jstauth

Here is a link to a companion post to the Fortune article with more detail on the study: https://www.quantopian.com/posts/research-an-update-to-inves...

You can check out the full analysis and data set in a shared IPython notebook.

The study was done over a 12 year period because that's the full length of the pricing data available in the backtester Karen was using to do the study. It would be interesting to extend the analysis back in time as well.

NB: I work with Karen at Quantopian and we both have two X chromosomes :)

That's a great question, I've been working up a more detailed analysis of implementation shortfall (and thinking about ways to make this automated) - but your question prompted me to post a quick comparison here that you can look at for some current results: https://www.quantopian.com/posts/live-results-vs-backtest-re...

Full disclosure, I work for Quantopian and I wrote this strategy.

Thanks. The drawdown is not ready for prime time indeed, I haven't traded it for that (among other) reasons. But I like the idea a lot and the futures linked ETFs seemed like an easier place to start that the contracts themselves.

Very cool. I have revisited the WTI/Brent spread trade a few times in the last few years - actually wrote a simple algo using Quandl data for USO and BNO, two ETFs linked to the WTI and Brent futures. I would love to update it to use this continuous futures contract logic and see how it does. https://www.quantopian.com/posts/brent-slash-wti-spread-fetc...

While I'd love to take all the credit (blame?), the reformed academic in my feels compelled to admit that the idea to look for predictive value in stock loan data is not original to me. The finance literature has some fascinating articles on this dating back as far as the late 80s (look for Desai 2002, J of Finance, Asquith 2005 J Fin Econ, or most recently http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1570451).

The intuition behind this signal as a market inefficiency, or 'anomaly' is that the market sees short sellers as informed investors, the so called 'smart money', and there is a herding effect to follow their trades which generates abnormal returns. The same logic can be applied to disclosed insider trades or institutional holdings filings made public via the SEC's EDGAR database.

Fawce's slick implementation of a 'Days to Cover' signal is a great way to highlight the power of aiming new tools like Quantopian at freely available public data stores (which exist expressly to increase market transparency). And sure, it doesn't go the whole way for you on execution details like borrow costs, liquidity etc. but those aspects tend to be unique to each trader.