# 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("AMZN", "TSLA", "RGR","WMT")
prices <- tq_get(x    = symbols, 
                 get  = "stock.prices",
                 from = "2012-12-31",
                 to   = "2024-11-13")

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] "AMZN" "RGR"  "TSLA" "WMT"
# weights
weights <- c(0.25, 0.25, 0.2, 0.1)
weights
## [1] 0.25 0.25 0.20 0.10
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 4 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AMZN       0.25
## 2 RGR        0.25
## 3 TSLA       0.2 
## 4 WMT        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: 143 × 2
##    date       returns
##    <date>       <dbl>
##  1 2013-01-31 0.0650 
##  2 2013-02-28 0.00342
##  3 2013-03-28 0.00850
##  4 2013-04-30 0.0651 
##  5 2013-05-31 0.128  
##  6 2013-06-28 0.0146 
##  7 2013-07-31 0.0839 
##  8 2013-08-30 0.0328 
##  9 2013-09-30 0.0997 
## 10 2013-10-31 0.0146 
## # ℹ 133 more rows

5 Compute Sharpe Ratio

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 × 3
##   `ESSharpe(Rf=0%,p=95%)` `StdDevSharpe(Rf=0%,p=95%)` `VaRSharpe(Rf=0%,p=95%)`
##                     <dbl>                       <dbl>                    <dbl>
## 1                   0.137                       0.240                    0.176

6 Plot: Rolling Sharpe Ratio

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: 120 × 2
##    date       rolling_sr
##    <date>          <dbl>
##  1 2014-12-31      0.323
##  2 2015-01-30      0.316
##  3 2015-02-27      0.367
##  4 2015-03-31      0.328
##  5 2015-04-30      0.339
##  6 2015-05-29      0.281
##  7 2015-06-30      0.292
##  8 2015-07-31      0.281
##  9 2015-08-31      0.233
## 10 2015-09-30      0.142
## # ℹ 110 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("2024-11-13"), y = 0.5, 
             label = "This portfolio has done 
             decently well since 2023.",
             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?

When looking at my portfolio, I can tell that it has performed okay overtime. A lot of ups and down with the sharpe ratio, and the sharpe ratio never hits over 1.0. The biggest structural break happened at the beginning of 2023. I think that the reason behind this break was because of the state of the economy. During these times it was believed we were heading towards a recession, causing a lack of confidence in investors.