Butterfly Backtesting Ideas (Part 1)
Posted by Mark on June 12, 2017 at 06:23 | Last modified: February 22, 2017 10:02I have completed one exhaustive butterfly backtest on dynamic iron butterflies (DIBF). While helpful for offering up some context, it left much to be desired.
Butterflies seem to be all the rage in trading communities these days so the main reason my backtest failed to impress is because the results were inconclusive. Slippage really made the difference between a trade that was profitable and one that was not. While tantalizing to think I can overcome slippage by simply entering a GTC limit order and waiting for a fill, unables do occur. Backtesting cannot fully determine the impact of unables primarily due to limited granularity of data (30-minute intervals).
I have some methodological issues that may have negatively impacted the results. The dynamic nature of the strategy means some trades were symmetric and others were asymmetric. An asymmetric butterfly will have a lower max loss potential to the upside. Even though most losses seem to have taken place on the downside, having a much larger upside loss potential (100%) hurts because the downside loss potential is the same either way (100%).
Aside from some trades being symmetric, those that were not had varying degrees of asymmetry. The greater the asymmetry, the lower potential loss to the upside in terms of ROI (%). Perhaps this should be standardized.
The need for standardization feeds directly into the next issue: use of percentages (ROI) instead of PnL. Because margin requirements ranged from $1,401 to $12,400, I used percentages to avoid having to normalize (e.g. two contracts of a $5,000 trade equates to one contract of a $10,000 trade). ROI is unaffected by margin requirements. Now consider a downside loss. Asymmetric and symmetric butterflies can both experience -100% ROIs when PnL is [much] worse for the asymmetric due to the embedded put credit spread. This doesn’t feel right.
One thing I could do with the DIBF backtest is normalize for margin requirement then recalculate the trade statistics based on PnL. This might serve as confirmation that I was on the right track with the initial analysis.
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