# 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("VOO", "NVDA", "GME", "GOOGL", "TSLA")

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] "GME"   "GOOGL" "NVDA"  "TSLA"  "VOO"
# 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 GME        0.25
## 2 GOOGL      0.25
## 3 NVDA       0.2 
## 4 TSLA       0.2 
## 5 VOO        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  0.0224
##  2 2013-02-28  0.0280
##  3 2013-03-28  0.0511
##  4 2013-04-30  0.152 
##  5 2013-05-31  0.135 
##  6 2013-06-28  0.0731
##  7 2013-07-31  0.0963
##  8 2013-08-30  0.0435
##  9 2013-09-30  0.0466
## 10 2013-10-31  0.0270
## # 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                       0.425

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      0.493
##  2 2015-01-30      0.461
##  3 2015-02-27      0.479
##  4 2015-03-31      0.425
##  5 2015-04-30      0.393
##  6 2015-05-29      0.362
##  7 2015-06-30      0.299
##  8 2015-07-31      0.283
##  9 2015-08-31      0.230
## 10 2015-09-30      0.197
## # 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?

Based on the 24 month rolling graph my portfolio had a very significant decline from 0.5 to 0.05 between 2015 and late 2016 which was the structural break within my portfolio. I believe the reason there was a structural break during this period was saved due to the presidency at the time. The Sharpe ratio started to recover in late 2016 to early 2017 remaining pretty consistent at 0.35 for the remainder of 2017 into 2018.