Require an aligned return window before reviewing outcomes

Define the meaning of a return row

Each report row contains a model_return and benchmark_return expressed as decimal returns for the same period. A value of 0.01 means one percent for that period. The date is its period key, not a trade timestamp. A meaningful comparison also requires consistent currency, income treatment and valuation boundaries, even though the teaching schema cannot verify those economic definitions.

The engine accepts finite returns greater than minus one and no greater than ten. These are implementation bounds for the synthetic examples, not plausible-performance assumptions or a representation of every possible investment instrument. A minus one return, representing complete loss under this wealth convention, is rejected; the calculator does not model post-wipeout continuation.

Test the intersection trap

Imagine three expected months. The model returns are January 0.02, February minus 0.04 and March 0.01, but the benchmark is missing February. An inner join on dates would retain January and March and omit the model’s negative month. The resulting comparison is a different two-month sample, not the contracted three-month review.

The report instead withholds the complete performance block and names the gap. It does not forward-fill the benchmark, replace the missing return with zero or treat a shorter intersection as the complete window. A separately labeled partial study could be useful, but it would need a new scope and should preserve the excluded-period evidence.

Keep feature coverage independent

Feature drift and outcome performance answer different questions. A null feature in February can leave the model and benchmark returns entirely complete. Under the report contract, that feature null creates a quality review and an observed-subset drift label while allowing the complete outcome calculation to remain available.

Now reverse the case: every feature is present, but the March model return is null. A stable feature histogram cannot supply the missing return. The performance block remains withheld. This is why the report records null counts for each field and explains metric-specific eligibility rather than issuing one all-purpose data-quality percentage.

Check comparability beyond calendar matching

Two rows sharing a month-end key can still describe different horizons. One might include a late valuation while another stops at a different market close. Similarly, a price-only benchmark and a return including distributions are not directly equivalent. The fixture contains invented numbers and does not infer these conventions from their magnitude.

For an actual review, record the return definition and the benchmark’s intended role before looking at relative results. CFA Institute’s GIPS handbook is a primary reference on performance calculation conventions, but citing it does not make this simple report GIPS compliant. The tool receives already calculated period returns; it does not reconstruct valuations or adjust external cash flows.

Make withholding a useful result

A withheld block should state the affected dates, missing field, expected count and required repair. Retain the valid inputs rather than replacing the whole report with a generic failure message. Quality and other eligible diagnostics can still help an investigator determine whether the issue is delivery, transformation or an incorrectly specified review window.

Once a verified correction arrives, rerun the same window and preserve the original incomplete report alongside the replacement. The publication’s static examples demonstrate that workflow without connecting to production accounts. Complete alignment establishes eligibility for the stated arithmetic, not the accuracy of source returns, investment skill or approval to continue a real strategy.

Keep in the decision record

• Never let an inner join silently redefine the review window.
• Feature nulls and return nulls have different consequences.
• Aligned arithmetic still requires economic comparability.

Source notes

1. CFA Institute — GIPS Standards Handbook for Firms, November 2020
2. W3C — Data Quality Vocabulary, Working Group Note, 15 December 2016
3. W3C — PROV-O, Recommendation, 30 April 2013

Sources inform the stated methods and governance context. All example monitoring observations are synthetic. No regulatory approval or production monitoring certification is implied.

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