Build a Preferred Financing Backsolve and Identification Console

A valuation specialist assessing recent financing evidence.

Investigate total equity values consistent with a financing price and specified investor rights. Separate primary financing evidence from secondary sales and unsupported valuation shortcuts.

Key: transaction_id + security_class + rights_version.

Fields: observed_price, units, primary_or_secondary, transaction_date, rights, calibration_parameters.

Document nonprice consideration and missing terms. Do not infer common value from preferred price alone.

Determine which transaction terms are supported by evidence.

Calibrate a forward allocation model to the observed security price.

Test parameter bounds and alternative rights interpretations.

Export residuals and an identification limitations report.

A transaction evidence viewer with calibration controls and multiple feasible valuation results.

A bounded calibration service using the approved allocation engine.

A financing evidence memo with a complete parameter and rights history.

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.

Forward pricing of the calibrated class matches its target within the stated tolerance.

One transaction cannot identify several unconstrained parameters.

Secondary trades remain separately labeled.

A change in rights invalidates an unchanged-price calibration assumption.

In the illustrative linear calibration, identical security proceeds imply different total equity when the supplied rights adjustment changes.

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

Full target: Create fictional preferred financing documents, transaction prices, side letters, and cap tables. Include a secondary sale with different rights and transaction circumstances.

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

SciPy, pandas, Streamlit

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.

The linear fixture is a toy calibration, not an economically complete preferred-stock pricing formula.

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.

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.