Quantitative Power In Action
We’ll go through some examples, but first let’s get some data. The FANG data set will be used which consists of stock prices for FB, AMZN, NFLX, and GOOG from the beginning of 2013 to the end of 2016.
data(FANG)
FANG## # A tibble: 4,032 × 8
## symbol date open high low close volume adjusted
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 FB 2013-01-02 27.44 28.18 27.42 28.00 69846400 28.00
## 2 FB 2013-01-03 27.88 28.47 27.59 27.77 63140600 27.77
## 3 FB 2013-01-04 28.01 28.93 27.83 28.76 72715400 28.76
## 4 FB 2013-01-07 28.69 29.79 28.65 29.42 83781800 29.42
## 5 FB 2013-01-08 29.51 29.60 28.86 29.06 45871300 29.06
## 6 FB 2013-01-09 29.67 30.60 29.49 30.59 104787700 30.59
## 7 FB 2013-01-10 30.60 31.45 30.28 31.30 95316400 31.30
## 8 FB 2013-01-11 31.28 31.96 31.10 31.72 89598000 31.72
## 9 FB 2013-01-14 32.08 32.21 30.62 30.95 98892800 30.95
## 10 FB 2013-01-15 30.64 31.71 29.88 30.10 173242600 30.10
## # ... with 4,022 more rowsExample 1: Use quantmod periodReturn to Convert Prices to Returns
The quantmod::periodReturn() function generates returns by periodicity. We’ll go through a couple usage cases.
Example 1A: Getting and Charting Annual Returns
We want to use the adjusted closing prices column (adjusted for stock splits, which can make it appear that a stock is performing poorly if a split is included). We set ohlc_fun = Ad. We research the periodReturn function, and we found that it accepts type = "arithmetic" and period = "yearly", which returns the annual returns.
FANG_annual_returns <- FANG %>%
group_by(symbol) %>%
tq_transmute(ohlc_fun = Ad,
mutate_fun = periodReturn,
period = "yearly",
type = "arithmetic")
FANG_annual_returns## Source: local data frame [16 x 3]
## Groups: symbol [4]
##
## symbol date yearly.returns
## <chr> <date> <dbl>
## 1 FB 2013-12-31 0.95178579
## 2 FB 2014-12-31 0.42763027
## 3 FB 2015-12-31 0.34145101
## 4 FB 2016-12-30 0.09927383
## 5 AMZN 2013-12-31 0.54984265
## 6 AMZN 2014-12-31 -0.22177086
## 7 AMZN 2015-12-31 1.17783149
## 8 AMZN 2016-12-30 0.10945565
## 9 NFLX 2013-12-31 3.00141286
## 10 NFLX 2014-12-31 -0.07214057
## 11 NFLX 2015-12-31 1.34378372
## 12 NFLX 2016-12-30 0.08235711
## 13 GOOG 2013-12-31 0.54954725
## 14 GOOG 2014-12-31 -0.05965347
## 15 GOOG 2015-12-31 0.44163478
## 16 GOOG 2016-12-30 0.01705145Charting annual returns is just a quick use of the ggplot2 package.
FANG_annual_returns %>%
ggplot(aes(x = date, y = yearly.returns, fill = symbol)) +
geom_bar(stat = "identity") +
geom_hline(yintercept = 0, color = palette_light()[[1]]) +
scale_y_continuous(labels = scales::percent) +
labs(title = "FANG: Annual Returns",
subtitle = "Get annual returns quickly with tq_transmute!",
y = "Annual Returns", x = "") +
facet_wrap(~ symbol, ncol = 2) +
theme_tq() +
scale_fill_tq()
Example 1B: Getting Daily Log Returns
Daily log returns follows a similar approach. Normally I go with a transmute function, tq_transmute, because the periodReturn function accepts different periodicity options, and anything other than daily will blow up a mutation. But, in our situation the period returns periodicity is the same as the stock prices periodicity (both daily), so we can use either. We want to use the adjusted closing prices column (adjusted for stock splits, which can make it appear that a stock is performing poorly if a split is included), so we set ohlc_fun = Ad. We researched the periodReturn function, and we found that it accepts type = "log" and period = "daily", which returns the daily log returns.
