# 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("NFLX","TSLA","AAPL")
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] "AAPL" "NFLX" "TSLA"
# weights
weights <- c(.25, .25, .2)
weights
## [1] 0.25 0.25 0.20
w_tble <- tibble(symbols, weights)
w_tble
## # A tibble: 3 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AAPL       0.25
## 2 NFLX       0.25
## 3 TSLA       0.2

4 Build a portfolio

portfolio_returns_tbl <- asset_returns_tbl %>%
    
    tq_portfolio(assets_col = asset, 
                 returns_col = returns,
                 weights = w_tble, 
                 rebalance_on ="months", 
                 col_rename = "Returns")

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date        Returns
##    <date>        <dbl>
##  1 2013-01-31  0.126  
##  2 2013-02-28  0.0111 
##  3 2013-03-28  0.0191 
##  4 2013-04-30  0.104  
##  5 2013-05-31  0.136  
##  6 2013-06-28 -0.0301 
##  7 2013-07-31  0.114  
##  8 2013-08-30  0.103  
##  9 2013-09-30  0.0429 
## 10 2013-10-31 -0.00445
## # … 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                       0.410

6 Plot: Rolling Sharpe Ratio

Calculate_rolling_SharpeRatio <- function(data)  {
    
    rolling_SR <- SharpeRatio(R = data,
                Rf = rfr,
                FUN = "StdDev")
    
    return(rolling_SR)
}


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      0.548
##  2 2015-01-30      0.529
##  3 2015-02-27      0.552
##  4 2015-03-31      0.476
##  5 2015-04-30      0.478
##  6 2015-05-29      0.455
##  7 2015-06-30      0.500
##  8 2015-07-31      0.470
##  9 2015-08-31      0.377
## 10 2015-09-30      0.310
## # … with 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 = str_glue("This portfolio started off really well 
                              then dropped until late 2016 
                              and has started to go back up."),
             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?

It had a major drop off from 2015 to late 2017 but has continued steady increase after 2017. Im not really too sure why it had a break in November 2016 but most likely some form of economic shrtage.