# 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("IVV", "VOO", "VTSAX", "FBGRX", "VSMPX")
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] "FBGRX" "IVV"   "VOO"   "VSMPX" "VTSAX"
# weights
weights <- c(0.2, 0.2, 0.2, 0.2, 0.2)
weights
## [1] 0.2 0.2 0.2 0.2 0.2
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 FBGRX       0.2
## 2 IVV         0.2
## 3 VOO         0.2
## 4 VSMPX       0.2
## 5 VTSAX       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  0.0395
##  2 2013-02-28  0.0103
##  3 2013-03-28  0.0282
##  4 2013-04-30  0.0143
##  5 2013-05-31  0.0230
##  6 2013-06-28 -0.0126
##  7 2013-07-31  0.0452
##  8 2013-08-30 -0.0204
##  9 2013-09-30  0.0297
## 10 2013-10-31  0.0345
## # 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                       0.422

6 Plot: Rolling Sharpe Ratio

# Create 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      0.675
##  2 2015-01-30      0.547
##  3 2015-02-27      0.586
##  4 2015-03-31      0.511
##  5 2015-04-30      0.489
##  6 2015-05-29      0.477
##  7 2015-06-30      0.464
##  8 2015-07-31      0.443
##  9 2015-08-31      0.301
## 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 pretty bad since 2015", 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 very well in 2015 then started to crash hard up until late 2016 when it started to return to where it was in 2015.The Code Along 9 assignment had a structural break in Nov 2016 due to the election.