R/get_price_data.R
In lorenzbr/PortfolioTracker: Track the Performance of Your Investment Portfolio
Defines functions
get_ticker_prices
get_prices_from_yahoo
update_latest_prices
append_latest_prices
append_latest_prices_db
update_prices_based_on_transactions
update_db_prices_based_on_transactions
Documented in
append_latest_prices
append_latest_prices_db
get_prices_from_yahoo
get_ticker_prices
update_db_prices_based_on_transactions
update_latest_prices
update_prices_based_on_transactions
#' Update and store prices in database based on new transactions
#'
#' @description Update and store prices in the database (currently csv files)
#' shared by all users. For each ticker, prices are written to a separate csv file.
#' Prices are updated if incoming transactions are older than the latest price
#' that is available according to a list with first and last date of a given ticker
#' (this list is stored in a csv as well). Prices are also updated if there are
#' no prices available for the given ticker.
#'
#' @usage update_db_prices_based_on_transactions(df_transactions, db_path,
#' external_search = TRUE)
#' @param df_transactions A data frame. Results from
#' \code{\link[BankStatementParser]{get_transactions}}.
#' At least three variables/columns are required: \emph{transaction_type},
#' \emph{isin} and \emph{transaction_date}
#' @param db_path A single character string containing the directory of the database.
#' @param external_search Logical; if TRUE, the function searches external
#' sources to find the ticker.
#'
#' @export
update_db_prices_based_on_transactions <- function(df_transactions, db_path,
external_search = TRUE) {
get_db_names(db_path)
## Only purchase transactions are relevant
df_transactions <- df_transactions[df_transactions$transaction_type == "Purchase", ]
isins <- unique(df_transactions$isin)
## With external search it may take very long (due to crawling a website)
update_ticker_isin(isins = isins,
path_tickers = path.database,
file_tickers = file.tickers.db,
external_search = external_search)
## Get table that converts ISIN to ticker (which is needed by Yahoo Finance)
df_isin_ticker <- data.table::fread(file.path(path.database, file.tickers.db))
df_transactions <- merge(df_transactions, df_isin_ticker,
by = "isin", all.x = TRUE)
df_transactions <- df_transactions[!is.na(df_transactions$ticker), ]
if (nrow(df_transactions) > 0) {
## Be aware of the specific date format
df_transactions$transaction_date <- as.Date(
df_transactions$transaction_date, format = "%d-%m-%Y")
## Keep oldest transactions for each ticker because younger transactions
## produce price data which are a subset of older transactions
## Base solution is more convoluted but should be FASTER than dplyr for
## small data (select minimum date and if more than one choose 1 randomly)
df_transactions <- as.data.frame(df_transactions)
col_names_transactions <- names(df_transactions)
df <- stats::aggregate(
transaction_date ~ ticker, data = df_transactions, FUN = min)
df_transactions <- merge(df_transactions, df,
by = c("ticker", "transaction_date"))
df_transactions <- do.call(rbind,
lapply(split(df_transactions,
df_transactions$ticker),
function(x) x[sample(nrow(x), 1), ]))
df_transactions <- df_transactions[, col_names_transactions]
row.names(df_transactions) <- 1:nrow(df_transactions)
df_price_range <- get_available_price_date_range(
path.database, file.ticker.price.available.db
)
today <- Sys.Date()
for (i in 1:nrow(df_transactions)) {
tryCatch({
transaction_date <- df_transactions$transaction_date[i]
ticker <- df_transactions$ticker[i]
## Select ticker, first and latest date in price available df
df_prices_same_ticker <- df_price_range[df_price_range$ticker == ticker, ]
## Initialize earliest_date (required to have an existing valid date)
earliest_date <- as.Date("1900-01-01", format = "%Y-%m-%d")
if (nrow(df_prices_same_ticker) == 1) {
earliest_date <- df_prices_same_ticker$first_date
## If new transaction is older than earliest date:
## Update prices from transaction_date until earliest_date - 1 day
if (transaction_date < earliest_date && !is.na(earliest_date))
today <- earliest_date - 1
}
## If transaction_date is older than earliest_date of existing transactions
## or no prices with same ticker exist, get prices
if (transaction_date < earliest_date || nrow(df_prices_same_ticker) == 0) {
df_ticker_prices <- get_prices_from_yahoo(ticker = ticker,
from = transaction_date,
to = today,
user_specific_exchange = FALSE)
if (!is.null(df_ticker_prices)) {
#### Update price availability date range
## If prices are already available for given ticker, update dates
## in price availability csv
from <- as.Date(min(df_ticker_prices$date), format = "%Y-%m-%d")
to <- as.Date(max(df_ticker_prices$date), format = "%Y-%m-%d")
is_current_ticker <- df_price_range$ticker == ticker
if (length(df_price_range$first_date[is_current_ticker]) > 0) {
if (from < earliest_date)
df_price_range$first_date[is_current_ticker] <- from
if (to > df_prices_same_ticker$last_date)
df_price_range$last_date[is_current_ticker] <- to
} else {
df_new <- data.frame(ticker = ticker, first_date = from,
last_date = to)
df_price_range <- rbind(df_price_range, df_new)
}
data.table::fwrite(df_price_range,
file.path(path.database,
file.ticker.price.available.db))
#### Update csv with prices
filename_prices <- paste0("prices_", ticker, ".csv")
file_path_prices <- file.path(path.prices.db, filename_prices)
## Append prices in correct order and store again as csv
if (file.exists(file_path_prices)) {
df_ticker_prices_all <- data.table::fread(file_path_prices)
## Order of row binding data frames important because this should be
## correct already
df_ticker_prices <- rbind(df_ticker_prices,
df_ticker_prices_all)
## Order by date: latest date should be first (on top) - Is it redundant
## because of rbind?
df_ticker_prices <- df_ticker_prices[order(df_ticker_prices$date), ]
}
data.table::fwrite(df_ticker_prices, file_path_prices)
}
}
}, error = function(cond) {
message("Original message:")
message(cond)
})
}
}
}
#' Update and store prices as csv based on new transactions
#'
#' @usage update_prices_based_on_transactions(df_transactions, path,
#' external_search = TRUE)
#' @param df_transactions A data frame. Results from
#' \code{\link[BankStatementParser]{get_transactions}}.
#' At least three variables/columns are required: \emph{transaction_type},
#' \emph{isin} and \emph{transaction_date}
#' @param path A single character string. Path where data are stored.
