# Load packages

# Core
library(tidyverse)
library(tidyquant)

Goal

Collect individual returns into a portfolio by assigning a weight to each stock

five stocks: “SPY”, “EFA”, “IJS”, “EEM”, “AGG”

from 2012-12-31 to 2017-12-31

1 Import stock prices

symbols <- c( "SPY", "EFA", "IJS", "EEM", "AGG")

prices <- tq_get(x = symbols,
                 get = "stock.prices",
                 from = "2012-12-31",
                 to = "2017-12-31")

2 Convert prices to returns

asset_returns_tbl <- prices %>%
  group_by(symbol) %>%
  tq_transmute(select = adjusted, 
               mutate_fun = periodReturn, 
               period = "monthly",
               type = "log") %>%
  slice(-1) %>%
  
  ungroup () %>%

set_names(c("asset","date", "returns"))

3 Assign a weight to each asset

# symbols
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "AGG" "EEM" "EFA" "IJS" "SPY"
#weights
weights <- c(0.25, 0.25, 0.2, 0.2, 0.1)
weights
## [1] 0.25 0.25 0.20 0.20 0.10
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AGG        0.25
## 2 EEM        0.25
## 3 EFA        0.2 
## 4 IJS        0.2 
## 5 SPY        0.1

4 Build a portfolio

# ?tq_portfolio()
portfolio_returns_tbl <- asset_returns_tbl %>% 
  
  tq_portfolio(assets_col = asset, returns_col =  returns, 
               weights = w_tbl,
               rebalance_on = "months", 
               col_rename = "returns" )
portfolio_returns_tbl
## # A tibble: 60 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-01-31  0.0204 
##  2 2013-02-28 -0.00239
##  3 2013-03-28  0.0121 
##  4 2013-04-30  0.0174 
##  5 2013-05-31 -0.0128 
##  6 2013-06-28 -0.0247 
##  7 2013-07-31  0.0321 
##  8 2013-08-30 -0.0224 
##  9 2013-09-30  0.0511 
## 10 2013-10-31  0.0301 
## # ℹ 50 more rows

5 Calculate Sharpe Ratio

# Define Risk free rate
rfr <- 0.0003

portfolio_SharpeRatio_tbl <- portfolio_returns_tbl %>% 
  
  tq_performance(Ra = returns, 
                 performance_fun = SharpeRatio,
                 Rf              = rfr,
                 FUN             = "StdDev")
portfolio_SharpeRatio_tbl
## # A tibble: 1 × 1
##   `StdDevSharpe(Rf=0%,p=95%)`
##                         <dbl>
## 1                       0.239

6 Plot

Histogram

portfolio_returns_tbl %>% 
  
  ggplot(aes(x = returns)) +
  geom_histogram(bindwidth = 0.01, fill = "cornflowerblue", alpha = 0.5) +
  
  geom_vline(xintercept = rfr, color = "green", size = 1) +
  
  annotate(geom = "text", x = rfr + 0.002, y = 13, 
           label = "risk free rate",
           angle = 90) +
  labs(y = "count")

Scatterplot around rfr

portfolio_returns_tbl %>% 
  
  # Add new variable
  mutate(excess_returns = if_else(returns > rfr,
                                 "rfr_above",
                                 "rfr_below")) %>% 
  # Plot
  ggplot(aes(x = date, y = returns)) +
  geom_point(aes(color = excess_returns)) + 
  geom_hline(yintercept = rfr, color = "cornflowerblue", 
             linetype = 3, size = 1)+
  geom_vline(xintercept = as.Date("2012-11-01"),
             color = "cornflowerblue", size = 1, alpha = 0.5) +
  annotate(geom = "text",
           x = as.Date("2012-11-01"), y = - 0.04,
           label = "Election", size = 5, angle = 90) +
  annotate(geom = "text",
           x = as.Date("2017-05-01"), y = -0.01,
           label = str_glue("No Returns below rfr after the 2016 election"), 
           color = "green") +
  labs(y = "monthly returns", x = NULL) +
  theme(legend.position = "none")

rolling sharpe ratio

# Create custom function
Calculate_rolling_SharpeRatio <- function(data) {
  
  rolling_SR <- SharpeRatio(R = data,
                            Rf = rfr, 
                            FUN = "StdDev")
  return(rolling_SR)
}

# Define Window
Window <- 24

#Transform data: calculate Sharpe ratio
rolling_sr_tbl <- portfolio_returns_tbl %>% 
  
  tq_mutate(select = returns,
            mutate_fun = rollapply,
            width = Window,
            FUN = Calculate_rolling_SharpeRatio, 
            col_rename = "rolling_sr")

rolling_sr_tbl
## # A tibble: 60 × 3
##    date        returns rolling_sr
##    <date>        <dbl>      <dbl>
##  1 2013-01-31  0.0204          NA
##  2 2013-02-28 -0.00239         NA
##  3 2013-03-28  0.0121          NA
##  4 2013-04-30  0.0174          NA
##  5 2013-05-31 -0.0128          NA
##  6 2013-06-28 -0.0247          NA
##  7 2013-07-31  0.0321          NA
##  8 2013-08-30 -0.0224          NA
##  9 2013-09-30  0.0511          NA
## 10 2013-10-31  0.0301          NA
## # ℹ 50 more rows
rolling_sr_tbl %>% 
  ggplot(aes(x = date, y = rolling_sr)) +
  geom_line(color = "cornflowerblue") +

#Labeling
labs(x = NULL, y = "Rolling Sharpe Ratio") +
  
annotate(geom = "text",
         x = as.Date("2016-06-01"), y = 0.5,
         label = "this portfolio has done quite well since 2016.",
         color = "red", size = 5)