# Loads tidyquant, tidyverse, lubridate, xts, quantmod, TTR
library(tidyquant)
TSLA <- tq_get("TSLA", get = "stock.prices", from = "2011-01-01", to = today())
TSLA
## # A tibble: 1,995 x 7
## date open high low close volume adjusted
## <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2011-01-03 26.8 27 25.9 26.6 1283000 26.6
## 2 2011-01-04 26.7 27.0 26.0 26.7 1187400 26.7
## 3 2011-01-05 26.5 26.9 26.2 26.8 1446700 26.8
## 4 2011-01-06 26.8 28 26.8 27.9 2061200 27.9
## 5 2011-01-07 28 28.6 27.9 28.2 2247900 28.2
## 6 2011-01-10 28.2 28.7 28.0 28.5 1342700 28.5
## 7 2011-01-11 28.6 28.7 26.9 27.0 1710200 27.0
## 8 2011-01-12 27.0 27.4 26.5 27.0 964400 27.0
## 9 2011-01-13 27.0 27.0 26.2 26.2 723600 26.2
## 10 2011-01-14 26.2 26.6 25.6 25.8 1192000 25.8
## # ... with 1,985 more rows
TSLA_MA <-
TSLA %>%
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)
TSLA_MA
## # A tibble: 1,995 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 26.8 27 25.9 26.6 1283000 26.6 NA NA
## 2 2011-01-04 26.7 27.0 26.0 26.7 1187400 26.7 NA NA
## 3 2011-01-05 26.5 26.9 26.2 26.8 1446700 26.8 NA NA
## 4 2011-01-06 26.8 28 26.8 27.9 2061200 27.9 NA NA
## 5 2011-01-07 28 28.6 27.9 28.2 2247900 28.2 NA NA
## 6 2011-01-10 28.2 28.7 28.0 28.5 1342700 28.5 NA NA
## 7 2011-01-11 28.6 28.7 26.9 27.0 1710200 27.0 NA NA
## 8 2011-01-12 27.0 27.4 26.5 27.0 964400 27.0 NA NA
## 9 2011-01-13 27.0 27.0 26.2 26.2 723600 26.2 NA NA
## 10 2011-01-14 26.2 26.6 25.6 25.8 1192000 25.8 NA NA
## # ... with 1,985 more rows
TSLA_MA %>%
select(date, close, SMA.50, SMA.200) %>%
gather(key = type, value = price, close:SMA.200)
## # A tibble: 5,985 x 3
## date type price
## <date> <chr> <dbl>
## 1 2011-01-03 close 26.6
## 2 2011-01-04 close 26.7
## 3 2011-01-05 close 26.8
## 4 2011-01-06 close 27.9
## 5 2011-01-07 close 28.2
## 6 2011-01-10 close 28.5
## 7 2011-01-11 close 27.0
## 8 2011-01-12 close 27.0
## 9 2011-01-13 close 26.2
## 10 2011-01-14 close 25.8
## # ... with 5,975 more rows
# Visualize Moving Average Crossover
TSLA_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")
F <- tq_get("F", get = "stock.prices", from = "2011-01-01", to = today())
F
## # A tibble: 1,995 x 7
## date open high low close volume adjusted
## <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2011-01-03 17.0 17.3 17 17.2 70794200 13.1
## 2 2011-01-04 17.4 17.7 17.0 17.4 106477200 13.2
## 3 2011-01-05 17.3 18.1 17.2 17.9 112378000 13.6
## 4 2011-01-06 18.0 18.3 18.0 18.2 96284400 13.9
## 5 2011-01-07 18.4 18.4 18.1 18.3 99029500 13.9
## 6 2011-01-10 18.4 18.5 18.2 18.3 65348400 14.0
## 7 2011-01-11 18.5 18.5 18.1 18.3 61312500 13.9
## 8 2011-01-12 18.4 18.8 18.3 18.7 66841400 14.3
## 9 2011-01-13 18.8 19.0 18.5 18.7 100459700 14.2
## 10 2011-01-14 18.6 18.8 18.6 18.6 50279400 14.2
## # ... with 1,985 more rows
F_MA <-
F %>%
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)
F_MA
