# 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("GM", "NOK", "GOOGL", "HMC", "NVDA")

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] "GM"    "GOOGL" "HMC"   "NOK"   "NVDA"
# 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 GM         0.25
## 2 GOOGL      0.25
## 3 HMC        0.2 
## 4 NOK        0.2 
## 5 NVDA       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, rebalance_on = "months",
             col_rename = "returns")

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-01-31  0.0125 
##  2 2013-02-28 -0.00567
##  3 2013-03-28 -0.0109 
##  4 2013-04-30  0.0570 
##  5 2013-05-31  0.0339 
##  6 2013-06-28  0.0114 
##  7 2013-07-31  0.0332 
##  8 2013-08-30 -0.0306 
##  9 2013-09-30  0.143  
## 10 2013-10-31  0.0860 
## # ℹ 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                       0.275

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     0.294 
##  2 2015-01-30     0.261 
##  3 2015-02-27     0.330 
##  4 2015-03-31     0.322 
##  5 2015-04-30     0.238 
##  6 2015-05-29     0.239 
##  7 2015-06-30     0.178 
##  8 2015-07-31     0.190 
##  9 2015-08-31     0.173 
## 10 2015-09-30     0.0883
## # 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 2016.",
             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?
My portfolio started out good in 2015, then took a big dip of about .35 on the rolling shape ratio in 2016. That was rough, but thankfully it recovered slowly but surely with an increased of that lost .35 which finally kept it stable in 2018. I think the reason for the decrease was because of the presidential elecotion at the time. It was a difficult time in our country in all aspects.