Quantocracy’s Daily Wrap for 01/24/2018

This post will be about comparing a volatility signal using three different variations of implied volatility indices to predict when to enter a short volatility position. In volatility trading, there are three separate implied volatility indices that have a somewhat long history for tradingthe VIX (everyone knows this one), the VXV (more recently changed to be called the VIX3M), which is like

There is a large literature that reports time-specific anomalies in equity markets such as the Monday effect, the January effect and the Halloween effect. This study is the first to report intra-day time-of-day, day-of-week, and month-of-year effects for Bitcoin returns and trading volume. Using more than 15 million price and trading volume observations from seven global Bitcoin exchanges reveal

Imagine the following. You spent time developing a strategy with a compounded annual return of 24% and max drawdown of 18%. Profitable 10 of the last 11 years. An average 21 day rolling correlation with the SPY of .20. Passes your out-of-sample testing. Passes your parameter sensitivity testing. Raise your hand if you would trade this? I would be the guy jumping up and down saying yes!. Now

Machine Learning is one of the most popular approaches in Artificial Intelligence. Over the past decade, Machine Learning has become one of the integral parts of our life. It is implemented in a task as simple as recognizing human handwriting or as complex as self-driving cars. It is also expected that in a couple of decades, the more mechanical repetitive task will be over. With the increasing

Rankings are everywhere. They are sometimes useful and, at other times, contradicting. In such a case, we need to come up with a consensus ranking but how do we evaluate ranking consensus? The other day I was reading about something called rank aggregation, which is just a fancy name for combining preferences expressed through rankings. I bet you know that rankings are everywhere. The page