FANG_daily_log_returns <- FANG %>%
group_by(symbol) %>%
tq_transmute(ohlc_fun = Ad,
mutate_fun = periodReturn,
period = "daily",
type = "log",
col_rename = "monthly.returns")FANG_daily_log_returns %>%
ggplot(aes(x = monthly.returns, fill = symbol)) +
geom_density(alpha = 0.5) +
labs(title = "FANG: Charting the Daily Log Returns",
x = "Monthly Returns", y = "Density") +
theme_tq() +
scale_fill_tq() +
facet_wrap(~ symbol, ncol = 2)
Example 2: Use xts to.period to Change the Periodicity from Daily to Monthly
The xts::to.period function is used for periodicity aggregation (converting from a lower level periodicity to a higher level such as minutes to hours or months to years). Because we are seeking a return structure that is on a different time scale than the input (daily versus weekly), we need to use a transmute function. We select tq_transmute and pass the open, high, low, close and volume using OHLC format via ohlc_fun = OHLCV. Looking at the documentation for to.period, we see that it accepts a period argument that we can set to "weeks". The result is the OHLCV data returned with the dates changed to one day per week.
FANG %>%
group_by(symbol) %>%
tq_transmute(ohlc_fun = OHLCV, mutate_fun = to.period, period = "months")## Source: local data frame [192 x 7]
## Groups: symbol [4]
##
## symbol date open high low close volume
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 FB 2013-01-31 29.15 31.47 28.74 30.98 190744900
## 2 FB 2013-02-28 26.84 27.30 26.34 27.25 83027800
## 3 FB 2013-03-28 26.09 26.17 25.52 25.58 28585700
## 4 FB 2013-04-30 27.13 27.85 27.01 27.77 36245700
## 5 FB 2013-05-31 24.63 24.95 24.27 24.35 35925000
## 6 FB 2013-06-28 24.68 24.98 24.42 24.88 96778900
## 7 FB 2013-07-31 37.96 38.31 36.33 36.80 154828700
## 8 FB 2013-08-30 42.02 42.26 41.06 41.29 67735100
## 9 FB 2013-09-30 50.14 51.60 49.80 50.23 100095000
## 10 FB 2013-10-31 47.16 52.00 46.50 50.21 248809000
## # ... with 182 more rowsA common usage case is to reduce the number of points to smooth time series plots. Let’s check out difference between daily and monthly plots.
Without Periodicity Aggregation
FANG_daily <- FANG %>%
group_by(symbol)
FANG_daily %>%
ggplot(aes(x = date, y = adjusted, color = symbol)) +
geom_line(size = 1) +
labs(title = "Daily Stock Prices",
x = "", y = "Adjusted Prices", color = "") +
facet_wrap(~ symbol, ncol = 2, scales = "free_y") +
scale_y_continuous(labels = scales::dollar) +
theme_tq() +
scale_color_tq()
With Periodicity Aggregation
FANG_monthly <- FANG %>%
group_by(symbol) %>%
tq_transmute(ohlc_fun = Ad, mutate_fun = to.period, period = "months")
FANG_monthly %>%
ggplot(aes(x = date, y = adjusted, color = symbol)) +
geom_line(size = 1) +
labs(title = "Monthly Stock Prices",
x = "", y = "Adjusted Prices", color = "") +
facet_wrap(~ symbol, ncol = 2, scales = "free_y") +
scale_y_continuous(labels = scales::dollar) +
theme_tq() +
scale_color_tq()
Example 3: Use TTR runCor to Visualize Rolling Correlations of Returns
Return correlations are a common way to analyze how closely an asset or portfolio mimics a baseline index or fund. We will need a set of returns for both the stocks and baseline. The stock will be the FANG data set and the baseline will be the Spdr XLK technology sector. We have the prices for the “FANG” stocks, so we use tq_get to retrieve the “XLK” prices. The returns can be calculated from the “adjusted” prices using the process in Example 1.
