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Bullish Iron Butterflies (Part 2)

With the wheels turning as a result of the last four posts (here, here, here, and here), I have decided to do an analysis of losers sorted by MFE.

I ran the analysis by simulating a reduction in transaction fees (TF) from $26/contract to either $11/contract or $6/contract. How many losers would otherwise be winners?

BIBF Loser Analysis by TF (5-28-17)

The average loss remained around 100%. The number of losses, however, decreased dramatically. Reducing TF to $11/contract and $6/contract cut the total number of losses by over 31% and over 57%, respectively.

As impressive as this may seem, the numbers are distorted because they are percentages of percentages. The losing trades are a small fraction of the whole. In order to estimate the overall impact, I counted the losers-turned-winners as +10% and adjusted the average loss downward per the numbers shown above. Here are the revised trade statistics:

BIBF Estimated Revised Trade Statistics by TF (5-28-17)

For TF $6/contract, we’re now looking at a marginally (PF 1.14) profitable trade.

I believe an average loss being over seven times the average win does significant damage to the trade statistics and I can think of two ways to improve upon this going forward.

First, I can explore implementation of a stop-loss (SL). I need to look at MAE distributions of the winners to see if a SL makes sense. Winners with MAE worse than the SL will become losers and that will hurt.

Second, I can stratify performance by spread width. The statistics so far have hidden the fact that margin requirement varies dramatically across the collection of trades. Narrower butterflies have a lower probability of profit and if these amount to wasted margin then perhaps I would realize some benefit by making the narrower trades wider. Another alternative would be to trade narrower butterflies in smaller size and leave additional capital on the sidelines for dilution (e.g. a 100% loss might then be -50% although a 10% winner might then be +5%).

One important issue to address is whether I need to repeat the entire backtest with $6/contract TF’s before I proceed with the above analyses. I will sleep on that.

Comments (2)

[…] far I have done several things with the BIBF analysis: considered the impact of transaction fees (TF), looked at width-adjusted ROI, identified a relationship between spread width and underlying […]

[…] generally find these numbers more encouraging than the bullish iron butterfly because the latter is not profitable with TF greater than $0.06/contract. The PCS is marginally […]

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