yahoofinancer Cookbook: 15 Practical Recipes

1. Download Historical Equity Prices

Retrieve daily Open, High, Low, Close, Adjusted Close, and Volume (OHLCV) price series for a single security using either functional or object-oriented (R6) interfaces.

# Functional API
aapl_prices <- yf_download_prices(
  tickers  = "AAPL",
  period   = "1y",
  interval = "1d"
)

head(aapl_prices)
#> # A tibble: 6 × 8
#>   symbol date                 open  high   low close adj_close   volume
#>   <chr>  <dttm>              <dbl> <dbl> <dbl> <dbl>     <dbl>    <dbl>
#> 1 AAPL   2025-08-25 13:30:00  226.  229.  226.  227.      226. 30983100
#> 2 AAPL   2025-08-26 13:30:00  227.  229.  225.  229.      228. 54575100
#> 3 AAPL   2025-08-27 13:30:00  229.  231.  228.  230.      230. 31259500
#> 4 AAPL   2025-08-28 13:30:00  231.  233.  229.  233.      232. 38074700
#> 5 AAPL   2025-08-29 13:30:00  233.  233.  231.  232.      231. 39418400
#> 6 AAPL   2025-09-02 13:30:00  229.  231.  227.  230.      229. 44075600

# R6 Class Interface
aapl_obj <- Ticker$new("AAPL")
aapl_history <- aapl_obj$get_history(period = "1y", interval = "1d")

head(aapl_history)
#> # A tibble: 6 × 8
#>   symbol date                 open  high   low close adj_close   volume
#>   <chr>  <dttm>              <dbl> <dbl> <dbl> <dbl>     <dbl>    <dbl>
#> 1 AAPL   2025-08-25 13:30:00  226.  229.  226.  227.      226. 30983100
#> 2 AAPL   2025-08-26 13:30:00  227.  229.  225.  229.      228. 54575100
#> 3 AAPL   2025-08-27 13:30:00  229.  231.  228.  230.      230. 31259500
#> 4 AAPL   2025-08-28 13:30:00  231.  233.  229.  233.      232. 38074700
#> 5 AAPL   2025-08-29 13:30:00  233.  233.  231.  232.      231. 39418400
#> 6 AAPL   2025-09-02 13:30:00  229.  231.  227.  230.      229. 44075600

Variations & Tips

Explicit Date Boundaries: Query fixed historical windows using start and end:

aapl_custom <- yf_download_prices(
  tickers  = "AAPL",
  start    = "2024-01-01",
  end      = "2024-12-31",
  interval = "1d"
)

Inspect Security Metadata via R6:

cat("Currency:    ", aapl_obj$currency, "\n")
#> Currency:     USD
cat("Exchange:    ", aapl_obj$exchange_name, "\n")
#> Exchange:     NMS
cat("Timezone:    ", aapl_obj$timezone, "\n")
#> Timezone:     EDT

2. Batch Download Multiple Tickers

Retrieve and stack price series for a diversified universe of equities into a single long-format tibble in one vectorized call.

symbols <- c("AAPL", "MSFT", "GOOGL", "NVDA", "TCS.NS")

basket_prices <- yf_download_prices(
  tickers  = symbols,
  period   = "6mo",
  interval = "1d"
)

# Inspect observation counts per ticker
basket_prices |>
  count(symbol)
#> # A tibble: 5 × 2
#>   symbol     n
#>   <chr>  <int>
#> 1 AAPL     125
#> 2 GOOGL    125
#> 3 MSFT     125
#> 4 NVDA     125
#> 5 TCS.NS   125

Variations & Tips

Faceted Multi-Asset Price Plot: Compare absolute price trajectories across assets using ggplot2:

ggplot(basket_prices, aes(x = date, y = adj_close, color = symbol)) +
  geom_line(show.legend = FALSE) +
  facet_wrap(~ symbol, scales = "free_y") +
  labs(
    title = "Historical Price Series by Security",
    x     = "Date",
    y     = "Adjusted Close Price"
  ) +
  theme_minimal()

plot of chunk recipe-2-faceted-plot

3. Intraday Price Series & Timeframes

Retrieve high-frequency intraday candles (1m, 5m, 15m, 60m) to examine intraday volatility, liquidity patterns, and trading microstructure.

intraday_5m <- yf_download_prices(
  tickers  = "MSFT",
  period   = "5d",
  interval = "5m"
)

head(intraday_5m)
#> # A tibble: 6 × 8
#>   symbol date                 open  high   low close adj_close  volume
#>   <chr>  <dttm>              <dbl> <dbl> <dbl> <dbl>     <dbl>   <dbl>
#> 1 MSFT   2026-08-18 13:30:00  482.  483.  477.  479.      479. 1477683
#> 2 MSFT   2026-08-18 13:35:00  479.  483.  479.  483.      483.  388264
#> 3 MSFT   2026-08-18 13:40:00  483.  484.  483.  483.      483.  437979
#> 4 MSFT   2026-08-18 13:45:00  483.  483.  482.  482.      482.  238777
#> 5 MSFT   2026-08-18 13:50:00  482.  483.  480.  482.      482.  477575
#> 6 MSFT   2026-08-18 13:55:00  482.  482.  479.  480.      480.  351820

