Moving Average Analysis
- Q1 Import S&P500 for the last one year.
- Q2 Calculate 15-day and 50-day simple moving averages.
- Q3 Transform data to long form from wide form for graphing.
- Q4 Visualize data.
- Q5 List all bullish or bearish crossovers with dates and closing prices.
- Q6 If you had invested $1 million on the day of the first bullish crossover and sold your shares on the following bearish crosover, how much would you have won or lost?
- Q7 Try 50-day and 100-day simple moving average instead. Does your answer in Q6 change?
- Q8 Hide the messages and the code, but display results of the code from the webpage.
- Q9 Display the title and your name correctly at the top of the webpage.
- Q10 Use the correct slug.
The code for the moving average analysis below is taken from: https://www.r-bloggers.com/tidyquant-bringing-quantitative-financial-analysis-to-the-tidyverse/.
library(tidyquant)
library(tidyverse)
# Conduct the trend analysis (moving average) for the Apple stock.
# Import data
from = today() - years(1)
stock <- tq_get("AMZN", get = "stock.prices", from = from)
stock
## # A tibble: 251 x 8
## symbol date open high low close volume adjusted
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 AMZN 2019-04-12 1848. 1852. 1841. 1843. 3114400 1843.
## 2 AMZN 2019-04-15 1842 1847. 1819. 1845. 3724400 1845.
## 3 AMZN 2019-04-16 1851. 1870. 1848 1863. 3044600 1863.
## 4 AMZN 2019-04-17 1873. 1876. 1860. 1865. 2893500 1865.
## 5 AMZN 2019-04-18 1869. 1871. 1859. 1862. 2749900 1862.
## 6 AMZN 2019-04-22 1855. 1888. 1846. 1887. 3373800 1887.
## 7 AMZN 2019-04-23 1891. 1929. 1890. 1924. 4640400 1924.
## 8 AMZN 2019-04-24 1925 1930. 1898. 1902. 3675800 1902.
## 9 AMZN 2019-04-25 1917 1922. 1900. 1902. 6099100 1902.
## 10 AMZN 2019-04-26 1929 1951 1898 1951. 8432600 1951.
## # … with 241 more rows# Calculate 50-day and 100-day simple moving average
stock <-
stock %>%
# Calculate 50-day simple moving average
tq_mutate(select = close, mutate_fun = SMA, n = 15) %>%
# Rename 50-day SMA to SMA.short
rename(SMA.short = SMA) %>%
tq_mutate(select = close, mutate_fun = SMA, n = 50) %>%
rename(SMA.long = SMA)
stock
## # A tibble: 251 x 10
## symbol date open high low close volume adjusted SMA.short SMA.long
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 AMZN 2019-04-12 1848. 1852. 1841. 1843. 3114400 1843. NA NA
## 2 AMZN 2019-04-15 1842 1847. 1819. 1845. 3724400 1845. NA NA
## 3 AMZN 2019-04-16 1851. 1870. 1848 1863. 3044600 1863. NA NA
## 4 AMZN 2019-04-17 1873. 1876. 1860. 1865. 2893500 1865. NA NA
## 5 AMZN 2019-04-18 1869. 1871. 1859. 1862. 2749900 1862. NA NA
## 6 AMZN 2019-04-22 1855. 1888. 1846. 1887. 3373800 1887. NA NA
## 7 AMZN 2019-04-23 1891. 1929. 1890. 1924. 4640400 1924. NA NA
## 8 AMZN 2019-04-24 1925 1930. 1898. 1902. 3675800 1902. NA NA
## 9 AMZN 2019-04-25 1917 1922. 1900. 1902. 6099100 1902. NA NA
## 10 AMZN 2019-04-26 1929 1951 1898 1951. 8432600 1951. NA NA
## # … with 241 more rows# Transform to long form to wide form for graphing
stock_long <-
stock %>%
select(date, close, SMA.short, SMA.long) %>%
# Transform to long form
gather(key = "type", value = "price", close:SMA.long)
stock_long
## # A tibble: 753 x 3
## date type price
## <date> <chr> <dbl>
## 1 2019-04-12 close 1843.
## 2 2019-04-15 close 1845.
## 3 2019-04-16 close 1863.
## 4 2019-04-17 close 1865.
## 5 2019-04-18 close 1862.
## 6 2019-04-22 close 1887.
## 7 2019-04-23 close 1924.
## 8 2019-04-24 close 1902.
## 9 2019-04-25 close 1902.
## 10 2019-04-26 close 1951.
## # … with 743 more rows# Visualize
stock_long %>%
ggplot(aes(x = date, y = price, col = type)) +
geom_line() +
theme(legend.position="bottom") +
labs(title = "Simple Moving Averages are a Breeze with tidyquant",
x = NULL,
y = "Stock Prices")
Q1 Import S&P500 for the last one year.
