Analysing Trades: Compute Profit/Loss, Resize and more
Functions to help analyse trades (as opposed to profit-and-loss series)
scale_trades(amount, price, timestamp, aggregate = FALSE,
fun = NULL, ...)
split_trades(amount, price, timestamp, aggregate = FALSE)
limit(amount, price, timestamp, lim, tol = 1e-8)
scale_to_unity(amount)
close_on_first(amount)
tw_exposure(amount, timestamp, start, end, abs.value = TRUE)amount |
notionals |
price |
a vector of prices |
timestamp |
a vector. |
aggregate |
|
fun |
a function |
lim |
a maximum absolute position size |
start |
optional time |
end |
optional time |
abs.value |
logical. If |
... |
passed on to fun |
tol |
numeric |
scale_trades takes a vector of notionals, prices and scales all
trades along the paths so that the maximum exposure is 1.
The default fun divides every element of a vector n by
max(abs(cumsum(n))). If user-specified, the function fun
needs to take a vector of notionals (changes in position.)
split_trades decomposes a trade list into single trades, where a
single trade comprises those trades from a zero position to the next
zero position.
Either a list or a list-of-lists.
Enrico Schumann
n <- c(1,1,-3,-1,2) p <- 100 + 1:length(n) timestamp <- 1:length(n) split_trades(n, p, timestamp) split_trades(n, p, timestamp, TRUE) ## almost like the original series scale_trades(n, p, timestamp) scale_trades(n, p, timestamp, TRUE) ## each _trade_ gets scaled
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.