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",

"http://download.finance.yahoo.com",

from, to)

GetRateFromUrl <- function(str) {

message("Reading from ", str)

tryCatch(read.csv(url(str), header=FALSE)[1, 1],

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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