I built a tool to track my own CS2 (Counter-Strike 2) skin inventory — what it's worth, what I actually made after the Steam Market's 15% fee. Once the data was flowing, an obvious question showed up: do any of the "signals" people chase in this market — momentum, retired cases, cheap vs. expensive — actually predict anything? Or is it noise with a narrative attached?

So I backtested them. Here's what held up, what didn't, and the two bugs the backtest caught in its own scoring model before I caught them.

The setup

I used the price-history cache my tool (SkinVault) already builds — three years of data across 373 price series, about 50,000 samples. For each candidate factor (momentum, price level, case-retirement status, and a few others), I measured its information coefficient (IC): the rank correlation, across items on the same date, between the factor's value and the item's return over some later horizon (7, 30, 90, 365 days).

The "same date" part matters more than it sounds.…