# 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,
                  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
symbol <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "SPY" "EFA" "IJS" "EEM" "AGG"
# 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 SPY        0.25
## 2 EFA        0.25
## 3 IJS        0.2 
## 4 EEM        0.2 
## 5 AGG        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  0.0308  
##  2 2013-02-28 -0.000870
##  3 2013-03-28  0.0187  
##  4 2013-04-30  0.0206  
##  5 2013-05-31 -0.00535 
##  6 2013-06-28 -0.0229  
##  7 2013-07-31  0.0412  
##  8 2013-08-30 -0.0255  
##  9 2013-09-30  0.0544  
## 10 2013-10-31  0.0352  
## # … with 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")

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 = 10, 
             label = "Risk Free Rate",
             angle = 90) + 
    
    labs(y = "Count",
         x = "Returns")

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("2016-11-30"), y = -0.05,
             label = "Election", size = 5, 
             angle = 90) + 
    annotate(geom = "text", 
             x = as.Date("2017-05-01"), y = -0.01, 
             label = "No Returns Below RFR 
             After the 2016 Election", color = "purple") + 
    
    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    0.312  
##  2 2015-01-30    0.237  
##  3 2015-02-27    0.301  
##  4 2015-03-31    0.259  
##  5 2015-04-30    0.259  
##  6 2015-05-29    0.262  
##  7 2015-06-30    0.269  
##  8 2015-07-31    0.202  
##  9 2015-08-31    0.128  
## 10 2015-09-30    0.00795
## # … with 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)