# Asset Returns
FANG_returns_monthly <- FANG %>%
group_by(symbol) %>%
tq_transmute(ohlc_fun = Ad,
mutate_fun = periodReturn,
period = "monthly")
# Baseline Returns
baseline_returns_monthly <- "XLK" %>%
tq_get(get = "stock.prices",
from = "2013-01-01",
to = "2016-12-31") %>%
tq_transmute(ohlc_fun = Ad,
mutate_fun = periodReturn,
period = "monthly")Next, join the asset returns with the baseline returns by date.
returns_joined <- left_join(FANG_returns_monthly,
baseline_returns_monthly,
by = "date")
returns_joined## Source: local data frame [192 x 4]
## Groups: symbol [?]
##
## symbol date monthly.returns.x monthly.returns.y
## <chr> <date> <dbl> <dbl>
## 1 FB 2013-01-31 0.1064285714 -0.01375376
## 2 FB 2013-02-28 -0.1204002582 0.00782312
## 3 FB 2013-03-28 -0.0612844037 0.02577700
## 4 FB 2013-04-30 0.0856137608 0.01750903
## 5 FB 2013-05-31 -0.1231544833 0.02792210
## 6 FB 2013-06-28 0.0217658727 -0.02891755
## 7 FB 2013-07-31 0.4790996977 0.03726707
## 8 FB 2013-08-30 0.1220109272 -0.01040025
## 9 FB 2013-09-30 0.2165172871 0.02532125
## 10 FB 2013-10-31 -0.0003981883 0.05024969
## # ... with 182 more rowsThe TTR::runCor function can be used to evaluate rolling correlations using the xy pattern. Looking at the documentation (?runCor), we can see that the arguments include x and y along with a few additional arguments including n for the width of the rolling correlation. Because the scale is monthly, we’ll go with n = 6 for a 6-month rolling correlation. The col_rename argument enables easy renaming of the output column(s).
FANG_rolling_corr <- returns_joined %>%
tq_transmute_xy(x = monthly.returns.x,
y = monthly.returns.y,
mutate_fun = runCor,
n = 6,
col_rename = "rolling.corr.6")And, we can plot the rolling correlations for the FANG stocks.
FANG_rolling_corr %>%
ggplot(aes(x = date, y = rolling.corr.6, color = symbol)) +
geom_hline(yintercept = 0, color = palette_light()[[1]]) +
geom_line(size = 1) +
labs(title = "FANG: Six Month Rolling Correlation to XLK",
x = "", y = "Correlation", color = "") +
facet_wrap(~ symbol, ncol = 2) +
theme_tq() +
scale_color_tq()
Example 4: Use TTR MACD to Visualize Moving Average Convergence Divergence
In reviewing the available options in the TTR package, we see that MACD will get us the Moving Average Convergence Divergence (MACD). In researching the documentation, the return is in the same periodicity as the input and the functions work with OHLC functions, so we can use tq_mutate(). MACD requires a price, so we select close using Cl.
FANG_macd <- FANG %>%
group_by(symbol) %>%
tq_mutate(ohlc_fun = Cl,
mutate_fun = MACD,
nFast = 12,
nSlow = 26,
nSig = 9,
maType = SMA) %>%
mutate(diff = macd - signal) %>%
select(-(open:volume))
FANG_macd## Source: local data frame [4,032 x 6]
