# 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("RTX", "GD", "LMT", "BA")
prices <- tq_get(x    = symbols, 
                 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
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "BA"  "GD"  "LMT" "RTX"
# weights
weights <- c(0.35, 0.30, 0.20, 0.15)
weights
## [1] 0.35 0.30 0.20 0.15
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 4 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 BA         0.35
## 2 GD         0.3 
## 3 LMT        0.2 
## 4 RTX        0.15

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 -0.0224
##  2 2013-02-28  0.0349
##  3 2013-03-28  0.0727
##  4 2013-04-30  0.0405
##  5 2013-05-31  0.0642
##  6 2013-06-28  0.0184
##  7 2013-07-31  0.0764
##  8 2013-08-30 -0.0114
##  9 2013-09-30  0.0773
## 10 2013-10-31  0.0423
## # … with 50 more rows

5 Calculate 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                       0.555

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: 37 × 2
##    date       rolling_sr
##    <date>          <dbl>
##  1 2014-12-31      0.785
##  2 2015-01-30      0.888
##  3 2015-02-27      0.901
##  4 2015-03-31      0.800
##  5 2015-04-30      0.665
##  6 2015-05-29      0.619
##  7 2015-06-30      0.572
##  8 2015-07-31      0.554
##  9 2015-08-31      0.434
## 10 2015-09-30      0.359
## # … with 27 more rows
rolling_sr_tbl %>% 
    ggplot(aes(x = date, y = rolling_sr)) + 
    geom_line(color = "slateblue") + 
    
    # Labeling
    labs(x = NULL, y = "Rolling Sharpe Ratio", 
         title = paste0("Rolling ", window, "-Month Sharpe Ratio")) + 
    theme(plot.title = element_text(hjust = 0.5))

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?

The portfolio had a break around February of 2015. The only thing that I could find that happened in early 2015 that would effect the portfolio is the agreement between Ukraine and Russia. This agreement included a ceasefire and a withdraw of heavy weapons. This means that the demand for defense vehicles and systems went down. This would cause these defense and aerospace stocks to decrease.