What is the justification for your Sharpe versus market value curves?
er_funcs = (
lambda x: np.exp(-x/50)*0.5 -0.2,
lambda x: np.exp(-x/40)*0.2 - 0.02,
lambda x: np.exp(-x/70)*0.15
vols = (
Does this imply a certain theoretical form of slippage? Or are you basing the curves on empirical evidence?
the problem of maximizing the Sharpe Ratio of a portfolio of uncorrelated trading algorithms under different capital bases.
I'm a little confused by this approach, since presumably you would never have uncorrelated trading algorithms (unless by some magic, the ones you are selecting for the Q fund are perfectly uncorrelated). Are you just making the simplifying assumption to do a "back-of-the-envelope" illustration? Or would you have factored out the correlations, so that you are only dealing with the uncorrelated returns?
Very active topic for us right now. Seems like zipline slippage is underestimating at low order sizes and overestimating at high order sizes. Definitely something we need to improve.
In the context of your internal discussions, is there any dialog around how you might share with users actual trade data (owned by Quantopian) so that they could empirically determine slippage? As you say, "The main headwind when trading larger amounts comes instead from slippage and the fact that large orders drive up the price" but if users don't have access to the data, then they won't be able to manage it. Also, I would think that given the vastness of the industry, there would be vendors who would provide accurate slippage models, on a case-by-case basis. Maybe you could purchase the data/models? Or maybe the data/models tend to be proprietary to trading firms?
For more realistic simulations we are using the Almgren slippage model
If it is more accurate, then why not update the default Quantopian slippage model, required by the contest? Or maybe that is the plan?