# Load packages

# Core
library(tidyverse)
library(tidyquant)

Goal

Visualize and examine changes in the underlying trend in the downside risk of your portfolio in terms of kurtosis.

Choose your stocks.

from 2012-12-31 to present

1 Import stock prices

symbols <- c("AMZN", "AAPL", "TSLA", "NFLX", "GOOGL")

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 %>%

    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 <- asset_returns_tbl %>% distinct(asset) %>% pull
symbols
## [1] "AAPL"  "AMZN"  "GOOGL" "NFLX"  "TSLA"
weight <- c(0.25, 0.25, 0.2, 0.2, 0.1)
weight
## [1] 0.25 0.25 0.20 0.20 0.10
w_tbl <- tibble(symbols, weight)
w_tbl
## # A tibble: 5 × 2
##   symbols weight
##   <chr>    <dbl>
## 1 AAPL      0.25
## 2 AMZN      0.25
## 3 GOOGL     0.2 
## 4 NFLX      0.2 
## 5 TSLA      0.1

4 Build a 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  0.115 
##  2 2013-02-28  0.0226
##  3 2013-03-28  0.0107
##  4 2013-04-30  0.0573
##  5 2013-05-31  0.0998
##  6 2013-06-28 -0.0261
##  7 2013-07-31  0.107 
##  8 2013-08-30  0.0462
##  9 2013-09-30  0.0585
## 10 2013-10-31  0.0830
## # ℹ 50 more rows

5 Compute Sharpe Ratio

rfr <- 0.0003

portfolio_sharpe_tbl <- portfolio_returns_tbl %>%

    tq_performance(Ra = returns,
                   Rf = rfr,
                   performance_fun = SharpeRatio,
                   FUN = "StdDev") 

portfolio_sharpe_tbl
## # A tibble: 1 × 1
##   `StdDevSharpe(Rf=0%,p=95%)`
##                         <dbl>
## 1                       0.465

6 Plot: Rolling Sharpe Ratio

portfolio_returns_tbl %>%

    ggplot(aes(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, size = 5) +
    labs(y = "count")

portfolio_returns_tbl %>%

    # Transform data
    mutate(returns_excess = if_else(returns > rfr, "above_rfr", "below_rfr")) %>%

    ggplot(aes(date, returns, color = returns_excess)) +
    geom_point(show.legend = FALSE) +

    # risk free rate
    geom_hline(yintercept = rfr, linetype = "dotted", size = 1, color = "cornflowerblue") +

    # election date
    geom_vline(xintercept = as.Date("2016-11-30"), size = 1, color = "cornflowerblue") +

    # formatting
    scale_x_date(breaks = scales::pretty_breaks(n = 7)) +

    # labeling
    annotate(geom = "text",
             x = as.Date("2017-01-01"), y = -0.04,
             label = "Election", angle = 90, size = 5) +
    annotate(geom = "text",
             x = as.Date("2017-06-01"), y = -0.01,
             label = str_glue("No returns below the RFR
                              after the 2016 election"),
             color = "red", size = 4) +
    labs(y = "percent monthly returns",
         x = NULL)

calculate_rolling_sharpeRatio <- function(df) {

    SharpeRatio(df,
                Rf = rfr,
                FUN = "StdDev")

}

# dump(list = "calculate_rolling_sharpeRatio",
#      file = "00_scripts/calculate_rolling_sharpeRatio.R")

# Set the length of periods for rolling calculation
window <- 24

# Calculate rolling sharpe ratios
rolling_sharpe_tbl <- portfolio_returns_tbl %>%

    tq_mutate(select = returns,
              mutate_fun = rollapply,
              width = window,
              align = "right",
              FUN = calculate_rolling_sharpeRatio,
              col_rename = "sharpeRatio") %>%
    na.omit()

rolling_sharpe_tbl
## # A tibble: 37 × 3
##    date        returns sharpeRatio
##    <date>        <dbl>       <dbl>
##  1 2014-12-31 -0.0597        0.502
##  2 2015-01-30  0.0934        0.498
##  3 2015-02-27  0.0649        0.525
##  4 2015-03-31 -0.0498        0.461
##  5 2015-04-30  0.107         0.480
##  6 2015-05-29  0.0475        0.457
##  7 2015-06-30  0.00838       0.490
##  8 2015-07-31  0.122         0.493
##  9 2015-08-31 -0.0366        0.424
## 10 2015-09-30 -0.0308        0.357
## # ℹ 27 more rows
rolling_sharpe_tbl %>%

    ggplot(aes(date, sharpeRatio)) +
    geom_line(color = "cornflowerblue") +

    labs(title = paste0("Rolling ", window, "-Month Sharpe Ratio"),
         y = "rolling Sharpe Ratio",
         x = NULL) +
    theme(plot.title = element_text(hjust = 0.5)) +

    annotate(geom = "text",
             x = as.Date("2016-06-01"), y = 0.5,
             label = "This portfolio has done quite well since 2016.",
             size = 5, color = "red")

My portfolio has performed quite well with one structure break around February-march of 2016. The structural break in November of 2016 is most likely the election of the new president, Donald Trump, and the reaction to the president, the trump rally.