# 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("SPY", "EFA", "IJS", "EEM", "AGG")

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
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "AGG" "EEM" "EFA" "IJS" "SPY"
#weights
weights <- c(0.25,0.25,0.2,0.2,0.1)
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 AGG        0.25
## 2 EEM        0.25
## 3 EFA        0.2 
## 4 IJS        0.2 
## 5 SPY        0.1

4 Build a portfolio

# ?tq_portfolio

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

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-01-31  0.0204 
##  2 2013-02-28 -0.00220
##  3 2013-03-28  0.0127 
##  4 2013-04-30  0.0173 
##  5 2013-05-31 -0.0113 
##  6 2013-06-28 -0.0233 
##  7 2013-07-31  0.0342 
##  8 2013-08-30 -0.0231 
##  9 2013-09-30  0.0513 
## 10 2013-10-31  0.0305 
## # ℹ 50 more rows

5 Caluculate CAPM BETA

##5.1
# Get market returns
market_returns_tbl <- tq_get("SPY",
                             get = "stock.prices",
                             from = "2012-12-31",
                             to = "2017-12-31") %>%
    # Convert prices to returns
    tq_transmute(select     = adjusted, 
                 mutate_fun = periodReturn, 
                 period     = "monthly",
                 type       = "log", 
                 col_rename = "returns") %>%
    
    slice(-1)

5.2 Join Returns

portfolio_market_returns_tbl <- left_join(market_returns_tbl,
                                          portfolio_returns_tbl,
                                          by = "date") %>%
  set_names("date", "market_returns", "portfolio_returns")
# 5.3 Calculating CAPM Beta 

portfolio_market_returns_tbl %>%
    tq_performance(Ra = portfolio_returns,
                   Rb = market_returns,
                    performance_fun = CAPM.beta)
## # A tibble: 1 × 1
##   CAPM.beta.1
##         <dbl>
## 1       0.761

6 Plot

 ###Scatter with regression line from ggplot 

portfolio_market_returns_tbl %>%
    ggplot(aes(market_returns, portfolio_returns)) +
    geom_point(color = "cornflowerblue") +
    geom_smooth(method = "lm", se = FALSE,
                size = 1.5, color = tidyquant::palette_light()[3]) +
    labs(x = "market returns",
         y = "portfolio returns")

actual_fitted_long_tbl <- portfolio_market_returns_tbl %>%
  
  # Linear Regression Model
  lm(portfolio_returns ~ market_returns, data = .) %>%
  
  # Get Fitted and Actual Returns
  broom::augment() %>%
  
  # Add Date
  mutate(date = portfolio_market_returns_tbl$date) %>%
  select(date, portfolio_returns, .fitted) %>%
  
  # Transform Data to Long
  pivot_longer(cols = c(portfolio_returns, .fitted), 
               names_to = "type", values_to = "returns")

actual_fitted_long_tbl %>%
  
  ggplot(aes(x = date, y = returns, color = type)) +
  geom_line()