In mirca/fingraph: Learning Graphs for Financial Markets

library(knitr)
opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  fig.align = "center",
  fig.retina = 2,
  out.width = "75%",
  dpi = 96
)
knit_hooks$set(pngquant = hook_pngquant)

fingraph

This repo contains ADMM implementations to estimate weighted undirected graphs (Markov random fields) under Student-t assumptions with applications to financial markets.

Installation

fingraph depends on the development version of spectralGraphTopology, which can be installed as:

> devtools::install_github("convexfi/spectralGraphTopology")

The stable version of fingraph can be installed directly from CRAN:

> install.packages("fingraph")

Microsoft Windows

On MS Windows environments, make sure to install the most recent version of Rtools.

Usage

Learning a graph of cryptocurrencies

library(igraph)
library(fingraph)
library(fitHeavyTail)
library(xts)
set.seed(123)

# load crypto prices into an xts table
crypto_prices <- readRDS("examples/crypto/crypto-prices.rds")
colnames(crypto_prices)

# compute log-returns
log_returns <- diff(log(crypto_prices), na.pad = FALSE)

# estimate a weighted, undirected graph (markov random field)
graph_mrf <- learn_kcomp_heavytail_graph(scale(log_returns),
                                         k = 8,
                                         heavy_type = "student",
                                         nu = fit_mvt(scale(log_returns))$nu,
                                         verbose = FALSE)

# plot network
net <- graph_from_adjacency_matrix(graph_mrf$adjacency,
                                   mode = "undirected",
                                   weighted = TRUE)
cfg <- cluster_fast_greedy(as.undirected(net))
la_kcomp <- layout_nicely(net)
V(net)$label.cex = 1
plot(cfg, net, vertex.label = colnames(crypto_prices),
     layout = la_kcomp,
     vertex.size = 4.5,
     col = "black",
     edge.color = c("#686de0"),
     vertex.label.family = "Helvetica",
     vertex.label.color = "black",
     vertex.label.dist = 1.25,
     vertex.shape = "circle",
     edge.width = 20*E(net)$weight,
     edge.curved = 0.1)

Learning a network of S&P500 stocks

library(xts)
library(igraph)
library(fingraph)
library(fitHeavyTail)
library(readr)
set.seed(123)

# load table w/ stocks and their sectors
SP500 <- read_csv("examples/stocks/SP500-sectors.csv")

# load stock prices into an xts table
stock_prices <- readRDS("examples/stocks/stock-data-2014-2018.rds")
colnames(stock_prices)

# compute log-returns
log_returns <- diff(log(stock_prices), na.pad = FALSE)

# estimate a weighted, undirected graph (markov random field)
graph_mrf <- learn_kcomp_heavytail_graph(scale(log_returns),
                                         rho = 10,
                                         k = 3,
                                         heavy_type = "student",
                                         nu = fit_mvt(scale(log_returns))$nu,
                                         verbose = FALSE)

# map stock names and sectors
stock_sectors <- c(SP500$GICS.Sector[SP500$Symbol %in% colnames(stock_prices)])
stock_sectors_index <- as.numeric(as.factor(stock_sectors))

# plot network
net <- graph_from_adjacency_matrix(graph_mrf$adjacency,
                                   mode = "undirected",
                                   weighted = TRUE)
la_kcomp <- layout_nicely(net)
V(net)$label.cex = 1
colors <- c("#FD7272", "#55E6C1", "#25CCF7")
V(net)$color <- colors[stock_sectors_index]
V(net)$type <- stock_sectors_index
V(net)$cluster <- stock_sectors_index
E(net)$color <- apply(as.data.frame(get.edgelist(net)), 1,
                      function(x) ifelse(V(net)$cluster[x[1]] == V(net)$cluster[x[2]],
                                        colors[V(net)$cluster[x[1]]], 'grey'))
plot(net, vertex.label = colnames(stock_prices),
     layout = la_kcomp,
     vertex.size = 4.5,
     vertex.label.family = "Helvetica",
     vertex.label.dist = 1.25,
     vertex.label.color = "black",
     vertex.shape = "circle",
     edge.width = 20*E(net)$weight,
     edge.curved = 0.1)