Quantocracy’s Daily Wrap for 11/30/2017

In the last post we introduced the concept of using adaptive volatility in order to have a flexible lookback as a function of market conditions. We used the R-squared of price as a proxy for the strength of the trend in the underlying market in order to vary the half-life in an exponential moving average framework. The transition function used an exponential formula to translate to a smoothing

VIX related products (ETNs, futures and options) are becoming popular financial instruments, for both hedging and speculation, these days. The volatility index VIX was developed in the early 90s. In its early days, it led the derivative markets. Today the dynamics has changed. Now there is strong evidence that the VIX futures market leads the cash index. In this post we are going to look at

Any serious quant trading research with machine learning models necessitates the use of a framework that abstracts away the model implementation from the model specification. This is particularly crucial for deep learning techniques as production-grade models require training on GPUs to make them computationally tractable. However, direct programming of GPUs requires knowledge of proprietary

Kory Hoang is not a veteran traderhes not someone who has been doing this 10-20 years. Hes someone who has been doing this for only a few years, yet hes begun to make decent gains on his trading capital. Kory is also not a full-time trader well, at the time of recording thisa few weeks agohe wasnt! Kory was a private equity analyst for Pitchbook, who traded on the side. But

Much has been made of Factor Investing, and even Vanguard is launching a suite of actively-managed factor ETFs. But even now, with Vanguard offering factor ETFs, there are many investors that only invest passively into an index fund, such as the SP500 or EAFE index. These investors will cite the numerous studies showing that (on average) active management fails compared to an index fund (there are