# Load packages

# Core
library(tidyverse)
library(tidyquant)
library(dplyr)

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",
                 fro = "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)

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")

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 = .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) +
    
    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 = 4)