Performance Analysis with tidyquant

library(tidyverse)

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library(tidyquant)

## Loading required package: lubridate
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## Attaching package: 'lubridate'
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## Loading required package: PerformanceAnalytics
## Loading required package: xts
## Loading required package: zoo
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## Loading required package: quantmod
## Loading required package: TTR
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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
## # … with 206 more rows

Get Baseline and Convert to Returns

Rb <- c("XLK") %>%
    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 = "Rb")
Rb

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

Join the Two Tables

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

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

Calculate CAPM

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

## # A tibble: 1 × 12
##   ActiveP…¹  Alpha Annua…²  Beta `Beta-` `Beta+` Corre…³ Corre…⁴ Infor…⁵ R-squ…⁶
##       <dbl>  <dbl>   <dbl> <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
## 1        NA 0.0216      NA 0.880   0.150   0.737   0.285       0      NA   0.081
## # … with 2 more variables: TrackingError <dbl>, TreynorRatio <dbl>, and
## #   abbreviated variable names ¹​ActivePremium, ²​AnnualizedAlpha, ³​Correlation,
## #   ⁴​`Correlationp-value`, ⁵​InformationRatio, ⁶​`R-squared`