Your First Stock Analysis with yahoofinancer

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Overview

In this guide, you will learn how to extract historical stock prices from Yahoo Finance, understand the returned data structure, compute a 50-day Simple Moving Average (SMA), and produce a publication-ready price and trend chart using ggplot2—with zero prior finance or algorithmic trading experience required.

Required Packages

# Install required packages if not already installed:
# install.packages(c("yahoofinancer", "dplyr", "ggplot2", "zoo", "scales"))

library(yahoofinancer)
library(dplyr)
library(ggplot2)
library(zoo)
library(scales)

1. Download Historical Stock Prices

To download historical stock data, use the functional helper yf_download_prices(). By default, specifying a ticker and a relative time period (e.g., "1y" for 1 year) pulls daily Open, High, Low, Close, Adjusted Close, and Volume (OHLCV) records.

# Fetch 1 year of daily historical data for Apple Inc. (AAPL)
aapl <- yf_download_prices(
  tickers  = "AAPL",
  period   = "1y",
  interval = "1d"
)

head(aapl)
#> # A tibble: 6 × 8
#>   symbol date                 open  high   low close adj_close   volume
#>   <chr>  <dttm>              <dbl> <dbl> <dbl> <dbl>     <dbl>    <dbl>
#> 1 AAPL   2025-08-18 13:30:00  232.  233.  230.  231.      230. 41235600
#> 2 AAPL   2025-08-19 13:30:00  231.  233.  229.  231.      230. 38945200
#> 3 AAPL   2025-08-20 13:30:00  230.  230.  226.  226.      225. 45120300
#> 4 AAPL   2025-08-21 13:30:00  226.  227.  224.  225.      224. 39870100
#> 5 AAPL   2025-08-22 13:30:00  226.  229.  225.  228.      227. 42319800
#> 6 AAPL   2025-08-25 13:30:00  226.  229.  226.  227.      226. 37651000

(Note: Output timestamps and values are illustrative; your query results will reflect the latest available market trading sessions.)

Understanding the Data Columns

Column	Type	Description
`symbol`	`character`	Ticker symbol representing the asset (e.g., `"AAPL"`).
`date`	`POSIXct` (`<dttm>`)	Timestamp representing the trading session.
`open`	`numeric`	Price at the opening bell.
`high`	`numeric`	Highest price reached during the session.
`low`	`numeric`	Lowest price reached during the session.
`close`	`numeric`	Final settlement price at the closing bell.
`adj_close`	`numeric`	Price adjusted for stock splits and dividend distributions.
`volume`	`numeric`	Total number of shares traded during the session.

2. Compute a 50-Day Moving Average

A Simple Moving Average (SMA) calculates the average closing price over a sliding window of $N$ days. The 50-day moving average (SMA-50) is one of the most widely followed trend indicators:

When the price is above the 50-day SMA, the asset is considered to be in an intermediate uptrend.
When the price is below the 50-day SMA, it indicates intermediate downward pressure.

We can compute this rolling metric easily using dplyr and zoo::rollmean():

aapl_trend <- aapl |>
  arrange(date) |>
  mutate(
    sma_50 = rollmean(close, k = 50, fill = NA, align = "right")
  )

tail(aapl_trend)
#> # A tibble: 6 × 9
#>   symbol date                 open  high   low close adj_close   volume sma_50
#>   <chr>  <dttm>              <dbl> <dbl> <dbl> <dbl>     <dbl>    <dbl>  <dbl>
#> 1 AAPL   2026-08-11 13:30:00  224.  226.  223.  225.      225. 40124300   221.
#> 2 AAPL   2026-08-12 13:30:00  225.  228.  224.  227.      227. 43105200   221.
#> 3 AAPL   2026-08-13 13:30:00  227.  230.  226.  229.      229. 48721100   222.
#> 4 AAPL   2026-08-14 13:30:00  229.  231.  228.  230.      230. 42390800   222.
#> 5 AAPL   2026-08-15 13:30:00  230.  232.  229.  231.      231. 39912000   223.
#> 6 AAPL   2026-08-18 13:30:00  231.  233.  230.  232.      232. 41050000   223.

(Note: The first 49 rows will contain NA for sma_50 until a full 50-day window has accumulated.)

