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")- active_return - Active return
- adj_ann_sharpe_ratio - Adjusted Annualized Sharpe ratio
- ann_return - Annualized return
- ann_risk - Annualized Risk
- ann_sharpe_ratio - Annualized Sharpe ratio
- ann_sortino_ratio - Annualized Sortino ratio
- avg_drawdown - Average Drawdown
- burke_ratio - Burke ratio
- calmar_ratio - Calmar ratio
- cdrawdown - Continuous drawdown
- downside_potential - Downside Potential
- downside_risk - Downside risk
- drawdown - Drawdown
- drawdown_info - Drawdown information
- excess_return - Excess return
- hist_cvar - Historical Conditional Value At Risk
- hist_var - Historical Value At Risk
- martin_ratio - Martin ratio
- max_drawdown - Max drawdown
- omega_ratio - Omega ratio
- pain_index - Pain index
- pain_ratio - Pain ratio
- param_cvar - Parametric Conditional Value At Risk
- param_var - Parametric Value At Risk
- sharpe_ratio - Sharpe ratio
- sortino_ratio - Sortino ratio
- sterling_ratio - Sterling ratio
- ulcer_index - Ulcer index
- upside_potential - Upside potential
- upside_risk - Upside risk
- kurtosis - Kurtosis
- skewness - Skewness
- normpdf - Normal probability density function (pdf)
- norminv - Inverse of the normal cumulative distribution function (cdf)
- rand_ts - Random time series for testing
- as_xts - Convert data to xts class with pre-calculated dates from today (backward)
- windowing - Create a training/test set with sliding windows (rolling, anchored, with/without overlapping)
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))