#' @param external_search Logical; if TRUE, the function searches external
#' sources to find the ticker.
#'
#' @export
#' @importFrom magrittr %>%
#' @importFrom rlang .data
update_prices_based_on_transactions <- function(df_transactions, path,
external_search = TRUE) {
get_user_names(path)
## To get prices, only purchase transactions are relevant
df_transactions <- df_transactions[df_transactions$transaction_type == "Purchase", ]
isins <- unique(df_transactions$isin)
update_ticker_isin(isins = isins,
path_tickers = path.tickers,
file_tickers = file.tickers,
external_search = external_search)
isin_ticker_exists <- file.exists(file.path(path.tickers, file.tickers))
if (isin_ticker_exists) {
## Get table that converts ISIN to ticker (which is needed by Yahoo Finance)
df_isin_ticker <- data.table::fread(file.path(path.tickers, file.tickers))
df_transactions <- merge(df_transactions, df_isin_ticker, by = "isin",
all.x = TRUE)
df_transactions <- df_transactions[!is.na(df_transactions$ticker), ]
df_transactions$transaction_date <- as.Date(df_transactions$transaction_date,
format = "%d-%m-%Y")
## Keep oldest transactions for each ticker because younger transactions
## produce price data which are a subset of older transactions
df_transactions <- df_transactions %>%
dplyr::group_by(.data$ticker) %>%
dplyr::filter(transaction_date == min(.data$transaction_date))
df_transactions <- df_transactions %>%
dplyr::group_by(.data$ticker) %>%
dplyr::sample_n(size = 1)
## Get current date
today <- Sys.Date()
## For loop over all transactions in file
## To do: alternatively, use new function and apply function (probably NOT faster)
for (i in 1:nrow(df_transactions)) {
tryCatch({
## Select transaction date and ticker
transaction_date <- df_transactions$transaction_date[i]
ticker <- df_transactions$ticker[i]
## Check whether prices for this ticker based on transaction date already exists:
## if no: continue
## if yes: 1) if the focal date is younger, don't do anything 2) if the
## focal date is older, continue
## Get all prices with same ticker
df_prices_same_ticker <- data.frame(
filename = list.files(path.prices.raw, pattern = ticker))
## Initialize earliest_date (required to have an existing valid date)
earliest_date <- as.Date("1900-01-01", format = "%Y-%m-%d")
## Identify earliest date for each of those files and compare to transaction_date
if (nrow(df_prices_same_ticker) > 0) {
df_prices_same_ticker$first_date <- stringr::str_match(
df_prices_same_ticker$filename, "from_(.*?)_to")[, 2]
df_prices_same_ticker$first_date <- as.Date(
df_prices_same_ticker$first_date, format = "%Y-%m-%d")
min_date <- min(df_prices_same_ticker$first_date)
df_prices_same_ticker <- df_prices_same_ticker[df_prices_same_ticker$first_date == min_date, ]
earliest_date <- unique(df_prices_same_ticker$first_date)
## If new transaction is older than earliest date updated "to" date
if (transaction_date < earliest_date && !is.na(earliest_date)) {
today <- earliest_date - 1
}
}
filename_data_raw_prices <- paste0("prices_ticker_", ticker, "_from_",
transaction_date, "_to_", today, ".csv")
## If transaction_date is older than earliest_date of existing transactions
## or no prices with same ticker exist, do this
if (transaction_date < earliest_date || nrow(df_prices_same_ticker) == 0) {
## Check whether such a file exists already, then no need to download again
if (!file.exists(file.path(path.prices.raw, filename_data_raw_prices))) {
df_ticker_prices <- get_prices_from_yahoo(ticker = ticker,
from = transaction_date,
to = today)
filename_data_raw_prices <- paste0("prices_ticker_", ticker,
"_from_",
min(df_ticker_prices$date),
"_to_",
max(df_ticker_prices$date),
".csv")
data.table::fwrite(df_ticker_prices, file.path(path.prices.raw,
filename_data_raw_prices))
}
}
}, error = function(cond) {
message("Original message:")
message(cond)
}
)
}
}
}
#' Append most recent prices to existing files
#'
#' @description Append prices for all tickers specified as an argument.
#' The existing csv files are used to append prices.
#'
#' @usage append_latest_prices_db(db_path, tickers)
#' @param db_path A single character string. Path where data are stored.
#' @param tickers A vector of character strings containing tickers.
#'
#' @export
append_latest_prices_db <- function(db_path, tickers) {
get_db_names(db_path)
df_price_range <- get_available_price_date_range(
path.database, file.ticker.price.available.db
)
## Limit tickers to set of user relevant tickers
df_price_range <- df_price_range[df_price_range$ticker %in% tickers, ]
if (nrow(df_price_range) > 0) {
today <- Sys.Date()
## Keep only tickers which are not up to date
df_price_range <- df_price_range[df_price_range$last_date < today, ]
## If at least one ticker is not up to date
if (nrow(df_price_range) > 0) {
## Loop over all tickers: get prices from Yahoo Finance API
for (i in 1:nrow(df_price_range)) {
skip_to_next <- FALSE
tryCatch({
## Date "from" is the last date + one day (since last date exists already)
from <- as.Date(df_price_range$last_date[i]) + 1
ticker <- df_price_range$ticker[i]
if (today > from) {
df_updated_prices <- get_prices_from_yahoo(ticker = ticker,
from = from,
to = today,
user_specific_exchange = FALSE)
## For some unknown reasons (time zones??), the date "from - 1" is
## returned -> remove this day
df_updated_prices <- df_updated_prices[df_updated_prices$date != from - 1, ]
if (!is.null(df_updated_prices) && nrow(df_updated_prices) > 0) {
## Update price availability date range
to <- as.Date(max(df_updated_prices$date), format = "%Y-%m-%d")
is_current_ticker <- df_price_range$ticker == ticker
df_price_range$last_date[is_current_ticker] <- to
data.table::fwrite(df_price_range,
file.path(path.database,
file.ticker.price.available.db))
## Append updated prices in csv with price data for current ticker
data.table::fwrite(df_updated_prices,
file.path(path.prices.db,
paste0("prices_", ticker, ".csv")),
append = TRUE)
}
}
}, error = function(e) {
skip_to_next <- TRUE
})
if (skip_to_next) next
}
}
}
}
#' Append most recent prices to existing files
#'
#' @description Append prices for all tickers in the specified path. The existing
#' csv files are used.