3. Visualize Price and Trend with ggplot2

With ggplot2, we can plot the daily closing price alongside the smoothed 50-day moving average:

# Convert POSIXct timestamp to Date for clean daily axis scaling
ggplot(aapl_trend, aes(x = as.Date(date))) +
  # Closing price line
  geom_line(aes(y = close, color = "Closing Price"), linewidth = 0.85) +
  # 50-Day Moving Average line
  geom_line(aes(y = sma_50, color = "50-Day SMA"), linewidth = 1.05, na.rm = TRUE) +
  # Custom colors and formatting
  scale_color_manual(
    name = "Series",
    values = c("Closing Price" = "#1f77b4", "50-Day SMA" = "#e6550d")
  ) +
  scale_x_date(date_breaks = "2 months", date_labels = "%b %Y") +
  scale_y_continuous(labels = dollar_format()) +
  labs(
    title = "Apple Inc. (AAPL) — 1-Year Price History",
    subtitle = "Daily Closing Price with 50-Day Simple Moving Average (SMA-50)",
    x = "Date",
    y = "Price (USD)",
    caption = "Source: Yahoo Finance via yahoofinancer"
  ) +
  theme_minimal(base_size = 12) +
  theme(
    legend.position = "top",
    plot.title = element_text(face = "bold", size = 14),
    panel.grid.minor = element_blank()
  )

4. Minimal Reproducible Example

Below is the complete, self-contained workflow in a single copy-pasteable script:

library(yahoofinancer)
library(dplyr)
library(ggplot2)
library(zoo)
library(scales)

# 1. Download 1 year of daily historical prices
aapl <- yf_download_prices("AAPL", period = "1y", interval = "1d")

# 2. Compute 50-day Simple Moving Average
aapl_trend <- aapl |>
  arrange(date) |>
  mutate(sma_50 = rollmean(close, k = 50, fill = NA, align = "right"))

# 3. Plot price chart with SMA overlay
ggplot(aapl_trend, aes(x = as.Date(date))) +
  geom_line(aes(y = close, color = "Closing Price"), linewidth = 0.85) +
  geom_line(aes(y = sma_50, color = "50-Day SMA"), linewidth = 1.05, na.rm = TRUE) +
  scale_color_manual(
    name = "Series",
    values = c("Closing Price" = "#1f77b4", "50-Day SMA" = "#e6550d")
  ) +
  scale_x_date(date_breaks = "2 months", date_labels = "%b %Y") +
  scale_y_continuous(labels = dollar_format()) +
  labs(
    title = "Apple Inc. (AAPL) — 1-Year Price History",
    subtitle = "Daily Closing Price with 50-Day Simple Moving Average (SMA-50)",
    x = "Date",
    y = "Price (USD)",
    caption = "Source: Yahoo Finance via yahoofinancer"
  ) +
  theme_minimal(base_size = 12) +
  theme(
    legend.position = "top",
    plot.title = element_text(face = "bold", size = 14),
    panel.grid.minor = element_blank()
  )

5. Summary

In this guide, you learned how to:

Download market data: Query clean OHLCV time series with yf_download_prices().
Compute indicators: Use dplyr and zoo::rollmean() to compute rolling metrics.
Visualize trends: Build publication-ready charts comparing price action and trend indicators using ggplot2.

6. Going Further

Now that you have fetched your first dataset and built a chart, here are several next steps and related features to explore:

Custom Date Ranges: Specify exact date boundaries instead of relative periods:

aapl_custom <- yf_download_prices("AAPL", start = "2024-01-01", end = "2024-12-31")

Different Frequencies: Query weekly (interval = "1wk") or monthly (interval = "1mo") bars for multi-year trend analysis.
Global Equities & Indices: Fetch international securities using standard exchange suffixes (e.g., "RELIANCE.NS" for NSE India or "AZN.L" for London) or market indices (e.g., Index$new("^GSPC") for the S&P 500).

Object-Oriented R6 Interface: For stateful workflows, market metadata, and fundamental valuation metrics, use the Ticker class:

aapl_obj <- Ticker$new("AAPL")

# Inspect security metadata and valuation measures
aapl_obj$currency
aapl_obj$valuation_measures

# Historical prices via R6
aapl_obj$get_history(period = "1y", interval = "1d")

Cookbook Recipes: For 15 in-depth quantitative recipes including drawdown analysis, technical indicators (EMA, RSI, MACD, Bollinger Bands), and portfolio performance modeling, see vignette("cookbook", package = "yahoofinancer").

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They may not be fully stable and should be used with caution. We make no claims about them.
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