August 11th, 2014

As a follow-up to **Winning by Default**, I wanted to show intermediary test results. The objective is to show the evolution of such a trading strategy from day one. There was no need to enhance its performance level beyond the rudimentary settings as was done in **Winning by Default**. This is more of a what-if scenario analyzing a trading strategy's long-term behavior and system metrics.

I used the same trading script as in the first part of **Winning by Default**, but this time, I did tests over increasing trading intervals, starting from 1 year ago to now and going back 50 years using the following year sequence: 1, 2, 3, 5, 10, 15, 20, 25, 30, 35, 40, 45 and 50. This way, one could see the progression over time of what was designed to be a long-term trading strategy. Such tests would show the evolution of the portfolio and how it behaved over time with respect to selected metrics (profits, trades, and win rates).

This is like having a trading script and asking the question: what would have been the result if I had started some 5, 10, 20, or 30+ years ago? This would force the trading strategy to show its merits over the whole range of selected trading intervals from 1 to 50 years.

In **Winning by Default**, the trading strategy adhered to the AlphaPower basic principles of accumulating shares over the long term while trading over the process. As such, the strategy would slowly accumulate shares and, with time, accumulate more as the equity increases. The strategy used was not the best of strategies. It has many faults, meaning there is much room for improvement, but nonetheless, it would show the effect of adopting the principles cited above. One of its more serious faults is the underutilization of its capital resources and this from the very beginning. Another one is that it is not very good with its entries or its exits. But those can be fixed or remedied.

The trading strategy starts rather slowly to accumulate shares. In fact, in its first year, less than 12% of available funds are put to work. This means that one would have much less volatility than the market as a whole since 88% of available reserves would be in cash. Even after its 2nd year, less than 35% of capital would have been placed in positions. The main objective of this strategy is not to produce immediate results but to obtain the highest possible long-term performance with minimal risks. And from such a start, I would say that those objectives were being met. Thereby, one starts with a market underexposure which in the beginning will result in less initial returns, less risk, lower drawdowns, and less portfolio volatility.

These simulations go from 1 year to 50 years in duration (Aug. 1964 – Aug. 2014) and use the DOW 30 + 2 dataset as previously. The profit picture over the 13 tested trading intervals looked like this:

**Fig. 1 Profit Distribution** – (1 to 50 years)

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As can be seen, very little happened in the first year, but as time progressed, the trading strategy improved with time. The strategy was able to expand and generate increasing profits over its 50-year trading history.

The above chart should be read as: if I started 20 years ago, what would have been the outcome? The answer can be read in the 20Y column. It gives the liquidating value or total accumulated profits over the last 20 years for each of the stocks in the portfolio.

From the chart below, we can easily see the exponential expansion of the total profit curve:

**Fig. 2 Profit Distribution Chart**

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Quite remarkable. Not only did the strategy not break down, but it continued to expand and prosper. This can also be seen in its winning trades metric.

**Fig. 3 Winning Trades Distribution**

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From Fig. 3, as the trading horizon was expanding, the winning percentage of trades would increase. The positions' win rate is composed of already closed and still opened positions. Observe that it did not break down either. In fact, it improved steadily over time, over its entire 50-year history. This is not the outcome of some random events.

**Fig. 4 Win Rate Distribution**

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The further back in time you wanted to start executing this trading script, the higher the percentage of winning trades and the higher its profit potential.

**Strategy Analysis**

The ETS trading strategy has been so considerably modified that it totally changed its nature to the point that it hardly resembles the original trading script. The tests were done with for anchored date yesterday, and all trading intervals ended there. So the 1-year test is for the last year, and the 20-year test is for the last 20 years, and so on. This does not change much of the tables and charts presented. The strategy would still start slowly and accumulate shares over time to end at the same point. The changes would be in the stocks that don't have 50 years of data history. Anchoring the data from 50 years ago and going from there would only slightly change the curves giving the strategy a slower start and a slightly stronger finish. But it would still end with the same 50-year numbers.