#'
#' @usage append_latest_prices(path)
#' @param path A single character string. Path where data are stored.
#'
#' @export
append_latest_prices <- function(path) {
## Currently existing csv file names carry the date period for which prices available
## To do: use external csv with price availability
get_user_names(path)
filename.prices.raw.with.ticker <- list.files(path.prices.raw)
if (length(filename.prices.raw.with.ticker) > 0) {
df_files_price_data <- data.frame(filename = filename.prices.raw.with.ticker)
df_files_price_data$first_date <- stringr::str_match(df_files_price_data$filename,
"from_(.*?)_to")[, 2]
df_files_price_data$last_date <- stringr::str_match(df_files_price_data$filename,
"to_(.*?).csv")[, 2]
df_files_price_data$ticker <- stringr::str_match(df_files_price_data$filename,
"ticker_(.*?)_from")[, 2]
## Keep latest date for each ticker
## To do: there should be only one file for each ticker anyways! So, no need
## to do this!?
df_files_price_data2 <- stats::aggregate(last_date ~ ticker,
data = df_files_price_data, max)
df_files_price_data <- merge(df_files_price_data, df_files_price_data2,
by = c("ticker", "last_date"))
today <- Sys.Date()
## Keep only tickers which are not up to date
df_files_price_data <- df_files_price_data[df_files_price_data$last_date < today, ]
## If at least one ticker is not up to date
if (nrow(df_files_price_data) > 0) {
## Loop over all tickers: get prices from Yahoo Finance API
for (i in 1:nrow(df_files_price_data)) {
skip_to_next <- FALSE
tryCatch({
## Date "from" is the last date + one day (since last date exists already)
from <- as.Date(df_files_price_data$last_date[i]) + 1
ticker <- df_files_price_data$ticker[i]
current_filename <- df_files_price_data$filename[i]
if (today > from) {
df_updated_prices <- get_prices_from_yahoo(ticker = ticker,
from = from,
to = today)
if (!is.null(df_updated_prices)) {
filename_prices_raw <- paste0("prices_ticker_", ticker,
"_from_", df_files_price_data$first_date[i],
"_to_", max(df_updated_prices$date),
".csv")
## Possibly too slow: better to use different approach: create a
## separate csv file with ticker, first_date and last_date
file.rename(from = file.path(path.prices.raw, current_filename),
to = file.path(path.prices.raw, filename_prices_raw))
## Append updated prices in csv with price data for current ticker
data.table::fwrite(df_updated_prices,
file.path(path.prices.raw, filename_prices_raw),
append = TRUE)
}
}
}, error = function(e) {
skip_to_next <- TRUE
})
if (skip_to_next) next
}
}
}
}
#' Update most recent prices and store in csv files
#'
#' Update prices for all tickers in the specified path. A new csv is created
#' for the updated prices. Better use \code{\link{append_latest_prices}} which is
#' more efficient.
#'
#' @usage update_latest_prices(path)
#' @param path A single character string. Path where data are stored.
#'
#' @export
update_latest_prices <- function(path) {
get_user_names(path)
filename.prices.raw.with.ticker <- list.files(path.prices.raw)
if (length(filename.prices.raw.with.ticker) > 0) {
df_files_price_data <- data.frame(filename = filename.prices.raw.with.ticker)
df_files_price_data$last_date <- stringr::str_match(df_files_price_data$filename,
"to_(.*?).csv")[, 2]
df_files_price_data$ticker <- stringr::str_match(df_files_price_data$filename,
"ticker_(.*?)_from")[, 2]
df_files_price_data <- stats::aggregate(last_date ~ ticker,
data = df_files_price_data, max)
today <- Sys.Date()
## Keep only tickers which are not up to date
df_files_price_data <- df_files_price_data[df_files_price_data$last_date < today, ]
## If at least one ticker is not up to date
if (nrow(df_files_price_data) > 0) {
for (i in 1:nrow(df_files_price_data)) {
skip_to_next <- FALSE
tryCatch({
## Date from is last date + 1 day
from <- as.Date(df_files_price_data$last_date[i]) + 1
ticker <- df_files_price_data$ticker[i]
if (today > from) {
df_updated_prices <- get_prices_from_yahoo(ticker = ticker,
from = from,
to = today)
if (!is.null(df_updated_prices)) {
filename_prices_raw <- paste0("prices_ticker_", ticker,
"_from_", min(df_updated_prices$date),
"_to_", max(df_updated_prices$date),
".csv")
data.table::fwrite(df_updated_prices, file.path(path.prices.raw,
filename_prices_raw))
}
}
}, error = function(e) {
skip_to_next <- TRUE
})
if (skip_to_next) next
}
}
}
}
#' Get prices for a given ticker from the Yahoo Finance API
#'
#' @description Get prices for a given ticker from the Yahoo Finance API. Wrapper around
#' \code{\link{get_ticker_prices}} with further arguments. E.g., select the stock exchange.
#'
#' @usage get_prices_from_yahoo(ticker, from, to, preferred_exchange = "Xetra",
#' stock_exchanges = c(".DE", ".F", ".SG", ".MU", ".DU"),
#' method = "simple", user_specific_exchange = TRUE)
#' @param ticker A single character string. Ticker symbol.
#' @param from A single character string. Start date.
#' @param to A single character string. End date.
#' @param preferred_exchange A single character string. Stock exchange
#' (default is \emph{Xetra})
#' @param stock_exchanges A vector of single character strings. Possible stock
#' exchanges to get prices from.
#' @param method A single character string (default: \emph{simple}). The method
#' how to get prices from Yahoo. Use the R package \emph{quantmod} or a \emph{simple}
#' and fast solution.
#' @param user_specific_exchange Logical indicating whether prices are taken from
#' exchange that is preferred by the user (i.e., stored in the user's profile).
#' Default is \emph{TRUE}.
#'
#' @return A data frame containing dates, prices (open, high, low, close, adjusted) and volume.
