# Load packages
# Core
library(tidyverse)
library(tidyquant)
Collect individual returns into a portfolio by assigning a weight to each stock
five stocks: “SPY”, “EFA”, “IJS”, “EEM”, “AGG”
from 2012-12-31 to 2017-12-31
symbols <- c("SPY", "EFA", "IJS", "EEM", "AGG")
prices <- tq_get(x= symbols,
get ="stock.prices",
from = "2012-12-31",
to = "2017-12-31")
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"))
# 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
# ?tq_portfolio
portfolio_returns_tbl <- asset_returns_tbl %>%
tq_portfolio(assets_col = asset,
returns_col = returns,
weights = w_tbl,
replace_on = "months")
portfolio_returns_tbl
## # A tibble: 60 × 2
## date portfolio.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
portfolio_sd_tidyquant_builtin_percent <- portfolio_returns_tbl %>%
tq_performance(Ra = portfolio.returns,
performance_fun = table.Stats) %>%
select(Stdev) %>%
mutate(tq_sd = round(Stdev, 4))
portfolio_sd_tidyquant_builtin_percent
## # A tibble: 1 × 2
## Stdev tq_sd
## <dbl> <dbl>
## 1 0.0235 0.0235
# Mean of Portfolio Returns
portfolio_mean_tidyquant_builtin_percent <-
mean(portfolio_returns_tbl$portfolio.returns)
portfolio_mean_tidyquant_builtin_percent
## [1] 0.006173971
##6 Plot ###Expected Return vs Risk
# Expected Return vs Risk
sd_mean_tbl <- asset_returns_tbl %>%
group_by(asset) %>%
tq_performance(Ra = returns,
performance_fun = table.Stats) %>%
select(Mean = ArithmeticMean, Stdev) %>%
ungroup() %>%
# mutate(Stdev = portfolio_sd_tidyquant_builtin_percent$Stdev * 100) %>%
# Add Portfolio SD
add_row(tibble(asset = "Portfolio",
Mean = portfolio_mean_tidyquant_builtin_percent,
Stdev = portfolio_sd_tidyquant_builtin_percent$tq_sd))
sd_mean_tbl
## # A tibble: 6 × 3
## asset Mean Stdev
## <chr> <dbl> <dbl>
## 1 AGG 0.0017 0.0086
## 2 EEM 0.0028 0.0419
## 3 EFA 0.006 0.0326
## 4 IJS 0.0119 0.0396
## 5 SPY 0.0121 0.0272
## 6 Portfolio 0.00617 0.0235
sd_mean_tbl %>%
ggplot(aes(x = Stdev, y = Mean, color = asset)) +
geom_point() +
ggrepel::geom_text_repel(aes(label = asset))
###24 Months Rolling Volitility
rolling_sd_tbl <- portfolio_returns_tbl %>%
tq_mutate(select = portfolio.returns,
mutate_fun = rollapply,
width = 24,
FUN = sd,
col_rename = "rolling_sd") %>%
na.omit() %>%
select(date,rolling_sd)
rolling_sd_tbl
## # A tibble: 37 × 2
## date rolling_sd
## <date> <dbl>
## 1 2014-12-31 0.0239
## 2 2015-01-30 0.0241
## 3 2015-02-27 0.0250
## 4 2015-03-31 0.0251
## 5 2015-04-30 0.0251
## 6 2015-05-29 0.0249
## 7 2015-06-30 0.0246
## 8 2015-07-31 0.0241
## 9 2015-08-31 0.0264
## 10 2015-09-30 0.0249
## # ℹ 27 more rows
rolling_sd_tbl %>%
ggplot(aes(x = date, y = rolling_sd)) +
geom_line(color = "cornflowerblue") +
#Formatting
scale_y_continuous(labels = scales::percent_format()) +
#Labeling
labs(x = NULL,
y = NULL,
title = "24-Month Rolling Volitility") +
theme(plot.title = element_text(hjust = 0.5))