What the strategy shows is that over time, it wins. It might start small, but with time, its stock accumulative stance pays off big. The strategy in its present form lacks in certain areas, all of which could easily be enhanced or "repaired" to produce even more in the profit department. However, I do have better trading strategies where this kind of work has already been done. I just wanted to show this one solely for illustrative purposes, to demonstrate how the strategy evolved from start to finish.

One would have to conclude that certain trading strategies not only can last for quite extended periods of time (50+ years) but can prosper over the whole interval and be quite profitable. The same would apply had I taken the 985 Russell stocks as an example. Only the numbers would have been much larger.

Also, with better trading techniques and better use of available resources, one could push performance to much higher levels.

It is worth mentioning that by having a long-term profitable trading strategy, one can now extract the programmed trading rules from the strategy and play them discretionarily if desired. At least you know that they could survive and be profitable over a long period of time, even going back half a century. I would have more confidence in a trading strategy that showed it could have survived over the past 50 years than just going blind.

As was said in the Winning by Default article, the trading script obeys its governing payoff matrix equation:

A(t) = A(0) + Σ(**H**(1 + r + g + T)^t.*Δ**P**).

This equation says that the reinvestment policy g and the contribution from the trading activity T can help push performance to higher levels, all other things being equal. Thereby, slowly increasing the trading activity over the long-term time horizon would increase overall performance. This is easy to demonstrate. Just redo the tests with an added push on trade activity, as was done in the previous article. For comparison, I will use the same DOW dataset over the same time intervals.

This would also show that one can or could gain some "control" over the long-term trading objectives, which would have a primary goal of generating more long-term profits.

**Enhancing Performance**

Increasing trading activity over the long term would also be a slow process but still an exponential one. It would start small and gradually increase over time. The point would be to make it meaningful. Adding some changes and procedures to the program in order to raise the trading activity T and the reinvestment policy g could generate something like the following:

**Fig. 5 Profit Distribution Enhanced**

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This is not just linear scaling or a single period kind of event but a progressive enhancement that spans the entire trading history of the applied methodology. Maybe the best way to show this is by presenting the data difference table for the enhanced strategy:

**Fig. 6 Profit Distribution Enhanced Differences**

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These differences also follow an exponential curve:

**Fig. 7 Profit Distribution Enhanced Differences Curve**

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It is with time that the enhanced trading strategy will show the power behind its added procedures as it builds on its history and previous achievements. This modified ETS trading strategy starts small, with low portfolio volatility, underperforming the market from the start, and this is by design. It is with time that, progressively, it will not only catch up to market averages but surpass them with ease.

There are better trading strategies than this that can show more aggressiveness, better use of available capital resources to further increase the general trading activity T thereby generating more profits that can be reinvested in buying still more shares. As was illustrated in this research note, you are not interested in a trade here and there but in the whole trading evolution over extended periods of time. And the more, the better, even going beyond half a century.

It was the trading principles involved that were under study here, not the ETS trading strategy itself. Even for the "enhanced" version of this trading script, and as was mentioned in the previous article, it would not be part of the list of tested strategies. The main reason being I don't like the way it trades, there are better techniques out there. I don't like its entries, I don't like its exits or its general trading behavior for that matter. And to top it off, if you compared it to the other 4 already tested trading strategies, it would come in last, way below what should be considered better trading practices and techniques. See the following four strategies for the 25-year portfolio comparisons: **BBB Mod 01**, **DEVX V6**, **LMK 0.06**, and **SupRes V3**.

What was under study was the payoff matrix equation: A(t) = A(0) + Σ(**H**(1 + r + g + T)^t.*Δ**P**), especially the exponential time part (^t). When applying the stock accumulation procedures to which were added trading procedures with the objective of building a long-term portfolio, one generates a positive feedback loop. The increased profitable trading activity feeds the system to acquire even more shares and has the effect of increasing portfolio value the longer you maintain your procedures, as was illustrated in the charts above.

The above payoff matrix equation becomes a tool to build long-term portfolios of whatever size you may want and where you would know by "default" that you would win, and the longer you stayed in the game, the more you would win.

Created... August 11, 2014, © Guy R. Fleury. All rights reserved.