Build the complete application

## User
An appraiser reviewing a probability-weighted expected return model.

## Build
Compare documented private-company exit scenarios with different timing and class payouts. Preserve scenario probabilities, dilution, discounting, and evidence for every assumption.

## Input Contract
- Key: scenario_id + security_class + valuation_date.
- Fields: exit_value, exit_time, probability, class_payout, diluted_units, discount_assumption.
- Probabilities sum to one. Allocate and discount within each scenario before aggregation.

## Workflow
1. Review scenario evidence and mutually exclusive outcomes.
2. Apply scenario-specific capitalization and payout rules.
3. Discount class payouts under documented conventions.
4. Export weighted class values and probability sensitivities.

## Interface
An exit-scenario matrix with probability validation, timeline charts, and class-value comparisons.

## Deliverables
- A scenario-specific payout and discounting model.
- A review memo showing probability and dilution sensitivity.
- Runnable local application, setup commands, and a dependency lockfile.
- Versioned input data, data dictionary, acquisition instructions, and rights notes.
- Calculation traces, source references, assumption records, and exported review artifacts.
- A deterministic baseline and tests for normal, incomplete, contradictory, and failure-recovery cases.
- A recorded complete workflow with a reproducible manifest and explicit limitations.

## Acceptance Criteria
- Reject probabilities outside zero to one or a nonunit total.
- Do not divide all scenarios by one share count when dilution differs.
- Do not discount an already present-valued payout twice.
- Explain risk treatment to avoid undisclosed duplication in probabilities and discount rates.

## Contrast Case
Keep discounted class values fixed and change their probabilities. The weighted result changes without changing any exit payout.

## Dataset Scope
The attached starter files are prototype inputs. Expected starter answers are visible. They are not independent holdouts.

Full target: Author fictional sale, public offering, continued operation, and downside scenarios. Supply scenario-specific rights, dilution, exit times, and discount conventions.

Complete target contracts and expert review remain pending. Public observations are not historical release vintages.

## Tools
NumPy, pandas, openpyxl

Use an available model appropriate to the task. Record provider, model identifier, version or retrieval date, settings, cost, and latency. Use deterministic code for calculations. Models may extract, retrieve, or explain evidence. They must not invent missing records, override approval rules, or claim professional authority.

## Constraints
Scenario probabilities are documented judgments, not measured frequencies unless supported by suitable evidence.

- No fabricated records, source citations, professional endorsements, or expert-adjudication claims.
- No future-data leakage into dated decisions, model fitting, or historical valuation inputs.
- No unauthorized payments, account restrictions, regulatory filings, or production changes.
- No automated appraisal certification, tax-compliance determination, or unsupported fraud accusation.
- No concealed uncertainty or silently invented inputs required for a numerical conclusion.

## Scoring
- Correct domain calculations and justified methods with documented assumptions. Weight: 30.
- Source lineage, temporal integrity, rights checks, and explicit synthetic-data labels. Weight: 25.
- A working application with review, export, error handling, and reproducible state. Weight: 20.
- Adversarial cases, uncertainty, missing-data behavior, and sensitivity analysis. Weight: 15.
- Evidence-linked conclusions that distinguish facts, assumptions, and unresolved judgments. Weight: 10.
Score each dimension from zero to two. Weighted score = sum(weight * score / 2). A failed safety gate blocks acceptance.