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Only calculate price ratios when company in key_ratios is demoninated in USD and allow specifying the specific exchange #50

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Only calculate price ratios when company in key_ratios is demoninated in USD and allow specifying the specific exchange #50

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3812ae4
Modification to tq_get to allow fetching key ratios from arbitrary ex…
mwaldstein Jun 14, 2017
472647d
Allowed get='key.ratios' to specify the specific exchange to query an…
mwaldstein Jun 23, 2017
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1 change: 1 addition & 0 deletions NEWS.md
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Original file line number Diff line number Diff line change
Expand Up @@ -2,6 +2,7 @@

* Improvements
* Added `pkgdown` integration.
* `tq_get()` where `get = "key.ratios"` now permits specifying a specific exchange - e.g. "EPA:UBI". This enables fetching basic non-US company financials. In the event finances aren't in USD, ratios involving stock price - e.g. "Price to Earnings" - will no be calculated.

* Fixes:
* Require new `quantmod` version 0.4-8 to fix Oanda and Yahoo bugs.
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150 changes: 88 additions & 62 deletions R/tq_get.R
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Expand Up @@ -445,20 +445,31 @@ tq_get_util_2 <- function(x, get, complete_cases, map, ...) {

# Convert x to uppercase
x <- stringr::str_to_upper(x) %>%
stringr::str_trim(side = "both") %>%
stringr::str_replace_all("[[:punct:]]", "")
stringr::str_trim(side = "both")

# If the request has a ':', assume that it is in the form of EXCHANGE:SYMBOL
# Allows both forcing a specific exchange source and making requests from non-default exchanges
if ( stringr::str_detect(x,":") ) {
split_req <- stringr::str_split(x,":",2)
stock_exchange <- c(split_req[[1]][1])
x <- split_req[[1]][2]
} else {
# Three Default URLs to try
stock_exchange <- c("XNAS", "XNYS", "XASE") # mornginstar gets from various exchanges
}

# This may need to be less agressive as some symbols have periods
x <- stringr::str_replace_all(x,"[[:punct:]]", "")

tryCatch({

# Download file
stock_exchange <- c("XNAS", "XNYS", "XASE") # mornginstar gets from various exchanges
url_base_1 <- 'http://financials.morningstar.com/finan/ajax/exportKR2CSV.html?&callback=?&t='
url_base_2 <- '&region=usa&culture=en-US&cur=&order=asc'
# Three URLs to try
url <- paste0(url_base_1, stock_exchange, ":", x, url_base_2)

# Try various stock exchanges
for(i in 1:3) {
for(i in 1:length(url)) {
text <- httr::RETRY("GET", url[i], times = 5) %>%
httr::content()

Expand All @@ -477,6 +488,10 @@ tq_get_util_2 <- function(x, get, complete_cases, map, ...) {
}
}

if (is.null(text)) {
stop("Could not fetch key_ratios")
}

# Read lines
text <- text %>%
xml2::as_list() %>%
Expand Down Expand Up @@ -510,7 +525,7 @@ tq_get_util_2 <- function(x, get, complete_cases, map, ...) {
rep("Efficiency", 8)),
group = 1:85
)
} else {
} else if (length(text) == 110) {
# Patch for stocks with 110 lines
skip_rows <- c(1:2, 19:21, 31:32, 41:44, 49, 54, 59, 64:66, 71:73, 94:95, 100:102)

Expand All @@ -534,6 +549,9 @@ tq_get_util_2 <- function(x, get, complete_cases, map, ...) {
rep("Efficiency", 8)),
group = c(1:52, 54:85)
)
} else {
# Catch unknown return from morningstar request
stop("Unexpected return from Morningstar")
}

text <- text[-skip_rows]
Expand All @@ -548,7 +566,6 @@ tq_get_util_2 <- function(x, get, complete_cases, map, ...) {
)
)


# Combine tibble parts into raw data
key_ratios_raw <- dplyr::bind_cols(key_ratios_1, key_ratios_2)

Expand All @@ -567,61 +584,70 @@ tq_get_util_2 <- function(x, get, complete_cases, map, ...) {
dplyr::select(section, sub.section, group, category, date, value)

# Calculate valuations

# Get stock prices
from = lubridate::today() - lubridate::years(12)
valuations_2 <- tq_get(x, get = "stock.prices", from = from) %>%
tq_transmute_xy(adjusted, mutate_fun = to.period, period = "years") %>%
dplyr::mutate(year = lubridate::year(date)) %>%
dplyr::select(year, date, adjusted)

