Quarto RPubs Test Document

Last Updated: 2023-01-29 16:49:04

Code

# load libraries
library(tidyverse) # because, always
library(tidyquant) # for tq_get() to pull financial data
library(plotly) # for interactive charts

# You need to have the open source Google Fonts Roboto font installed on your computer.  Here is a blog describing how to do this: https://babichmorrowc.github.io/post/2019-10-11-google-fonts/ . I'll add better step-by-step instructions later. 

pull_dashboard_data <- function(start_date = as.Date("2000-01-01")) {
  
  # Data to pull from FRED (Federal Reserve Economic Data)
  fred_tbl <- tribble(
    ~"symbol", ~"indicator",
    "DFF", "Fed Funds Rate",
    "DTWEXBGS", "Broad USD Index"
    )
  
  # Data to pull from Yahoo Finance. Data updated more frequently & with less of a lag than FRED.
  yahoo_tbl <- tribble(
    ~"symbol", ~"indicator",
    "^GSPC", "S&P 500",
    "BZ=F","Oil Price",
    "^TNX", "US 10yr Yield"
    )
  
  # pull the FRED data
  fred_data <- fred_tbl %>%
    tq_get(get = "economic.data", from = start_date)
  
  # FRED only has the last 10 years of S&P 500 data, but Yahoo has it from the 1920s onward
  yahoo_data <- yahoo_tbl %>%
    tq_get(from = start_date) %>%
    # make the columns the same as the FRED data
    select(symbol:date, price = close)
  
  #combine into one tibble
  combined_data <- fred_data %>%
    bind_rows(yahoo_data) %>%
    select(indicator, date, level = price)
}
## use this function to pull all the data
dashboard_data <- pull_dashboard_data()


### Set Range Selector Options for Plotly plots
# This code adds:
## makes the chart default to showing 1 year of data
## adds a range slider
## adds range selector buttons
## limits the chart to showing weekdays so that the lines don't show gaps for every weekend, as is the default for plotly. 
x_axis_range_options <- list(
  # it's obvious that the x axis is the date
  title = "",
  # makes 1 year the default view
  range = c(max(dashboard_data$date)-365, max(dashboard_data$date)),
  # makes the range slider visible
  rangeslider = list(visible = T, type = "date"),
  # specify the range selector buttons
  rangeselector=list(
    buttons=list(
      list(count=1, label="1m", step="month", stepmode="backward"),
      list(count=6, label="6m", step="month", stepmode="backward"),
      list(count=1, label="YTD", step="year", stepmode="todate"),
      list(count=1, label="1y", step="year", stepmode="backward"),
      list(count=5, label="5y", step="year", stepmode="backward"),
      list(count=10, label="10y", step="year", stepmode="backward"),
      list(step="all", label = "All")
      )),
  # eliminate weekends
  rangebreaks=list(bounds=list("sat", "mon") #hide weekends
))

Introduction

Financial markets are complex. The objective of this dashboard is to give readers a 40,000 ft view of global financial markets in as few indicators as possible. The dashboard seeks to:

1. Avoid cognitive overload by focusing on only five indicators.

2. Illuminate big-picture trends in global financial markets.

3. Inform where readers might want to dig deeper.

This dashboard is meant to anchor and inform the 15 minute trading floor meeting at the beginning of each class for Sustainable Finance: Applications and Methods at the Johns Hopkins School of Advanced International Studies (SAIS).

It is a work in progress, and will evolve & improve over time.

Indicators

This dashboard focuses on five indicators central to global financial markets.

• Interest Rates:

  • Fed Funds Rate

  • US 10-Year Treasury Yield

• Risk Assets:

  • S&P 500

• Commodities:

  • Oil Prices (Brent)

• Currencies:

  • Broad USD Index

Dashboard

Interest Rates

Code

dashboard_data %>%
  filter(date >= ymd("2000-01-01")) %>%
  filter(indicator %in% c("Fed Funds Rate", "US 10yr Yield")) %>%
  mutate(weekday = lubridate::wday(date, label = TRUE)) %>%
  filter(!weekday %in% c("Sat", "Sun")) %>%
  plot_ly(x = ~date, y = ~level, hoverinfo = "text+name", text = ~paste0("Level: ", round(level,2),"%", "<br>",
                                                                    "Date: ", date)) %>%
  add_lines(width = 10, color = ~indicator, colors = c("#114B5F", "#028090"), line = list(width = 4)) %>%
  layout(xaxis = x_axis_range_options) %>%
  layout(#font = list(family = "Roboto Condensed"),
         title = list(text = "<b>Interest Rates: Fed Funds Rate & 10yr UST<b>", 
                      font = list(size = 25)
                      ),
         # add a margin between the title & graph
         margin = list(t = 70),
         yaxis = list(
           title = list(
             text = "<b>Level<b>", 
             font = list(size = 16)
             ),
           ticksuffix = "%"
           )
  )

Discussion

Federal Funds Rate: is the main interest rate tool of the US Federal Reserve. Expectations about the future path of the Fed Funds Rate are a primary driver of risk appetite in global financial markets. Think of expectations about future Fed policy as the symphony conductor directing global financial markets. Data from FRED.

10 Year US Treasury Yields: is one of the deepest, most liquid financial markets in the world. Because it is traded actively, it embeds market expectations about future growth and inflation that will drive Federal Reserve monetary policy. Rapid moves in US Treasury yields are a strong signal that markets are repricing risk appetite. When markets face great uncertainty, such as at the beginning of the COVID pandemic, global investors will buy US Treasury Bonds in a Flight to Safety. Data from Yahoo Finance (updates faster than FRED).