F <- getSymbols("F", 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)
GOOGL <- getSymbols("GOOGL", src = "yahoo", from = "2015-01-01", to = "2025-06-01", auto.assign = FALSE)title: “Business Analytics Mini-bootcamp - Summer 2025 (day 2) - Stock Market Data Analysis using quantmod and R” author: “Justin Maliska, taught by Zhenning ‘Jimmy’ Xu, Ph.D.” format: html editor: visual —
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 F, TSLA, GOOGL, and AMZN.
Context: Tools like
quantmodare 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
quantmodandPerformanceAnalyticsin strategy design, backtesting, and risk analysis.
Load Stock Data
Reflection Question:
What are some advantages of using programmatic data collection (via getSymbols) over manually downloading CSV files from Yahoo Finance?
Answer
I think that an advantage of using programmatic data collection is that is it being updated in real time instead of using a previously downloaded CSV file that may have old data.
View Sample Data
head(GOOGL) GOOGL.Open GOOGL.High GOOGL.Low GOOGL.Close GOOGL.Volume
2015-01-02 26.6300 26.7900 26.3940 26.4775 26480000
2015-01-05 26.3575 26.3995 25.8875 25.9730 41182000
2015-01-06 26.0250 26.0605 25.2775 25.3320 54456000
2015-01-07 25.5475 25.5745 25.1825 25.2575 46918000
2015-01-08 25.0755 25.3750 24.7510 25.3455 73054000
2015-01-09 25.4090 25.4300 24.9325 25.0360 42000000
GOOGL.Adjusted
2015-01-02 26.31965
2015-01-05 25.81816
2015-01-06 25.18098
2015-01-07 25.10692
2015-01-08 25.19440
2015-01-09 24.88675barChart(GOOGL, name = "GOOGL Volume Bar Chart")
chartSeries(GOOGL, type = "bar", theme = chartTheme("white"))
chartSeries(GOOGL, type = "line", theme = chartTheme("white"))
chartSeries(GOOGL, 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?
Answer
These different charts give you different ways of seeing the average selling price.
Daily and Yearly Returns
plot(dailyReturn(GOOGL), main = "GOOGL Daily Returns")
plot(yearlyReturn(GOOGL), main = "GOOGL 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(GOOGL, file = "GOOGL_stock.csv", row.names = FALSE, col.names = TRUE, sep = ",")Convert to Data Frame and Visualize
GOOGL_df <- data.frame(GOOGL)
GOOGL_df$time <- index(GOOGL)
head(GOOGL_df) GOOGL.Open GOOGL.High GOOGL.Low GOOGL.Close GOOGL.Volume
2015-01-02 26.6300 26.7900 26.3940 26.4775 26480000
2015-01-05 26.3575 26.3995 25.8875 25.9730 41182000
2015-01-06 26.0250 26.0605 25.2775 25.3320 54456000
2015-01-07 25.5475 25.5745 25.1825 25.2575 46918000
2015-01-08 25.0755 25.3750 24.7510 25.3455 73054000
2015-01-09 25.4090 25.4300 24.9325 25.0360 42000000
GOOGL.Adjusted time
2015-01-02 26.31965 2015-01-02
2015-01-05 25.81816 2015-01-05
2015-01-06 25.18098 2015-01-06
2015-01-07 25.10692 2015-01-07
2015-01-08 25.19440 2015-01-08
2015-01-09 24.88675 2015-01-09## Do you still remember the ggplot functions we used yesterday?
ggplot(GOOGL_df, aes(x = time, y = GOOGL.Adjusted)) +
geom_point() +
labs(title = "GOOGL 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)
What is one key insight or skill you learned today? Briefly explain it in your own words.
Answer
One thing that I learned from this lab was how to import data for stocks.
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.
Answer
Using R instead of only relying on EXCEL is something I would like to apply to my work life and my personal life.
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/