May 6, 2019
The trading strategy described in my latest book (Reengineering Your Stock Portfolio) has some singular long-term properties. It started with a trading strategy (published on Quantopian) which was modified, step by step, to enrich its final outcome. Instead of trying to optimize alpha-factors or some of its parameters, equations and administrative procedures were used to direct and control this innovative strategy's trading behavior.
The last version used in my simulations did generate outstanding long-term results, but, personally, it was not my main concern since that was in line with my prior research (see Beyond the Efficient Frontier). The strategy simply did what it should have done, what was expected of it and directed to do.
What was, however, was the trading dynamics. And if the statistics of the mechanics of a trade were interesting, then the strategy overall would have to show a profit, no matter what its size. Evidently, the larger the better since it would also show a kind of proof of concept that this innovative methodology (guiding equations) was a viable alternative to usual long-term trading methods.
My first interest in that strategy was its architecture. I saw a template for doing more.
Right off, with no code modification, it was scalable. This is part of my collection of acid tests. If a trading strategy is not scalable, what real use can it have? You want your equity to grow with time and the strategy cannot handle it? Then, what are its future prospects?
Another of my acid tests is longevity. Can the strategy you are looking at, at the very least, withstand past extended trading intervals? Again, if it can not, then what are its chances going forward?
These are extremely simple strategy tests. The first is solved by throwing more or less money at it, while the second by giving it more time. Both these measures are independent of the trading strategy itself since they are set outside the strategy and prior to its execution. Yet, you need those answers before you even put any effort into improving the strategy's code or its trading philosophy.
They are rudimentary tests, and if you do not do them, you are not doing your homework, or you are ready to waste a lot of your time. However, I view that as your choice, not mine.
Some pointed out that the trading strategy operated from hindsight. All trading strategy simulations do. They operate on what is historical data.
Due to my old age and experience, I went back to my earlier simulations during the dot.com bubble era to find some of the same stocks being analyzed in trading strategies. Someone younger, not having lived that period, has less of an understanding of it. For me, most of the selected stocks have been in my selectable set for over the past 20 years. Why should it change now because I am doing a new simulation? At least, I now have a glimpse of what might have been if. Also, since I started my website in 2011, the same trading philosophy (accumulate shares over the long term and trade over the process), even if it was developed earlier, has been at the heart of every simulation made.
What is important is that a trading strategy has a signature. It is made to do what it does. Some internal dynamics that will trigger trades in a specific fashion. For instance, say you set a 10% profit target on a trade, irrelevant of which stock it might be. You expect your program to execute that trade that it be on past or future data. Going forward, you might not know which stock will hit its profit target or when, but it does not matter. Whenever such a target will be hit, you want your program to execute the trade. That is part of the strategy's mechanics.
You could make another stock selection, it would not change the trade dynamics. It would certainly change the overall results since the daily opportunity set would be completely different. And whichever tradable stocks you might want to use, they should follow your own selection criteria set.
Innovations, at first, are usually tossed aside. But this trading strategy, I can assure you, is not rocket science. Slowly, with time, it will become more acceptable to more people.
Created. May 6, 2019, © Guy R. Fleury. All rights reserved.