Performance Analysis with Tidyquant

Quick Example of Performance Analysis with Tidyquant

# Load Packages
library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
## ✔ purrr     1.0.2     
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(tidyquant)

## Registered S3 method overwritten by 'quantmod':
##   method            from
##   as.zoo.data.frame zoo 
## ── Attaching core tidyquant packages ──────────────────────── tidyquant 1.0.9 ──
## ✔ PerformanceAnalytics 2.0.4      ✔ TTR                  0.24.4
## ✔ quantmod             0.4.26     ✔ xts                  0.14.0── Conflicts ────────────────────────────────────────── tidyquant_conflicts() ──
## ✖ zoo::as.Date()                 masks base::as.Date()
## ✖ zoo::as.Date.numeric()         masks base::as.Date.numeric()
## ✖ dplyr::filter()                masks stats::filter()
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## ✖ xts::last()                    masks dplyr::last()
## ✖ PerformanceAnalytics::legend() masks graphics::legend()
## ✖ quantmod::summary()            masks base::summary()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

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") %>%
    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
## # ℹ 62 more rows

3 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
## # ℹ 206 more rows

4 Calculate the CAPM

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

## # A tibble: 1 × 12
##   ActivePremium  Alpha AnnualizedAlpha  Beta `Beta-` `Beta+` Correlation
##           <dbl>  <dbl>           <dbl> <dbl>   <dbl>   <dbl>       <dbl>
## 1            NA 0.0216              NA 0.880   0.150   0.737       0.285
## # ℹ 5 more variables: `Correlationp-value` <dbl>, InformationRatio <dbl>,
## #   `R-squared` <dbl>, TrackingError <dbl>, TreynorRatio <dbl>