Low Volatility vs. High Volatility Days


I read a blog post recently that began “suppose you have a trading system that works well on low-volatility days…” and I thought, hmm. Is that a thing? Is there an edge to low-volatility days vs high volatility days? Let’s turn this blog post into a speculator’s version of Dude, What Would Happen?

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Get Shorty (again, research, not the movie…)

I’m running a high risk of running out of movies with “short” in the title. So this had better be the last blog post on the subject!

In my previous post (here), I looked at a short-sale signal where a stock was shorted after it averaged 3% gains each day over five days (in any distribution). At the end of five days, it had to be up 15%. Yes, I could have just looked at it that way, but whatever, it all works out the same.

The graphs looked pretty good. Seemed like the basis for a system, yeah?

Not so fast.

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Data Mining vs Out of Sample Data

So in this last post, I data-mined the hell out of the S&P500 index (well ok SPY) and found an “anomaly”: every time SPY drops more than 1% from the previous close to the current close, you wait (that’s Day 0). You then buy at the close 13 days later, and sell at the close of Day 14. This showed significantly better return than if you did the same thing but owned all the Day 16s instead. Here’s the graph from the last post.

Screen Shot 2016-02-04 at 8.21.02 PM

But with only 177 samples of data between 2010-2015, that’s probably just a fluke….right?

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Autocorrelation of SPY, and the Redneck Correlogram

I’ve been reading books by Michael Halls-Moore and my head hurts. Not having any formal training in statistics, I only understand about half of the material. None the less, I found his discussion of ‘correlograms’ interesting. I even installed R on my computer (even though I haven’t fully grasped Python yet!) and was able to make some correlograms with R. However not knowing anything about R (sensing a theme here?), I thought I’d come up with my own version of a correlogram using AmiBroker and Google Sheets. A ‘redneck correlogram’ if you will.

So what is a correlogram, you ask? Here’s a link to a wiki page on the subject. My interpretation: it’s a tool to see if a time series of data (i.e. stock prices) is autocorrelated (i.e. is there some connection between price movements down the line from the day in question).

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