# 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 Kurtosis

# 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 × 3
##   `ESSharpe(Rf=0%,p=95%)` `StdDevSharpe(Rf=0%,p=95%)` `VaRSharpe(Rf=0%,p=95%)`
##                     <dbl>                       <dbl>                    <dbl>
## 1                   0.121                       0.239                    0.168

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("2016-12-01"), 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 a 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")  +
    
    #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)