Quiz 2
Kyle Dunnell
03/04/2020
# Load packages
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## as.zoo.data.frame zoo## Version 0.4-0 included new data defaults. See ?getSymbols.## ══ Need to Learn tidyquant? ══════════════════════════════════════════════════════════
## Business Science offers a 1-hour course - Learning Lab #9: Performance Analysis & Portfolio Optimization with tidyquant!
## </> Learn more at: https://university.business-science.io/p/learning-labs-pro </>library(tidyverse)## ── Attaching packages ───────────────────────────────────────────── tidyverse 1.3.0 ──## ✓ ggplot2 3.2.1 ✓ purrr 0.3.3
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## x lubridate::union() masks base::union()# Import stock prices
stock_prices <- tq_get(c("WMT", "TGT", "AMZN"), get = "stock.prices", from = "2020-01-01")
# Calculate daily returns
stock_returns <-
stock_prices %>%
group_by(symbol) %>%
tq_mutate(select = adjusted, mutate_fun = periodReturn, period = "daily")
stock_returns## # A tibble: 126 x 9
## # Groups: symbol [3]
## symbol date open high low close volume adjusted daily.returns
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 WMT 2020-01-02 119. 120. 119. 119. 6764900 119. 0
## 2 WMT 2020-01-03 118. 119. 118. 118. 5399200 118. -0.00883
## 3 WMT 2020-01-06 117. 118. 117. 118. 6445500 118. -0.00204
## 4 WMT 2020-01-07 117. 118. 116. 117. 6846900 117. -0.00926
## 5 WMT 2020-01-08 116. 117. 116. 116. 5875800 116. -0.00343
## 6 WMT 2020-01-09 116. 117. 116. 117. 5563700 117. 0.0103
## 7 WMT 2020-01-10 117. 117. 116. 116. 6054800 116. -0.00835
## 8 WMT 2020-01-13 116. 117. 115. 116. 6112600 116. -0.00430
## 9 WMT 2020-01-14 115. 116. 115. 116. 6585800 116. 0.00259
## 10 WMT 2020-01-15 115. 116. 115. 115. 7454200 115. -0.00775
## # … with 116 more rowsQ1 filter Select stock returns of January 31, 2020.
Hint: See the code in 1.2.2 Selecting observations.
jan31 <- filter (stock_returns, date == "2020-01-31")
jan31## # A tibble: 3 x 9
## # Groups: symbol [3]
## symbol date open high low close volume adjusted daily.returns
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 WMT 2020-01-31 116. 116. 114. 114. 7775800 114. -0.0179
## 2 TGT 2020-01-31 113. 114. 110. 111. 6961900 110. -0.0343
## 3 AMZN 2020-01-31 2051. 2056. 2002. 2009. 15567300 2009. 0.0738Q2 Which of the three stocks performed best on January 31, 2020?
Hint: Answer the question by comparing daily returns of each stock on January 31, 2020. Amazon performed the best with a higher closing price than opening price. Both the other stocks had negative returns ## Q3 Plot the distribution of daily returns by stock using boxplots. Hint: See the code in 4.3.3 Box plots. Add an appropriate title and labels for both axes.
ggplot(stock_returns,
aes(x = symbol,
y = daily.returns)) +
geom_boxplot() +
labs(title = "Distribution of Daily Returns", x = "Symbol", y = "Daily Returns")
## Q4 Based on the boxplot above, which of the three stocks performed best this year? Hint: Answer the question by comparing median and outliers of each stock. Amazon performed the ebst agaimn with a higher median average stock than both walmart and Target. ## Q5 Calculate mean daily returns for each stock. Hint: See the code in 4.3.1 Bar chart (on summary statistics).
avgreturns <- stock_returns %>%
group_by(symbol) %>%
summarize(mean_returns = mean(daily.returns))
avgreturns## # A tibble: 3 x 2
## symbol mean_returns
## <chr> <dbl>
## 1 AMZN 0.000326
## 2 TGT -0.00382
## 3 WMT -0.00111Q6 Plot mean daily returns using bar charts.
Hint: See the code in 4.3.1 Bar chart (on summary statistics). Add an appropriate title and labels for both axes.
ggplot(avgreturns,
aes(x = symbol,
y = mean_returns)) +
geom_bar(stat = "identity") +
labs(title = "Mean Daily Returns", x = "Symbol", y = "Mean Returns")
## Q7 Create the line plot of stock prices for all three stocks in one graph. Hint: Google search something like “ggplot2 multiple lines”.
ggplot(stock_prices, aes(x = date, y = adjusted, group = symbol)) +
geom_line(aes(color = symbol)) +
scale_color_manual(values = c("goldenrod", "darkred", "steelblue")) +
labs(y = "Adjusted Stock Price", x = "Date", title = "Daily Stock Prices")
## Q7.a filter Create the same line plot as in Q7, but without Amazon. Note: Insert a new code chunk below, copy and paste the code in Q7, and revise it using the dplyr::filter function. This is an extra credit question worth 10 points. However, the total number of points you could earn for this quiz is capped at 100 points. In other words, the extra credit can only offset any one question you missed in the first seven questions.
stock_prices2 <- tq_get(c("WMT", "TGT"), get = "stock.prices", from = "2020-01-01")
ggplot(stock_prices2, aes(x = date, y = adjusted, group = symbol)) +
geom_line(aes(color = symbol)) +
scale_color_manual(values = c("darkred", "steelblue")) +
labs(y = "Adjusted Stock Price", x = "Date", title = "Daily Stock Prices for Target vs. Walmart")
## Q8 Hide the messages, but display the code and its results on the webpage. Hint: Use message, echo and results in the chunk options. Refer to the RMarkdown Reference Guide.