## # A tibble: 1,995 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 17.0 17.3 17 17.2 70794200 13.1 NA NA
## 2 2011-01-04 17.4 17.7 17.0 17.4 106477200 13.2 NA NA
## 3 2011-01-05 17.3 18.1 17.2 17.9 112378000 13.6 NA NA
## 4 2011-01-06 18.0 18.3 18.0 18.2 96284400 13.9 NA NA
## 5 2011-01-07 18.4 18.4 18.1 18.3 99029500 13.9 NA NA
## 6 2011-01-10 18.4 18.5 18.2 18.3 65348400 14.0 NA NA
## 7 2011-01-11 18.5 18.5 18.1 18.3 61312500 13.9 NA NA
## 8 2011-01-12 18.4 18.8 18.3 18.7 66841400 14.3 NA NA
## 9 2011-01-13 18.8 19.0 18.5 18.7 100459700 14.2 NA NA
## 10 2011-01-14 18.6 18.8 18.6 18.6 50279400 14.2 NA NA
## # ... with 1,985 more rows
F_MA %>%
select(date, close, SMA.50, SMA.200) %>%
gather(key = type, value = price, close:SMA.200)
## # A tibble: 5,985 x 3
## date type price
## <date> <chr> <dbl>
## 1 2011-01-03 close 17.2
## 2 2011-01-04 close 17.4
## 3 2011-01-05 close 17.9
## 4 2011-01-06 close 18.2
## 5 2011-01-07 close 18.3
## 6 2011-01-10 close 18.3
## 7 2011-01-11 close 18.3
## 8 2011-01-12 close 18.7
## 9 2011-01-13 close 18.7
## 10 2011-01-14 close 18.6
## # ... with 5,975 more rows
F_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")
GM <- tq_get("GM", get = "stock.prices", from = "2011-01-01", to = today())
GM
## # A tibble: 1,995 x 7
## date open high low close volume adjusted
## <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2011-01-03 37.3 38 37.0 37.1 24874900 30.2
## 2 2011-01-04 37.1 38.0 36.7 37.9 32363400 30.9
## 3 2011-01-05 37.5 38.3 37.5 38.1 22503900 31.0
## 4 2011-01-06 38.2 39.5 38.1 38.9 38556900 31.7
## 5 2011-01-07 38.8 39.3 38.5 39.0 19901100 31.8
## 6 2011-01-10 39.3 39.4 38.4 38.6 18341600 31.4
## 7 2011-01-11 38.7 39.4 38.5 38.8 14856500 31.6
## 8 2011-01-12 39.0 39.4 38.4 38.6 16773900 31.5
## 9 2011-01-13 38.7 38.7 38.1 38.3 11358300 31.2
## 10 2011-01-14 38.2 38.5 38.0 38.2 5894200 31.1
## # ... with 1,985 more rows
GM_MA <-
GM %>%
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)
GM_MA
## # A tibble: 1,995 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 37.3 38 37.0 37.1 24874900 30.2 NA NA
## 2 2011-01-04 37.1 38.0 36.7 37.9 32363400 30.9 NA NA
## 3 2011-01-05 37.5 38.3 37.5 38.1 22503900 31.0 NA NA
## 4 2011-01-06 38.2 39.5 38.1 38.9 38556900 31.7 NA NA
## 5 2011-01-07 38.8 39.3 38.5 39.0 19901100 31.8 NA NA
## 6 2011-01-10 39.3 39.4 38.4 38.6 18341600 31.4 NA NA
## 7 2011-01-11 38.7 39.4 38.5 38.8 14856500 31.6 NA NA
## 8 2011-01-12 39.0 39.4 38.4 38.6 16773900 31.5 NA NA
## 9 2011-01-13 38.7 38.7 38.1 38.3 11358300 31.2 NA NA
## 10 2011-01-14 38.2 38.5 38.0 38.2 5894200 31.1 NA NA
## # ... with 1,985 more rows
GM_MA %>%
select(date, close, SMA.50, SMA.200) %>%
gather(key = type, value = price, close:SMA.200)
## # A tibble: 5,985 x 3
## date type price
## <date> <chr> <dbl>
## 1 2011-01-03 close 37.1
## 2 2011-01-04 close 37.9
## 3 2011-01-05 close 38.1
## 4 2011-01-06 close 38.9
## 5 2011-01-07 close 39.0
## 6 2011-01-10 close 38.6
## 7 2011-01-11 close 38.8
## 8 2011-01-12 close 38.6
## 9 2011-01-13 close 38.3
## 10 2011-01-14 close 38.2
## # ... with 5,975 more rows
GM_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")