Variations & Tips

Hourly Candle Tracking: Download 1-hour candles over the past month:

intraday_1h <- yf_download_prices(
  tickers  = "MSFT",
  period   = "1mo",
  interval = "60m"
)

Intraday Volume Distribution: Visualize intraday trading volume bars:

ggplot(intraday_5m, aes(x = date, y = volume)) +
  geom_col(fill = "#4f46e5", alpha = 0.8) +
  scale_y_continuous(labels = label_number(scale_cut = cut_short_scale())) +
  labs(
    title = "MSFT 5-Minute Intraday Volume",
    x     = "Timestamp (UTC)",
    y     = "Volume"
  ) +
  theme_minimal()

plot of chunk recipe-3-volume-plot

4. Real-Time Market Overview & Regional Filtering

Retrieve live market snapshots across international equity indices, currencies, and commodities, then filter for specific regional exchanges.

market_overview <- get_market_summary(as_tibble = TRUE)

# Filter for benchmark indices and currencies in market overview
us_overview <- market_overview |>
  filter(grepl("S&P|Nasdaq|Dow|EUR/USD|Gold|Crude", short_name, ignore.case = TRUE))

print(us_overview)
#> # A tibble: 6 × 9
#>   symbol   short_name     regular_market_price regular_market_change
#>   <chr>    <chr>                         <dbl>                 <dbl>
#> 1 ES=F     S&P Futures                 7697                27.2     
#> 2 YM=F     Dow Futures                53689               200       
#> 3 NQ=F     Nasdaq Futures             29330               224.      
#> 4 CL=F     Crude Oil                     82.6              -2.41    
#> 5 GC=F     Gold                        4696.               -1.90    
#> 6 EURUSD=X EUR/USD                        1.17             -0.000408
#> # ℹ 5 more variables: regular_market_change_percent <dbl>,
#> #   regular_market_previous_close <dbl>, market_state <chr>, exchange <chr>,
#> #   market_time <dttm>

Variations & Tips

Filter for Commodities & Crypto:

commodities_crypto <- market_overview |>
  filter(grepl("Gold|Crude|Silver|BTC|ETH", short_name, ignore.case = TRUE))

Filter for Major European Indices:

european_market <- market_overview |>
  filter(grepl("DAX|FTSE|CAC|ESTX", short_name, ignore.case = TRUE))

Discover Trending Securities: Retrieve real-time trending symbols for a target country:

trending_us <- get_trending("US", count = 5)
print(trending_us)
#> [1] "AVGO" "SNOW" "HPE"  "NTAP" "PLTR"

5. Download Benchmark Index History

Retrieve historical price series for major benchmark indices (^GSPC, ^IXIC, ^NSEI, ^BSESN) and normalize prices to a common base of 100 for comparative performance tracking.

benchmark_symbols <- c("^GSPC", "^IXIC", "^NSEI", "^BSESN")

benchmark_prices <- yf_download_prices(
  tickers  = benchmark_symbols,
  period   = "1y",
  interval = "1d"
)

# Normalize price levels (Base = 100)
normalized_indices <- benchmark_prices |>
  group_by(symbol) |>
  arrange(date, .by_group = TRUE) |>
  mutate(indexed_price = (close / first(close)) * 100) |>
  ungroup()

ggplot(normalized_indices, aes(x = date, y = indexed_price, color = symbol)) +
  geom_line(linewidth = 0.8) +
  labs(
    title = "Global Benchmark Performance (Base = 100)",
    x     = "Date",
    y     = "Normalized Growth",
    color = "Index"
  ) +
  theme_minimal()

plot of chunk recipe-5-core

Variations & Tips

Indian Sectoral Indices: Track major sector sub-indices:

sectoral_symbols <- c("^CNXIT", "^NSEBANK", "^CNXAUTO")
sectoral_prices  <- yf_download_prices(sectoral_symbols, period = "1y", interval = "1d")

Index Class R6 Query & Latest Quote:

nifty <- Index$new("^NSEI")
nifty_quote <- yf_get_index_quotes("^NSEI")
cat("Index Symbol: ", nifty$symbol, "\n")
#> Index Symbol:  ^NSEI
cat("Latest Close: ", tail(nifty_quote$close, 1), "\n")
#> Latest Close:  24219.1

6. Historical Currency & Forex Conversions

Convert foreign asset valuations into a local base currency by retrieving spot and historical foreign exchange rates via ISO 4217 currency pairs.

