In frdanconia/shiny_etf_portfolio: ShinyETF

library(tidyverse) 
library(Quandl)
library(xts)
library(dygraphs)
Quandl.api_key("d9EidiiDWoFESfdk5nPy")

Inputs{.sidebar}

# We are choosing a few commodities and a few economic indicators. There are others, like 
# unemployment, consumer confidence, home building, retail spending. 
# Remember this is a commodities flexdashboard. 


commodityChoices <- c(
                  "Copper" = "CHRIS/CME_HG1.1", #daily 2017-5-2
                  "WTI oil" = "FRED/DCOILWTICO.1", # daily 2017-4-24
                  "Iron Ore" = "ODA/PIORECR_USD", # monthly, 2017-3-31
                  "Platinum" = "LPPM/PLAT.1", #daily, 2017-5-2
                  "Palladium" = "LPPM/PALL.1",
                  "Silver" = "LBMA/SILVER.1") # daily 2017-5

selectInput("commodity",
            "Commodity",
            choices = commodityChoices, 
            selected = "Copper")

econIndicatorChoices <- c(
                  "10-Yr Yield" = "FRED/DGS10", # daily 2017-5
                  "US CPI" = "RATEINF/INFLATION_USA",# monthly 2017-3
                  "Japan CPI" = "RATEINF/INFLATION_JPN",
                  "EU CPI" = "RATEINF/INFLATION_EUR") 

selectInput("econIndicator",
            "Economic Indicator",
            choices = econIndicatorChoices, 
            selected = "10-yr Yield")


dateRangeInput("dateRange",
               "Date range",
               start = "1990-01-01",
               end   = "2017-03-30")

actionButton("go", "Submit")

ratio_indicator <- eventReactive(input$go, {

Quandl.api_key("d9EidiiDWoFESfdk5nPy")

start_date <- format(input$dateRange[1])
end_date <- format(input$dateRange[2])

# Create a vector of 3 data set codes
# 1) commodity chosen by user
# 2) gold quandl code
# 3) economic indicator chosen by user

gold_code <- "CHRIS/CME_GC1.1"
data_set_codes <- c(input$commodity, gold_code, input$econIndicator)

# Pipe the data_set_codes vector to Quandl via the map() function
# Note we can still set the start and end date and object type
# as we always can with Quandl.

quandlData <- data_set_codes  %>% 
        # Pipe the datasets vector to Quandl via the map() function.
        map(Quandl,
            start_date = start_date,
            end_date = end_date,
            collapse = "monthly",
            type = "xts") %>%
        # Replace all NAs using map() and na.locf().
        map(na.locf, formLast = TRUE) %>%
        # Merge to one xts object using map() and merge().
        reduce(merge) %>% 
        # Add nicer column names.
        `colnames<-`(c(names(commodityChoices[commodityChoices == input$commodity]), 
                            "Gold",
                            names(econIndicatorChoices[econIndicatorChoices == input$econIndicator]))) 

# Create a column and add the price ratio.
quandlData$ratio <- quandlData[,1]/quandlData[,2]

# Save just the ratio and the economic indicator data.
ratio_indicator  <- merge(quandlData$ratio, quandlData[,3])

# Add more general names.
colnames(ratio_indicator) <- c("ratio","indicator")       

return(ratio_indicator)
})

Row

Chart 1

dygraphOutput("ratio_indicator")

output$ratio_indicator <- renderDygraph({
  dygraph(ratio_indicator()) %>% 
  # Add the rollPeriod for smoothing.
  dyRoller(rollPeriod = 3) %>% 
  # Create two independent axes.
  dyAxis("y", label = "USD") %>%
  dyAxis("y2", label = "Percent (%)", independentTicks = TRUE) %>%
  # Assign each time series to an axis.
  dySeries("ratio", axis = 'y', 
           label = paste(names(commodityChoices[commodityChoices == input$commodity]), 
                         "/Gold (LHS)", sep = ""), color = "blue") %>% 
  dySeries("indicator", axis = 'y2', 
           label = paste(names(econIndicatorChoices[econIndicatorChoices == input$econIndicator]), 
                         "(RHS)", sep = ""), color = "red")
})

Row

Chart 2

dygraphOutput("rollingCorrelation")

output$rollingCorrelation <- renderDygraph({

rolling_cor <- rollapply(ratio_indicator(), 24,
                         function(x) cor(x[, 1], x[, 2], use = "pairwise.complete.obs"),
                         by.column = FALSE)

names(rolling_cor) <- paste(names(commodityChoices[commodityChoices == input$commodity]),
                            "/Gold ",
                            names(econIndicatorChoices[econIndicatorChoices == input$econIndicator]), 
                            " Correlation", sep = "")

  avg <- round(mean(rolling_cor,  na.rm = T), 2)
  mini <- round(min(rolling_cor,  na.rm = T), 2)
  maxi <- round(max(rolling_cor,  na.rm = T), 2)


dygraph(rolling_cor, main = paste(names(commodityChoices[commodityChoices == input$commodity]),
                            "/Gold ",
                            names(econIndicatorChoices[econIndicatorChoices == input$econIndicator]), 
                            " Correlation", sep = "")) %>% 
  dyRangeSelector(dateWindow = c("2015-01-01", "2016-12-31"))  %>% 
  dyLimit(avg, color = 'purple') %>% 
  dyLimit(mini, color = 'red') %>% 
  dyLimit(maxi, color = 'blue') %>% 
  dyEvent("2016-11-08", "Trump!", labelLoc = "bottom")
})