Check AI-generated quant code for look-ahead bias: a FiClaw data-timing checklist
Review data availability, historical universes, signal timing, execution and preprocessing for future-data leakage in AI-generated quant strategy code.
Published by FiClaw 产品团队 · Published · Updated
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For every input, ask whether it was available at the historical decision time. Distinguish the observation period, publication or availability time, signal calculation and earliest feasible execution. Then inspect historical universe membership and preprocessing. FiClaw's code generation and historical backtests still require this review.
For researchers generating strategy code, using financial factors or investigating unusually strong backtest results.
Example code review request: expose timing assumptions
Review this strategy before optimizing returns. List each field's observation period, availability time, signal time and execution time. Flag negative shifts, centered windows, full-sample normalization, future-value backfills and current index members used throughout history. For each concern, identify the code, supporting evidence and a proposed correction. Mark unverified data explicitly.
An observation period is not an availability timestamp
A financial figure describing a quarter was not necessarily available when that quarter ended. Check its publication or availability time and whether later revisions replaced the original value. State limitations when historical versions are unavailable.
A signal requiring the completed daily close needs a compatible execution assumption. Inspect any strategy that uses the full closing observation while assuming an order filled before that information was available.
Inspect universes and full-sample calculations
Applying today's index constituents to all earlier periods changes the historical opportunity set and can omit delisted or removed securities. Confirm historical membership and instrument status before defining what the test can establish.
Review negative shifts, centered windows, backward filling, statistics fitted across the entire sample and selection based on subsequent returns. These operations require an explanation of information timing rather than automatic acceptance or rejection.
Turn concerns into traceable FiClaw research evidence
Put timing constraints in the specification and trace selected trades from source fields and universe membership through signals to fills. Retain code and configuration before and after corrections, then explain the result changes.
A successful run does not establish freedom from look-ahead bias. FiClaw can assist with code explanations and diagnosis; source documentation and actual records are still needed to establish availability. Leave unresolved questions visible in the conclusion.
Trace the information behind a historical trade
| Object | Evidence | Typical concern |
|---|---|---|
| Input field | Observation period, availability and historical version | Treating period-end as publication time |
| Universe | Historical membership and listing status | Keeping only today's surviving members |
| Signal | Window direction, preprocessing scope and calculation time | Later observations influencing an earlier decision |
| Execution | Order time, matching assumptions and fill log | Assuming a fill before information was available |
Frequently asked questions
Does runnable code mean there is no look-ahead bias?
No. Syntax and interface checks cannot establish historical information availability. Review field timing, universe membership, window direction and execution records.
Can shifting every signal by one day fix all leakage?
No. A signal delay does not repair historical-universe errors, revised financial values or preprocessing fitted on future observations. Check each information source separately.
Does FiClaw guarantee point-in-time versions for every field?
This guide makes no such guarantee. FiClaw connects to historical data through QuantAPI; historical revisions, universe membership and field availability must be confirmed against the data documentation or with the provider.
Method references
- scikit-learn: common pitfalls and data leakage
Background on separating fitting from evaluation; financial disclosure timing still requires source-specific evidence.
Related resources
- A-share data coverage and boundaries →
- Factor research checklist →
- Out-of-sample and walk-forward validation →