Seasonality and Day-of-Week Effects in Raw Feeds

A raw alternative data feed — web traffic, credit-card transactions, satellite counts, app downloads — is rarely a clean stream of underlying business activity. It's usually contaminated with mechanical calendar patterns that have nothing to do with the thing being measured: retail foot traffic naturally spikes on weekends, credit-card processors batch and report transactions with a lag that varies by day of week, and many providers show artificial dips or gaps around public holidays simply because fewer people are generating data, not because underlying demand changed.

If these patterns aren't identified and removed, they can easily be mistaken for genuine predictive signal — a strategy that appears to "predict" retail sales might just be re-discovering that Saturdays are busier than Tuesdays, a fact any calendar already tells you for free, contributing zero real alpha despite looking statistically significant in a naive backtest. The standard fix is seasonal adjustment: estimating and subtracting out the recurring day-of-week, day-of-month, and holiday components before looking for anything else in the series, often using a rolling average of the same weekday across recent weeks as a baseline.

This matters most for data with irregular collection cadence, where a provider's own operational quirks — a batch job that runs only on business days, a scraper that misses holidays — get baked directly into the feed and are easy to confuse with the underlying economic reality the data is supposed to represent.