#'
#' @export
get_prices_from_yahoo <- function(ticker, from, to, preferred_exchange = "Xetra",
stock_exchanges = c(".DE", ".F", ".SG",
".MU", ".DU"),
method = "simple",
user_specific_exchange = TRUE) {
if (user_specific_exchange) {
path_tick <- path.tickers
file_tick_ex <- file.ticker.exchange
} else if (!user_specific_exchange) {
path_tick <- path.database
file_tick_ex <- file.ticker.exchange.db
}
## Check if specific exchange for ticker has been saved in file.
## Then, takes this one
ticker_exchange_file_exists <- file.exists(file.path(path_tick, file_tick_ex))
## Initiate: ticker exchange pair does not exists (in the following code
## this will be checked)
ticker_exchange_pair_exists <- FALSE
if (ticker_exchange_file_exists) {
df_ticker_exchanges <- data.table::fread(file.path(path_tick, file_tick_ex))
## Needed because using "ticker" produces same data frame (?)
ticker_current <- ticker
df_ticker_exchange <- df_ticker_exchanges[df_ticker_exchanges$ticker == ticker_current, ]
ticker_exchange_pair_exists <- nrow(df_ticker_exchange) == 1
if (ticker_exchange_pair_exists) {
stock_exchange <- df_ticker_exchange$exchange
ticker_yahoo <- paste0(ticker, stock_exchange)
}
}
if (!exists("ticker_yahoo")) {
## Produce final ticker for Yahoo Finance
if (preferred_exchange == "Xetra") {
stock_exchange <- ".DE"
} else if (preferred_exchange == "Frankfurt") {
stock_exchange <- ".F"
} else if (preferred_exchange == "Stuttgart") {
stock_exchange <- ".SG"
} else if (preferred_exchange == "Muenchen") {
stock_exchange <- ".MU"
} else if (preferred_exchange == "Duesseldorf") {
stock_exchange <- ".DU"
}
ticker_yahoo <- paste0(ticker, stock_exchange)
}
## Get prices from Yahoo API
try({
if (method == "quantmod") {
ticker_prices <- quantmod::getSymbols(ticker_yahoo,
from = from,
to = to,
auto.assign = FALSE,
warnings = FALSE)
} else if (method == "simple") {
ticker_prices <- get_ticker_prices(ticker_yahoo,
from = from,
to = to)
}
})
## If not yet found and ticker with exchanges does not exist, iterate over all
## other stock exchanges to get prices
if (!ticker_exchange_file_exists && !exists("ticker_prices")) {
iter_stock_exchanges <- 1
while (!exists("ticker_prices") && iter_stock_exchanges <= length(stock_exchanges)) {
stock_exchange <- stock_exchanges[iter_stock_exchanges]
ticker_yahoo <- paste0(ticker, stock_exchange)
iter_stock_exchanges <- iter_stock_exchanges + 1
try({
if (method == "quantmod") {
ticker_prices <- quantmod::getSymbols(ticker_yahoo,
from = from,
to = to,
auto.assign = FALSE,
warnings = FALSE)
} else if (method == "simple") {
ticker_prices <- get_ticker_prices(ticker_yahoo,
from = from,
to = to)
}
})
}
} else if (ticker_exchange_file_exists) {
if (nrow(df_ticker_exchange) != 1) {
iter_stock_exchanges <- 1
while (!exists("ticker_prices") &&
iter_stock_exchanges <= length(stock_exchanges)) {
stock_exchange <- stock_exchanges[iter_stock_exchanges]
ticker_yahoo <- paste0(ticker, stock_exchange)
iter_stock_exchanges <- iter_stock_exchanges + 1
try({
if (method == "quantmod") {
ticker_prices <- quantmod::getSymbols(Symbols = ticker_yahoo,
from = from, to = to,
auto.assign = FALSE,
warnings = FALSE)
} else if (method == "simple") {
ticker_prices <- get_ticker_prices(ticker = ticker_yahoo,
from = from,
to = to)
}
})
}
}
}
if (exists("ticker_prices")) {
## Store stock exchange and ticker in csv file if it not yet exists
df_ticker_exchange <- data.frame(ticker = ticker, exchange = stock_exchange)
if (ticker_exchange_file_exists && !ticker_exchange_pair_exists) {
df_ticker_exchanges <- data.table::fread(file.path(path_tick, file_tick_ex))
df_ticker_exchanges <- rbind(df_ticker_exchanges, df_ticker_exchange)
df_ticker_exchanges <- unique(df_ticker_exchanges)
data.table::fwrite(df_ticker_exchanges, file.path(path_tick, file_tick_ex))
} else if (!ticker_exchange_file_exists) {
data.table::fwrite(df_ticker_exchange, file.path(path_tick, file_tick_ex))
}
## Convert time series with prices to data frame
df_ticker_prices <- data.frame(ticker_prices)
if (method == "quantmod") {
names(df_ticker_prices) <- gsub(pattern = paste0(ticker_yahoo, "\\."),
replacement = "",
names(df_ticker_prices))
df_ticker_prices$date <- rownames(df_ticker_prices)
rownames(df_ticker_prices) <- 1:nrow(df_ticker_prices)
} else if (method == "simple") {
names(df_ticker_prices)[startsWith(names(df_ticker_prices), "Adj")] <- "Adjusted"
## Columns of data frame need to have same order as quantmod method
df_ticker_prices <- df_ticker_prices[, c("Open", "High", "Low", "Close",
"Volume", "Adjusted", "Date")]
}
names(df_ticker_prices) <- tolower(names(df_ticker_prices))
df_ticker_prices$date <- as.Date(df_ticker_prices$date)
df_ticker_prices <- df_ticker_prices[!is.na(df_ticker_prices$adjusted), ]
df_ticker_prices <- df_ticker_prices[!is.null(df_ticker_prices$adjusted), ]
df_ticker_prices <- df_ticker_prices[df_ticker_prices$adjusted != "NA", ]
df_ticker_prices <- df_ticker_prices[df_ticker_prices$adjusted != "null", ]
return(df_ticker_prices)
} else if (!exists("ticker_prices")) {
return(NULL)
}
}
#' Get prices from the Yahoo Finance API fast and simple
#'
#' @usage get_ticker_prices(ticker, from, to)
#' @param ticker A single character string. Ticker symbol.
#' @param from A single character string. Start date.
#' @param to A single character string. End date.
#'
#' @return A data frame containing dates, prices (open, high, low, close, adjusted) and volume.
