Bank research
Data vintages in bank AI research: a macro and scenario review checklist
A historical series downloaded today may differ from what a researcher saw at the time. Freeze the source version before interpreting changes in macro views, charts or historical experiments. This checklist guides task design; it does not establish an included macro database or bank-wide multi-asset platform.
Published by FiClaw 产品团队 · Published · Updated
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Bank AI research should record observation periods, original release times, actual institutional availability, retrieval timestamps and data vintages, alongside units, frequency and transformations. Explain revisions, method changes and scenario changes separately. FiClaw's public core is A-share quant research; historical macro vintages, bond data and internal research systems require individual access assessment.
Key takeaways
- Observation periods describe the measured period; availability describes when a researcher could use it.
- Separate changes to data, methods and scenarios when explaining a revised conclusion.
- Hand over source snapshots, transformations, assumptions and human interpretation together.
Record distinct time fields for each data item
FRED documentation distinguishes currently available data from information available at a historical point; ALFRED can retrieve the latter. This illustrates the vintage concept and does not establish a FiClaw FRED or ALFRED integration. Verify each source's version mechanism separately.
| Field | Meaning | Review focus |
|---|---|---|
| Observation period | Month, quarter or other measured period | Frequency, seasonal adjustment and statistical scope |
| Original release time | When the source first published or revised the value | Retain date; intraday work also needs time and time zone |
| Institutional availability | When the supplier and internal workflow made it usable | Do not equate source release with internal availability |
| Retrieval time and vintage | Snapshot obtained for this run and version identifier | Separate initial values, revisions and this file; retain snapshots |
Use two snapshots to explain a research change
Consider an example without actual figures: a macro chart completed at T changes after the next update, including both historical and latest values. First hold the method fixed and calculate on old and new snapshots. Then hold the new snapshot fixed to examine method changes, and vary scenario assumptions separately. Disclose interaction effects where they occur.
FRED's release-date documentation notes that source release dates need not equal availability on its websites. Institutional research should also retain supplier and internal availability times. Do not silently replace then-available information with later revisions when evaluating historical judgments or experiments; disclose missing vintage evidence.
- 01Units and frequency: distinguish year-on-year, period-on-period, annualized, adjusted and unadjusted values.
- 02Transformations and alignment: record aggregation, gaps, lags and mixed-frequency methods.
- 03Revisions and corrections: retain old values or files and change explanations instead of only overwriting them.
- 04Scenarios and forecasts: identify the author, conditions and evidence; keep assumptions separate from observed facts.
A copyable bank research data and scenario brief
Department / use: [economics / financial markets / wealth research] Question: [what to explain or test] Data: [authorized source, series or file ID, units, frequency, seasonal adjustment] Timing: [observation period, original release, institutional availability, time zone] Snapshot: [retrieval time, version or file ID; historical vintages available?] Calculation: [transformations, aggregation, gaps, lags and alignment] Baseline scenario: [fixed assumptions, evidence and owner] Changed scenario: [what changes, what stays fixed, why] Difference analysis: [revisions / methods / scenarios and interactions] Handoff: [snapshots, calculations, charts, open issues and review] Keep unverified versions and timing explicit; do not invent historical data or forecasts.
Scope FiClaw around the actual task
Bank teams can first validate supported specifications, Python, A-share historical tests, diagnosis and parameter comparison. For macro or bond topics, list sources, vintages, models and intended outputs before assessing interfaces and implementation. An A-share experiment does not establish bond valuation, multi-asset forecasting or balance-sheet analysis.
Use public or redacted samples to compare data conventions, calculation consistency, visible unresolved issues and total human review effort. Assess internal access, permissions, archiving and deployment separately. Research, credit, pricing, suitability, trading and reporting remain within the bank's respective roles and processes.
Frequently asked questions
Is an observation date sufficient for macro research?
No. Record releases and revision vintages, actual availability and retrieval times. Data with the same observation date may have different values when downloaded at different times.
Can current values substitute for missing historical vintages?
Not silently. Disclose missing vintages and their implications, limit conclusions, or obtain appropriate source evidence before validation.
Does FiClaw cover all bank macro and bond research?
The current public core is A-share quant strategy research. Macro databases, vintages, bond data, valuation, forecasts and internal bank systems need separate authorization, interface and implementation assessment.
Method and product references
Related reading
Define one bank research task
Describe the department, question, available data and expected output to scope the pilot and access assessment.