inst/doc/TQ02-quant-integrations-in-tidyquant.R

In tidyquant: Tidy Quantitative Financial Analysis

## ---- echo = FALSE, message = FALSE, warning = FALSE--------------------------
knitr::opts_chunk$set(message = FALSE,
                      warning = FALSE,
                      fig.width = 8, 
                      fig.height = 4.5,
                      fig.align = 'center',
                      out.width='95%')
# devtools::load_all() # Travis CI fails on load_all()

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# Loads tidyquant, lubridate, xts, quantmod, TTR 
library(tidyverse)
library(tidyquant)

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tq_transmute_fun_options() %>% str()

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# Get zoo functions that work with tq_transmute and tq_mutate
tq_transmute_fun_options()$zoo

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# Get xts functions that work with tq_transmute and tq_mutate
tq_transmute_fun_options()$xts

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# Get quantmod functions that work with tq_transmute and tq_mutate
tq_transmute_fun_options()$quantmod

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# Get TTR functions that work with tq_transmute and tq_mutate
tq_transmute_fun_options()$TTR

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# Get PerformanceAnalytics functions that work with tq_transmute and tq_mutate
tq_transmute_fun_options()$PerformanceAnalytics

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data("FANG")

FANG

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

## ---- fig.height = 4.5--------------------------------------------------------
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()

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FANG_daily_log_returns <- FANG %>%
    group_by(symbol) %>%
    tq_transmute(select     = adjusted, 
                 mutate_fun = periodReturn, 
                 period     = "daily", 
                 type       = "log",
                 col_rename = "monthly.returns")

## ---- fig.height = 4.5--------------------------------------------------------
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)

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

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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()

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

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# Asset Returns
FANG_returns_monthly <- FANG %>%
    group_by(symbol) %>%
    tq_transmute(select     = adjusted, 
                 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(select     = adjusted, 
                 mutate_fun = periodReturn,
                 period     = "monthly")

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returns_joined <- left_join(FANG_returns_monthly, 
                            baseline_returns_monthly,
                            by = "date")
returns_joined

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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")

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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()

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FANG_macd <- FANG %>%
    group_by(symbol) %>%
    tq_mutate(select     = close, 
              mutate_fun = MACD, 
              nFast      = 12, 
              nSlow      = 26, 
              nSig       = 9, 
              maType     = SMA) %>%
    mutate(diff = macd - signal) %>%
    select(-(open:volume))
FANG_macd

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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()

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

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

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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())

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# Get stock pairs
stock_prices <- c("MA", "V") %>%
    tq_get(get  = "stock.prices",
           from = "2015-01-01",
           to   = "2016-12-31") %>%
    group_by(symbol) 

stock_pairs <- stock_prices %>%
    tq_transmute(select     = adjusted,
                 mutate_fun = periodReturn,
                 period     = "daily",
                 type       = "log",
                 col_rename = "returns") %>%
    spread(key = symbol, value = returns)

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stock_pairs %>%
    ggplot(aes(x = V, y = MA)) +
    geom_point(color = palette_light()[[1]], alpha = 0.5) +
    geom_smooth(method = "lm") +
    labs(title = "Visualizing Returns Relationship of Stock Pairs") +
    theme_tq()

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lm(MA ~ V, data = stock_pairs) %>%
    summary()

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regr_fun <- function(data) {
    coef(lm(MA ~ V, data = timetk::tk_tbl(data, silent = TRUE)))
}

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stock_pairs <- stock_pairs %>%
         tq_mutate(mutate_fun = rollapply,
                   width      = 90,
                   FUN        = regr_fun,
                   by.column  = FALSE,
                   col_rename = c("coef.0", "coef.1"))
stock_pairs

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stock_pairs %>%
    ggplot(aes(x = date, y = coef.1)) +
    geom_line(size = 1, color = palette_light()[[1]]) +
    geom_hline(yintercept = 0.8134, size = 1, color = palette_light()[[2]]) +
    labs(title = "MA ~ V: Visualizing Rolling Regression Coefficient", x = "") +
    theme_tq()

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stock_prices %>%
    tq_transmute(adjusted, 
                 periodReturn, 
                 period = "daily", 
                 type = "log", 
                 col_rename = "returns") %>%
    mutate(wealth.index = 100 * cumprod(1 + returns)) %>%
    ggplot(aes(x = date, y = wealth.index, color = symbol)) +
    geom_line(size = 1) +
    labs(title = "MA and V: Stock Prices") +
    theme_tq() + 
    scale_color_tq()

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FANG %>%
    group_by(symbol) %>%
    tq_transmute(adjusted, periodReturn, period = "daily") %>%
    tq_transmute(daily.returns, Return.clean, alpha = 0.05) %>%
    tq_transmute(daily.returns, Return.excess, Rf = 0.03 / 252)