# 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("NFLX", "AMZN", "GOOG")
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 <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "AMZN" "GOOG" "NFLX"
# weights
weights <- c(0.4, 0.3, 0.3)
weights
## [1] 0.4 0.3 0.3
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 3 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AMZN        0.4
## 2 GOOG        0.3
## 3 NFLX        0.3

4 Build a portfolio

portfolio_returns_tbl <- asset_returns_tbl %>%
    
    tq_portfolio(assets_col   = asset,
                 returns_co   = 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  0.216  
##  2 2013-02-28  0.0545 
##  3 2013-03-28  0.00262
##  4 2013-04-30  0.0315 
##  5 2013-05-31  0.0539 
##  6 2013-06-28 -0.00525
##  7 2013-07-31  0.0791 
##  8 2013-08-30  0.00290
##  9 2013-09-30  0.0784 
## 10 2013-10-31  0.122  
## # … with 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")

portfolio_SharpeRatio_tbl
## # A tibble: 1 × 1
##   `StdDevSharpe(Rf=0%,p=95%)`
##                         <dbl>
## 1                       0.417

6 Plot: 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      0.333
##  2 2015-01-30      0.321
##  3 2015-02-27      0.325
##  4 2015-03-31      0.283
##  5 2015-04-30      0.326
##  6 2015-05-29      0.318
##  7 2015-06-30      0.330
##  8 2015-07-31      0.361
##  9 2015-08-31      0.347
## 10 2015-09-30      0.285
## # … with 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 donw quite qell 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?