Build an Inflation Compensation Evidence Monitor

A rates researcher preparing an evidence-led market note.

Compare matched nominal and inflation-indexed yields. Explain observed compensation changes without treating the yield spread as a direct measure of expected inflation.

Key: date + maturity_years + instrument_definition.

Fields: nominal_yield, real_yield, unit, release_time, missing_reason.

Use percentage-point units consistently. Missing real yields do not imply a zero spread.

Align dates, maturities, and quotation conventions.

Compute matched yield spreads.

Separate observed changes from hypothetical premium adjustments.

Export a note with unresolved explanations and data gaps.

Matched-curve charts, a premium-assumption panel, and an evidence table for each proposed explanation.

A rate alignment and spread calculation service.

An export separating observations from economic interpretation.

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.

Reject maturity mismatches before subtraction.

A 0.1 percentage-point change displays as ten basis points.

Missing observations remain missing.

No output labels the spread alone as expected inflation.

Keep nominal yield fixed and increase real yield. Compensation falls even when no inflation forecast input changes.

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

Full target: Acquire five years of matched-maturity nominal and real yield observations. Add source-documented liquidity proxies only when their coverage is explicit.

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

pandas, SciPy, 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.

Do not claim a causal decomposition without identified premium estimates.

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.