# 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

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

prices <- tq_get(x = symbol,
                 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 <- 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 Sharp Ratio

 rfr <- 0.0003

portfolio_SharpRatio <- portfolio_returns_tbl %>%
    
    tq_performance(Ra              = returns, 
                   performance_fun = SharpeRatio,
                   Rf              = rfr,
                   FUN             = "StdDev")

portfolio_SharpRatio
## # 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("2016-12-01"),
             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 Sharp Ratio

Calculate_rolling_SharpRatio <- function(data) {
    
    
    rolling_SR <- SharpeRatio(R   = data, 
                              Rf  = rfr, 
                              FUN = "StdDev")
    return(rolling_SR)
}

# Define Window
window <- 24


# Transform Data: calculate Rolling Sharp Ratio

rolling_sr_tbl <- portfolio_returns_tbl %>%
    
    tq_mutate(select     = returns,
              mutate_fun = rollapply, 
              width      = window, 
              FUN        = Calculate_rolling_SharpRatio,
              col_rename = "rolling_sr") %>%
    select(-returns) %>%
    na.omit()



rolling_sr_tbl %>%
    
    ggplot(aes(x = date,
               y = rolling_sr)) +
    geom_line(color = "cornflowerblue") +
    
    # Labeling
    labs(x = NULL, y = "Rolling Sharp 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)