fixed issue #19

NEWS.md

@@ -1,7 +1,8 @@
 ## Version 2.5.9 (2020-11-17)
 
 - removed rownames from output ([issue 18](https://github.com/msperlin/BatchGetSymbols/issues/18))
-- Fixed bug in NA price
+- Made sure that, on "weekly" mode, the first day of week is a monday ([issue 19](https://github.com/msperlin/BatchGetSymbols/issues/19))
+- Added input how.to.aggregate that will allow the user to aggregate de data by the last or first prices of intervals ([issue 19](https://github.com/msperlin/BatchGetSymbols/issues/19))
 
 ## Version 2.5.8 (2020-05-08)
 

R/BatchGetSymbols.R

@@ -16,6 +16,7 @@
 #' @param bench.ticker The ticker of the benchmark asset used to compare dates. My suggestion is to use the main stock index of the market from where the data is coming from (default = ^GSPC (SP500, US market))
 #' @param type.return Type of price return to calculate: 'arit' (default) - aritmetic, 'log' - log returns.
 #' @param freq.data Frequency of financial data ('daily', 'weekly', 'monthly', 'yearly')
+#' @param how.to.aggregate defines whether to aggregate the data using the first observations of the period or last ('first', 'last')
 #' @param thresh.bad.data A percentage threshold for defining bad data. The dates of the benchmark ticker are compared to each asset. If the percentage of non-missing dates
 #'  with respect to the benchmark ticker is lower than thresh.bad.data, the function will ignore the asset (default = 0.75)
 #' @param do.complete.data Return a complete/balanced dataset? If TRUE, all missing pairs of ticker-date will be replaced by NA or closest price (see input do.fill.missing.prices). Default = FALSE.
@@ -51,6 +52,7 @@ BatchGetSymbols <- function(tickers,
                             bench.ticker = '^GSPC',
                             type.return = 'arit',
                             freq.data = 'daily',
+                            how.to.aggregate = 'first',
                             do.complete.data = FALSE,
                             do.fill.missing.prices = TRUE,
                             do.cache = TRUE,
@@ -72,6 +74,11 @@ BatchGetSymbols <- function(tickers,
     stop(paste0('Input type.ret should be one of:\n\n', paste0(possible.values, collapse = '\n')))
   }
 
+  possible.values <- c('first', 'last')
+  if (!any(how.to.aggregate %in% possible.values)) {
+    stop(paste0('Input how.to.aggregate should be one of:\n\n', paste0(possible.values, collapse = '\n')))
+  }
+
   # check for NA
   if (any(is.na(tickers))) {
     my.msg <- paste0('Found NA value in ticker vector.',
@@ -238,26 +245,52 @@ BatchGetSymbols <- function(tickers,
                        'monthly' = '1 month',
                        'yearly' = '1 year')
 
-    week.vec <- seq(as.Date(paste0(lubridate::year(min(df.tickers$ref.date)), '-01-01')),
+    # find the first monday (see issue #19)
+    # https://github.com/msperlin/BatchGetSymbols/issues/19
+    temp_dates <- seq(as.Date(paste0(lubridate::year(min(df.tickers$ref.date)), '-01-01')),
+                      as.Date(paste0(lubridate::year(max(df.tickers$ref.date))+1, '-12-31')),
+                      by = '1 day')
+
+    temp_weekdays <- lubridate::wday(temp_dates, week_start = 1)
+    first_idx <- min(which(temp_weekdays == 1))
+    first_monday <- temp_dates[first_idx]
+
+    week.vec <- seq(first_monday,
                     as.Date(paste0(lubridate::year(max(df.tickers$ref.date))+1, '-12-31')),
                     by = str.freq)
 
     df.tickers$time.groups <- cut(x = df.tickers$ref.date, breaks = week.vec, right = FALSE)
 
     # set NULL vars for CRAN check: "no visible binding..."
     time.groups <- volume <- price.open <- price.close <- price.adjusted <- NULL
+    price.high <- price.low <- NULL
+
+    if (how.to.aggregate == 'first') {
+      df.tickers <- df.tickers %>%
+        group_by(time.groups, ticker) %>%
+        summarise(ref.date = min(ref.date),
+                  volume = sum(volume, na.rm = TRUE),
+                  price.open = first(price.open),
+                  price.high = max(price.high),
+                  price.low = min(price.low),
+                  price.close = first(price.close),
+                  price.adjusted = first(price.adjusted)) %>%
+        #select(-time.groups) %>%
+        arrange(ticker, ref.date)
+    } else if (how.to.aggregate == 'last') {
+      df.tickers <- df.tickers %>%
+        group_by(time.groups, ticker) %>%
+        summarise(ref.date = min(ref.date),
+                  volume = sum(volume, na.rm = TRUE),
+                  price.open = first(price.open),
+                  price.high = max(price.high),
+                  price.low = min(price.low),
+                  price.close = last(price.close),
+                  price.adjusted = last(price.adjusted)) %>%
+        #select(-time.groups) %>%
+        arrange(ticker, ref.date)
+    }
 
-    df.tickers <- df.tickers %>%
-      group_by(time.groups, ticker) %>%
-      summarise(ref.date = min(ref.date),
-                volume = sum(volume, na.rm = TRUE),
-                price.open = first(price.open),
-                price.high = max(price.close),
-                price.low = min(price.close),
-                price.close = first(price.close),
-                price.adjusted = first(price.adjusted)) %>%
-      #select(-time.groups) %>%
-      arrange(ticker, ref.date)
 
     df.tickers$time.groups <- NULL
   }