Generating permutations of conditional frequencies and using only the relevant ones automagically

Alright bois, let's get to it.

I believe this is a machine learning problem but I am not good enough to know how to use it. I suspect random forests.

Some of my discretionary trading uses conditional frequencies: "when X has happened in the past, what does Y look like relative to Z as a frequency distribution". This helps me zero in on levels I might want to trade in conjunction with other things.

So for example: when a stonk index opens 3% below yesterday's open, what is the frequency of today's close relative to yesterday's OHLC+midpoint.

it annoys me that I have to write code to figure this out for each particular instance. So for the above case, I'd do something like this:

mask = df.open.pct_change().abs() >= 0.03 (df[mask].close > df[mask].pervious_close).sum()/len(df[mask]) 

What I want to do is have an algorithm that looks at where things are opening and automatically figures out the pertinent conditional frequencies. I believe machine learning basically is an engine to compute conditional frequencies with some fuzzing which is why I think it is relevant here but I am struggling to make the connection.

So, geniuses of /r/algotrading (myself obviously excluded), how would you do this?

Submitted October 14, 2020 at 05:38PM by Pepper-Salt
via https://ift.tt/2IqZRW9

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