#'
#' @export
get_ticker_prices <- function(ticker, from, to) {
## See https://www.datasciencecentral.com/getting-historical-data-from-yahoo-finance-in-r/
## Be careful with excessive downloads because no limitation is included in this function
url <- paste0("https://query1.finance.yahoo.com/v7/finance/download/",
ticker,
"?period1=",
as.integer(as.POSIXct(from)),
"&period2=",
as.integer(as.POSIXct(to)),
"&interval=1d&events=history")
df <- data.table::fread(url)
return(df)
}
lorenzbr/PortfolioTracker documentation built on Feb. 11, 2023, 8:27 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions get_ticker_prices get_prices_from_yahoo update_latest_prices append_latest_prices append_latest_prices_db update_prices_based_on_transactions update_db_prices_based_on_transactions
Documented in append_latest_prices append_latest_prices_db get_prices_from_yahoo get_ticker_prices update_db_prices_based_on_transactions update_latest_prices update_prices_based_on_transactions
#' Update and store prices in database based on new transactions
#'
#' @description Update and store prices in the database (currently csv files)
#' shared by all users. For each ticker, prices are written to a separate csv file.
#' Prices are updated if incoming transactions are older than the latest price
#' that is available according to a list with first and last date of a given ticker
#' (this list is stored in a csv as well). Prices are also updated if there are
#' no prices available for the given ticker.
#'
#' @usage update_db_prices_based_on_transactions(df_transactions, db_path,
#' external_search = TRUE)
#' @param df_transactions A data frame. Results from
#' \code{\link[BankStatementParser]{get_transactions}}.
#' At least three variables/columns are required: \emph{transaction_type},
#' \emph{isin} and \emph{transaction_date}
#' @param db_path A single character string containing the directory of the database.
#' @param external_search Logical; if TRUE, the function searches external
#' sources to find the ticker.
#'
#' @export
update_db_prices_based_on_transactions <- function(df_transactions, db_path,
external_search = TRUE) {
get_db_names(db_path)
## Only purchase transactions are relevant
df_transactions <- df_transactions[df_transactions$transaction_type == "Purchase", ]
isins <- unique(df_transactions$isin)
## With external search it may take very long (due to crawling a website)
update_ticker_isin(isins = isins,
path_tickers = path.database,
file_tickers = file.tickers.db,
external_search = external_search)
## Get table that converts ISIN to ticker (which is needed by Yahoo Finance)
df_isin_ticker <- data.table::fread(file.path(path.database, file.tickers.db))
df_transactions <- merge(df_transactions, df_isin_ticker,
by = "isin", all.x = TRUE)
df_transactions <- df_transactions[!is.na(df_transactions$ticker), ]
if (nrow(df_transactions) > 0) {
## Be aware of the specific date format
df_transactions$transaction_date <- as.Date(
df_transactions$transaction_date, format = "%d-%m-%Y")
## Keep oldest transactions for each ticker because younger transactions
## produce price data which are a subset of older transactions
## Base solution is more convoluted but should be FASTER than dplyr for
## small data (select minimum date and if more than one choose 1 randomly)
df_transactions <- as.data.frame(df_transactions)
col_names_transactions <- names(df_transactions)
df <- stats::aggregate(
transaction_date ~ ticker, data = df_transactions, FUN = min)
df_transactions <- merge(df_transactions, df,
by = c("ticker", "transaction_date"))
df_transactions <- do.call(rbind,
lapply(split(df_transactions,
df_transactions$ticker),
function(x) x[sample(nrow(x), 1), ]))
df_transactions <- df_transactions[, col_names_transactions]
row.names(df_transactions) <- 1:nrow(df_transactions)
df_price_range <- get_available_price_date_range(
path.database, file.ticker.price.available.db
)
today <- Sys.Date()
for (i in 1:nrow(df_transactions)) {
tryCatch({
transaction_date <- df_transactions$transaction_date[i]
ticker <- df_transactions$ticker[i]
## Select ticker, first and latest date in price available df
df_prices_same_ticker <- df_price_range[df_price_range$ticker == ticker, ]
## Initialize earliest_date (required to have an existing valid date)
earliest_date <- as.Date("1900-01-01", format = "%Y-%m-%d")
if (nrow(df_prices_same_ticker) == 1) {
earliest_date <- df_prices_same_ticker$first_date
## If new transaction is older than earliest date:
## Update prices from transaction_date until earliest_date - 1 day
if (transaction_date < earliest_date && !is.na(earliest_date))
today <- earliest_date - 1
}
## If transaction_date is older than earliest_date of existing transactions
## or no prices with same ticker exist, get prices
if (transaction_date < earliest_date || nrow(df_prices_same_ticker) == 0) {
df_ticker_prices <- get_prices_from_yahoo(ticker = ticker,
from = transaction_date,
to = today,
user_specific_exchange = FALSE)
if (!is.null(df_ticker_prices)) {
#### Update price availability date range
## If prices are already available for given ticker, update dates
## in price availability csv
from <- as.Date(min(df_ticker_prices$date), format = "%Y-%m-%d")
to <- as.Date(max(df_ticker_prices$date), format = "%Y-%m-%d")
is_current_ticker <- df_price_range$ticker == ticker
if (length(df_price_range$first_date[is_current_ticker]) > 0) {
if (from < earliest_date)
df_price_range$first_date[is_current_ticker] <- from
if (to > df_prices_same_ticker$last_date)
df_price_range$last_date[is_current_ticker] <- to
} else {
df_new <- data.frame(ticker = ticker, first_date = from,
last_date = to)
df_price_range <- rbind(df_price_range, df_new)
}
data.table::fwrite(df_price_range,
file.path(path.database,
file.ticker.price.available.db))
#### Update csv with prices
filename_prices <- paste0("prices_", ticker, ".csv")
file_path_prices <- file.path(path.prices.db, filename_prices)
## Append prices in correct order and store again as csv
if (file.exists(file_path_prices)) {
df_ticker_prices_all <- data.table::fread(file_path_prices)
## Order of row binding data frames important because this should be
## correct already
df_ticker_prices <- rbind(df_ticker_prices,
df_ticker_prices_all)
## Order by date: latest date should be first (on top) - Is it redundant
## because of rbind?
df_ticker_prices <- df_ticker_prices[order(df_ticker_prices$date), ]
}
data.table::fwrite(df_ticker_prices, file_path_prices)
}
}
}, error = function(cond) {
message("Original message:")
message(cond)
})
}
}
}
#' Update and store prices as csv based on new transactions
#'
#' @usage update_prices_based_on_transactions(df_transactions, path,
#' external_search = TRUE)
#' @param df_transactions A data frame. Results from
#' \code{\link[BankStatementParser]{get_transactions}}.
#' At least three variables/columns are required: \emph{transaction_type},
#' \emph{isin} and \emph{transaction_date}
#' @param path A single character string. Path where data are stored.
