# Load packages

# Core
library(tidyverse)
library(tidyquant)

Goal

Collect individual returns into a portfolio by assigning a weight to each stock

two stocks: “TSLA”, “DELL”

from 2012-12-31 to 2017-12-31

1 Import stock prices

symbols <- c("TSLA", "DELL")

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] "DELL" "TSLA"
# weights
weights <- c(0.50,0.50)
weights
## [1] 0.5 0.5
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 2 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 DELL        0.5
## 2 TSLA        0.5

4 Build a portfolio

#?tq_portfolio

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

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date       returns
##    <date>       <dbl>
##  1 2013-01-31  0.0510
##  2 2013-02-28 -0.0371
##  3 2013-03-28  0.0421
##  4 2013-04-30  0.177 
##  5 2013-05-31  0.297 
##  6 2013-06-28  0.0468
##  7 2013-07-31  0.112 
##  8 2013-08-30  0.115 
##  9 2013-09-30  0.0674
## 10 2013-10-31 -0.0949
## # ℹ 50 more rows

5 Calculate Sharp Ratio

# Define risk free rate
rfr <- 0.0003

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

portfolio_SharpRatio_tbl
## # A tibble: 1 × 1
##   `StdDevSharpe(Rf=0%,p=95%)`
##                         <dbl>
## 1                       0.308

6 Plot

Histogram 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 = 1.5,) +
    annotate(geom = "text", x = rfr + 0.002, y = 13, 
             label = "Risk free rate",
             angle = 90) +
    labs(y = "count")

Scatterplot of returns around risk free rate

portfolio_returns_tbl %>%
    
    # Add a new variable
    mutate(excess_returns = if_else(returns > rfr, 
                                    "rfr_above",
                                    "rfr_below")) %>%
    # Plot
    ggplot(aes(x= date, y  = returns)) +
    geom_point(aes(color = excess_returns)) +
    geom_hline(yintercept = rfr, color = "cornflowerblue",
               linetype = 3, linewidth = 1) +
    geom_vline(xintercept = as.Date("2016-11-01"),
               color = "green", linewidth = 1, alpha = 0.8) +
    
    theme(legend.position = "none") +
    annotate(geom = "text",
             x = as.Date("2016-12-01"), y = -0.04,
             label = "Election", linewidth = 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 = "blue") +
    
    labs(y = "monthly returns", x = NULL)

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)

   }

# 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     0.409 
##  2 2015-01-30     0.362 
##  3 2015-02-27     0.381 
##  4 2015-03-31     0.342 
##  5 2015-04-30     0.318 
##  6 2015-05-29     0.263 
##  7 2015-06-30     0.256 
##  8 2015-07-31     0.197 
##  9 2015-08-31     0.114 
## 10 2015-09-30     0.0735
## # ℹ 27 more rows
rolling_sr_tbl %>%
    
    ggplot(aes(x = date, y = rolling_sr)) +
    geom_line(color = "cornflowerblue") +
    
    # Labelling
    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.
             Structural break was around February 2016 and bounced around
             until August where it continued to rise ",
             color = "red",
             size = 3)