Simulation — Scenario Manager

Test the idea before it touches live capital.

Scenario discipline

A controlled condition, not a vibe.

Borrowing the QA doctrine: a scenario is a contract — a controlled test condition with explicit expected behavior, which turns testing into objective comparison instead of opinion. Scenario_Profiles in CP3 then compares the active profile against alternates over real evidence: was another weighting structurally superior, persistently?

Persistence rule: a profile must look better over multiple weeks and months, not one flattering sample.
Scenario outputs remain sandboxed until deliberately promoted — the R&D isolation principle.

Promotion workflow

From idea to production, with a paper trail.

A candidate change follows a fixed path: define the scenario contract with expected behavior, run it against evidence in the sandbox, compare against the Standard baseline over a persistence window, document the verdict, and only then promote deliberately. Every step leaves a record — because 'I tried it and it felt better' is exactly the process MARS exists to replace.

01Persistence windows span multiple weeks and months; one flattering sample never promotes anything.

02Rejected scenarios stay in the library with their verdicts — negative knowledge is still knowledge.

03Promotion updates the Legend weights or rules explicitly, so production changes are visible diffs, not silent drift.

The core idea

How MARS uses this

Every proposed rule change - stop policy, branch weights, fee model - is cloned into a scenario profile and resampled against the same evidence. The grid renders the trade-offs, and only profiles whose edge survives without deepening the P10 tail earn candidate status for live promotion.

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

How it benefits you

System changes stop being vibes-based. The tempting tweak that costs 0.07R of expectancy for a modest drawdown saving gets rejected by arithmetic before it silently taxes six months of trading - and promising candidates carry their evidence with them into review.

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

Worked example

Weekly EV tagged GREEN / YELLOW / RED against expectancy thresholds, with the rolling-EV line separating persistent edge from one lucky week.

Reference

The scenario levers — what each knob stresses

Lever	What it stresses	The question it answers
Win-rate shift	Edge durability	How much hit-rate decay can the system absorb?
Risk-per-trade change	Deployment aggression	Does faster compounding survive the drawdown cost?
Branch-mix drift	Blend dependence	What if trend branches fire less often than modeled?
Fee & slippage load	Friction drag	At what cost level does thin EV go negative?
Losing-streak injection	Sequence risk	Can the gate ladder contain a cold start?

Inside this module

2 pages go deeper than this one.

Scenario Discipline

Go deeper →

Promotion Workflow

Go deeper →

Connected inside MARS

This module doesn't work alone.

Connected — 01

Monte Carlo Lab

Open system page →

Connected — 02

Variant Attribution Module

Open system page →

Connected — 03

Certified Validation QA

Open system page →

Go deeper

Operator briefs on this territory.

Deep dive — 01

Define expected behavior before running the test.

Write the expected behavior first, or the run will mean whatever you wanted it to.

Read the full brief →

Deep dive — 02

Sandbox → persistence window → documented verdict → promotion.

Four gates between a promising idea and the production rail — and the order is the protection.

Read the full brief →

Deep dive — 03

Why R&D never touches the Standard baseline.

Experiments aren't dangerous — they're persuasive. The wall exists for the second problem.

Read the full brief →

Take it further

Scenario discipline is easier to respect when testing is cheap. The Foundry is the sandbox side of that discipline — build the case there, promote it here.

Open the AlphaRail Foundry Lab ↗

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