One very simple approach is to use the optimize
NetExposure constraint (see https://www.quantopian.com/help#module-quantopian_optimize_constraints). Even if one isn't using
order_optimal_portfolio to order there is still the
calculate_optimal_portfolio method. Use this to input the original weights and it will calculate a set of new weights meeting any constraints. Something like this
# Assume weights is an existing series of weights
objective = opt.TargetWeights(weights)
net_exposure_constraint = opt.NetExposure(max_short_exposure, 1.0)
opt_weights = opt.calculate_optimal_portfolio(objective, constraints=[net_exposure_constraint])
One word of caution. The optimize methods have a very literal approach to the world and the results may not always be as expected. Take for example the code above. It will return a series of weights meeting the max short weight criteria. However, part of the way it arrives at this is to limit gross exposure (or leverage) to something much less than 1. Probably not what one expected but it does meet the constraints.
I find using the
calculate_optimal_portfolio helpful in this regard. One can look at the results of the optimization and make any adjustments before doing the actual ordering either with the
order_optimal_portfolio or basic
Attached is an algo which iteratively executes
calculate_optimal_portfolio until the gross leverage is at an acceptable level. Also note that the
NetExposure constraint expects an input of exposure and not 'short weight'. There is a conversion for this in the notebook.
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