Many asset management firms are happily enjoying record revenue and profits driven not by inorganic growth or skillful portfolio management but by a seemingly endless increase in US equity prices. These firms are effectively commodity producers entirely dependent on the price of an index over which the firm has no control. The options market presents an easy, cheap, and liquid form of protection in the form of puts with which the firm could hedge its revenues, its clients, or both. However, many of these firms in blissful disregard for the brutality of asymmetric arithmetic choose to ignore this opportunity for protection.
Here is some very quick and ugly code with which anyone can evaluate various put options for the hedge and their potential outcomes. I hope someone, somewhere might benefit from the idea
Jeroen Ooms (@opencpu) provides R
users a magical polyglot world of R, JavaScript, C, and C++. This is my
attempt to both thank him and highlight some of all that he has done.
Much of my new R depends on his work.
`jsonlite` is an ultra-fast reliable tool to convert and create `json`
in `R`. It's fast because like much Jeroen's work, he leverages
`C`/`C++` libraries. `shiny` and `htmlwidgets` both depend on
`jsonlite`.
V8
V8 gives R its own embedded
JavaScript engine to leverage functionality in JavaScript that might not
exist in R. For example, the
WebCola constraint-based
layout engine offers valuable technology not available within R. Let's
partially recreate the smallgroups
example
all in R. You might notice that the previously mentioned jsonlite is
essential to this workflow.
library(V8)
library(jsonlite)
library(scales)
ctx = new_context(global="window")
ctx$source("https://cdn.rawgit.com/tgdwyer/WebCola/master/WebCola/cola.min.js")
## [1] "true"
### small grouped example
group_json <- fromJSON(
system.file(
"htmlwidgets/lib/WebCola/examples/graphdata/smallgrouped.json",
package = "colaR"
)
)
# need to get forEach polyfill
ctx$source(
"https://cdnjs.cloudflare.com/ajax/libs/es5-shim/4.1.10/es5-shim.min.js"
)
# code to recreate small group example
js_group <- '
// console.assert does not exists
console = {}
console.assert = function(){};
var width = 960,
height = 500
graph = {
"nodes":[
{"name":"a","width":60,"height":40},
{"name":"b","width":60,"height":40},
{"name":"c","width":60,"height":40},
{"name":"d","width":60,"height":40},
{"name":"e","width":60,"height":40},
{"name":"f","width":60,"height":40},
{"name":"g","width":60,"height":40}
],
"links":[
{"source":1,"target":2},
{"source":2,"target":3},
{"source":3,"target":4},
{"source":0,"target":1},
{"source":2,"target":0},
{"source":3,"target":5},
{"source":0,"target":5}
],
"groups":[
{"leaves":[0], "groups":[1]},
{"leaves":[1,2]},
{"leaves":[3,4]}
]
}
var g_cola = new cola.Layout()
.linkDistance(100)
.avoidOverlaps(true)
.handleDisconnected(false)
.size([width, height]);
g_cola
.nodes(graph.nodes)
.links(graph.links)
.groups(graph.groups)
.start()
'
# run the small group JS code in V8
ctx$eval(js_group)
## [1] "[object Object]"
Now, WebCola has done the hard work and laid out our nodes and links,
so let's get their positions.
We got layout coordinates above. Let's use another one of Jeroen's
packages rjade that provides
jade (now called
pug) templates through V8. rjade
will let us build a SVG graph with our layout.
library(rjade)
library(htmltools)
svg <- jade_compile(
'
doctype xml
svg(version="1.1",xmlns="http://www.w3.org/2000/svg",xmlns:xlink="http://www.w3.org/1999/xlink",width="960px",height="500px")
each l in lines
line(style={fill:none, stroke:"lightgray"})&attributes({"x1": l.x1, "x2": l.x2, "y1": l.y1, "y2": l.y2})
each val in rects
g
rect(style={fill: fillColor})&attributes({"x": val.x - val.width/2, "y": val.y - val.height/2, "height": val.height - 6, "width": val.width - 6, rx: 5, ry: 5})
text&attributes({"x": val.x, "y": val.y, "dy": ".2em", "text-anchor":"middle"})= val.name
'
,pretty=T
)(rects = nodes, lines = links, fillColor = "lightgray")
HTML(svg)
rsvg
If we are not in the browser though with inline SVG support, we very
likely will want a static image format such as png or jpeg. Of
course, Jeroen has that covered also with the crazy-speedy
rsvg. Jeroen offers
base64, but in this case we
will use base64enc, since it allows raw.
