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.
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
Attachment Excel Spreadsheet