If you’ve ever backtested a strategy using fundamental data, you’ve probably made a critical assumption without realizing it: that the numbers you’re seeing reflect what was actually known at the time. In reality, many datasets show restated, future-corrected values — and that’s a problem.

Most financial data vendors aggregate fundamentals after the fact, without tagging when the data became available to the public. That means your backtest might be using Q4 earnings before they were released, inflating model performance with hindsight bias. This subtle error can lead to drastically overstated results — and faulty decisions downstream.

At Finvera, we handle fundamentals differently. Our API includes point-in-time tagging, which means every metric — revenue, EBITDA, EPS, you name it — is linked to its original filing date, not just the fiscal period it belongs to. If a company files late, or restates a number months later, our system captures that distinction.

We also map XBRL filings with high fidelity, resolving inconsistencies across formats, industries, and issuers. So you get structured, reliable data that works whether you’re modeling small-cap biotech or global industrials. And because we normalize tags and fields up front, you don’t waste time writing patchwork logic just to run a screen.

This kind of rigor is especially important for quant funds, financial engineers, and researchers who care about truth over trend. Because if your model is built on faulty assumptions, it doesn’t matter how good your math is — your output is compromised before you even hit “run.”