Business Analytics Mini-bootcamp - Summer 2025 (day 2) - Stock Market Data Analysis using quantmod and R

Author

Your Name, taught by Zhenning ‘Jimmy’ Xu, Ph.D.

Introduction

This Quarto notebook explores stock market data using R packages like quantmod, PerformanceAnalytics, and tidyverse. We analyze and visualize data for multiple tickers including QQQ, TSLA, WMT, and AMZN.

Context: Tools like quantmod are widely used in algorithmic trading platforms and fintech firms. For example:

Lemon Markets, a European brokerage API platform, uses similar R-based or Python-based workflows to empower retail and institutional clients.

QuantInsti, a leading training provider in algorithmic trading, uses packages like quantmod and PerformanceAnalytics in strategy design, backtesting, and risk analysis.

Load Stock Data

QQQ <- getSymbols("QQQ", src = "yahoo", from = "2020-01-01", to = "2025-06-01", auto.assign = FALSE)
TSLA <- getSymbols("TSLA", src = "yahoo", from = "2015-01-01", to = "2025-06-01", auto.assign = FALSE)
WMT <- getSymbols("WMT", src = "yahoo", from = "2015-01-01", to = "2025-06-01", auto.assign = FALSE)

Reflection Question:

What are some advantages of using programmatic data collection (via getSymbols) over manually downloading CSV files from Yahoo Finance?

View Sample Data

head(WMT)

           WMT.Open WMT.High  WMT.Low WMT.Close WMT.Volume WMT.Adjusted
2015-01-02 28.75667 28.90667 28.51667  28.63333   13505400     23.32091
2015-01-05 28.57333 28.77333 28.50333  28.55000   20937000     23.25303
2015-01-06 28.66000 28.91667 28.59667  28.77000   24615300     23.43222
2015-01-07 28.92667 29.56000 28.89000  29.53333   25495200     24.05393
2015-01-08 29.73667 30.22333 29.69000  30.15667   38140800     24.56162
2015-01-09 30.10667 30.13000 29.75000  29.78333   25567500     24.25755

barChart(WMT, name = "WMT Volume Bar Chart")

chartSeries(WMT, type = "bar", theme = chartTheme("white"))

chartSeries(WMT, type = "line", theme = chartTheme("white"))

chartSeries(WMT, type = "candlesticks", theme = chartTheme("white"))

Reflection Question:

Explore each chart type. How do line, bar, and candlestick charts differ in the kind of information they emphasize?

Daily and Yearly Returns

plot(dailyReturn(WMT), main = "WMT Daily Returns")

plot(yearlyReturn(WMT), main = "WMT Yearly Returns")

Discussion Idea:

QuantInsti trains traders to analyze returns across multiple timeframes. Why might a yearly return plot give a different impression than a daily return plot?

write.table(WMT, file = "WMT_stock.csv", row.names = FALSE, col.names = TRUE, sep = ",")

Convert to Data Frame and Visualize

WMT_df <- data.frame(WMT)
WMT_df$time <- index(WMT)

head(WMT_df)

           WMT.Open WMT.High  WMT.Low WMT.Close WMT.Volume WMT.Adjusted
2015-01-02 28.75667 28.90667 28.51667  28.63333   13505400     23.32091
2015-01-05 28.57333 28.77333 28.50333  28.55000   20937000     23.25303
2015-01-06 28.66000 28.91667 28.59667  28.77000   24615300     23.43222
2015-01-07 28.92667 29.56000 28.89000  29.53333   25495200     24.05393
2015-01-08 29.73667 30.22333 29.69000  30.15667   38140800     24.56162
2015-01-09 30.10667 30.13000 29.75000  29.78333   25567500     24.25755
                 time
2015-01-02 2015-01-02
2015-01-05 2015-01-05
2015-01-06 2015-01-06
2015-01-07 2015-01-07
2015-01-08 2015-01-08
2015-01-09 2015-01-09

## Do you still remember the ggplot functions we used yesterday?
ggplot(WMT_df, aes(x = time, y = WMT.Adjusted)) +
  geom_point() +
  labs(title = "WMT Adjusted Closing Price",
       x = "Date", y = "Adjusted Price")

Critical Thinking:

Why might financial analysts prefer adjusted closing prices over raw closing prices when visualizing or modeling stock performance?

Technical Indicators: Bollinger Bands

What Are Bollinger Bands?

Bollinger Bands are a technical analysis tool used to measure a stock’s price volatility and identify potential buying or selling opportunities.

They consist of three lines:

Middle Band: This is usually a simple moving average (SMA) — often a 20-day average.

Upper Band: This is the middle band plus 2 standard deviations.

Lower Band: This is the middle band minus 2 standard deviations.

These bands move with the stock price and expand or contract based on how volatile the stock is.

🧠 Why Use Bollinger Bands? They help answer questions like:

Is the stock price relatively high or low compared to recent history?

Is the stock entering a period of increased volatility?

📌 How to Read Bollinger Bands When the price touches or moves above the upper band: → The stock might be overbought (price could be too high). → Some traders see this as a sell signal.

When the price touches or falls below the lower band: → The stock might be oversold (price could be too low). → This could be a buy signal.

AMZN <- getSymbols("AMZN", src = "yahoo", auto.assign = FALSE)

chartSeries(AMZN, subset = 'last 3 months', theme = chartTheme("white"))

addBBands(n = 20, sd = 2, maType = "SMA", draw = "bands", on = -1)

Reflection:

Bollinger Bands are popular in technical analysis. What kind of market behavior do the upper and lower bands signal? How might a quant fund use this indicator?

What is one key insight or skill you learned today? Briefly explain it in your own words.

What is one concept or tool from today’s session that you would like to apply in the future? Describe how you might use it.

References

quantmod website: https://www.quantmod.com/

10 great R packages for stock market data:https://dev.to/lemon-markets/10-great-r-packages-for-stock-market-data-1ocp

QuantInsti: Guide to quantmod: https://blog.quantinsti.com/a-guide-on-r-quantmod-package-how-to-get-started/

chartSeries documentation: https://www.rdocumentation.org/packages/quantmod/versions/0.4.27/topics/chartSeries

Shorting strategy in R: https://blog.quantinsti.com/shorting-high-algo-trading-strategy-r/