Traders' Roundtable

I’ve come across an interesting feature of the R-Multiple Distribution & Profit Contribution Graphs.

It wasn’t obvious to me at first and I thought it might be helpful to point it out.

The feature became apparent to me in simulations where there were high R trades that were trimmed in size many times by risk management as they continued to increase in value.

The feature is that each trim ‘created’ a new trade (OK, no surprise). And each ‘trim’ trade added one observation to the R-Multiple Distribution graph with the trim trade’s R value going into the R-Multiple Profit Contribution graph.

So if you have high R trades and lots of trims to manage risk, then your R graphs may ‘look’ much better than a very similar system without trimming.

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Here’s an (extreme) example to illustrate:

Case 1: System has one trade: one entry and one exit that is high R and no risk management via reduce positions. R graphs have one bar each. See ‘Case 1’ image.

Case 2: Same system, same entry date & size but add risk management by reducing positions. This time we have 29 small trims to manage risk plus the final sale. See ‘Case 2’ image.

The CAGR and final equity of Case 1 & Case 2 (not shown) are fairly close so I would consider the two similar in many respects.

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Here’s why I’m bringing this up:

Let’s say you have a much larger simulation with many more securities and it has mediocre results.

If you add Case 1’s single trade to this system’s R graphs, you probably won’t think there is a lot of change. Nice to add that one high R trade but probably nothing earth-shaking.

But … if you sprinkle Case 2’s 29 high R trims and final sale into the mediocre system’s R graphs, they could easily make it look like a great system.

I think you may be expressing disappointment that R multiples (Rm = Dollars of Profit / Dollars of Risk) don't necessarily have the same denominator in every case. Trade entries that are paired with a single exit (trades that don't get "trimmed" in your terminology) have a large denominator, while trade entries that get split into many trades due to many different exit points ("trims") have small denominators.

You could of course define your own measure-of-goodness which has, by definition, the same denominator in every case. An example that seems sensible to me, is the "Percent Profit Factor" calculation built into Blox. This is a ratio

PPF = 100 * (dollars of profit on the trade) / (Account equity dollars on the day of entry)

You could define the numerator to be "total dollars of profit on all pieces of the trade including scale-ins and scale-outs and trims and pyramids" if you wished. Now you get the same numerical result whether or not Blox schmeers the entry and exit over many little pieces. Which, it seems to me, is approximately the way you wish that Rmultiples were counted.

Thanks sluggo - that was the unstated point of my post:

Don't assume that the observations in your R-Multiple Graphs have the same 'weight' or denominator.

But isn't some sort of common size or weight precisely the point of using R-multiples !?!

I'd like to move any discussion of this post to a forum with public access.

Please post any further replies here:

viewtopic.php?p=28867#28867

Thanks, steve