# 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("LME", "GME", "LMO", "UNH", "UPS")

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] "GME" "LME" "LMO" "UNH" "UPS"
# 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 GME        0.25
## 2 LME        0.25
## 3 LMO        0.2 
## 4 UNH        0.2 
## 5 UPS        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: 80 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-01-31  0.0754 
##  2 2013-02-28 -0.0208 
##  3 2013-03-28  0.00287
##  4 2013-04-30  0.110  
##  5 2013-05-31 -0.103  
##  6 2013-06-28  0.125  
##  7 2013-07-31  0.207  
##  8 2013-08-30 -0.0594 
##  9 2013-09-30 -0.0344 
## 10 2013-10-31 -0.0789 
## # ℹ 70 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                      0.0351

6 Plot: 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: 57 × 2
##    date       rolling_sr
##    <date>          <dbl>
##  1 2014-11-28    0.0573 
##  2 2014-12-31   -0.0136 
##  3 2015-01-30    0.00538
##  4 2015-02-27    0.0231 
##  5 2015-03-24   -0.0197 
##  6 2015-03-31    0.0800 
##  7 2015-04-17    0.0272 
##  8 2015-04-30   -0.0871 
##  9 2015-05-20   -0.0522 
## 10 2015-05-28   -0.0870 
## # ℹ 47 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 2017.",
             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?

My portfolio has overall a poor sharp ratio that is less desirable to the average investor and may not be favored at all, but over time has increased in recent years. There were two structural breaks, one in 2016, and another in 2015. One as said in November and the second was in late 2015 around November as well, I believe both were linked to election news of some sort and who was the favored candidate, as the sharp rebound in November 2016 could be a shock of Trump winning presidency.