CVS
Tim Larocque
December 8, 2018
# Loads tidyquant, tidyverse, lubridate, xts, quantmod, TTR
library(tidyquant)CVS <- tq_get("CVS", get = "stock.prices", from = "2011-01-01", to = today())
CVS
## # A tibble: 1,997 x 7
## date open high low close volume adjusted
## <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2011-01-03 34.9 35.2 34.8 35.0 8164600 30.2
## 2 2011-01-04 35 35.1 34.4 35.0 9891900 30.2
## 3 2011-01-05 34.9 35.4 34.8 35.4 10946500 30.5
## 4 2011-01-06 35.3 35.3 34.8 35.0 8680000 30.2
## 5 2011-01-07 35 35.3 34.8 35.0 8220600 30.2
## 6 2011-01-10 35.0 35.3 35.0 35.2 6579400 30.3
## 7 2011-01-11 35.2 35.7 35.1 35.3 8869300 30.5
## 8 2011-01-12 35.4 36.0 34.7 34.8 13151500 30.0
## 9 2011-01-13 34.8 34.9 34.5 34.9 8165500 30.1
## 10 2011-01-14 34.9 35.2 34.6 35.1 7016700 30.2
## # ... with 1,987 more rowsCVS_MA <-
CVS %>%
tq_mutate(select = close, mutate_fun = SMA, n = 50) %>%
rename(SMA.50 = SMA) %>%
tq_mutate(select = close, mutate_fun = SMA, n = 200) %>%
rename(SMA.200 = SMA)
CVS_MA
## # A tibble: 1,997 x 9
## date open high low close volume adjusted SMA.50 SMA.200
## <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2011-01-03 34.9 35.2 34.8 35.0 8164600 30.2 NA NA
## 2 2011-01-04 35 35.1 34.4 35.0 9891900 30.2 NA NA
## 3 2011-01-05 34.9 35.4 34.8 35.4 10946500 30.5 NA NA
## 4 2011-01-06 35.3 35.3 34.8 35.0 8680000 30.2 NA NA
## 5 2011-01-07 35 35.3 34.8 35.0 8220600 30.2 NA NA
## 6 2011-01-10 35.0 35.3 35.0 35.2 6579400 30.3 NA NA
## 7 2011-01-11 35.2 35.7 35.1 35.3 8869300 30.5 NA NA
## 8 2011-01-12 35.4 36.0 34.7 34.8 13151500 30.0 NA NA
## 9 2011-01-13 34.8 34.9 34.5 34.9 8165500 30.1 NA NA
## 10 2011-01-14 34.9 35.2 34.6 35.1 7016700 30.2 NA NA
## # ... with 1,987 more rowsCVS_MA %>%
select(date, close, SMA.50, SMA.200) %>%
gather(key = type, value = price, close:SMA.200)
## # A tibble: 5,991 x 3
## date type price
## <date> <chr> <dbl>
## 1 2011-01-03 close 35.0
## 2 2011-01-04 close 35.0
## 3 2011-01-05 close 35.4
## 4 2011-01-06 close 35.0
## 5 2011-01-07 close 35.0
## 6 2011-01-10 close 35.2
## 7 2011-01-11 close 35.3
## 8 2011-01-12 close 34.8
## 9 2011-01-13 close 34.9
## 10 2011-01-14 close 35.1
## # ... with 5,981 more rows# Transform to long form to wide form for graphing
CVS_MA %>%
select(date, close, SMA.50, SMA.200) %>%
gather(key = type, value = price, close:SMA.200)
## # A tibble: 5,991 x 3
## date type price
## <date> <chr> <dbl>
## 1 2011-01-03 close 35.0
## 2 2011-01-04 close 35.0
## 3 2011-01-05 close 35.4
## 4 2011-01-06 close 35.0
## 5 2011-01-07 close 35.0
## 6 2011-01-10 close 35.2
## 7 2011-01-11 close 35.3
## 8 2011-01-12 close 34.8
## 9 2011-01-13 close 34.9
## 10 2011-01-14 close 35.1
## # ... with 5,981 more rows
# Visualize Moving Average Crossover
CVS_MA %>%
select(date, close, SMA.50, SMA.200) %>%
gather(key = type, value = price, close:SMA.200) %>%
filter(date >= "2018-01-01") %>%
ggplot(aes(x = date, y = price, col = type)) +
geom_line() +
theme(legend.position="bottom") +
labs(x = NULL,
y = "Stock Prices")
```