performance of the ETFRot strategy

ETFRot is an ETF rotation strategy that I’ve been working on for a while now (almost a year). Essentially it uses a couple of momentum and volatility indicators to rank ETFs in a basket spanning across asset classes, and then trades the top ranked ETF. When the top ranked ETF changes, it “rotates” into the the new one. Simple logic, simple to trade.

To test the influence of data mining bias, I ran a walk forward optimization, optimizing the parameters on in sample data up to X and then using those optimized parameters to trade and evaluate the strategy in X+1. Repeat, now including X+1 in the in-sample and testing out of sample on X+2. If data mining bias is rampant, we would expect performance of the optimized parameters to be poor out of sample: they would have little predictive power. After doing a walk forward optimization of ETFRot, the parameters seem to be intrinsically predictive rather than just curve-fit:


It starts in 2005 because I used 2003-2005 (before 2003 some ETFs in my basket didn’t exist) as my first in-sample sample. The second chart compares ETFRot performance with the SPY. It seems to have done very well since 2008, which is when the market tanked. Maybe ETFRot is capturing a market regime shift that happened in 2008…

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