Could a resident Q Guro take a look and provide feedback on this taking into account the following:
In an institutional capacity, there is no leverage factor.
Commissions are close to 5% of retail (even free commission has a slippage factor).
All of the dialogue on Q centers around leverage costs and commissions as the ultimate test of whether a model is "worthy" or not.
However, the Q fund should have an institutional footprint where these costs are not as important when measuring the viability of the underlying model. From what I've been reading (mind you I am only a sideline observer), it appears many good models are developed with the intention of gaining access to Q fund capital, but are being written as if they will be charged margin rates and retail commissions.
Is this perhaps a way for Q to create a built in cushion that results in an increase in absolute returns purely from finance cost and commission savings?
With this algo, assuming zero capital costs, and adjusting data granularity such that trade frequency is increased without having to worry about commissions eating into profits would create something that while not worthy of the Q fund, could be worthy of a hedge fund.
I would love to see variations on this algo with the above assumptions factored in that produce significantly higher returns without worrying about excess volatility. At the hedge fund level, portfolio stops can be used to control downside risk. I haven't figured out how to incorporate a portfolio level stop on Q such that when the portfolio is stopped out and goes to 100% cash (or alternative security), the book is reestablished as if the algo was reset the next day.
Any additional insight would be highly educational for me.