README.Rmd

---
title: "fingraph README"
output:
  html_document:
    keep_md: true
---

```{r, echo = FALSE}
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
[![codecov](https://codecov.io/gh/convexfi/fingraph/branch/main/graph/badge.svg?token=OhreF1p2Yt)](https://app.codecov.io/gh/convexfi/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:
```{r, eval = FALSE}
> devtools::install_github("convexfi/spectralGraphTopology")
```

The stable version of **fingraph** can be installed directly from CRAN:
```{r, eval = FALSE}
> 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
```{r plot_crypto_network, message=FALSE}
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
```{r plot_sp500_stocks_network, message=FALSE}
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)
```

## Citation
If you made use of this software please consider citing:

-   [Cardoso JVM](https://mirca.github.io), [Ying J](https://github.com/jxying),
    [Palomar DP](https://www.danielppalomar.com) (2021).
    [Graphical Models in Heavy-Tailed Markets](https://papers.nips.cc/paper/2021/hash/a64a034c3cb8eac64eb46ea474902797-Abstract.html).
    [Advances in Neural Information Processing Systems](https://neurips.cc/Conferences/2021) (NeurIPS’21).

## Links
- [RFinance'23 Slides](https://github.com/mirca/rfinance-talk/blob/main/rfinance.pdf)
- [NeurIPS’21 Slides](https://palomar.home.ece.ust.hk/papers/2021/CardosoYingPalomar-NeurIPS2021-slides.pdf)
- [NeurIPS'21 Poster](https://palomar.home.ece.ust.hk/papers/2021/CardosoYingPalomar-NeurIPS2021-poster.png)
- [NeurIPS'21 Supplementary Material](https://palomar.home.ece.ust.hk/papers/2021/CardosoYingPalomar-NeurIPS2021-supplemental.pdf)
- [CRAN Package](https://cran.r-project.org/package=fingraph)