# 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", "GOOG","MSFT", "AAPL")
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] "AAPL" "GOOG" "MSFT" "TSLA"
# weights
weights <- c(0.25, 0.25, 0.25, 0.25)
weights
## [1] 0.25 0.25 0.25 0.25
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 4 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AAPL       0.25
## 2 GOOG       0.25
## 3 MSFT       0.25
## 4 TSLA       0.25

4 Build a portfolio

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

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date       portfolio.returns
##    <date>                 <dbl>
##  1 2013-01-31           0.00997
##  2 2013-02-28          -0.00509
##  3 2013-03-28           0.0267 
##  4 2013-04-30           0.134  
##  5 2013-05-31           0.183  
##  6 2013-06-28          -0.00804
##  7 2013-07-31           0.0707 
##  8 2013-08-30           0.0795 
##  9 2013-09-30           0.0358 
## 10 2013-10-31           0.0317 
## # … with 50 more rows

5 Compute Sharpe Ratio

rfr <- 0.0003
portfolio_SharpeRatio_tbl <- portfolio_returns_tbl %>%
    
    tq_performance(Ra = portfolio.returns, 
                   performance_fun = SharpeRatio,
                   Rf              = rfr,
                   FUN             = "StdDev")

portfolio_SharpeRatio_tbl
## # A tibble: 1 × 1
##   `StdDevSharpe(Rf=0%,p=95%)`
##                         <dbl>
## 1                       0.437

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 = portfolio.returns, 
              mutate_fun = rollapply, 
              width = window, 
              FUN = Calculate_rolling_SharpeRatio,
              col_rename = "rolling_sr") %>%
    
    select(-portfolio.returns) %>%
    na.omit()

rolling_sr_tbl
## # A tibble: 37 × 2
##    date       rolling_sr
##    <date>          <dbl>
##  1 2014-12-31      0.624
##  2 2015-01-30      0.569
##  3 2015-02-27      0.618
##  4 2015-03-31      0.536
##  5 2015-04-30      0.529
##  6 2015-05-29      0.507
##  7 2015-06-30      0.501
##  8 2015-07-31      0.490
##  9 2015-08-31      0.361
## 10 2015-09-30      0.322
## # … 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 been struggling since 2015, but is slowly recovering.",
             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?

My portfolio has struggled from the start of 2015, and is slowly recovering fro, its largely disappointing results. In early 2016, around January/February there was a structural break which happened again in July 2016 and November 2016, which is in line with the technology sector crash in 2016. My portfolio consists of GOOG, TSLA, MSFT, and AAPL, all of which struggled mightily with a bear market outlook and Fed Rate hikes during this time period. Consumer spending was Unsustainable during this time and investors began to show this as my portfolio had a structural break that is slowly recovering from very poor performance. GOOG was a huge reason for technology struggles, as they continuosly reported poor earnign results throughout uch of 2016.