Week 10: Code along 9
CHAPTER 6 Sharpe Ratio
Shane McCormick
2026-11-01
# 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 2012-12-31 to 2017-12-31
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")
prices## # A tibble: 6,300 × 8
## symbol date open high low close volume adjusted
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 SPY 2012-12-31 140. 143. 140. 142. 243935200 113.
## 2 SPY 2013-01-02 145. 146. 145. 146. 192059000 116.
## 3 SPY 2013-01-03 146. 146. 145. 146. 144761800 115.
## 4 SPY 2013-01-04 146. 147. 146. 146. 116817700 116.
## 5 SPY 2013-01-07 146. 146. 145. 146. 110002500 116.
## 6 SPY 2013-01-08 146. 146. 145. 146. 121265100 115.
## 7 SPY 2013-01-09 146. 146. 146. 146. 90745600 116.
## 8 SPY 2013-01-10 147. 147. 146. 147. 130735400 117.
## 9 SPY 2013-01-11 147. 147. 147. 147. 113917300 117.
## 10 SPY 2013-01-14 147. 147. 146. 147. 89567200 116.
## # ℹ 6,290 more rows2 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"))
asset_returns_tbl## # A tibble: 300 × 3
## asset date returns
## <chr> <date> <dbl>
## 1 AGG 2013-01-31 -0.00623
## 2 AGG 2013-02-28 0.00589
## 3 AGG 2013-03-28 0.000986
## 4 AGG 2013-04-30 0.00964
## 5 AGG 2013-05-31 -0.0202
## 6 AGG 2013-06-28 -0.0158
## 7 AGG 2013-07-31 0.00269
## 8 AGG 2013-08-30 -0.00830
## 9 AGG 2013-09-30 0.0111
## 10 AGG 2013-10-31 0.00829
## # ℹ 290 more rows3 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 0.10w_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 0.14 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",
col_rename = "returns")
portfolio_returns_tbl## # A tibble: 60 × 2
## date returns
## <date> <dbl>
## 1 2013-01-31 0.0204
## 2 2013-02-28 -0.00239
## 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 rows5 Calculate 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
## `SharpeRatio(Rf=0%,p=95%)`
## <dbl>
## 1 0.2396 Plot
Histogram of Returns with risk free rate
portfolio_returns_tbl %>%
ggplot(aes(x = returns)) +
geom_histogram(binwidth = .01,
fill = "cornflowerblue",
alpha = 0.5) +
geom_vline(xintercept = rfr,
color = "green",
size = 1) +
annotate(geom = "text",
x = rfr + .0025,
y = 13,
label = "risk free rate",
angle = 90)
labs(y = "count")## <ggplot2::labels> List of 1
## $ y: chr "count"Scatterplot of Returns around Risk Free Rate
portfolio_returns_tbl %>%
# Add a new variable
mutate(excess_returns = if_else(returns > rfr, "rfr_above", "rfr_below")) %>%
# Plot
ggplot(aes(x = date, y = returns)) +
geom_point(aes(color = excess_returns)) +
geom_hline(yintercept = rfr,
color = "cornflowerblue",
linetype = 3,
size = 1) +
geom_vline(xintercept = as.Date("2016-11-01"),
color = "cornflowerblue",
size = 1,
alpha = 0.5) +
theme(legend.position = "none") +
annotate(geom = "text",
x = as.Date("2016-12-31"),
y = -.04,
label = "Election", size = 5,
angle = 90) +
annotate(geom = "text",
x = as.Date("2017-05-01"),
y = -.01,
label = str_glue("No returns below RFR
after the 2016 election."), color = "green") +
labs(y = "monthly returns", x = NULL)
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 0.230
## 2 2015-01-30 0.178
## 3 2015-02-27 0.240
## 4 2015-03-31 0.210
## 5 2015-04-30 0.214
## 6 2015-05-29 0.222
## 7 2015-06-30 0.238
## 8 2015-07-31 0.162
## 9 2015-08-31 0.0950
## 10 2015-09-30 -0.0279
## # ℹ 27 more rowsrolling_sr_tbl %>%
ggplot(aes(x = date, y = rolling_sr)) +
geom_line(color = "cornflowerblue") +
# Labeling
labs(x = NULL, y = "Rolling Sharpe Ratio", title = "Rolling 24-Month Sharpe Ratio") +
annotate(geom = "text",
x = as.Date("2016-06-01"), y = 0.5,
label = "This portfolio has done quite well since 2016",
color = "red", size = 5)