pq_performance calculates performance metrics based on returns of market price or portfolio. The performance analysis functions are calling from PerformanceAnalytics package, which includes many widely used performance metrics.
pq_performance(dt, Ra, Rb = NULL, perf_fun, ...)a list/dataframe of time series datasets.
the column name of asset returns.
the column name of baseline returns, defaults to NULL.
performance function from PerformanceAnalytics package, see pq_perf_funs.
additional parameters, the arguments used in PerformanceAnalytics functions.
library(pedquant)
library(data.table)
# load data
data(dt_banks)
data(dt_ssec)
# calculate returns
datret1 = pq_return(dt_banks, 'close', freq = 'monthly', rcol_name = 'Ra')
datret2 = pq_return(dt_ssec, 'close', freq = 'monthly', rcol_name = 'Rb')
# merge returns of assets and baseline
datRaRb = merge(
rbindlist(datret1)[, .(date, symbol, Ra)],
rbindlist(datret2)[, .(date, Rb)],
by = 'date', all.x = TRUE
)
# claculate table.CAPM metrics
perf_capm = pq_performance(datRaRb, Ra = 'Ra', Rb = 'Rb', perf_fun = 'table.CAPM')
rbindlist(perf_capm, idcol = 'symbol')
#> symbol Alpha Beta Beta+ Beta- R-squared Annualized Alpha Correlation
#> 1: 601288.SS 0.0048 0.6490 0.9538 0.4703 0.4898 0.0592 0.6998
#> 2: 601328.SS 0.0057 0.7513 0.9886 0.5825 0.4789 0.0701 0.6920
#> 3: 601398.SS 0.0051 0.6739 0.7942 0.6160 0.5239 0.0625 0.7238
#> 4: 601939.SS 0.0061 0.9472 1.1829 0.7994 0.5138 0.0763 0.7168
#> 5: 601988.SS 0.0040 0.7474 0.9353 0.7874 0.5312 0.0485 0.7289
#> Correlation p-value Tracking Error Active Premium Information Ratio
#> 1: 0 0.1788 0.0440 0.2459
#> 2: 0 0.1961 0.0522 0.2661
#> 3: 0 0.1719 0.0478 0.2782
#> 4: 0 0.2202 0.0566 0.2571
#> 5: 0 0.1780 0.0328 0.1842
#> Treynor Ratio
#> 1: 0.1192
#> 2: 0.1139
#> 3: 0.1205
#> 4: 0.0950
#> 5: 0.0886