Jeroen's newest package magick
is in my mind the coolest. magick gives us all the power of
ImageMagick as easy R
functions, and is pure wizardry. I am still shocked that it compiled
first try with absolutely no problems.
I should note that this document was assembled in rmarkdown. RStudio
gives us lots of tools for working with rmarkdown, but Jeroen gives us
a powerful tool
commonmark. Let's use it
to give our readers other options for output.
This would convert markdown to LaTeX. As a test, I used commonmark to make the html for this post.
Conclusion and Thanks
There are of course more packages, but I'll stop here. Jeroen Ooms truly
is a wizard, and the R community is extraordinarily blessed to have
him. Thanks so much Jeroen.
For even more wizardry, be sure to check out
opencpu from Jeroen, which makes R
available as a web service.
Just because I’m ignorant doesn’t mean I won’t try things. Feel free to correct any ignorance that follows. More than anything I would like to feature the new htmlwidget visNetwork. I thought the example from Minimum Spanning Trees in R applied to currency data (similar to this research paper Minimum Spanning Tree Application in the Currency Market) would be a good way to demonstrate this fancy new widget. We’ll grab the currency data from FRED using quantmod code from this old post Eigen-who?.
Code
# get MST using code from this post # https://mktstk.wordpress.com/2015/01/03/minimum-spanning-trees-in-r/
library(quantmod) # #get currency data from the FED FRED data series Korea <- getSymbols("DEXKOUS",src="FRED",auto.assign=FALSE) #load Korea Malaysia <- getSymbols("DEXMAUS",src="FRED",auto.assign=FALSE) #load Malaysia Singapore <- getSymbols("DEXSIUS",src="FRED",auto.assign=FALSE) #load Singapore Taiwan <- getSymbols("DEXTAUS",src="FRED",auto.assign=FALSE) #load Taiwan China <- getSymbols("DEXCHUS",src="FRED",auto.assign=FALSE) #load China Japan <- getSymbols("DEXJPUS",src="FRED",auto.assign=FALSE) #load Japan Thailand <- getSymbols("DEXTHUS",src="FRED",auto.assign=FALSE) #load Thailand Brazil <- getSymbols("DEXBZUS",src="FRED",auto.assign=FALSE) #load Brazil Mexico <- getSymbols("DEXMXUS",src="FRED",auto.assign=FALSE) #load Mexico India <- getSymbols("DEXINUS",src="FRED",auto.assign=FALSE) #load India USDOther <- getSymbols("DTWEXO",src="FRED",auto.assign=FALSE) #load US Dollar Other Trading Partners USDBroad <- getSymbols("DTWEXB",src="FRED",auto.assign=FALSE) #load US Dollar Broad #combine all the currencies into one big currency xts currencies<-merge(Korea, Malaysia, Singapore, Taiwan, China, Japan, Thailand, Brazil, Mexico, India, USDOther, USDBroad)
In a couple of hours, I got this crude but working rendering complete with a d3.js brush to get the scale. Then since this is sort of a finance blog, I imagined we found an old correlation heatmap like the one in Pretty Correlation Map of PIMCO Funds. Although, we could guess at the correlation values, I thought it would be a lot more fun to get live values. Try it out below.
Brush over the scale / legend
Input scale min and max
Mouseover color areas in the chart
As I said, it is rough, but it works. It needs a little UI work :)
As I said in my last post, I don’t know what I’m doing, so I have no basis for discussing or arguing time series clustering. After reading the paper a couple of times, I think I understand their points, and I do not think what I am doing is “meaningless”. In their financial time series examples, they use prices and speak of trying to find patterns. I simply want to classify which years are most alike by various characteristics, such as autocorrelation of returns not prices, distribution of returns, and all sorts of other classifiers.
More than anything this whole exercise gave me a good excuse to dig much, much deeper. Iongtime readers might be wondering where are the interactive plots. I wanted to share what I have done so far hoping that readers might elaborate, argue, or point me in good directions.
