# 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("TSLA", "HD", "MSFT", "META", "WMT")
prices <- tq_get(x = symbols,
                 get = "stock.prices",
                 from = "2012-12-31",
                 to = "2017-12-31")

2 Convert prices to returns (monthly)

asset_return_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_return_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "HD"   "META" "MSFT" "TSLA" "WMT"
#weights
weights <- c(0.25, 0.25, 0.20, 0.20, 0.10)
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 HD         0.25
## 2 META       0.25
## 3 MSFT       0.2 
## 4 TSLA       0.2 
## 5 WMT        0.1

4 Build a portfolio

# ?tq_portfolio()

portfolio_returns_tbl <- asset_return_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  0.0860
##  2 2013-02-28 -0.0357
##  3 2013-03-28  0.0190
##  4 2013-04-30  0.137 
##  5 2013-05-31  0.112 
##  6 2013-06-28  0.0190
##  7 2013-07-31  0.136 
##  8 2013-08-30  0.0650
##  9 2013-09-30  0.0824
## 10 2013-10-31 -0.0153
## # … 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                       0.555

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 Date: 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      0.706
##  2 2015-01-30      0.575
##  3 2015-02-27      0.659
##  4 2015-03-31      0.614
##  5 2015-04-30      0.588
##  6 2015-05-29      0.547
##  7 2015-06-30      0.545
##  8 2015-07-31      0.529
##  9 2015-08-31      0.406
## 10 2015-09-30      0.349
## # … 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 Done Quite Well Since Late 2016.", 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?

Since the start of 2015, my portfolio has seen a very steady drop off. The lowest structural break that my portfolio saw took place in December 2016. All of my portfolio does not fall into one industry so it can be challenging trying to link all downfalls to one certain event. Although my portfolio has not reached the same high as it had in 2015, it is still trending in a positive direction which leaves me optimistic for the future.