Qapi provides a light-weight library for quantitative analysis in finance. The package aims to optimize metrics for local data analysis, shiny web app or plumber-based microservice. Code is minimal with a limited packages dependency and basic input requirements. No missing values handling.
The package can be easily applied to matrix
, data.frame
, data.table
, list
, etc. with apply
family of functions or custom helpers. It works with vectors and matrices.
Functions are implemented in pure R, for big data manipulation is advisable to use parallelization or C-based calculations.
Install the development version from GitHub:
# install.packages("remotes")
remotes::install_github("maxto/qapi")
library(qapi)
# rand time series
ts_mat <- rand_ts(m = 20,n = 3,as_ret = T,method="matrix")
ts_xts <- rand_ts(m = 20,n = 3,as_ret = T,method="xts")
ts_df <- rand_ts(m = 20,n = 3,as_ret = T,method="data.frame")
# single column or vector
sharpe_ratio(ts_mat[,1])
sharpe_ratio(ts_xts[,1])
sharpe_ratio(ts_df$s1)
# for multiple columns
apply(ts_mat,2,function(a)sharpe_ratio(a))
apply(ts_xts,2,function(a)sharpe_ratio(a))
apply(ts_df,2,function(a)sharpe_ratio(a))
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