Data Homework: TidyQuant Tutorials

Core Functions

##Prerequisites

# Load the tidyquant package to get started.
#Loads tidyquant, lubridate, xts, quantmod, TTR 
library(tidyverse)
library(tidyquant)

#Stock Indexes
tq_index_options()
#Set x as one of the options in the list of options above to get the desired stock index / exchange.
tq_index("SP500")

Get Quantitative Data

tq_get_options()
#yahoo finance
aapl_prices  <- tq_get("AAPL", get = "stock.prices", from = " 1990-01-01")
aapl_prices 
x8411T <- tq_get("8411.T", get = "stock.prices.japan", from = "2016-01-01", to  = "2016-12-31")
#FRED Economic Data
wti_price_usd <- tq_get("DCOILWTICO", get = "economic.data")
wti_price_usd 

##Quandl API

quandl_api_key("<your-api-key>")

#Mutate Quantitative Data

data("FANG")
FANG

##Transmute Quantitative Data, tq_transmute

FANG %>%
    group_by(symbol) %>%
    tq_transmute(select = adjusted, mutate_fun = to.monthly, indexAt = "lastof")

##Mutate Quantitative Data, tq_mutate

FANG %>%
    group_by(symbol) %>%
    tq_mutate(select     = close, 
              mutate_fun = MACD, 
              col_rename = c("MACD", "Signal"))

#Quantitative Analysis Package Integrations in tidyquant ##Getting and Charting Annual Returns

FANG_annual_returns <- FANG %>%
    group_by(symbol) %>%
    tq_transmute(select     = adjusted, 
                 mutate_fun = periodReturn, 
                 period     = "yearly", 
                 type       = "arithmetic")
FANG_annual_returns

###Charting annual returns is just a quick use of the ggplot2 package.
FANG_annual_returns %>%
    ggplot(aes(x = date, y = yearly.returns, fill = symbol)) +
    geom_col() +
    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, scales = "free_y") +
    theme_tq() + 
    scale_fill_tq()

##Getting Daily Log Returns

FANG_daily_log_returns <- FANG %>%
    group_by(symbol) %>%
    tq_transmute(select     = adjusted, 
                 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)

##Use xts to.period to Change the Periodicity from Daily to Monthly

FANG %>%
    group_by(symbol) %>%
    tq_transmute(select     = open:volume, 
                 mutate_fun = to.period, 
                 period     = "months")
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()

#Scaling and Modeling with tidyquant ##Scaling the Mutation of Financial Data

data("FANG")
FANG

FANG_returns_yearly <- FANG %>%
    group_by(symbol) %>%
    tq_transmute(select     = adjusted, 
                 mutate_fun = periodReturn, 
                 period     = "yearly", 
                 col_rename = "yearly.returns") 
FANG_returns_yearly %>%
    ggplot(aes(x = year(date), y = yearly.returns, fill = symbol)) +
    geom_bar(position = "dodge", stat = "identity") +
    labs(title = "FANG: Annual Returns", 
         subtitle = "Mutating at scale is quick and easy!",
         y = "Returns", x = "", color = "Blue") +
    scale_y_continuous(labels = scales::percent) +
    coord_flip() +
    theme_tq() +
    scale_fill_tq()

#Charting with tidyquant ##Line Chart

#Use FANG data set
data("FANG") 
# Get AAPL and AMZN Stock Prices
AAPL <- tq_get("AAPL", get = "stock.prices", from = "2016-09-01", to = "2017-12-31")
AMZN <- tq_get("AMZN", get = "stock.prices", from = "2001-01-01", to = "2017-12-31")
AAPL %>%
    ggplot(aes(x = date, y = close)) +
    geom_line() +
    labs(title = "AAPL Line Chart", y = "Closing Price", x = "") + 
    theme_tq()

end <- as_date("2016-12-31")

AAPL %>%
    ggplot(aes(x = date, y = close)) +
    geom_barchart(aes(open = open, high = high, low = low, close = close)) +
    labs(title = "AAPL Bar Chart", y = "Closing Price", x = "") + 
    theme_tq()

##Charting Multiple Securities

start <- end - weeks(6)
FANG %>%
    filter(date >= start - days(2 * 15)) %>%
    ggplot(aes(x = date, y = close, group = symbol)) +
    geom_candlestick(aes(open = open, high = high, low = low, close = close)) +
    labs(title = "FANG Candlestick Chart", 
         subtitle = "Experimenting with Mulitple Stocks",
         y = "Closing Price", x = "") + 
    coord_x_date(xlim = c(start, end)) +
    facet_wrap(~ symbol, ncol = 2, scale = "free_y") + 
    theme_tq()

##Adding BBands to multiple stocks

start <- end - weeks(24)
FANG %>%
    filter(date >= start - days(2 * 20)) %>%
    ggplot(aes(x = date, y = close, 
               open = open, high = high, low = low, close = close, 
               group = symbol)) +
    geom_barchart() +
    geom_bbands(ma_fun = SMA, sd = 2, n = 20, linetype = 5) +
    labs(title = "FANG Bar Chart", 
         subtitle = "BBands with SMA Applied, Experimenting with Multiple Stocks", 
         y = "Closing Price", x = "") + 
    coord_x_date(xlim = c(start, end)) +
    facet_wrap(~ symbol, ncol = 2, scales = "free_y") + 
    theme_tq()