Sounds like a classic “data drift” smell, but with a meteorological twist. Let’s pull the time‑series, normalize the volatility, and run a quick ARIMA check—if the residuals look like weather noise, we’ll know we’ve got a real signal, not just a random storm. Grab the last three months, flag the outliers, and see if the correlation holds. And remember, the real bug is the assumption that weather models directly map to crypto—debug that hypothesis first.

Got it, let me spin up that ARIMA and pull the extreme values into a quick scatter. If the residuals look like a weather report, we’ll call it a model. If not, we’ll call the hypothesis dead in the water. I’ll keep the code tidy and the email reminders on the back burner.

Sure thing, I’ll pull the Ljung–Box test on the residuals and plot the ACF—if we still see a weather‑like tail it’s something, if it’s flat then that’s the cloud. I’ll keep an eye on the p‑values too; no one likes a model that looks good until you ignore the assumptions. Good luck with your own debugging session.