Regardless of your interest in time series clustering, you might enjoy the dplyr and piping that I used to generate the results. Also, I have not seen dplyr do applied to autocorrelation ACF, so you might want to check that out in the last snippet of code.
All of the code for this post and last post is in this Github repo.
sp5 %>>% # dplyr doesn't like xts, so make a data.frame ( data.frame( date = index(.) ,price = .[,1,drop=T] ) ) %>>% # add a column for Year mutate( year = as.numeric(format(date,"%Y"))) %>>% # group by our new Year column group_by( year ) %>>% # within each year, find what day in the year so we can join mutate( pos = rank(date) ) %>>% mutate( roc = price/lag(price,k=1) - 1 ) %>>% # can remove date select( -c(date,price) ) %>>% as.data.frame %>>% # years as columns as pos as row spread( year, roc ) %>>% # remove last year since assume not complete ( .[,-ncol(.)] ) %>>% # remove pos since index will be same select( -pos ) %>>% # fill nas with previous value na.fill( 0 ) %>>% t %>>% (~sp_wide) %>>% # use TSclust diss; notes lots of METHOD options diss( METHOD="ACF" ) %>>% hclust %>>% (~hc) %>>% ape::as.phylo() %>>% treewidget #%>>% #htmlwidgets::as.iframe(file="index.html",selfcontained=F,libdir = "./lib")
library(lattice) library(ggplot2) # get wide to long the hard way # could have easily changed to above pipe to save long # as an intermediate step # but this makes for a fun lapply # and also we can add in our cluster here sp_wide %>>% ( lapply( rownames(.) ,function(yr){ data.frame( year = as.Date(paste0(yr,"-01-01"),"%Y-%m-%d") ,cluster = cutree(hc,10)[yr] ,pos = 1:length(.[yr,]) ,roc = .[yr,] ) } ) ) %>>% (do.call(rbind,.)) %>>% (~sp_long)
sp_long %>>% ggplot( aes( x = roc, group = year, color = factor(cluster) ) ) %>>% + geom_density() %>>% + facet_wrap( ~ cluster, ncol = 1 ) %>>% + xlim(-0.05,0.05) %>>% + labs(title='Density of S&P 500 Years Clustered by TSclust') %>>% + theme_bw() %>>% # thanks to my friend Zev Ross for his cheatsheet + theme( plot.title = element_text(size=15, face="bold", hjust=0) ) %>>% + theme( legend.position="none" ) %>>% + scale_color_brewer( palette="Paired" )
# explore autocorrelations sp5 %>>% # dplyr doesn't like xts, so make a data.frame ( data.frame( date = index(.) ,price = .[,1,drop=T] ) ) %>>% # add a column for Year mutate( year = as.numeric(format(date,"%Y"))) %>>% # group by our new Year column group_by( year ) %>>% # within each year, find what day in the year so we can join mutate( pos = rank(date) ) %>>% mutate( roc = price/lag(price,k=1) - 1 ) %>>% # can remove date select( -c(date,price) ) %>>% as.data.frame %>>% # years as columns as pos as row spread( year, roc ) %>>% # remove last year since assume not complete ( .[,-ncol(.)] ) %>>% t -> sP
sp_long %>>% group_by( cluster, year ) %>>% do( . %>>% ( clustd ~ acf(clustd$roc,plot=F) %>>% (a ~ data.frame( cluster = clustd[1,2] ,year = clustd[1,1] ,lag = a$lag[-1] ,acf = a$acf[-1] ) ) ) ) %>>% as.data.frame %>>% ggplot( aes( x = factor(cluster), y = acf, color = factor(cluster) ) ) %>>% + geom_point() %>>% + facet_wrap( ~lag, ncol = 4 ) %>>% + labs(title='ACF of S&P 500 Years Clustered by TSclust') %>>% + theme_bw() %>>% # thanks to my friend Zev Ross for his cheatsheet + theme( plot.title = element_text(size=15, face="bold", hjust=0) ,legend.title=element_blank() ) %>>% + theme(legend.position="none") %>>% + scale_color_brewer(palette="Paired")
If you’ve made it this far, I would love to hear from you.
