Through the years I have sought to improve my returns. Initially I sought market knowledge; then I focused on self-awareness and knowledge of self - I especially looked for ways to improve my decision making skills. Nowadays, I focus at improving my net returns by delving into the nuances of the Expectancy Return and Expectancy Ratio.

There are quite a few variations of both formulae floating around. For Expectancy Return I use:

(Avg$ Win x Win Rate) - (Avg$Loss x Loss Rate) = expected $ Return.

Where:

  • Avg$Win = Profitable $ Returns/Total Winning Trades
  • Win Rate = Total number Win Trades/Total Trades
  • Avg$Loss = Losing $ Returns/Losing Trades
  • Loss Rate = Total number Losing Trades/Total Trades

I have explained elsewhere (The Art of Position Sizing) that I prefer to normalize my results by using percentage of the price that initiated the trade. But for this blog. let’s use the idea with which most are comfortable: $ Return.

Note also that the above formula can be summarised to (Total-Losing$)/Total Trades; but while the results would be the same, the first rendition separates two important components:

  1. The Avg$Win and Avg$Loss: these are the result of our entry and exit and are totally within our control. And,
  2. The WinRate and LossRate: these are more a factor of the interaction of the Ebb and Flow of our plan and the Market. (For ‘Ebb and Flow’, see (The Art of Position Sizing).

If we understand how important (1) is to our being profitable, we’ll tailor our trade management and position sizing according to its statistical history.

Most times we manage trades based on our perception of the technical and/or the fundamental picture. The Ray Wave, for example, provides an objectively definable edge in identifying where a swing structure may end. (See The Ray Wave I). But maximization of our profit lies less with the Ray Wave’s intricacies than with understanding our profit/loss profile.

For example if we have a 40% hit rate; and Avg$Win of $75.00 with an Avg$Loss of $50, we have no edge i.e. over a large sample size, we’ll not make or lose money. But if we can raise the Avg$win to $100, we can expect to make $10.00 per trade.

We can take the Expectancy Return and turn it into a ratio:

(Ave$win x WinRate)/(Avg$Loss x Loss Rate) = Expectancy Ratio

Now taking that information we reconcile the idioms:

  1. Never let a winning trade turn into a loss. And,
  2. Let your profits run

Once we know the minimum ratio we need to provide an edge of ‘x’, we don’t have a winning trade until the minimum ratio is reached. For example, if our minimum ratio for an edge is 1.5, and we have risked $100 on this trade, we need to see at least $150.00 in open profits before we can say we have a winning trade. Once that objective is attained we implement our defensive strategies to let our profits run. The strategies may involve partial exits (like my Rule of 3) or bringing the trailing stop to breakeven etc.

This is not to say that on occasions, we’ll not exit a position before attaining our minimum ratio - there will be times when we perceive that market conditions have changed and we need to exit. The key words are, ‘on occasions’. If we consistently take profits before attaining our minimum historical ratio, then, in the long run, we must be unprofitable.

Tomorrow I’ll examine how these ideas have led to the amending of my Rule of 3 See (Rule of 3); I’ll also examine their application to John Sweeney’s Maximum Adverse and Maximum Favourable Excursions.

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