Performance Analysis with TidyQuant

Quick Example of Performance Analysis with TidyQuant

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

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

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

Calculate CAPM

RaRb_capm <- RaRb  |>
  tq_performance(Ra = Ra,
                 Rb = Rb,
                 performance_fun = table.CAPM,
                 scale = 12)
RaRb_capm

## # A tibble: 1 × 17
##   ActivePremium  Alpha AlphaRobust AnnualizedAlpha  Beta `Beta-` `Beta-Robust`
##           <dbl>  <dbl>       <dbl>           <dbl> <dbl>   <dbl>         <dbl>
## 1         0.188 0.0216      0.0097           0.292 0.880   0.150          1.31
## # ℹ 10 more variables: `Beta+` <dbl>, `Beta+Robust` <dbl>, BetaRobust <dbl>,
## #   Correlation <dbl>, `Correlationp-value` <dbl>, InformationRatio <dbl>,
## #   `R-squared` <dbl>, `R-squaredRobust` <dbl>, TrackingError <dbl>,
## #   TreynorRatio <dbl>