# A Currency Graph

July 27, 2011
By

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Here’s a graph in which nodes (and edges) represent currencies (and exchange rates):

library(igraph)
currencies <- factor(c("EUR", "USD", "JPY", "GBP"))
df <- subset(expand.grid(from=currencies, to=currencies),
from != to)
GetExchangeRates <- function(from, to) {
urls <- sprintf("%s/d/quotes.csv?s=%s%s=X&f=b",
from, to)
GetRateFromUrl <- function(str) {
error = function(e) NA)
}
sapply(urls, GetRateFromUrl)
}
# If a url connection fails, the corresponding rate will be NA
df\$rate <- GetExchangeRates(df\$from, df\$to)
g <- graph.data.frame(df, directed=TRUE)
g\$layout <- layout.fruchterman.reingold(g)
E(g)\$label <- E(g)\$rate
V(g)\$label <- V(g)\$name
dev.new(width=10, height=10)
plot(g, main=sprintf("Exchange Rates on %s", Sys.Date()))
savePlot("exchange_rate_graph.png")

I’d like to emulate this post and look for profitable cycles using R. Here’s a first attempt:

# Look for negative-cost cycles
E(g)\$weight <- -log(E(g)\$rate)
shortest.paths(g)

In this case, the shortest.paths function complains that it “cannot run the Bellman-Ford algorithm” because a “negative loop [was] detected while calculating shortest paths” — great! There’s a negative-cost cycle in there somewhere. But what’s the easiest way to actually find that cycle using R — does anyone have any tips?

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