Hint: Copy and revise the importing part of the code from above.
library(tidyquant)
library(tidyverse)
# Conduct the trend analysis (moving average) for the Apple stock.
# Import data
from = today() - years(1)
stock <- tq_get("^GSPC", get = "stock.prices", from = from)
stock
## # A tibble: 251 x 8
## symbol date open high low close volume adjusted
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 ^GSPC 2019-04-12 2901. 2911. 2898. 2907. 3688490000 2907.
## 2 ^GSPC 2019-04-15 2908. 2910. 2896. 2906. 3088330000 2906.
## 3 ^GSPC 2019-04-16 2912. 2916. 2901. 2907. 3402210000 2907.
## 4 ^GSPC 2019-04-17 2916. 2918 2895. 2900. 3602300000 2900.
## 5 ^GSPC 2019-04-18 2905. 2908. 2892. 2905. 3506850000 2905.
## 6 ^GSPC 2019-04-22 2899. 2910. 2896. 2908. 2997950000 2908.
## 7 ^GSPC 2019-04-23 2910. 2936. 2909. 2934. 3635030000 2934.
## 8 ^GSPC 2019-04-24 2934 2937. 2926. 2927. 3448960000 2927.
## 9 ^GSPC 2019-04-25 2929. 2933. 2913. 2926. 3425280000 2926.
## 10 ^GSPC 2019-04-26 2926. 2940. 2918. 2940. 3248500000 2940.
## # … with 241 more rowsQ2 Calculate 15-day and 50-day simple moving averages.
Hint: Copy and revise the moving average part of the code from above.
# Calculate 50-day and 100-day simple moving average
stock <-
stock %>%
# Calculate 50-day simple moving average
tq_mutate(select = close, mutate_fun = SMA, n = 15) %>%
# Rename 50-day SMA to SMA.short
rename(SMA.short = SMA) %>%
tq_mutate(select = close, mutate_fun = SMA, n = 50) %>%
rename(SMA.long = SMA)
stock
## # A tibble: 251 x 10
## symbol date open high low close volume adjusted SMA.short SMA.long
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 ^GSPC 2019-04-12 2901. 2911. 2898. 2907. 3.69e9 2907. NA NA
## 2 ^GSPC 2019-04-15 2908. 2910. 2896. 2906. 3.09e9 2906. NA NA
## 3 ^GSPC 2019-04-16 2912. 2916. 2901. 2907. 3.40e9 2907. NA NA
## 4 ^GSPC 2019-04-17 2916. 2918 2895. 2900. 3.60e9 2900. NA NA
## 5 ^GSPC 2019-04-18 2905. 2908. 2892. 2905. 3.51e9 2905. NA NA
## 6 ^GSPC 2019-04-22 2899. 2910. 2896. 2908. 3.00e9 2908. NA NA
## 7 ^GSPC 2019-04-23 2910. 2936. 2909. 2934. 3.64e9 2934. NA NA
## 8 ^GSPC 2019-04-24 2934 2937. 2926. 2927. 3.45e9 2927. NA NA
## 9 ^GSPC 2019-04-25 2929. 2933. 2913. 2926. 3.43e9 2926. NA NA
## 10 ^GSPC 2019-04-26 2926. 2940. 2918. 2940. 3.25e9 2940. NA NA
## # … with 241 more rowsQ3 Transform data to long form from wide form for graphing.
Hint: Copy and revise the transformation part of the code from above.
# Transform to long form to wide form for graphing
stock_long <-
stock %>%
select(date, close, SMA.short, SMA.long) %>%
# Transform to long form
gather(key = "type", value = "price", close:SMA.long)
stock_long
## # A tibble: 753 x 3
## date type price
## <date> <chr> <dbl>
## 1 2019-04-12 close 2907.
## 2 2019-04-15 close 2906.
## 3 2019-04-16 close 2907.
## 4 2019-04-17 close 2900.
## 5 2019-04-18 close 2905.
## 6 2019-04-22 close 2908.
## 7 2019-04-23 close 2934.
## 8 2019-04-24 close 2927.
## 9 2019-04-25 close 2926.
## 10 2019-04-26 close 2940.
## # … with 743 more rowsQ4 Visualize data.
Hint: Copy and revise the visualization part of the code from above.
# Visualize
stock_long %>%
ggplot(aes(x = date, y = price, col = type)) +
geom_line() +
theme(legend.position="bottom") +
labs(title = "Simple Moving Averages are a Breeze with tidyquant",
x = NULL,
y = "Stock Prices")
Q5 List all bullish or bearish crossovers with dates and closing prices.
