# 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("TSLA", "DIS", "NKE", "GE", "SBUX")

prices <- tq_get(x = symbols,
                 get = "stock.prices",
                 from = "2012-12-31",
                 to = "2017-12-31")

2 Convert prices to returns

asset_returns_tbl <- prices %>%

    # Calculate monthly returns
    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

# symbols
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "DIS"  "GE"   "NKE"  "SBUX" "TSLA"
# weight
weights <- c(0.25, 
             0.25, 
             0.20, 
             0.20, 
             0.10)
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 DIS        0.25
## 2 GE         0.25
## 3 NKE        0.2 
## 4 SBUX       0.2 
## 5 TSLA       0.1

4 Build a portfolio

portfolio_returns_tbl <- asset_returns_tbl %>%

    tq_portfolio(assets_col = asset,
                 returns_col = returns,
                 weigh = w_tbl,
                 rebalance_on = "months",
                 col_rename = "returns")

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-01-31  0.0632 
##  2 2013-02-28  0.00669
##  3 2013-03-28  0.0408 
##  4 2013-04-30  0.0797 
##  5 2013-05-31  0.0742 
##  6 2013-06-28  0.0241 
##  7 2013-07-31  0.0552 
##  8 2013-08-30 -0.00643
##  9 2013-09-30  0.0845 
## 10 2013-10-31  0.0377 
## # ℹ 50 more rows

5 Calculate Sharpe Ratio

rfr <- 0.0003

portfolio_returns_tbl %>%
    
    tq_performance(Ra = returns,
                   performance_fun = SharpeRatio,
                   Rf              = rfr,
                   FUN             = "StdDev" )
## # A tibble: 1 × 1
##   `StdDevSharpe(Rf=0%,p=95%)`
##                         <dbl>
## 1                       0.336

6 Plot

Histogram of Returns with risk free rate

portfolio_returns_tbl %>%
    
    ggplot(aes(x = returns)) +
    geom_histogram(binwidth = 0.01, fill = "cornflowerblue", alpha = 0.5) +
    
    geom_vline(xintercept = rfr, color = "green", size = 1) +
    
    annotate(geom = "text",
             x = rfr + 0.002, y = 13,
             label = "risk free rate",
             angle = 90) +
    
    labs(y = "count")

Scatterolot of Returns around Risk Free Rate

portfolio_returns_tbl %>%
    
    mutate(excess_returns = if_else(returns > rfr,
                                    "rfr_above",
                                    "rfr_below")) %>%
    
    ggplot(aes(x = date, y = returns)) +
    geom_point(aes(color = excess_returns)) +
    geom_hline(yintercept = rfr, color = "cornflowerblue", 
               linetype = 3, size = 1) +
    geom_vline(xintercept = as.Date("2016-11-01"),
               color = "cornflowerblue", size = 1) +
    
    theme(legend.position = "none") +
    
    annotate(geom = "text",
             x = as.Date("2016-12-01"), y = -0.04,
             label = "Election", size = 5, angle = 90)+
    
    annotate(geom = "text",
             x = as.Date("2017-05-01"), y = -0.01,
             label = str_glue("No returns below RFR after the 2016 Election."), 
             color = "green")

Rolling Sharpe Ratio

calculate_rolling_SharpeRatio <- function(data) {
    
    rolling_SR <- SharpeRatio(R = data,
                Rf = rfr,
                FUN = "StdDev")
    
    return(rolling_SR)
}

window <- 24

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      0.718
##  2 2015-01-30      0.604
##  3 2015-02-27      0.658
##  4 2015-03-31      0.598
##  5 2015-04-30      0.590
##  6 2015-05-29      0.567
##  7 2015-06-30      0.572
##  8 2015-07-31      0.560
##  9 2015-08-31      0.422
## 10 2015-09-30      0.393
## # ℹ 27 more rows
rolling_sr_tbl %>%
    
    ggplot(aes(x = date, y = rolling_sr)) +
    geom_line(color = "cornflowerblue") +
    
    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)

6 Plot: Rolling Sharpe Ratio

How has your portfolio performed over time? Not very well since 2015

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?– I think the break was the election was a big deal in the market and people were doing a lot of different things with there money depending on which side of the election the company was on.