Quantocracy’s Daily Wrap for 04/15/2020

After our little detour into GARCHery, were back to discuss capital market expectations. In Mean expectations, we examined using the historical average return to set return expectations when constructing a portfolio. We noted hurdles to this approach due to factors like non-normal distributions, serial correlation, and ultra-wide confidence intervals. While we highlighted these obstacles and

My last two posts have shown Octave functions that use the Oanda API to access and download data. In the first of these posts I said that I would post more code for further functions as and when I write them. However, on further reflection this would be unnecessary as the generic form of any such function is: 1) create the required headers ## set up the headers query = [ 'curl -s –compressed

Many machine learning algorithms rely on distances between data points as their input, sometimes the only input, especially so for clustering and ranking algorithms. The celebrated k-nearest neighbors (KNN) algorithm is our example chief, but distances are also frequently used as an input in the natural language processing domain; You shall know a word by the company it keeps (Firth, J. R.

In a previous post, we showed that unhedged currency exposure adds unrewarded risk to our investment, hurting risk-adjusted-performance. This risk should either be neutralized through passive hedging; or mitigated and turned into profit with an active overlay, the latter being what ETS has been doing for the last 20 years. Now, lets say we dont want to get involved in currency matters and we

Developer: Etienne Botes and Douglas Siepman (Vortex Indicator). Concept: Dual momentum trading strategy based on Vortex Indicator. Research Goal: Performance verification of dual momentum signals. Specification: Table 1. Results: Figure 1-2. Trade Filter: Long Filter: Slow Positive Vortex Indicator (+VI1) is above Slow Negative Vortex Indicator (VI1). Short Filter: Slow Negative Vortex