Week 7: Code along 6
CHP 4 Standard Deviation
Andrew Cook
2024-10-7
# Load packages
# Core
library(tidyverse)
library(tidyquant)Goal
Collect individual returns into a portfolio by assigning a weight to each stock
five stocks: “SPY”, “EFA”, “IJS”, “EEM”, “AGG”
from 2019-12-31 to 2024-12-31
1 Import stock prices
# Choose stocks
symbols <- c("SPY", "EFA", "IJS", "EEM", "AGG")
# Using tq_get() ----
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 %>%
# Calculate monthly returns
group_by(symbol) %>%
tq_transmute(select = adjusted,
mutate_fun = periodReturn,
period = "monthly",
type = "log") %>%
slice(-1) %>%
ungroup() %>%
# remane
set_names(c("asset", "date", "returns"))
# period_returns = c("yearly", "quarterly", "monthly", "weekly")3 Assign a weight to each asset
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
w <- c(0.25,
0.25,
0.20,
0.20,
0.10)
w_tbl <- tibble(symbols, w)4 Build a portfolio
portfolio_returns_rebalanced_monthly_tbl <- asset_returns_tbl %>%
tq_portfolio(assets_col = asset,
returns_col = returns,
weights = w_tbl,
col_rename = "returns",
rebalance_on = "months")
portfolio_returns_rebalanced_monthly_tbl## # A tibble: 60 × 2
## date returns
## <date> <dbl>
## 1 2013-01-31 0.0204
## 2 2013-02-28 -0.00239
## 3 2013-03-28 0.0121
## 4 2013-04-30 0.0174
## 5 2013-05-31 -0.0128
## 6 2013-06-28 -0.0247
## 7 2013-07-31 0.0321
## 8 2013-08-30 -0.0224
## 9 2013-09-30 0.0511
## 10 2013-10-31 0.0301
## # ℹ 50 more rows# write_rds(portfolio_returns_rebalanced_monthly_tbl,
# "00_data/Ch03_portfolio_returns_rebalanced_monthly_tbl.rds")5 Compute Standard Deviation
portfolio_sd_tidyquant_builtin_percent <- portfolio_returns_rebalanced_monthly_tbl %>%
tq_performance(Ra = returns,
Rb = NULL,
performance_fun = table.Stats) %>%
select(Stdev) %>%
mutate(tq_sd = round(Stdev, 4) * 100)
portfolio_sd_tidyquant_builtin_percent## # A tibble: 1 × 2
## Stdev tq_sd
## <dbl> <dbl>
## 1 0.0235 2.356 Plot
# Figure 4.1 Dispersion of Portfolio Returns ----
portfolio_returns_rebalanced_monthly_tbl %>%
ggplot(aes(date, returns)) +
geom_point(color = "cornflowerblue", size = 2) +
labs(title = "Scatterplot of Returns by Date") +
theme(plot.title = element_text(hjust = 0.5))
# Figure 4.2 Scatter of Returns Colored by Distance from Mean ----
sd_plot <- sd(portfolio_returns_rebalanced_monthly_tbl$returns)
mean_plot <- mean(portfolio_returns_rebalanced_monthly_tbl$returns)
portfolio_returns_rebalanced_monthly_tbl %>%
mutate(hist_col = case_when(
returns > mean_plot + sd_plot ~ "high",
returns < mean_plot - sd_plot ~ "middle",
TRUE ~ "low"
)) %>%
# Plot
ggplot(aes(date, returns, col = hist_col)) +
geom_point(size = 2) +
labs(title = "Colored Scatter") +
theme(plot.title = element_text(hjust = 0.5))
# Figure 4.3 Scatter of Returns with Line at Standard Deviation ----
sd_plot <- sd(portfolio_returns_rebalanced_monthly_tbl$returns)
mean_plot <- mean(portfolio_returns_rebalanced_monthly_tbl$returns)
portfolio_returns_rebalanced_monthly_tbl %>%
mutate(hist_col = case_when(
returns > mean_plot + sd_plot ~ "high",
returns < mean_plot - sd_plot ~ "middle",
TRUE ~ "low"
)) %>%
# Plot
ggplot(aes(date, returns, col = hist_col)) +
geom_point(size = 2) +
labs(title = "Colored Scatter with Line") +
theme(plot.title = element_text(hjust = 0.5)) +
# Add lines
geom_hline(yintercept = mean_plot + sd_plot, linetype = "dotted", color = "purple") +
geom_hline(yintercept = mean_plot - sd_plot, linetype = "dotted", color = "purple")
# Figure 4.4 Asset and Portfolio Standard Deviation Comparison ----
portfolio_returns_rebalanced_monthly_tbl## # A tibble: 60 × 2
## date returns
## <date> <dbl>
## 1 2013-01-31 0.0204
## 2 2013-02-28 -0.00239
## 3 2013-03-28 0.0121
## 4 2013-04-30 0.0174
## 5 2013-05-31 -0.0128
## 6 2013-06-28 -0.0247
## 7 2013-07-31 0.0321
## 8 2013-08-30 -0.0224
## 9 2013-09-30 0.0511
## 10 2013-10-31 0.0301
## # ℹ 50 more rowsasset_returns_sd_tbl <- asset_returns_tbl %>%
group_by(asset) %>%
tq_performance(Ra = returns,
Rb = NULL,
performance_fun = table.Stats) %>%
select(asset, Stdev) %>%
ungroup() %>%
# Add portfolio sd
add_row(tibble(asset = "Portfolio",
Stdev = sd(portfolio_returns_rebalanced_monthly_tbl$returns)))
asset_returns_sd_tbl %>%
# Plot
ggplot(aes(asset, Stdev, col = asset)) +
geom_point() +
ggrepel::geom_text_repel(aes(label = asset),
data = asset_returns_sd_tbl %>%
filter(asset == "Portfolio")) +
labs(title = "")
# Figure 4.5 Expected Returns versus Risk ----
asset_returns_sd_mean_tbl <- asset_returns_tbl %>%
group_by(asset) %>%
tq_performance(Ra = returns,
Rb = NULL,
performance_fun = table.Stats) %>%
select(asset, Mean = ArithmeticMean, Stdev) %>%
ungroup() %>%
add_row(tibble(asset = "Portfolio",
Mean = mean(portfolio_returns_rebalanced_monthly_tbl$returns),
Stdev = sd(portfolio_returns_rebalanced_monthly_tbl$returns)))
asset_returns_sd_mean_tbl %>%
ggplot(aes(Stdev, Mean, col = asset)) +
geom_point() +
ggrepel::geom_text_repel(aes(label = asset))
# Assign a value to winder
window <- 24
port_rolling_sd_tbl <- portfolio_returns_rebalanced_monthly_tbl %>%
tq_mutate(select = returns,
mutate_fun = rollapply,
width = window,
FUN = sd,
col_rename = "rolling_sd") %>%
select(date, rolling_sd) %>%
na.omit()
# Figure 4.7 Rolling Volatility ggplot ----
port_rolling_sd_tbl %>%
ggplot(aes(date, rolling_sd)) +
geom_line(color = "cornflowerblue") +
scale_y_continuous(labels = scales::percent) +
scale_x_date(breaks = scales::breaks_pretty(n = 7))+
labs(title = "24-Month Rolling Volatility",
x = NULL,
y = NULL) +
theme(plot.title = element_text(hjust = 0.5))