# 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("AAPL", "TSLA", "NFLX", "DIS", "MTN")

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 <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "AAPL" "DIS"  "MTN"  "NFLX" "TSLA"
weight  <- c(0.25, 0.25, 0.2, 0.2, 0.1)
weight
## [1] 0.25 0.25 0.20 0.20 0.10
w_tbl <- tibble(symbols, weight)
w_tbl
## # A tibble: 5 × 2
##   symbols weight
##   <chr>    <dbl>
## 1 AAPL      0.25
## 2 DIS       0.25
## 3 MTN       0.2 
## 4 NFLX      0.2 
## 5 TSLA      0.1

4 Build a 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  0.102 
##  2 2013-02-28  0.0242
##  3 2013-03-28  0.0451
##  4 2013-04-30  0.0806
##  5 2013-05-31  0.0871
##  6 2013-06-28 -0.0431
##  7 2013-07-31  0.108 
##  8 2013-08-30  0.0608
##  9 2013-09-30  0.0437
## 10 2013-10-31  0.0315
## # ℹ 50 more rows

5 Compute Sharpe Ratio

rfr <- 0.0003

portfolio_sharpe_tbl <- portfolio_returns_tbl %>%

    tq_performance(Ra = returns,
                   Rf = rfr,
                   performance_fun = SharpeRatio,
                   FUN = "StdDev") 

portfolio_sharpe_tbl
## # A tibble: 1 × 1
##   `StdDevSharpe(Rf=0%,p=95%)`
##                         <dbl>
## 1                       0.521

6 Plot: Rolling Sharpe Ratio

Rolling Sharpe

calculate_rolling_sharpeRatio <- function(df) {

    SharpeRatio(df,
                Rf = rfr,
                FUN = "StdDev")

}


window <- 24

rolling_sharpe_tbl <- portfolio_returns_tbl %>%

    tq_mutate(select = returns,
              mutate_fun = rollapply,
              width = window,
              align = "right",
              FUN = calculate_rolling_sharpeRatio,
              col_rename = "sharpeRatio") %>%
    na.omit()

rolling_sharpe_tbl
## # A tibble: 37 × 3
##    date        returns sharpeRatio
##    <date>        <dbl>       <dbl>
##  1 2014-12-31 -0.0146        0.712
##  2 2015-01-30  0.0412        0.690
##  3 2015-02-27  0.0721        0.723
##  4 2015-03-31 -0.00563       0.668
##  5 2015-04-30  0.0780        0.668
##  6 2015-05-29  0.0571        0.657
##  7 2015-06-30  0.0273        0.766
##  8 2015-07-31  0.0451        0.756
##  9 2015-08-31 -0.0667        0.568
## 10 2015-09-30 -0.0327        0.482
## # ℹ 27 more rows
rolling_sharpe_tbl %>%

    ggplot(aes(date, sharpeRatio)) +
    geom_line(color = "cornflowerblue") +

    labs(title = paste0("Rolling ", window, "-Month Sharpe Ratio"),
         y = "rolling Sharpe Ratio",
         x = NULL) +
    theme(plot.title = element_text(hjust = 0.5)) +

    annotate(geom = "text",
             x = as.Date("2016-06-01"), y = 0.5,
             label = "",
             size = 5, color = "red")

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 portfolios Sharpe ratio has decreased from 2015 to 2016 and then slowly started to increase again. The Sharpe of my portfolio is not very good and the decline in 2015-2016 was not good at all. The structual break in assignment nine could have been caused by the presidencial election as shifts in the market tend to happen around that time.