# 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("LULU", "NKE", "UA") 
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] "LULU" "NKE"  "UA"
# weights
weights <- c(0.35, 0.45, 0.2)
weights
## [1] 0.35 0.45 0.20
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 3 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 LULU       0.35
## 2 NKE        0.45
## 3 UA         0.2

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 -0.0140  
##  2 2013-02-28 -0.00489 
##  3 2013-03-28  0.0107  
##  4 2013-04-30  0.104   
##  5 2013-05-31 -0.00480 
##  6 2013-06-28 -0.0458  
##  7 2013-07-31  0.0157  
##  8 2013-08-30  0.00711 
##  9 2013-09-30  0.0765  
## 10 2013-10-31 -0.000965
## # ℹ 50 more rows


## 5 Compute Sharpe Ratio


``` r
# 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                      0.0587

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     0.142 
##  2 2015-01-30     0.186 
##  3 2015-02-27     0.218 
##  4 2015-03-31     0.202 
##  5 2015-04-30     0.121 
##  6 2015-05-29     0.118 
##  7 2015-06-30     0.209 
##  8 2015-07-31     0.209 
##  9 2015-08-31     0.195 
## 10 2015-09-30     0.0992
## # ℹ 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 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? My portfolio was not doing well from 2015- the middle of 2016. It slowly started climbing back up until 2017 when it saw a huge increase in the rolling sharpe ratio and continued this into 2018. America votes in November every four years for a president, so I think with campaigns and everything that this would effect stocks and is the cause of the structural break in November 2016.My portfolio also saw this structural break in November.