# 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

symbols <- c("NVDA", "TSLA", "DELL", "MSFT", "AMZN")
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] "AMZN" "DELL" "MSFT" "NVDA" "TSLA"
#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 AMZN       0.25
## 2 DELL       0.25
## 3 MSFT       0.2 
## 4 NVDA       0.2 
## 5 TSLA       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, 
                 reabalance_on = "months",
                 col_rename = "returns")

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-01-31  0.0298 
##  2 2013-02-28  0.00247
##  3 2013-03-28  0.0190 
##  4 2013-04-30  0.0690 
##  5 2013-05-31  0.116  
##  6 2013-06-28  0.0165 
##  7 2013-07-31  0.0519 
##  8 2013-08-30  0.0537 
##  9 2013-09-30  0.0669 
## 10 2013-10-31 -0.0190 
## # ℹ 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")

6 Plot: Rolling Sharpe Ratio

# Create a custom function to calculate rolling sharpe ratio
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      0.452
##  2 2015-01-30      0.383
##  3 2015-02-27      0.425
##  4 2015-03-31      0.357
##  5 2015-04-30      0.374
##  6 2015-05-29      0.326
##  7 2015-06-30      0.303
##  8 2015-07-31      0.304
##  9 2015-08-31      0.246
## 10 2015-09-30      0.213
## # ℹ 27 more rows
rolling_sr_tbl %>%
    
    ggplot(aes(x = date, y = rolling_sr)) + 
               geom_line(color = "cornflowerblue") +
               

labs(x = NULL, y = "Rolling Sharpe Ratio") +
    
    annotate(geom = "text", 
        x = as.Date("2016-06-01"), 
        y = 0.5, 
    label = "This Portfolio crashed in mid 2015 but has since come back, skyrocketing in mid 2017", 
    color = "red",
     size = 4)

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?

I think the reason for the structural break in 2016 was the presidential election.