In this post, I’ll attempt to address a question I’ve seen tossed around time and again regarding quantstrat. “How do … Continue reading →

As a first step in visualizing/exploring the data from my last post, FOMC Dates - Scraping Data From Web Pages, I’ll plot the FOMC announcement dates along with the following price series: 2-Year and 10-Year US Treasury yields, S&P500 ETF (SPY) and USD Index ETF (UUP).I’ll use the quantmod R package to download the price data from...

If you would have invested in 1992 in the DAX ETF - provided it would have been around, of course - you would have earned a decent amount of money.That's the story of the passive guys and in my previous post I'm borrowing a few arguments of this guys t...

Pipes in R make my life incredibly easy, and I think my code easier to read. Note, there are a couple different flavors of pipes (see magrittr and pipeR). For now, I choose pipeR.library(quantmod)library(pipeR)library(ggplot2)getSymbols("^GSPC",from="1900-01-01",auto.assign=F) %>>% #get S&P 500 from Yahoo!Finance ( . ) %>>% #get end of year ROC( type="discrete", n=1

Part 2 of a series by Daniel Hanson, with contributions by Steve Su (author of the GLDEX package) Recap of Part 1 In our previous article, we introduced the four-parameter Generalized Lambda Distribution (GLD) and looked at fitting a 20-year set of returns from the Wilshire 5000 Index, comparing the results of two methods, namely the Method of Moments,...

by Daniel Hanson, with contributions by Steve Su (author of the GLDEX package). Part 1 of a series. Introduction As most readers are well aware, market return data tends to have heavier tails than that which can be captured by a normal distribution; furthermore, skewness will not be captured either. For this reason, a four parameter distribution such as...

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