@Mark
Your compute function is always run for every day of your backtest. The idea here is that each day you receive a trailing window of data ending on that day, and the responsibility of your compute is to produce one value per asset each time it's called.
Looking at your compute, I doubt that's a significant bottleneck for performance. The more likely issue is that you're loading 200 trailing rows of fundamentals data for every field you're using, but you only need a single row for many of them. I'd suggest trying to pull out the expressions that need long trailing windows into separate factors, computing that score separately, and then adding them together.
In the longer term, your example makes me think that we need a way to take a Filter and convert it back into a Factor by setting True to 1 and False to 0. This would let you write something like:
piotroski_net_income = (morningstar.income_statement.net_income.latest > 0).as_factor()
piotroski_cash_flow = (morningstar.cash_flow_statement.operating_cash_flow.latest > 0).as_factor()
...
piotroski_score = (piotroski_net_income + piotroski_cash_flow + ...)
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