#' @param external_search Logical; if TRUE, the function searches external
#' sources to find the ticker.
#'
#' @export
#' @importFrom magrittr %>%
#' @importFrom rlang .data
update_prices_based_on_transactions <- function(df_transactions, path,
external_search = TRUE) {
get_user_names(path)
## To get prices, only purchase transactions are relevant
df_transactions <- df_transactions[df_transactions$transaction_type == "Purchase", ]
isins <- unique(df_transactions$isin)
update_ticker_isin(isins = isins,
path_tickers = path.tickers,
file_tickers = file.tickers,
external_search = external_search)
isin_ticker_exists <- file.exists(file.path(path.tickers, file.tickers))
if (isin_ticker_exists) {
## Get table that converts ISIN to ticker (which is needed by Yahoo Finance)
df_isin_ticker <- data.table::fread(file.path(path.tickers, file.tickers))
df_transactions <- merge(df_transactions, df_isin_ticker, by = "isin",
all.x = TRUE)
df_transactions <- df_transactions[!is.na(df_transactions$ticker), ]
df_transactions$transaction_date <- as.Date(df_transactions$transaction_date,
format = "%d-%m-%Y")
## Keep oldest transactions for each ticker because younger transactions
## produce price data which are a subset of older transactions
df_transactions <- df_transactions %>%
dplyr::group_by(.data$ticker) %>%
dplyr::filter(transaction_date == min(.data$transaction_date))
df_transactions <- df_transactions %>%
dplyr::group_by(.data$ticker) %>%
dplyr::sample_n(size = 1)
## Get current date
today <- Sys.Date()
## For loop over all transactions in file
## To do: alternatively, use new function and apply function (probably NOT faster)
for (i in 1:nrow(df_transactions)) {
tryCatch({
## Select transaction date and ticker
transaction_date <- df_transactions$transaction_date[i]
ticker <- df_transactions$ticker[i]
## Check whether prices for this ticker based on transaction date already exists:
## if no: continue
## if yes: 1) if the focal date is younger, don't do anything 2) if the
## focal date is older, continue
## Get all prices with same ticker
df_prices_same_ticker <- data.frame(
filename = list.files(path.prices.raw, pattern = ticker))
## Initialize earliest_date (required to have an existing valid date)
earliest_date <- as.Date("1900-01-01", format = "%Y-%m-%d")
## Identify earliest date for each of those files and compare to transaction_date
if (nrow(df_prices_same_ticker) > 0) {
df_prices_same_ticker$first_date <- stringr::str_match(
df_prices_same_ticker$filename, "from_(.*?)_to")[, 2]
df_prices_same_ticker$first_date <- as.Date(
df_prices_same_ticker$first_date, format = "%Y-%m-%d")
min_date <- min(df_prices_same_ticker$first_date)
df_prices_same_ticker <- df_prices_same_ticker[df_prices_same_ticker$first_date == min_date, ]
earliest_date <- unique(df_prices_same_ticker$first_date)
## If new transaction is older than earliest date updated "to" date
if (transaction_date < earliest_date && !is.na(earliest_date)) {
today <- earliest_date - 1
}
}
filename_data_raw_prices <- paste0("prices_ticker_", ticker, "_from_",
transaction_date, "_to_", today, ".csv")
## If transaction_date is older than earliest_date of existing transactions
## or no prices with same ticker exist, do this
if (transaction_date < earliest_date || nrow(df_prices_same_ticker) == 0) {
## Check whether such a file exists already, then no need to download again
if (!file.exists(file.path(path.prices.raw, filename_data_raw_prices))) {
df_ticker_prices <- get_prices_from_yahoo(ticker = ticker,
from = transaction_date,
to = today)
filename_data_raw_prices <- paste0("prices_ticker_", ticker,
"_from_",
min(df_ticker_prices$date),
"_to_",
max(df_ticker_prices$date),
".csv")
data.table::fwrite(df_ticker_prices, file.path(path.prices.raw,
filename_data_raw_prices))
}
}
}, error = function(cond) {
message("Original message:")
message(cond)
}
)
}
}
}
#' Append most recent prices to existing files
#'
#' @description Append prices for all tickers specified as an argument.
#' The existing csv files are used to append prices.
#'
#' @usage append_latest_prices_db(db_path, tickers)
#' @param db_path A single character string. Path where data are stored.
#' @param tickers A vector of character strings containing tickers.
#'
#' @export
append_latest_prices_db <- function(db_path, tickers) {
get_db_names(db_path)
df_price_range <- get_available_price_date_range(
path.database, file.ticker.price.available.db
)
## Limit tickers to set of user relevant tickers
df_price_range <- df_price_range[df_price_range$ticker %in% tickers, ]
if (nrow(df_price_range) > 0) {
today <- Sys.Date()
## Keep only tickers which are not up to date
df_price_range <- df_price_range[df_price_range$last_date < today, ]
## If at least one ticker is not up to date
if (nrow(df_price_range) > 0) {
## Loop over all tickers: get prices from Yahoo Finance API
for (i in 1:nrow(df_price_range)) {
skip_to_next <- FALSE
tryCatch({
## Date "from" is the last date + one day (since last date exists already)
from <- as.Date(df_price_range$last_date[i]) + 1
ticker <- df_price_range$ticker[i]
if (today > from) {
df_updated_prices <- get_prices_from_yahoo(ticker = ticker,
from = from,
to = today,
user_specific_exchange = FALSE)
## For some unknown reasons (time zones??), the date "from - 1" is
## returned -> remove this day
df_updated_prices <- df_updated_prices[df_updated_prices$date != from - 1, ]
if (!is.null(df_updated_prices) && nrow(df_updated_prices) > 0) {
## Update price availability date range
to <- as.Date(max(df_updated_prices$date), format = "%Y-%m-%d")
is_current_ticker <- df_price_range$ticker == ticker
df_price_range$last_date[is_current_ticker] <- to
data.table::fwrite(df_price_range,
file.path(path.database,
file.ticker.price.available.db))
## Append updated prices in csv with price data for current ticker
data.table::fwrite(df_updated_prices,
file.path(path.prices.db,
paste0("prices_", ticker, ".csv")),
append = TRUE)
}
}
}, error = function(e) {
skip_to_next <- TRUE
})
if (skip_to_next) next
}
}
}
}
#' Append most recent prices to existing files
#'
#' @description Append prices for all tickers in the specified path. The existing
#' csv files are used.
