1. Certainly it's possible to forecast totally random data.
 2. The best method will depend on what "totally random" means, and on what "best" means. Let's suppose that "best" means "lowest expected [tag:mse]". 

 * If "totally random" means independent, identically distributed (iid) sales, then the best forecast will be the historical average.
 * If "totally random" means iid *increments* over the previous day's sales, i.e., a random walk, then the best forecast will be the last observation, also known as the "naive forecast". This data generating process is unplausible for sales, though a good first idea for stock prices.

"Best" = "lowest expected [tag:mape]" has [a different answer](https://stats.stackexchange.com/q/299712/1352). "Best" = "lowest expected [tag:mae]" may have [yet another answer](https://stats.stackexchange.com/q/355538/1352).

I suspect that you have something different in mind by "totally random". [We have a number of existing threads on forecasting daily data.](https://stats.stackexchange.com/search?q=%5Bforecasting%5D+daily) Browsing these should be useful.

Regarding your question about your specific data: This question is very broad, and I believe you would profit
from reading an introductory level textbook, e.g., the free online
[*Forecasting: Principles and Practice* by
Hyndman & Athanasopoulos](https://otexts.org/fpp2/).
If after reading this you still have more specific questions, then
please do ask them here. If you already *have* read such a textbook,
please edit your question to make it more specific. Thank you!