August 14, 2017

Following the Math of the Stock Trading Game

After publishing my latest book: From Zero-Beta to Alpha Generation, Reshaping a Stock Trading Strategy, a few questioned the presented stock trading strategy as if it might be unrealistic. That we could not reach those kinds of numbers. When all this stock trading strategy did was follow the math of the game.

With everything provided in that book, I think anyone could rebuild something similar or better. The benefit: it would now be their own code. A strategy design they would understand well enough to maybe give them the confidence needed to apply it. Or find in it trading procedures that they could apply elsewhere.

What I see is a simple piece of software. A program instructed to trade in a particular manner. It makes a selection from the 1,500 highest valued and most liquid listed companies, buys the top tier momentum stocks and shorts the most underperforming ones.

Evidently, it is a simple trend following system. It operates under the assumption that the defined trend, up or down, might continue over the short term. You will find a lot of academic literature stating the same thing: on average stock prices do move in the same direction, even within all the chaos. Stock prices do have some memory. There is a long term upward drift as illustrated by any long term stock index chart.

Since we are dealing with a program, it could be re-engineered by anyone having the means to carry it out. The successive simulations showed in the book could have been done with relative ease using Quantopian's platform and its Q1500US dataset. Take a look at the Quantopian forum where a lot of it was chronicled live,  and where you will also find a starting version of the code that was used which you could also transform. 

Understandably, if you do not have the capital, my last iteration of this strategy (test 78) would be totally out of reach. That code is not made for small accounts. See my previous analysis of this. 

Nonetheless, having a small account is not a valid argument against the strategy itself. It might only say that you do not have the capital or inclination to carry it out. In such a case, I would consider your time might be better spent finding more capital.

In trying to scale down the strategy's account size, one could reverse some of the steps taken that scaled it up. For instance, my first step would be to reduce the number of trade candidates to a level more in line with the smaller account size. Note, it might not be worthwhile to play the stock market 1 to 5 shares at a time. If you do not diversify enough, you are increasing your market exposure risk. And, if you trade a lot, frictional costs will eat up a greater portion of your profits.

The Atlas Trading Strategy

The notion of an atlas strategy dates way back. It stood for the ultimate trading strategy. The one, out of the gazillion of possibilities, that beats them all. Sure, there will be one at the top. But, be assured, this one has no pretense of being it. It is just a relatively simple trading strategy that follows the math of its game. I think a lot of strategies could do better than mine. I can see a multitude of them. We only need to design them.

However, at some point, we do have to make a choice. Pick one and go from there. So, take your best shot (highest alpha), or maybe choose the strategy you like best for whatever reason. At least, until you can find a better one.

The search for an atlas strategy should be unrelenting. There are whole families of these strategies out there with numerous variations on their respective themes. Your best trading strategies should already have reached this high alpha status.

Your fund has for simplified equation: F(t) = (1+L)∙F(0)∙(1 + rm + α - fc% - lc% - d%)t, where your alpha should not only be positive, it should exceed frictional and leveraging costs. This over the long term. Leveraging costs apply only if L>0, meaning that in the program it is greater than 1 (leverage > 1.00).

Looking at it from the above equation's perspective, there is one number that should be your center of attention. That is alpha. It depends on the portfolio management skills you or your program brings to the task. It puts in one number your trading strategy's edge. I view it as: can you really do better than your peers, read better than the averages?

This alpha is compounded. It makes all the difference. Its real power is not at the start of the game. It is at the other end of the time spectrum where you will find that a few extra alpha points could have made a huge difference.

But, by then, you will have reached destination and the market does not offer a rerun button. Therefore, you need to plan for those extra alpha points from the start. The "I should have" in this game does not carry any rewards. This was well illustrated in figure 4 of Post-Strategy Portfolio Analysis.

I have no way of saying that what was presented is better than others. All I can say is: it is pretty good, I liked it. It shows a lot of promise and merits more investigation. Especially, since there are still some weaknesses to take care of. Meaning it could still do better.

After analyzing the limiting factors in the original version of this program, I opted to transform it by first eliminating most of its tracking and trading procedures, to then add what more satisfied my views of its payoff matrix. Observation gave what the trading strategy did, adding new procedures made it do more, and at a much larger scale.

Some of the principles used can also apply to a lot of other strategy designs. Just looking at how a trading strategy is intended to trade over time can compel us to redesign them to do things differently.

It is not by doing the same thing as everyone else that you will get different results.

It is by looking at the trading problem differently. Stuff that others do not even consider or try for some reason or other.

Notwithstanding, there is a need for a background trading philosophy which is to be supported by a methodology that is anchored in math.

If you do not have a long term vision of where you are going, where do you think you will end up?

For those wondering why I am not publishing the strategy that generated the higher tests, the reason is very simple. Look at test 78 again and tell me why I should give the code away.

In the end, you will find that you have only one person you need to convince when designing trading strategies, and it is you.

If you want more, you will have to do more.

Created... August 14,  2017   © Guy R. Fleury. All rights reserved 

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