I'm getting a mystery Pipeline error (see the attached notebook):
ValueError: too many inputs The error does not result if the number of custom factors is reduced. So, I'm concluding that there is a limit on the number of custom factors. Is there a limit, or is something else going on?
Anyone? I'd like to understand if there is a limitation to the number of custom factors supported by Pipeline (and if it will be maintained as a limitation going forward), since it affects the overall algo structure.
I have ran into this problem too. I got around it bu calculating part of the factors using a very archaic aggregation in the pipeline for about 20 of my alpha factors, and the other 30 or so are in CustomFactor classes, which are executed in a similar manner as yours. If I figure anything else out I will let you know, as I am trying to get them all into CustomFactor classes to apply ML eventually. I am curious of Quantopian's answer, as I am going to need some room going forward too.
Hi Dane -
Oddly, if I do the alpha combination outside Pipeline I don't run into the same limitation. Below is a snippet of code I'm using in an algo. When I get the chance, I'll see if I can use something similar in the broken notebook I posted above.
def factor_pipeline():
factors = make_factors()
pipeline_columns = {}
for k,f in enumerate(factors):
pipeline_columns['alpha_'+str(k)] = SimpleMovingAverage([f(mask=QTradableStocksUS())],window_length=FACTOR_AVG_WINDOW,mask=QTradableStocksUS())
pipe = Pipeline(columns = pipeline_columns,
screen = QTradableStocksUS())
return pipe 
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