Performance Analysis with tidyquant

#Load Packages
library(tidyverse)
library(tidyquant)

1 Get stock prices and convert to returns

Ra <- c("AAPL", "GOOG", "NFLX") %>%
    tq_get(get  = "stock.prices",
           from = "2010-01-01",
           to   = "2015-12-31") %>%
    group_by(symbol) %>%
    tq_transmute(select     = adjusted,
                 mutate_fun = periodReturn,
                 period     = "monthly",
                 col_rename = "Ra")
Ra

## # A tibble: 216 × 3
## # Groups:   symbol [3]
##    symbol date            Ra
##    <chr>  <date>       <dbl>
##  1 AAPL   2010-01-29 -0.103 
##  2 AAPL   2010-02-26  0.0654
##  3 AAPL   2010-03-31  0.148 
##  4 AAPL   2010-04-30  0.111 
##  5 AAPL   2010-05-28 -0.0161
##  6 AAPL   2010-06-30 -0.0208
##  7 AAPL   2010-07-30  0.0227
##  8 AAPL   2010-08-31 -0.0550
##  9 AAPL   2010-09-30  0.167 
## 10 AAPL   2010-10-29  0.0607
## # ℹ 206 more rows

2 Get Baseline and Convert to returns

Rb <- "XLK" %>%
    tq_get(get  = "stock.prices",
           from = "2010-01-01",
           to   = "2015-12-31") %>%
    tq_transmute(select     = adjusted,
                 mutate_fun = periodReturn,
                 period     = "monthly",
                 col_rename = "Rb")
Rb

## # A tibble: 72 × 2
##    date            Rb
##    <date>       <dbl>
##  1 2010-01-29 -0.0993
##  2 2010-02-26  0.0348
##  3 2010-03-31  0.0684
##  4 2010-04-30  0.0126
##  5 2010-05-28 -0.0748
##  6 2010-06-30 -0.0540
##  7 2010-07-30  0.0745
##  8 2010-08-31 -0.0561
##  9 2010-09-30  0.117 
## 10 2010-10-29  0.0578
## # ℹ 62 more rows

3 Join the Two Tables

RaRb <- left_join(Ra, Rb, by = c("date" = "date"))
RaRb

## # A tibble: 216 × 4
## # Groups:   symbol [3]
##    symbol date            Ra      Rb
##    <chr>  <date>       <dbl>   <dbl>
##  1 AAPL   2010-01-29 -0.103  -0.0993
##  2 AAPL   2010-02-26  0.0654  0.0348
##  3 AAPL   2010-03-31  0.148   0.0684
##  4 AAPL   2010-04-30  0.111   0.0126
##  5 AAPL   2010-05-28 -0.0161 -0.0748
##  6 AAPL   2010-06-30 -0.0208 -0.0540
##  7 AAPL   2010-07-30  0.0227  0.0745
##  8 AAPL   2010-08-31 -0.0550 -0.0561
##  9 AAPL   2010-09-30  0.167   0.117 
## 10 AAPL   2010-10-29  0.0607  0.0578
## # ℹ 206 more rows

4 Calculate CAPM

RaRb_capm <- RaRb %>%
    tq_performance(Ra = Ra,
                   Rb = Rb,
                   performance_fun = table.CAPM)
RaRb_capm

## # A tibble: 3 × 13
## # Groups:   symbol [3]
##   symbol ActivePremium  Alpha AnnualizedAlpha  Beta `Beta-` `Beta+` Correlation
##   <chr>          <dbl>  <dbl>           <dbl> <dbl>   <dbl>   <dbl>       <dbl>
## 1 AAPL           0.119 0.0089           0.112 1.11    0.578  1.04        0.659 
## 2 GOOG           0.034 0.0028           0.034 1.14    1.39   1.16        0.644 
## 3 NFLX           0.447 0.053            0.859 0.384  -1.52   0.0045      0.0817
## # ℹ 5 more variables: `Correlationp-value` <dbl>, InformationRatio <dbl>,
## #   `R-squared` <dbl>, TrackingError <dbl>, TreynorRatio <dbl>

RaRb_skewness <- RaRb %>%
    tq_performance(Ra = Ra,
                   Rb = NULL,
                   performance_fun = skewness)
RaRb_skewness
Ra <- XILX %>%
    group_by(symbol) %>%
    tq_transmute(adjusted, periodReturn, period = "monthly", col_rename = "Ra")