Implement decision-time financial snapshots and replayable evidence - #1
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The repository previously contained only its project introduction. It now builds financial feature snapshots using both event and availability time, selects the highest known source revision, and handles tombstones and freshness without leaking later corrections into historical decisions.
Includes a SQLite window-function engine, an independent Python oracle, strict JSON contracts, record-level CSV lineage, an offline report, reproducible figures, bilingual READMEs and Chinese interview notes.
Validation: 40 tests passed locally, including 40 randomized SQL/oracle history comparisons, future-append invariance, timezone/freshness boundaries and rehashed-report corruption. The committed synthetic example replays successfully: 15 decisions, 45 lookups, 11 event-only baseline selections using future knowledge. Python 3.11/3.12 CI installs the package, tests it and replays saved and fresh evidence.
Scope: scalar observations and source-provided revision/availability contracts; no distributed performance or integrated CreditVintage pipeline is claimed.