#'
#' @usage append_latest_prices(path)
#' @param path A single character string. Path where data are stored.
#'
#' @export
append_latest_prices <- function(path) {
## Currently existing csv file names carry the date period for which prices available
## To do: use external csv with price availability
get_user_names(path)
filename.prices.raw.with.ticker <- list.files(path.prices.raw)
if (length(filename.prices.raw.with.ticker) > 0) {
df_files_price_data <- data.frame(filename = filename.prices.raw.with.ticker)
df_files_price_data$first_date <- stringr::str_match(df_files_price_data$filename,
"from_(.*?)_to")[, 2]
df_files_price_data$last_date <- stringr::str_match(df_files_price_data$filename,
"to_(.*?).csv")[, 2]
df_files_price_data$ticker <- stringr::str_match(df_files_price_data$filename,
"ticker_(.*?)_from")[, 2]
## Keep latest date for each ticker
## To do: there should be only one file for each ticker anyways! So, no need
## to do this!?
df_files_price_data2 <- stats::aggregate(last_date ~ ticker,
data = df_files_price_data, max)
df_files_price_data <- merge(df_files_price_data, df_files_price_data2,
by = c("ticker", "last_date"))
today <- Sys.Date()
## Keep only tickers which are not up to date
df_files_price_data <- df_files_price_data[df_files_price_data$last_date < today, ]
## If at least one ticker is not up to date
if (nrow(df_files_price_data) > 0) {
## Loop over all tickers: get prices from Yahoo Finance API
for (i in 1:nrow(df_files_price_data)) {
skip_to_next <- FALSE
tryCatch({
## Date "from" is the last date + one day (since last date exists already)
from <- as.Date(df_files_price_data$last_date[i]) + 1
ticker <- df_files_price_data$ticker[i]
current_filename <- df_files_price_data$filename[i]
if (today > from) {
df_updated_prices <- get_prices_from_yahoo(ticker = ticker,
from = from,
to = today)
if (!is.null(df_updated_prices)) {
filename_prices_raw <- paste0("prices_ticker_", ticker,
"_from_", df_files_price_data$first_date[i],
"_to_", max(df_updated_prices$date),
".csv")
## Possibly too slow: better to use different approach: create a
## separate csv file with ticker, first_date and last_date
file.rename(from = file.path(path.prices.raw, current_filename),
to = file.path(path.prices.raw, filename_prices_raw))
## Append updated prices in csv with price data for current ticker
data.table::fwrite(df_updated_prices,
file.path(path.prices.raw, filename_prices_raw),
append = TRUE)
}
}
}, error = function(e) {
skip_to_next <- TRUE
})
if (skip_to_next) next
}
}
}
}
#' Update most recent prices and store in csv files
#'
#' Update prices for all tickers in the specified path. A new csv is created
#' for the updated prices. Better use \code{\link{append_latest_prices}} which is
#' more efficient.
#'
#' @usage update_latest_prices(path)
#' @param path A single character string. Path where data are stored.
#'
#' @export
update_latest_prices <- function(path) {
get_user_names(path)
filename.prices.raw.with.ticker <- list.files(path.prices.raw)
if (length(filename.prices.raw.with.ticker) > 0) {
df_files_price_data <- data.frame(filename = filename.prices.raw.with.ticker)
df_files_price_data$last_date <- stringr::str_match(df_files_price_data$filename,
"to_(.*?).csv")[, 2]
df_files_price_data$ticker <- stringr::str_match(df_files_price_data$filename,
"ticker_(.*?)_from")[, 2]
df_files_price_data <- stats::aggregate(last_date ~ ticker,
data = df_files_price_data, max)
today <- Sys.Date()
## Keep only tickers which are not up to date
df_files_price_data <- df_files_price_data[df_files_price_data$last_date < today, ]
## If at least one ticker is not up to date
if (nrow(df_files_price_data) > 0) {
for (i in 1:nrow(df_files_price_data)) {
skip_to_next <- FALSE
tryCatch({
## Date from is last date + 1 day
from <- as.Date(df_files_price_data$last_date[i]) + 1
ticker <- df_files_price_data$ticker[i]
if (today > from) {
df_updated_prices <- get_prices_from_yahoo(ticker = ticker,
from = from,
to = today)
if (!is.null(df_updated_prices)) {
filename_prices_raw <- paste0("prices_ticker_", ticker,
"_from_", min(df_updated_prices$date),
"_to_", max(df_updated_prices$date),
".csv")
data.table::fwrite(df_updated_prices, file.path(path.prices.raw,
filename_prices_raw))
}
}
}, error = function(e) {
skip_to_next <- TRUE
})
if (skip_to_next) next
}
}
}
}
#' Get prices for a given ticker from the Yahoo Finance API
#'
#' @description Get prices for a given ticker from the Yahoo Finance API. Wrapper around
#' \code{\link{get_ticker_prices}} with further arguments. E.g., select the stock exchange.
#'
#' @usage get_prices_from_yahoo(ticker, from, to, preferred_exchange = "Xetra",
#' stock_exchanges = c(".DE", ".F", ".SG", ".MU", ".DU"),
#' method = "simple", user_specific_exchange = TRUE)
#' @param ticker A single character string. Ticker symbol.
#' @param from A single character string. Start date.
#' @param to A single character string. End date.
#' @param preferred_exchange A single character string. Stock exchange
#' (default is \emph{Xetra})
#' @param stock_exchanges A vector of single character strings. Possible stock
#' exchanges to get prices from.
#' @param method A single character string (default: \emph{simple}). The method
#' how to get prices from Yahoo. Use the R package \emph{quantmod} or a \emph{simple}
#' and fast solution.
#' @param user_specific_exchange Logical indicating whether prices are taken from
#' exchange that is preferred by the user (i.e., stored in the user's profile).
#' Default is \emph{TRUE}.
#'
#' @return A data frame containing dates, prices (open, high, low, close, adjusted) and volume.
