**Quantitative thoughts » EN**, and kindly contributed to R-bloggers)

Recently, I was busy testing the following strategy:

If SPY and VIX daily returns are positive, then short SPY at close and keep it for one day.

The strategy is dump simple and it has very good feature – short side. There are not so many successful short side strategies. For testing purpose I used daily Yahoo data from 1995 until present. For commissions and bid/ask spread I used 5 $ fee per 10 000 $ trade. Here we go:

Annualized Return 0.0421

Annualized Std Dev 0.0488

Annualized Sharpe (Rf=0%) 0.8621

t = 3.3787, df = 3811, p-value = 0.0007356

Up to this point is was relatively easy to make a test (the true is, that I spent some time cracking and hacking blotter package, but I will write about it in separate post). My second objective was the improvement of this strategy.

One of the way to understand the strategy is to look how the components are related to each other or correlated. To do that, I took daily returns of SPY and VIX at day 0 and plotted against SPY next day’s (day+1) returns.

What can I tell by looking at this plot? I couldn’t figure out any **linear** relation between returns of SPY and VIX at day+0 and returns of SPY at day+1. Should I try something like random forest method?

I tried to add some TA flavors, like RSI, but the improvements were not very significant. Simplicity is genius!

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**Quantitative thoughts » EN**.

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