I've read that some think this trading strategy is almost worthless. Even the original author thinks so. Now, that is not a high five recommendation. But...
It should be noted that Thomas' addition to the code changed the underlying trade mechanics. I've studied this phenomenon a lot over the years, and have been using it in my programs as well (not Q). Sure, you will probably be taking incremental risk, which often will turn out to be about almost the same as everyone else, but you could also generate higher alpha, be more profitable.
The reason would be simple as well: you would be reinvesting more of the generated profits. Providing a positive feedback to your trading strategy. I have applied this to stocks as I think it is one of the places where such procedures can prevail. I have not tested other environments, and don't intend to do so in the near future. So, view all I say as being related to US stocks only.
We have Kelly numbers, fix-fraction, optimal-f, and a lot of other portfolio weighting schemes. I see this one more like expectation opportunity weighting. You are allocating your equity to the highest ranking slopes (highest upward momentum) of a group of stocks on the premise that their relative trends will continue up in the near future.
The trade selection process should work in the future as it did in the past. You will always be able to rank stuff, past data, it is part of the information available to activate your decision surrogates.
If the premise is valid, a backtest of a sufficiently long duration should reveal this. And, I think, that is what Thomas' modifications show: higher profits. One can fiddle with the other parameters controlling this strategy, but I would find it more appropriate at this time to tackle some of its deficiencies and undesirable traits.
For me, even as it is, that strategy has merit.
The strategy is nonetheless partially shooting itself in the foot which tends to curtail its potential. Here are some other tests I've done.
Raising the profit target from 25% to 80%, and then 150%:
5 SMRS 85% Leverage : 80% profit target (was 25%)
6 SMRS 85% Leverage : 150% profit target (was 25%)
The scenario didn't change much, but still, as the profit target increased, this trading strategy starts to slow down. The slowdown is distributed over the entire time series.
On the other hand, increasing relative weights, does have an impact:
7 SMRS 85% Leverage : 150% profit target : increase relative weights
For all these charts, you don't see a major increase in volatility or drawdown. Anyone could bear a 2 to 3% additional points in drawdown for the added reward. Chart #7 says that you get $8.3M on a $100k stake over a 13.6 year period, just by changing the parameters, not the code. Bottom line, it is a 38% CAGR over the period.
This is still Thomas' trading logic at play. All I've changed are underlying assumptions, some of the numbers controlling the strategy. None of the code was changed. Not a single line of code was added. So, I'm not of the opinion the strategy is worthless, one just needs to look at it differently and really see what Thomas' addition does.
The program does have weaknesses that should be addressed. It would make it a much better program. I'm not familiar enough with Q at this time to make those changes. But that will change.
One of the weaknesses is that the program is hardly scalable in its present state. For instance, giving it $1M in capital resulted in:
8 SMRS 85% Leverage : 125% profit target
You still beat the index, but, one could do better... Just doing 10 #7 would already provide 4 times more performance than #8 with the same initial capital, with little more effort, since a machine would be doing the job.
Of note, such a trading method can not know in advance what stocks will be treated, that it be past or future. But it can always sort its past.
A better mix. The same variables, but pushing each in the right direction.
9 SMRS 85% Leverage : 125% profit target : faster response : $100k
Hope it's helpful.
Just to push it a little bit more...
10 SMRS 85% Leverage : 125% profit target : faster response : $100k + residual