The site is down for whatever reason, but daily return data isn't really highly correlated. The auto-correlation and partial auto-correlations are close to zero

The site is down for whatever reason, but daily return data isn't really highly correlated. The auto-correlation and partial auto-correlations are close to zero regardless of the lag you use. Monthly data shows slight acf and pacf for the previous period for most equities. Moreover, if you are just looking at the direction of daily movements (up or down), the series of moves fits a geometric distribution very well, with a slight bias in the up probability than the down one. But regardless, the fix you're suggesting is correct. You can't just take random days out of order. Even though daily returns show low acf/pacf, stocks show trends in the magnitude of their returns, but not necessarily their direction. For example, a given stock could be popping off with strong upward momentum, and still be down half the days. It's just that the returns are greater on the upside than downside. You see this kind of incorrect thinking a lot with people who don't work in quant finance: they try and predict the direction and sometimes magnitude of returns. In real life, no one actually does this, as it is practically impossible. Instead, you try and construct a portfolio in which you are exposed to some factor in which you think is correlated to out-performance. I personally think believe that forecasting direction and magnitude to be literally impossible with ML methods in daily data. I (and everyone else) has come to this conclusion after many failed attempts with every ML/AI tool in existence. Fortunately, it turns out you don't really need to forecast returns to deliver alpha and superior risk-adjusted returns. It's just the most obvious thing to do for people new to finance, but experienced with ML.