How to get average across all dates using rolling average?

The differences you're observing between your 33-day rolling average and monthly averages make complete sense when we consider how these calculations work:

1. Continuous vs. Fixed Periods: A 33-day average updates daily and isn't tied to calendar months, while monthly averages use fixed start/end dates.

2. Cross-Month Inclusion: Your June average, for instance, contains data from late May, creating different comparison points.

3. Smoothing Effect: The longer window naturally smooths out daily fluctuations more than monthly averages.

The Seasonal Factor You Should Consider

We should also account for potential seasonality in your time patterns. While I don't know your exact data, many financial datasets show monthly patterns - often peaking at period beginnings/ends.

Here's why this matters: Your 33-day window captures two peak moments - the end of one month and start of another. When these high-value periods combine in a single average, the result naturally appears inflated compared to looking at months individually.