# 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("HMC", "WMT", "TGT")

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] "HMC" "TGT" "WMT"
# weights 
weight <- c(0.25, 0.25, 0.5)
weight
## [1] 0.25 0.25 0.50
w_tbl <- tibble(symbols, weight)
w_tbl
## # A tibble: 3 × 2
##   symbols weight
##   <chr>    <dbl>
## 1 HMC       0.25
## 2 TGT       0.25
## 3 WMT       0.5

4 Build a portfolio

# ?tq_portfolio

portfolio_returns_tbl <- asset_returns_tbl %>%
    
    tq_portfolio(assets_col  = asset, 
                 returns_col = returns, 
                 weights     = w_tbl, 
                 reblance_on = "months", 
                 col_rename = "returns")

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-01-31  0.0227 
##  2 2013-02-28  0.0160 
##  3 2013-03-28  0.0589 
##  4 2013-04-30  0.0373 
##  5 2013-05-31 -0.0333 
##  6 2013-06-28 -0.00556
##  7 2013-07-31  0.0311 
##  8 2013-08-30 -0.0676 
##  9 2013-09-30  0.0244 
## 10 2013-10-31  0.0332 
## # … with 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                       0.128

6 Plot: Rolling Sharpe Ratio

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    0.182  
##  2 2015-01-30    0.146  
##  3 2015-02-27    0.153  
##  4 2015-03-31    0.105  
##  5 2015-04-30    0.0246 
##  6 2015-05-29    0.0464 
##  7 2015-06-30    0.0237 
##  8 2015-07-31    0.00843
##  9 2015-08-31   -0.00267
## 10 2015-09-30   -0.0351 
## # … 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 started to get better 
             since the beginning of 2017 and keep increasing ",
             color = "red",
             size = 5)

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?

Overall my portfolio has performed low compared to a good sharpe ratio. The structural break in my chart would be in the beginning of 2017. After this structural break, my portfolio has performed better and has been on a steady increase upwards. In the beginning of March, of 2017, Target faced one of their biggest price drop in active trade because this retail giant struggled to cope with the rapidly changing behavior of consumers. To fight this struggle Target revamped store locations and invested a lot of money to ensure prices are competitive.