- A bullish crossover if the 100-day moving average cross above the 200-day moving average.
- A bearish crossover if the 100-day moving average cross below the 200-day moving average.
Q7 Try 50-day and 100-day simple moving average instead. Does your answer in Q6 change?
library(tidyquant)
library(tidyverse)
# Conduct the trend analysis (moving average) for the Apple stock.
# Import data
from = today() - years(1)
stock <- tq_get("^GSPC", get = "stock.prices", from = from)
stock
## # A tibble: 251 x 8
## symbol date open high low close volume adjusted
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 ^GSPC 2019-04-12 2901. 2911. 2898. 2907. 3688490000 2907.
## 2 ^GSPC 2019-04-15 2908. 2910. 2896. 2906. 3088330000 2906.
## 3 ^GSPC 2019-04-16 2912. 2916. 2901. 2907. 3402210000 2907.
## 4 ^GSPC 2019-04-17 2916. 2918 2895. 2900. 3602300000 2900.
## 5 ^GSPC 2019-04-18 2905. 2908. 2892. 2905. 3506850000 2905.
## 6 ^GSPC 2019-04-22 2899. 2910. 2896. 2908. 2997950000 2908.
## 7 ^GSPC 2019-04-23 2910. 2936. 2909. 2934. 3635030000 2934.
## 8 ^GSPC 2019-04-24 2934 2937. 2926. 2927. 3448960000 2927.
## 9 ^GSPC 2019-04-25 2929. 2933. 2913. 2926. 3425280000 2926.
## 10 ^GSPC 2019-04-26 2926. 2940. 2918. 2940. 3248500000 2940.
## # … with 241 more rows# Calculate 50-day and 100-day simple moving average
stock <-
stock %>%
# Calculate 50-day simple moving average
tq_mutate(select = close, mutate_fun = SMA, n = 50) %>%
# Rename 50-day SMA to SMA.short
rename(SMA.short = SMA) %>%
tq_mutate(select = close, mutate_fun = SMA, n = 100) %>%
rename(SMA.long = SMA)
stock
## # A tibble: 251 x 10
## symbol date open high low close volume adjusted SMA.short SMA.long
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 ^GSPC 2019-04-12 2901. 2911. 2898. 2907. 3.69e9 2907. NA NA
## 2 ^GSPC 2019-04-15 2908. 2910. 2896. 2906. 3.09e9 2906. NA NA
## 3 ^GSPC 2019-04-16 2912. 2916. 2901. 2907. 3.40e9 2907. NA NA
## 4 ^GSPC 2019-04-17 2916. 2918 2895. 2900. 3.60e9 2900. NA NA
## 5 ^GSPC 2019-04-18 2905. 2908. 2892. 2905. 3.51e9 2905. NA NA
## 6 ^GSPC 2019-04-22 2899. 2910. 2896. 2908. 3.00e9 2908. NA NA
## 7 ^GSPC 2019-04-23 2910. 2936. 2909. 2934. 3.64e9 2934. NA NA
## 8 ^GSPC 2019-04-24 2934 2937. 2926. 2927. 3.45e9 2927. NA NA
## 9 ^GSPC 2019-04-25 2929. 2933. 2913. 2926. 3.43e9 2926. NA NA
## 10 ^GSPC 2019-04-26 2926. 2940. 2918. 2940. 3.25e9 2940. NA NA
## # … with 241 more rows# Transform to long form to wide form for graphing
stock_long <-
stock %>%
select(date, close, SMA.short, SMA.long) %>%
# Transform to long form
gather(key = "type", value = "price", close:SMA.long)
stock_long
## # A tibble: 753 x 3
## date type price
## <date> <chr> <dbl>
## 1 2019-04-12 close 2907.
## 2 2019-04-15 close 2906.
## 3 2019-04-16 close 2907.
## 4 2019-04-17 close 2900.
## 5 2019-04-18 close 2905.
## 6 2019-04-22 close 2908.
## 7 2019-04-23 close 2934.
## 8 2019-04-24 close 2927.
## 9 2019-04-25 close 2926.
## 10 2019-04-26 close 2940.
## # … with 743 more rows# Visualize
stock_long %>%
ggplot(aes(x = date, y = price, col = type)) +
geom_line() +
theme(legend.position="bottom") +
labs(title = "Simple Moving Averages are a Breeze with tidyquant",
x = NULL,
y = "Stock Prices")
Q8 Hide the messages and the code, but display results of the code from the webpage.
Hint: Use message, echo and results in the chunk options. Refer to the RMarkdown Reference Guide.