**BarroMetrics Views: Ebb and Flow II**

This last post in this series. I have decided to produce an e-book based on the series. I will be updating the entries by adding some info and expanding some sections.

Turning to today’s blog…..

The question I posed in the previous blog in the series was: “How do we tell when we are in Ebb or Flow phase?”

There are two answers to the question, a quantitative one and a qualitative one. The quantitative one is based upon a frequency distribution in Excel of our profits and losses.

For example, Figure 1 shows my frequency distribution for the years 2006 – 2014. (Note that because my patterns of losses and profits changed in 2015, I have had to create a new frequency distribution.). A word of explanation:

- The numbers with a yellow shading are profits and losses within the first standard deviation i.e. these are normal returns.
- The numbers with a green shading are numbers within the second standard deviation, i.e. these are moderately larger than normal losses or profits.
- The numbers with a white shading are numbers representing extreme profits and losses.

My **quantitative test for Ebb**: *“Three consecutive losses that were greater than 2.01 but less than 5.48.” *Whenever this used to occur, I would immediately reduce position size and implement my other Ebb protective measures. My **quantitative test for Flow**: *“Three consecutive profitable trades that are greater than 3.65%.”*

You will note, that my test for Ebb will reduce position size more quickly than I’d increase position size when in Flow. Also, note that once in Flow stage, I increase my position size to a maximum of 2.5 of normal.

You will recall from **Ebb and Flow I** that I have another rule which says that if I lose 7% or more, I shall immediately reduce position size. This rule is merely another measure to deal with the Ebb phase.

How do I determine the standard deviations in my frequency distribution?

From Figure 1, it’s clear that I do not use the normal mathematical formula. The reason I don’t is because the normal bell curve assumes that the average and the mode will be one and the same. In fact, most trading results have a skew. Consequently, a better way of assessing the standard deviations….

*Use Pete Steidlmayer’s method for calculating the value area (see How is the Value Area Calculated…) ***(use method B**).

That’s the quantitative way. Nowadays, I tend to rely more on my gut (i.e. the qualitative approach). It’s not hard for me to tell when I’ve entered into Ebb. Just about everything I do will be wrong:

- I’ll choose the wrong pairs.
- I’ll enter at the wrong zones.
- If I go long, the market will drop.
- If I go short, the market will rise.

To cope with Ebb, 2015 saw a change in strategy. Nowadays, whenever I believe I am in Ebb, the dominant strategy is to exit my positions as soon as they fail to behave in an optimal manner. The change in strategy has been successful.

For example, since mid-February 2016, I have had eight trades. Of these, one attained my core profit target, one was a scratch trade, and six were losing trades.

Despite the poor win-loss ratio, my results are positive to the tune of around 1.6%. I have achieved this result because the six losses amounted to -0.26% whereas the one profitable trade produced a result of 1.89%. Pre-2015, the six trades would have produced a loss of around 10 to 12%.

So you can say that the new strategy has been successful in reducing losses. It has reduced drawdown, and has successfully increased the positive expectancy return – the aim of the Ebb and Flow concept.

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Chris from Australia was kind enough to produce an Excel sheet for determining the consecutive theoretical number of losses and positive expectancy for blog readers. I attach his sheet.

I trust you have enjoyed reading the series as much as I enjoyed writing it.

FIGURE 1 Frequency Distribution

2016-04-25 Estimating consecutive losses and Expectancy

Attachment Excel Spreadsheet