# 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("X", "CMC", "ZEUS", "TSLA", "GOOG")
prices <- tq_get(x  = symbols,
    get = "stock.prices", 
     from = "2012-12-31")

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 <- asset_return_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "CMC"  "GOOG" "TSLA" "X"    "ZEUS"
weights <- c(0.2, 0.3, 0.2, 0.15, 0.15)
weights
## [1] 0.20 0.30 0.20 0.15 0.15
w_tbl <- tibble(symbols, weights)
w_tbl 
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 CMC        0.2 
## 2 GOOG       0.3 
## 3 TSLA       0.2 
## 4 X          0.15
## 5 ZEUS       0.15

4 Build a 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: 143 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-01-31  0.0469 
##  2 2013-02-28 -0.0139 
##  3 2013-03-28  0.0203 
##  4 2013-04-30  0.0272 
##  5 2013-05-31  0.181  
##  6 2013-06-28  0.00690
##  7 2013-07-31  0.0761 
##  8 2013-08-30  0.0186 
##  9 2013-09-30  0.0941 
## 10 2013-10-31  0.0530 
## # ℹ 133 more rows

5 Compute Sharpe Ratio

rfr <- 0.0003

porftolio_SharpRatio_tbl <- portfolio_returns_tbl %>% 
    
    tq_performance(Ra              = returns, 
                   performance_fun = SharpeRatio, 
                   Rf              = rfr, 
                   FUN             = "StdDev")

porftolio_SharpRatio_tbl
## # A tibble: 1 × 1
##   `StdDevSharpe(Rf=0%,p=95%)`
##                         <dbl>
## 1                       0.173

6 Plot: Rolling Sharpe Ratio

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

    return(rolling_SR)}
window <- 24
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: 120 × 2
##    date       rolling_sr
##    <date>          <dbl>
##  1 2014-12-31     0.368 
##  2 2015-01-30     0.239 
##  3 2015-02-27     0.280 
##  4 2015-03-31     0.251 
##  5 2015-04-30     0.236 
##  6 2015-05-29     0.199 
##  7 2015-06-30     0.178 
##  8 2015-07-31     0.110 
##  9 2015-08-31     0.0726
## 10 2015-09-30    -0.104 
## # ℹ 110 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("2018-06-01"), y = 0.5, 
             label = "This portfolio has done well 2016-2018
                      and then again 2020-2021.", 
             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?

November 2016 was an election year which can impact the market and different sectors. For me it improved my returns into 2018 where it had a peak before it dropped again. It would again rise in 2020 another election year. The same could be happening now as it looks like the returns are begining to trend upward.