# 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")
symbols
## [1] "SPY" "EFA" "IJS" "EEM" "AGG"
prices
## # A tibble: 6,300 × 8
##    symbol date        open  high   low close    volume adjusted
##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>     <dbl>    <dbl>
##  1 SPY    2012-12-31  140.  143.  140.  142. 243935200     114.
##  2 SPY    2013-01-02  145.  146.  145.  146. 192059000     117.
##  3 SPY    2013-01-03  146.  146.  145.  146. 144761800     117.
##  4 SPY    2013-01-04  146.  147.  146.  146. 116817700     117.
##  5 SPY    2013-01-07  146.  146.  145.  146. 110002500     117.
##  6 SPY    2013-01-08  146.  146.  145.  146. 121265100     117.
##  7 SPY    2013-01-09  146.  146.  146.  146.  90745600     117.
##  8 SPY    2013-01-10  147.  147.  146.  147. 130735400     118.
##  9 SPY    2013-01-11  147.  147.  147.  147. 113917300     118.
## 10 SPY    2013-01-14  147.  147.  146.  147.  89567200     118.
## # ℹ 6,290 more rows

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"))
asset_returns_tbl
## # A tibble: 300 × 3
##    asset date         returns
##    <chr> <date>         <dbl>
##  1 AGG   2013-01-31 -0.00623 
##  2 AGG   2013-02-28  0.00589 
##  3 AGG   2013-03-28  0.000985
##  4 AGG   2013-04-30  0.00964 
##  5 AGG   2013-05-31 -0.0202  
##  6 AGG   2013-06-28 -0.0158  
##  7 AGG   2013-07-31  0.00269 
##  8 AGG   2013-08-30 -0.00830 
##  9 AGG   2013-09-30  0.0111  
## 10 AGG   2013-10-31  0.00829 
## # ℹ 290 more rows

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 of Returns with risk free rate

portfolio_returns_tbl %>%
    ggplot(aes(x = returns)) +
    geom_histogram(binwidth = 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 of Returns around Risk Free Rate

portfolio_returns_tbl %>%
    # Add a 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("2016-11-01"),
               color = "cornflowerblue",
               size = 1,
               alpha = 0.5) +
    theme(legend.position = "none") +
    annotate(geom = "text",
             x = as.Date("2016-11-01"), 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)

Rolling Sharpe Ratio

# Create custom function to calculate rolling SR
Calculate_rolling_SharpeRatio <- function(data) {
    rolling_SR <- SharpeRatio(R = data, 
                Rf = rfr,
                FUN = "StdDev")
    return(rolling_SR)
}
#Define window
window <- 24
# Transform data: Calculate rolling 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") %>%
    select(-returns) %>%
    na.omit()

rolling_sr_tbl
## # A tibble: 37 × 2
##    date       rolling_sr
##    <date>          <dbl>
##  1 2014-12-31     0.230 
##  2 2015-01-30     0.178 
##  3 2015-02-27     0.240 
##  4 2015-03-31     0.210 
##  5 2015-04-30     0.214 
##  6 2015-05-29     0.222 
##  7 2015-06-30     0.238 
##  8 2015-07-31     0.162 
##  9 2015-08-31     0.0950
## 10 2015-09-30    -0.0279
## # ℹ 27 more rows
rolling_sr_tbl %>% 
    ggplot(aes(x = date, y = rolling_sr)) +
    geom_line(color = "cornflowerblue") +
    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)