I'm building a machine learning model with the aim of learning a daily strategy of buy or sell the stock.
I was wondering if I should use adjusted close price or something else to calculate returns (I was thinking about considering open price/close price the day before) and to evaluate the strategy. I know that adjusted prices offer a better representation of the price as they account for dividends and other things, but with in that way the results I would get are they consistent with reality? In other words, I fear it may happen that a strategy which is successful for returns calculated through adjusted prices would be not the same in a realistic world.