December 18th, 2016
In my previous post, it was said I would not trade in that fashion. For one, I do not have that kind of capital available. And two, I may be too chicken. I prefer a smoother ride. But, that does not mean that this particular trading strategy is wrong, or that we can not extract useful trading procedures from it. Even downplayed the strategy could make quite an impact.
The strategy did give more than an indication of where upper trading limits might reside. And based on the strategy's code, it could do even more. I was exploring to find where these limits were, and even at the presented level, the program had not reached them yet.
For your convenience, here is chart #1 again:
#1 Example Momentum - WOW
(click to enlarge)
What the chart does give is this: you can push an ordinary trading strategy to a much higher performance level simply by making changes to trading procedures having an impact on n*u*PT. The rest seems secondary, meaning, the how you do it is opened to about any method you might like. All you want is to have those trading procedures have a net positive impact.
Sometimes, you have to go further than everyone else, even if in doing so, you are alone. And it is not because you think differently, that you are necessarily wrong. It might just be that you have a different way of doing things.
From the previous post, the question should have been: is the simulation that generated chart #1 possible? The answer to that is: yes, as evidenced by the chart.
As said before, I would not trade exactly like that without correcting some of the strategy's flaws (at least what I consider as flaws). It is nonetheless a feasible trading strategy as is, even with all the wasted resources and shortcomings. This trading strategy trades that it likes it or not. It just trades, for no other particular reason than the price changed between rebalancing periods.
The strategy might start trading 7 stocks on its first trading day, but gradually, it will trade more and more, to the point that at the end it is taking over 30,000 trades each day it rebalances. There is nothing wrong with that, a machine is doing the work, and since the profits are there, why not take them. After all, the strategy is only doing what it is programmed to do.
It is to show that if I push hard on "n", the number of trades, a part of the major portfolio metrics of significance: A(t) = A(0) + (1+lev)*(1+a)*n*u*PT (see previous article). I can increase profits considerably by also raising "a" and leveraging it right up to a WOW level.
Chart #1 remains a viable trading strategy. It is very erratic with wide day to day variations and might require nerves of steel as can be seen in the volatility and beta numbers. The program seeks volatility and this can have quite an impact on u*PT.
Some might say: you did not account for commissions and slippage. Yes, they were accounted for. The program used the default Quantopian setting which already accounts for them. So, what you see in chart #1 does include frictional costs. Even so, they would add up to less than $10 million over the trading interval. Go ahead, double it, triple it, put it at $100 million if you want. It does not make a notable difference in the final result. One could see those frictional costs at most as a trivial consideration.
What I see in this trading strategy is just a different way, a different set of trading procedures, than what I usually use to push n*u*PT higher. As such, it adds to my arsenal of available trading techniques. It now becomes available should I need a marked down version of it.
The only thing not seen in the presented simulation is the interest charges related to the leveraging since the strategy does use leveraging. But there, whatever those charges might be, they would be more than compensated for by the excessively high return which would have taken a $10 million dollar account to a $ 22 billion profit.