#'
#' @export
get_prices_from_yahoo <- function(ticker, from, to, preferred_exchange = "Xetra",
stock_exchanges = c(".DE", ".F", ".SG",
".MU", ".DU"),
method = "simple",
user_specific_exchange = TRUE) {
if (user_specific_exchange) {
path_tick <- path.tickers
file_tick_ex <- file.ticker.exchange
} else if (!user_specific_exchange) {
path_tick <- path.database
file_tick_ex <- file.ticker.exchange.db
}
## Check if specific exchange for ticker has been saved in file.
## Then, takes this one
ticker_exchange_file_exists <- file.exists(file.path(path_tick, file_tick_ex))
## Initiate: ticker exchange pair does not exists (in the following code
## this will be checked)
ticker_exchange_pair_exists <- FALSE
if (ticker_exchange_file_exists) {
df_ticker_exchanges <- data.table::fread(file.path(path_tick, file_tick_ex))
## Needed because using "ticker" produces same data frame (?)
ticker_current <- ticker
df_ticker_exchange <- df_ticker_exchanges[df_ticker_exchanges$ticker == ticker_current, ]
ticker_exchange_pair_exists <- nrow(df_ticker_exchange) == 1
if (ticker_exchange_pair_exists) {
stock_exchange <- df_ticker_exchange$exchange
ticker_yahoo <- paste0(ticker, stock_exchange)
}
}
if (!exists("ticker_yahoo")) {
## Produce final ticker for Yahoo Finance
if (preferred_exchange == "Xetra") {
stock_exchange <- ".DE"
} else if (preferred_exchange == "Frankfurt") {
stock_exchange <- ".F"
} else if (preferred_exchange == "Stuttgart") {
stock_exchange <- ".SG"
} else if (preferred_exchange == "Muenchen") {
stock_exchange <- ".MU"
} else if (preferred_exchange == "Duesseldorf") {
stock_exchange <- ".DU"
}
ticker_yahoo <- paste0(ticker, stock_exchange)
}
## Get prices from Yahoo API
try({
if (method == "quantmod") {
ticker_prices <- quantmod::getSymbols(ticker_yahoo,
from = from,
to = to,
auto.assign = FALSE,
warnings = FALSE)
} else if (method == "simple") {
ticker_prices <- get_ticker_prices(ticker_yahoo,
from = from,
to = to)
}
})
## If not yet found and ticker with exchanges does not exist, iterate over all
## other stock exchanges to get prices
if (!ticker_exchange_file_exists && !exists("ticker_prices")) {
iter_stock_exchanges <- 1
while (!exists("ticker_prices") && iter_stock_exchanges <= length(stock_exchanges)) {
stock_exchange <- stock_exchanges[iter_stock_exchanges]
ticker_yahoo <- paste0(ticker, stock_exchange)
iter_stock_exchanges <- iter_stock_exchanges + 1
try({
if (method == "quantmod") {
ticker_prices <- quantmod::getSymbols(ticker_yahoo,
from = from,
to = to,
auto.assign = FALSE,
warnings = FALSE)
} else if (method == "simple") {
ticker_prices <- get_ticker_prices(ticker_yahoo,
from = from,
to = to)
}
})
}
} else if (ticker_exchange_file_exists) {
if (nrow(df_ticker_exchange) != 1) {
iter_stock_exchanges <- 1
while (!exists("ticker_prices") &&
iter_stock_exchanges <= length(stock_exchanges)) {
stock_exchange <- stock_exchanges[iter_stock_exchanges]
ticker_yahoo <- paste0(ticker, stock_exchange)
iter_stock_exchanges <- iter_stock_exchanges + 1
try({
if (method == "quantmod") {
ticker_prices <- quantmod::getSymbols(Symbols = ticker_yahoo,
from = from, to = to,
auto.assign = FALSE,
warnings = FALSE)
} else if (method == "simple") {
ticker_prices <- get_ticker_prices(ticker = ticker_yahoo,
from = from,
to = to)
}
})
}
}
}
if (exists("ticker_prices")) {
## Store stock exchange and ticker in csv file if it not yet exists
df_ticker_exchange <- data.frame(ticker = ticker, exchange = stock_exchange)
if (ticker_exchange_file_exists && !ticker_exchange_pair_exists) {
df_ticker_exchanges <- data.table::fread(file.path(path_tick, file_tick_ex))
df_ticker_exchanges <- rbind(df_ticker_exchanges, df_ticker_exchange)
df_ticker_exchanges <- unique(df_ticker_exchanges)
data.table::fwrite(df_ticker_exchanges, file.path(path_tick, file_tick_ex))
} else if (!ticker_exchange_file_exists) {
data.table::fwrite(df_ticker_exchange, file.path(path_tick, file_tick_ex))
}
## Convert time series with prices to data frame
df_ticker_prices <- data.frame(ticker_prices)
if (method == "quantmod") {
names(df_ticker_prices) <- gsub(pattern = paste0(ticker_yahoo, "\\."),
replacement = "",
names(df_ticker_prices))
df_ticker_prices$date <- rownames(df_ticker_prices)
rownames(df_ticker_prices) <- 1:nrow(df_ticker_prices)
} else if (method == "simple") {
names(df_ticker_prices)[startsWith(names(df_ticker_prices), "Adj")] <- "Adjusted"
## Columns of data frame need to have same order as quantmod method
df_ticker_prices <- df_ticker_prices[, c("Open", "High", "Low", "Close",
"Volume", "Adjusted", "Date")]
}
names(df_ticker_prices) <- tolower(names(df_ticker_prices))
df_ticker_prices$date <- as.Date(df_ticker_prices$date)
df_ticker_prices <- df_ticker_prices[!is.na(df_ticker_prices$adjusted), ]
df_ticker_prices <- df_ticker_prices[!is.null(df_ticker_prices$adjusted), ]
df_ticker_prices <- df_ticker_prices[df_ticker_prices$adjusted != "NA", ]
df_ticker_prices <- df_ticker_prices[df_ticker_prices$adjusted != "null", ]
return(df_ticker_prices)
} else if (!exists("ticker_prices")) {
return(NULL)
}
}
#' Get prices from the Yahoo Finance API fast and simple
#'
#' @usage get_ticker_prices(ticker, from, to)
#' @param ticker A single character string. Ticker symbol.
#' @param from A single character string. Start date.
#' @param to A single character string. End date.
#'
#' @return A data frame containing dates, prices (open, high, low, close, adjusted) and volume.
#'
#' @export
get_ticker_prices <- function(ticker, from, to) {
## See https://www.datasciencecentral.com/getting-historical-data-from-yahoo-finance-in-r/
## Be careful with excessive downloads because no limitation is included in this function
url <- paste0("https://query1.finance.yahoo.com/v7/finance/download/",
ticker,
"?period1=",
as.integer(as.POSIXct(from)),
"&period2=",
as.integer(as.POSIXct(to)),
"&interval=1d&events=history")
df <- data.table::fread(url)
return(df)
}
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.