# 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
symbols <- asset_returns_tbl %>% distinct (asset) %>% pull()
symbols
## [1] "AGG" "EEM" "EFA" "IJS" "SPY"
#weights
weight <- c(0.25,0.25,0.2,0.2,0.1)
weight
## [1] 0.25 0.25 0.20 0.20 0.10
w_tbl <- tibble(symbols,weight)
w_tbl
## # A tibble: 5 × 2
##   symbols weight
##   <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 RFR
rfr <- 0.0003
portfolio_sharpe <- portfolio_returns_tbl %>%
  
      tq_performance(Ra =                returns, 
                   performance_fun = SharpeRatio,
                   Rf =                rfr,
                   FUN = "StdDev")
portfolio_sharpe
## # A tibble: 1 × 1
##   `SharpeRatio(Rf=0%,p=95%)`
##                        <dbl>
## 1                      0.239

6 Plot

Histogram of Returns

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, y = 13, label = "risk free rate",
             angle = 90) +
    labs(y = "count")

Scatterplot

portfolio_returns_tbl %>%
    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 custom function
Rolling_Sharpe <- function(data) {
   rolling_SR <-  SharpeRatio(R = data,
                Rf = rfr, 
                FUN = "StdDev")
    return(rolling_SR)
}
#Set Window

window <-  24

rolling_sr_tbl <- portfolio_returns_tbl %>%
    tq_mutate(select = returns,mutate_fun = rollapply,
              width = window,
              FUN = Rolling_Sharpe,
              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 well since 2016",
             color = "red", size = 2)