# 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("SPY", "EFA", "IJS", "EEM", "AGG")

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", "return"))

3 Assign a weight to each asset (change the weigting scheme)

# symbols
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "AGG" "EEM" "EFA" "IJS" "SPY"
# 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 AGG        0.25
## 2 EEM        0.25
## 3 EFA        0.2 
## 4 IJS        0.2 
## 5 SPY        0.1

4 Build a portfolio

# ?tq_portfolio

portfolio_returns_tbl <- asset_returns_tbl %>%
    
    tq_portfolio(assets_col = asset, 
                returns_col = return, 
                weights     = w_tbl,
                rebalace_on = "months",
                col_rename  = "returns")

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-01-31  0.0204 
##  2 2013-02-28 -0.00220
##  3 2013-03-28  0.0127 
##  4 2013-04-30  0.0173 
##  5 2013-05-31 -0.0113 
##  6 2013-06-28 -0.0233 
##  7 2013-07-31  0.0342 
##  8 2013-08-30 -0.0231 
##  9 2013-09-30  0.0513 
## 10 2013-10-31  0.0305 
## # 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")

6 Plot: Rolling Sharpe Ratio

# Create a custom function to calculate rolling SR
Calculate_rolling_SharpeRatio <- function(data) {
    
    rollingSR  <- SharpeRatio(R = data,
                              Rf = rfr,
                              FUN = "StdDev")
    
    return(rollingSR)
    
}
# 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     0.250 
##  2 2015-01-30     0.190 
##  3 2015-02-27     0.252 
##  4 2015-03-31     0.223 
##  5 2015-04-30     0.221 
##  6 2015-05-29     0.231 
##  7 2015-06-30     0.246 
##  8 2015-07-31     0.173 
##  9 2015-08-31     0.109 
## 10 2015-09-30    -0.0134
## # 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 Code Along Assignment 9, the portfolio experienced a significant structural break in November 2016, which was driven by the shifting macroeconomic landscape and market reactions following the U.S. presidential election that spurred a sharp, sustained increase in risk-adjusted returns.