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More Dynamic PositionConcentration Constraints Tuned To Alpha
Stephen DeRubertis
posted Dec 22, 2017

Hello,
Is anyone aware of a way for Optimize API to provide more dynamic upper/lower bounds for position concentrations in a hedged algorithm?

Instead of position size constraints like this:
[ max=0.01 / min=-0.01 ]

I think it could be very beneficial to have something like more like this:
[ maxLong=0.01 / minLong=0.001 / maxShort=0.01 / minShort=0.001 ]

The goal is to maintain a larger breadth of bets in hopes of smoothing out the returns by letting Optimize API determine a proportional weighting relative to the alpha signal.

Is this possible?
Thank you

1 response
Dan Whitnable
Dec 23, 2017

Look at the 'PositionConcentration' constraint. https://www.quantopian.com/help#module-quantopian_optimize_constraints It allows for explicitly setting the minimum and maximum concentration for each asset. One could set the maximum percent proportional to the alpha for each asset. 


# Assume the alphas are in a series already. First will need to normalize them.  
weights = alphas / alphas.sum()

# However, this will probably create too great a restriction.  
# To give optimize some 'wiggle room' perhaps multiply by constant  
weights = weights * MY_WIGGLE_ROOM

# Then simply use this in the PositionConcentration constraint  
max_position_constraint  = opt.PositionConcentration(min_weights = -weights,  max_weights = weights)
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