# Get key ratios
valuations_1 <- key_ratios_bind %>%
dplyr::filter(section == "Financials") %>%
dplyr::filter(category %in% c("Revenue USD Mil",
"Shares Mil",
"Earnings Per Share USD",
"Book Value Per Share * USD",
"Operating Cash Flow USD Mil")) %>%
dplyr::mutate(year = lubridate::year(date)) %>%
dplyr::select(year, category, value) %>%
tidyr::spread(key = category, value = value) %>%
dplyr::mutate(`Revenue Per Share USD` = `Revenue USD Mil` / `Shares Mil`,
`Cash Flow Per Share USD` = `Operating Cash Flow USD Mil` / `Shares Mil`) %>%
dplyr::select(year,
`Earnings Per Share USD`,
`Revenue Per Share USD`,
`Book Value Per Share * USD`,
`Cash Flow Per Share USD`)

# Merge and calculate valuations
valuation <- dplyr::left_join(valuations_1, valuations_2, by = "year") %>%
dplyr::mutate(`Price to Earnings` = adjusted / `Earnings Per Share USD`,
`Price to Sales` = adjusted / `Revenue Per Share USD`,
`Price to Book` = adjusted / `Book Value Per Share * USD`,
`Price to Cash Flow` = adjusted / `Cash Flow Per Share USD`) %>%
dplyr::select(date,
`Price to Earnings`,
`Price to Sales`,
`Price to Book`,
`Price to Cash Flow`) %>%
tidyr::gather(key = category, value = value, -date) %>%
dplyr::select(category, date, value) %>%
dplyr::mutate(date = lubridate::as_date(date))

# Get last group number
last_group_num <- key_ratios_bind$group %>% max()

# Create valuation tibble and bind_rows
valuation_bind <- dplyr::bind_cols(
tibble::tibble(section = rep("Valuation Ratios", nrow(valuation))),
tibble::tibble(sub.section = rep("Valuation Ratios", nrow(valuation))),
tibble::tibble(group = rep(seq(last_group_num + 1, length.out = 4), each = 10)),
valuation)

key_ratios <- dplyr::bind_rows(key_ratios_bind, valuation_bind) %>%
dplyr::group_by(section) %>%
tidyr::nest()
currency = strsplit(text[2]," ")[[1]][2]

if (currency == "USD") {
# We can only trust stock price ratios if existing data is in USD
# Get stock prices
from = lubridate::today() - lubridate::years(12)
valuations_2 <- tq_get(x, get = "stock.prices", from = from) %>%
tq_transmute_xy(adjusted, mutate_fun = to.period, period = "years") %>%
dplyr::mutate(year = lubridate::year(date)) %>%
dplyr::select(year, date, adjusted)

# Get key ratios
valuations_1 <- key_ratios_bind %>%
dplyr::filter(section == "Financials") %>%
dplyr::filter(category %in% c("Revenue USD Mil",
"Shares Mil",
"Earnings Per Share USD",
"Book Value Per Share * USD",
"Operating Cash Flow USD Mil")) %>%
dplyr::mutate(year = lubridate::year(date)) %>%
dplyr::select(year, category, value) %>%
tidyr::spread(key = category, value = value) %>%
dplyr::mutate(`Revenue Per Share USD` = `Revenue USD Mil` / `Shares Mil`,
`Cash Flow Per Share USD` = `Operating Cash Flow USD Mil` / `Shares Mil`) %>%
dplyr::select(year,
`Earnings Per Share USD`,
`Revenue Per Share USD`,
`Book Value Per Share * USD`,
`Cash Flow Per Share USD`)

# Merge and calculate valuations
valuation <- dplyr::left_join(valuations_1, valuations_2, by = "year") %>%
dplyr::mutate(`Price to Earnings` = adjusted / `Earnings Per Share USD`,
`Price to Sales` = adjusted / `Revenue Per Share USD`,
`Price to Book` = adjusted / `Book Value Per Share * USD`,
`Price to Cash Flow` = adjusted / `Cash Flow Per Share USD`) %>%
dplyr::select(date,
`Price to Earnings`,
`Price to Sales`,
`Price to Book`,
`Price to Cash Flow`) %>%
tidyr::gather(key = category, value = value, -date) %>%
dplyr::select(category, date, value) %>%
dplyr::mutate(date = lubridate::as_date(date))

# Get last group number
last_group_num <- key_ratios_bind$group %>% max()

# Create valuation tibble and bind_rows
valuation_bind <- dplyr::bind_cols(
tibble::tibble(section = rep("Valuation Ratios", nrow(valuation))),
tibble::tibble(sub.section = rep("Valuation Ratios", nrow(valuation))),
tibble::tibble(group = rep(seq(last_group_num + 1, length.out = 4), each = 10)),
valuation)

key_ratios <- dplyr::bind_rows(key_ratios_bind, valuation_bind) %>%
dplyr::group_by(section) %>%
tidyr::nest()
} else {
# Cant calculate ratios for non-USD companies - just set to return key ratios.
key_ratios <- key_ratios_bind %>%
dplyr::group_by(section) %>%
tidyr::nest()
}

return(key_ratios)

Expand Down