## Groups: symbol [4]
##
## symbol date adjusted macd signal diff
## <chr> <date> <dbl> <dbl> <dbl> <dbl>
## 1 FB 2013-01-02 28.00 NA NA NA
## 2 FB 2013-01-03 27.77 NA NA NA
## 3 FB 2013-01-04 28.76 NA NA NA
## 4 FB 2013-01-07 29.42 NA NA NA
## 5 FB 2013-01-08 29.06 NA NA NA
## 6 FB 2013-01-09 30.59 NA NA NA
## 7 FB 2013-01-10 31.30 NA NA NA
## 8 FB 2013-01-11 31.72 NA NA NA
## 9 FB 2013-01-14 30.95 NA NA NA
## 10 FB 2013-01-15 30.10 NA NA NA
## # ... with 4,022 more rowsAnd, we can visualize the data like so.
FANG_macd %>%
filter(date >= as_date("2016-10-01")) %>%
ggplot(aes(x = date)) +
geom_hline(yintercept = 0, color = palette_light()[[1]]) +
geom_line(aes(y = macd, col = symbol)) +
geom_line(aes(y = signal), color = "blue", linetype = 2) +
geom_bar(aes(y = diff), stat = "identity", color = palette_light()[[1]]) +
facet_wrap(~ symbol, ncol = 2, scale = "free_y") +
labs(title = "FANG: Moving Average Convergence Divergence",
y = "MACD", x = "", color = "") +
theme_tq() +
scale_color_tq()
Example 5: Use xts apply.quarterly to Get the Max and Min Price for Each Quarter
The xts::apply.quarterly() function that is part of the period apply group can be used to apply functions by quarterly time segments. Because we are seeking a return structure that is on a different time scale than the input (quarterly versus daily), we need to use a transmute function. We select tq_transmute and pass the close price using OHLC format via ohlc_fun = Cl, and we send this subset of the data to the apply.quarterly function via the mutate_fun argument. Looking at the documentation for apply.quarterly, we see that we can pass a function to the argument, FUN. We want the maximum values, so we set FUN = max. The result is the quarters returned as a date and the maximum closing price during the quarter returned as a double.
FANG_max_by_qtr <- FANG %>%
group_by(symbol) %>%
tq_transmute(ohlc_fun = Ad,
mutate_fun = apply.quarterly,
FUN = max,
col_rename = "max.close") %>%
mutate(year.qtr = paste0(year(date), "-Q", quarter(date))) %>%
select(-date)
FANG_max_by_qtr## Source: local data frame [64 x 3]
## Groups: symbol [4]
##
## symbol max.close year.qtr
## <chr> <dbl> <chr>
## 1 FB 32.47 2013-Q1
## 2 FB 28.97 2013-Q2
## 3 FB 51.24 2013-Q3
## 4 FB 57.96 2013-Q4
## 5 FB 72.03 2014-Q1
## 6 FB 67.60 2014-Q2
## 7 FB 79.04 2014-Q3
## 8 FB 81.45 2014-Q4
## 9 FB 85.31 2015-Q1
## 10 FB 88.86 2015-Q2
## # ... with 54 more rowsThe minimum each quarter can be retrieved in much the same way. The data frames can be joined using left_join to get the max and min by quarter.
FANG_min_by_qtr <- FANG %>%
group_by(symbol) %>%
tq_transmute(ohlc_fun = Ad,
mutate_fun = apply.quarterly,
FUN = min,
col_rename = "min.close") %>%
mutate(year.qtr = paste0(year(date), "-Q", quarter(date))) %>%
select(-date)
FANG_by_qtr <- left_join(FANG_max_by_qtr, FANG_min_by_qtr,
by = c("symbol" = "symbol",
"year.qtr" = "year.qtr"))
FANG_by_qtr## Source: local data frame [64 x 4]
## Groups: symbol [?]
##
## symbol max.close year.qtr min.close
## <chr> <dbl> <chr> <dbl>
## 1 FB 32.47 2013-Q1 25.13
## 2 FB 28.97 2013-Q2 22.90
## 3 FB 51.24 2013-Q3 24.37
## 4 FB 57.96 2013-Q4 44.82
## 5 FB 72.03 2014-Q1 53.53
## 6 FB 67.60 2014-Q2 56.14
## 7 FB 79.04 2014-Q3 62.76
## 8 FB 81.45 2014-Q4 72.63
## 9 FB 85.31 2015-Q1 74.05
## 10 FB 88.86 2015-Q2 77.46
## # ... with 54 more rowsAnd, we can visualize the data like so.
FANG_by_qtr %>%
ggplot(aes(x = year.qtr, color = symbol)) +
geom_segment(aes(xend = year.qtr, y = min.close, yend = max.close),
size = 1) +
geom_point(aes(y = max.close), size = 2) +
geom_point(aes(y = min.close), size = 2) +
facet_wrap(~ symbol, ncol = 2, scale = "free_y") +
labs(title = "FANG: Min/Max Price By Quarter",
y = "Stock Price", color = "") +
theme_tq() +
scale_color_tq() +
scale_y_continuous(labels = scales::dollar) +
theme(axis.text.x = element_text(angle = 90, hjust = 1),
axis.title.x = element_blank())