January 16th, 2017
This is the continuation of Playing the Stock Market Game: Time is All
Repeatedly applying an automated trading strategy to a bunch of stocks in a backtest will produce the same answer every time. It is the output of a program. A recipe, a set of trading rules, procedures, coded instructions and software routines.
Since the output of a trading strategy can be expressed as a time function: A(t) = A0 + n∙u∙PT, then, A0 + n∙u∙PT is its unique signature. Leaving us with 3 portfolio metrics of consequence.
There may be millions of trading strategies out there, and many more not yet designed. Each has its unique signature. You apply any one of them, and you generate Ak(t) = A0 + n∙u∙PT.
Due to this equation, the output of any trading strategy can be compared to any other. It also means that if you run my trading strategy on your computer, we both get the same answers. It is just a program being executed.
There lies part of the problem: the strategy's signature. You only have 3 numbers. Two of which you can set yourself. The other a sequential counter of your trading activity.
People play a short-term stock market game as if in a none random-like environment.
Stock prices might not be totally random, but, in the short term, they sure have a high noise to signal ratio. So high, in fact, it is hard to distinguish and separate the background signal from the predominant ambient noise. The signal is literally buried in a turbulent sea of variance.
It is understandable. If price variations were less randomly distributed, then you would be able to make better predictions and be able to quantify them. Give them reliable occurrence probabilities, improve on allocation methods and betting procedures. There is a lot of money involved should you become good at it too.
However, in this statistical swarm of price variations, it is difficult to make highly accurate forecasts. Which in itself gives an indication as to the high degree of randomness in price fluctuations.
What most strategy developer design are trading strategies somewhat adapted to this unique past swarm of price variations. That too could be an expression of randomness since a lot of these trading strategies tend to break down going forward. Why do they break down if their past data was somewhat representative of their future? Were bets not made pursuant to their "predictive" analysis?
The future too is a unique occurrence. It offers no replays.
If a trading strategy has this distinctive signature, then, it is all there is. Or, is it? Is it all it can do?
Have we for only solution to find better and better strategy designs as a way to improve performance?
This quest has been on for decades, and still, most portfolio managers have a hard time beating averages.
Note, that the average is almost given away, as a kind of default value to anyone willing to participate for a long time. The market average does not set the bar that high having for secular trend a little less than a 10% CAGR (dividends included). So, how could you not outperform the averages?
Is your search reduced to continuously finding ever better trading strategies which become better adapted to their singular past? It is a real conundrum. The better they get, the more they are adapted to their past, over-fitted, and the more they might break down going forward. Becoming totally misaligned with reality and maybe even condemned to underperform averages, or worse, destroy one's portfolio.
Even with the high degree of price randomness, you still design trading strategies that backtest well on your selected stocks, but with a limited future. Are you bound to design trading strategies having a high propensity of achieving close to market averages? And this, not necessarily due to your program per se, but just because you participated in the game.
It is why you have to look at the problem differently. As simple as that.
It is not by doing what everyone else is doing that you will exceed what everyone else is doing.
It is by finding new ways, exploring untried avenues, looking at the total picture and not just from period to period. It is by giving your trading strategies a long-term vision that you will stand a chance of outperforming the majority out there.
If you don't do it, well, that is a matter of choice too. You can always rely on index funds to at least give you close to average returns over the long run. But there too, you will need to put in the time.
Redesign Your Trading Strategies
Then again, you might wish to redesign the whole thing from scratch. Your trading strategy might have a unique signature, but that does not mean it is the end of it.
On the contrary, it might just be a starting point, the initial block of a trading methodology. This does not mean you will be able to escape the portfolio's equation: A(t) = A0 + n∙u∙PT. The same equation would apply to a portfolio of diverse asset portfolios: Ʃ Ak(t).
Nonetheless, I do state: you could do better by controlling those 3 portfolio metrics. Redesigning them as time functions: A(t) = A0 + (1+f(a))∙n∙(1+f(b))∙u∙(1+f(c))∙PT. To increase performance, the only requirement would be: f(a) > 0, f(b) > 0, f(c) > 0, or any combination thereof.
It did not change the signature of the trading strategy. It only emphasized your long-term action. Stuff, procedures you could add to your trading strategy to make it behave the way you want it to behave.
For instance, you could increase the trade-unit with time: (1+f(b))t ∙ u. It will impact your portfolio for its entire duration, and show on your total return. Increasing u is increasing the bet size going forward, it does not change the decision points, only the quantity traded. But with the net result of increasing performance: n∙(1+f(b))t∙u∙PT > n∙u∙PT.
What would happen? With time, these functions would increase the importance of each of the 3 portfolio metrics, increase overall performance by a factor of (1+f(a))∙(1+f(b))∙(1+f(c)). This, without changing the strategy's signature, or its coded logic.
It shifts upward the CAGR curve, ever so slightly in the beginning, almost imperceptibly. But gradually, the CAGR curve would improve, widening its spread compared to a benchmark, and thereby be an alpha generator on its own. A self-engineered one at that.
You can outperform the averages just by changing your point of view on the problem. The strategy will stay automated. It will respond to these 3 scaling and controlling factors: (1+f(a))∙(1+f(b))∙(1+f(c)), and increase performance. Not due to randomness, but to your preset controls.
We are left with 3 questions. How can we increase n? How can we increase u? And, how can we increase PT? Better yet, how can we increase all three at the same time over the long haul? After all, at bean counting time, you want to have reached the finish line!
This is how you will exceed your trading strategy's signature.
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