# 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("GOOG", "GME", "NVDA", "V")

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"  "GOOG" "NVDA" "V"
# weights
weights <- c(0.25, 0.25, 0.25, 0.25)
weights
## [1] 0.25 0.25 0.25 0.25
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 4 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 GME        0.25
## 2 GOOG       0.25
## 3 NVDA       0.25
## 4 V          0.25

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")

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date       portfolio.returns
##    <date>                 <dbl>
##  1 2013-01-31           0.00716
##  2 2013-02-28           0.0451 
##  3 2013-03-28           0.0484 
##  4 2013-04-30           0.0804 
##  5 2013-05-31           0.0311 
##  6 2013-06-28           0.0607 
##  7 2013-07-31           0.0398 
##  8 2013-08-30          -0.00126
##  9 2013-09-30           0.0418 
## 10 2013-10-31           0.0666 
## # ℹ 50 more rows

5 Compute Sharpe Ratio

# Define risk free rate
rfr <- 0.0003

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

portfolio_SharpeRatio_tbl
## # A tibble: 1 × 1
##   `StdDevSharpe(Rf=0%,p=95%)`
##                         <dbl>
## 1                       0.460

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)
}

window <- 24

# Transform data: calculate rolling sharpe ratio
rolling_sr_tbl <- portfolio_returns_tbl %>%
    
    tq_mutate(select     = portfolio.returns, 
              mutate_fun = rollapply,
              width      = window,
              FUN        = Calculate_rolling_SharpeRatio,
              col_rename = "rolling_sr") %>%
    
    select(-portfolio.returns) %>%
    na.omit()

rolling_sr_tbl
## # A tibble: 37 × 2
##    date       rolling_sr
##    <date>          <dbl>
##  1 2014-12-31      0.472
##  2 2015-01-30      0.459
##  3 2015-02-27      0.474
##  4 2015-03-31      0.406
##  5 2015-04-30      0.361
##  6 2015-05-29      0.367
##  7 2015-06-30      0.266
##  8 2015-07-31      0.297
##  9 2015-08-31      0.294
## 10 2015-09-30      0.263
## # 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 decreased throughout 2015
                 it has been significantly increasing 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 February 2016. What do you think the reason is?

It seems at if my portfolio had some significantly rough times but improved greatly after around February 2016. From my rolling sharpe ratio graph, there appears to be several structural breaks. The one that is most impactful and noticeable is from around December 2015 to around February 2016. Around this time, there was a drop in oil prices, the global stock market was falling, and the Chinese market crashed. These are three of the many reasons that may have caused this structural break around early 2016, similarly in our Code Along Assignment 9. Around February 2016, the Code Along Assignment showed a similar structural break, therefore, it may have been for the same reasons. Overall, the economy seemed to have some downturns during late 2015 and early 2016 causing these significant structural breaks in both this graph and the Code Along Assignment 9 graph.