — Risk-Tier Performance

Which tiers earn their keep — and which just add variance.

How MARS uses this

The benchmark models how often each tier should be used given the gate history. MARS compares live tier deployment against that expectation each review cycle, reading over-use of high tiers as aggression drift and under-use as unmonetized authority - both invisible in raw P&L.

How it benefits you

Aggression creep gets caught while it is still a pattern on a chart rather than an oversized loss. Equally, unnecessary timidity shows up as a measurable gap, so you deploy the edge you have actually earned instead of leaving modeled expectancy on the table.

0%10%20%30%T1T2T3T4T5T6T7BENCHMARKLIVE

Live risk-tier usage against benchmark expectation, T1 through T7.

The key idea

The questions

Tier usage as a diagnostic.

Am I spending more time in high tiers than the model expects for my gate history? Are high-tier trades producing proportionally better outcomes, or just bigger swings? Is tier compression during defensive gates actually happening, or is override use quietly flattening the ladder?

  • Riding higher tiers too aggressively is visible here even while returns look good.
  • Under-using authorized tiers is also a finding: unnecessarily timid deployment leaves modeled edge unmonetized.

Review cadence

How tier reads enter the loop.

Tier usage is reviewed monthly alongside gate dwell and throttle behavior. The comparison is conditional: usage is judged against what the model expects given the gates actually visited — heavy T5 usage during a Growth-dominant quarter is normal; the same usage during Buffer dwell is a governance breach in progress.

  • Tier-conditional R: each tier must show outcomes proportional to its extra risk, or the ladder is being climbed for nothing.
  • Override log cross-check: elevated tier usage plus frequent Full Tier overrides is the classic aggression-drift signature.
  • Under-usage gets equal scrutiny — authorized tiers left idle is modeled expectancy left unmonetized.

Reference

Tier doctrine — what each deployment tier is for

Tier zoneRoleBenchmark expectation
T1–T2Survival and suppression tiersDominant only in deep defensive gates
T3–T4Workhorse middle deploymentWhere a healthy allocator spends most time
T5Earned accelerationRequires Recovery gate or better
T6–T7Elite deploymentRare by design — frequent use signals over-aggression
Tier vs gate capThe governing comparisonUsage must be judged against the gate cap, not raw frequency

Worked example

Four scenario profiles judged side by side: expectancy, adverse-tail cost, and a verdict. Changes graduate through this grid or not at all.

BASELINE

LIVE
EV +0.31RP10 DD −14.8%

current governing profile

TIGHTER STOPS

REJECTED
EV +0.24RP10 DD −11.2%

EV cost exceeds drawdown saving

TNP WEIGHT +10

SANDBOX
EV +0.36RP10 DD −19.6%

edge up, adverse tail deepens

FEE MODEL B

CANDIDATE
EV +0.33RP10 DD −14.9%

friction saving survives resampling

Before you go deeper

Inside this module

2 pages go deeper than this one.

01

The Questions

Go deeper →

02

Review Cadence

Go deeper →

Connected inside MARS

This module doesn't work alone.

Connected — 01

Seven-Tier Risk Model

Open system page →

Connected — 02

Dynamic 7-Tier Benchmark

Open system page →

Go deeper

Operator briefs on this territory.

Deep dive — 01

Which tiers actually earn their risk.

The ladder authorizes deployment. Attribution asks whether each rung's risk actually converts.

Read the full brief →

Deep dive — 02

How often tier performance gets re-examined — and why not more.

Slice a small sample seven ways and something always looks broken. Cadence is the cure.

Read the full brief →

Deep dive — 03

Spotting when a tier stops behaving like its simulation.

Tiers fail by drifting, not breaking. Compare each rung's live signature to its simulated one before the gap compounds.

Read the full brief →

Take it further

Tier bands are built from a simulated distribution. The Foundry lets you rebuild that distribution on a different profile and see which tiers survive the change.

Open the AlphaRail Foundry Lab ↗

Every module ships in the complete MARS package.

One price. Eleven workbooks, three TradingView indicators, and the full manual library — $497.

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