#' Create main df structure
#'
#' Create main df with important columns that sub-identify the ID (code.) such as stock., lastmod, etc.
#'
#' Dependencies: library0("plyr", "dplyr", "function0")
#' Return: df
#'
#' @param fdf fdf (final df without warrant)
#' @keywords main df
#' @export
main_df.f <- function(fdf){
.Deprecated("main_df_f")
libraryf(c("plyr", "dplyr", "function0"))
lastvalue.f <- function(a.v){return(a.v[length(a.v)])}
mdf1 <- fdf %>% group_by(code.) %>% summarise(stock. = lastvalue.f(stock.), date = lastvalue.f(date), chg.. = lastvalue.f(chg..),
lastmod = lastvalue.f(lastmod), status.ind = lastvalue.f(status.ind))
return(mdf1)
}
#' Create df that evaluates volume criteria
#'
#' s1_vol: mean volume of most recent 5 days > mean volume of past 6th to 10th days
#' Dependencies: library0("plyr", "dplyr", "function0")
#' Return df
#'
#' @param fdf fdf (final df without warrant)
#' @keywords volume criteria df
#' @export
volume_increase_df.f <- function(fdf){
.Deprecated("volume_increase_df_f")
libraryf(c("plyr", "dplyr", "function0"))
summarised_df1 <- fdf %>% group_by(code.) %>% summarise(meanvol5_0 = funlag(last.v = lastmod, lag = 0, n = 5, fun = max),
meanvol5_5 = funlag(last.v = lastmod, lag = 5, n = 5, fun = max))
summarised_df2 <- summarised_df1 %>% mutate(s1_vol = as.numeric(meanvol5_0 > meanvol5_5))
return(summarised_df2)
}
#' Create df that evaluates price breakout criteria
#'
#' s2_br: today's close > max of past 20 days lag 1 close
#' s3_br: today's close > max of past 20 days lag 5 close
#' Dependencies: library0("plyr", "dplyr", "function0")
#' Return df
#'
#' @param fdf fdf (final df without warrant)
#' @keywords price breakout criteria df
#' @export
breakout_strat_df.f <- function(fdf){
.Deprecated("breakout_strat_df_f")
libraryf(c("plyr", "dplyr", "function0"))
summarised_df1 <- fdf %>% group_by(code.) %>% summarise(latestclose = lastmod[length(lastmod)],
max20_1 = funlag(last.v = lastmod, lag = 1, n = 20, fun = max),
max20_5 = funlag(last.v = lastmod, lag = 5, n = 20, fun = max))
summarised_df2 <- summarised_df1 %>% mutate(s2_br = as.numeric(latestclose > max20_1), s3_br = as.numeric(latestclose > max20_5))
return(summarised_df2)
}
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