# Load packages

# Core
library(tidyverse)
library(tidyquant)

Goal

Visualize and examine changes in the underlying trend in the performance of your portfolio in terms of Sharpe Ratio.

Choose your stocks.

from 2012-12-31 to present

1 Import stock prices

symbols <- c("XOM", "QQQ", "SPY", "TSLA","CGC")

prices <- tq_get(x = symbols,
                 get = "stock.prices",
                 from = "2012-12-31",
                 to = "2017-12-31")

2 Convert prices to returns (monthly)

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 (change the weigting scheme)

# symbols 
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "CGC"  "QQQ"  "SPY"  "TSLA" "XOM"
# 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 CGC        0.25
## 2 QQQ        0.25
## 3 SPY        0.2 
## 4 TSLA       0.2 
## 5 XOM        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  0.0409 
##  2 2013-02-28 -0.0113 
##  3 2013-03-28  0.0324 
##  4 2013-04-30  0.0796 
##  5 2013-05-31  0.135  
##  6 2013-06-28  0.00985
##  7 2013-07-31  0.0738 
##  8 2013-08-30  0.0323 
##  9 2013-09-30  0.0437 
## 10 2013-10-31 -0.0127 
## # ℹ 50 more rows

5 Compute 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                       0.397

6 Plot: Rolling Sharpe Ratio

# Custom function
# necessary because we would not be able to specify FUN = "StdDev" otherwise

calculate_rolling_sharpeRatio <- function(df) {

    SharpeRatio(df,
                Rf = rfr,
                FUN = "StdDev")

}

# dump(list = "calculate_rolling_sharpeRatio",
#      file = "00_scripts/calculate_rolling_sharpeRatio.R")

# Set the length of periods for rolling calculation
window <- 24

# Calculate rolling sharpe ratios
rolling_sharpe_tbl <- portfolio_returns_tbl %>%

    tq_mutate(select = returns,
              mutate_fun = rollapply,
              width = window,
              align = "right",
              FUN = calculate_rolling_sharpeRatio,
              col_rename = "sharpeRatio") %>%
    na.omit()

rolling_sharpe_tbl
## # A tibble: 37 × 3
##    date        returns sharpeRatio
##    <date>        <dbl>       <dbl>
##  1 2014-12-31 -0.0664       0.354 
##  2 2015-01-30 -0.0659       0.259 
##  3 2015-02-27  0.0608       0.310 
##  4 2015-03-31 -0.0436       0.248 
##  5 2015-04-30  0.0463       0.229 
##  6 2015-05-29  0.00669      0.152 
##  7 2015-06-30  0.00654      0.149 
##  8 2015-07-31  0.00161      0.0930
##  9 2015-08-31 -0.0922      -0.0132
## 10 2015-09-30 -0.0234      -0.0679
## # ℹ 27 more rows
# Figure 7.5 Rolling Sharpe ggplot ----

rolling_sharpe_tbl %>%

    ggplot(aes(date, sharpeRatio)) +
    geom_line(color = "cornflowerblue") +

    labs(title = paste0("Rolling ", window, "-Month Sharpe Ratio"),
         y = "rolling Sharpe Ratio",
         x = NULL) +
    theme(plot.title = element_text(hjust = 0.5)) +

    annotate(geom = "text",
             x = as.Date("2016-06-01"), y = 0.5,
             label = "This portfolio has improved since 2016.",
             size = 5, color = "red")

How has your portfolio performed over time? Provide dates of the structural breaks, if any. The Code Along Assignment 9 had one structural break in November 2016. What do you think the reason is?

Over my portfolio has done quite well. Starting off at positive 0.4 and going to below 0.0 and then starting at beginning of 2016 has improved and reaching to over 0.6 by the beginning of 2018. There was a structural break in November 2016 based on an improvement in market conditions overall as my portfolio similiarly match the portfolio of our professor in Code Along 9.