# 1. Fetch Apple USD prices
aapl <- yf_download_prices("AAPL", period = "6mo") |>
  mutate(date_day = as.Date(date, tz = "UTC"))

# 2. Fetch USD/INR exchange rates
usd_inr <- currency_converter("USD", "INR", period = "6mo") |>
  mutate(date_day = as.Date(date, tz = "UTC")) |>
  select(date_day, fx_rate = close)

# 3. Join and compute share price in INR
aapl_inr <- aapl |>
  inner_join(usd_inr, by = "date_day") |>
  mutate(close_inr = close * fx_rate) |>
  select(date_day, close_usd = close, fx_rate, close_inr)

head(aapl_inr)
#> # A tibble: 6 × 4
#>   date_day   close_usd fx_rate close_inr
#>   <date>         <dbl>   <dbl>     <dbl>
#> 1 2026-02-25      274.    90.9    24932.
#> 2 2026-02-26      273.    91.0    24825.
#> 3 2026-02-27      264.    91.0    24042.
#> 4 2026-03-02      265.    91.1    24111.
#> 5 2026-03-03      264.    91.6    24150.
#> 6 2026-03-04      263.    92.0    24154.

Variations & Tips

Inspect Available ISO Currency Codes:

supported_currencies <- get_currencies()
head(supported_currencies)
#>   short_name          long_name symbol    local_long_name
#> 1        FJD      Fijian Dollar    FJD      Fijian Dollar
#> 2        MXN       Mexican Peso    MXN       Mexican Peso
#> 3        SCR  Seychellois Rupee    SCR  Seychellois Rupee
#> 4        CDF    Congolese Franc    CDF    Congolese Franc
#> 5        BBD   Barbadian Dollar    BBD   Barbadian Dollar
#> 6        GTQ Guatemalan Quetzal    GTQ Guatemalan Quetzal

Direct Forex Pair Download: Query exchange rates using the =X ticker convention:

fx_basket <- yf_download_prices(c("EURUSD=X", "GBPUSD=X", "USDJPY=X"), period = "3mo")

7. Validate Ticker Symbols Before Pipelines

Sanitize, filter, and audit arbitrary universes of ticker symbols prior to running batch download pipelines to prevent failures caused by delisted or malformed symbols.

raw_symbols <- c("AAPL", "INVALID_XYZ", "TCS.NS", "NOT_REAL_123", "MSFT")

# 1. Return valid tickers only
clean_symbols <- validate(raw_symbols)
print(clean_symbols)
#> [1] "AAPL"   "TCS.NS" "MSFT"

# 2. Named logical audit vector
validation_status <- validate(raw_symbols, return_logical = TRUE)
print(validation_status)
#>         AAPL  INVALID_XYZ       TCS.NS NOT_REAL_123         MSFT 
#>         TRUE        FALSE         TRUE        FALSE         TRUE

# 3. Clean inline before download
clean_prices <- yf_download_prices(
  tickers = validate(raw_symbols),
  period  = "3mo"
)

head(clean_prices)
#> # A tibble: 6 × 8
#>   symbol date                 open  high   low close adj_close   volume
#>   <chr>  <dttm>              <dbl> <dbl> <dbl> <dbl>     <dbl>    <dbl>
#> 1 AAPL   2026-05-26 13:30:00  310.  312.  308.  308.      308. 48000500
#> 2 AAPL   2026-05-27 13:30:00  308.  313.  308.  311.      311. 50430900
#> 3 AAPL   2026-05-28 13:30:00  311.  313.  310.  313.      312. 48220400
#> 4 AAPL   2026-05-29 13:30:00  312.  315   310.  312.      312. 70026800
#> 5 AAPL   2026-06-01 13:30:00  310.  311.  305.  306.      306. 48849900
#> 6 AAPL   2026-06-02 13:30:00  307.  315.  307.  315.      315. 44534700

Variations & Tips

Audit and Warning Log for Rejected Tickers:

status <- validate(raw_symbols, return_logical = TRUE)
invalid_tickers <- names(status[!status])

if (length(invalid_tickers) > 0) {
  warning("Dropped invalid tickers: ", paste(invalid_tickers, collapse = ", "))
}

8. Calculate Daily Percentage Returns

Calculate simple discrete percentage returns and continuous log returns across multiple securities using standardized adjusted close prices (adj_close).

symbols <- c("AAPL", "MSFT", "GOOGL")

returns_df <- yf_download_prices(symbols, period = "1y", interval = "1d") |>
  group_by(symbol) |>
  arrange(date, .by_group = TRUE) |>
  mutate(
    daily_return = (adj_close / lag(adj_close)) - 1,
    log_return   = log(adj_close / lag(adj_close))
  ) |>
  filter(!is.na(daily_return)) |>
  ungroup()

head(returns_df)
#> # A tibble: 6 × 10
#>   symbol date                 open  high   low close adj_close   volume
#>   <chr>  <dttm>              <dbl> <dbl> <dbl> <dbl>     <dbl>    <dbl>
#> 1 AAPL   2025-08-26 13:30:00  227.  229.  225.  229.      228. 54575100
#> 2 AAPL   2025-08-27 13:30:00  229.  231.  228.  230.      230. 31259500
#> 3 AAPL   2025-08-28 13:30:00  231.  233.  229.  233.      232. 38074700
#> 4 AAPL   2025-08-29 13:30:00  233.  233.  231.  232.      231. 39418400
#> 5 AAPL   2025-09-02 13:30:00  229.  231.  227.  230.      229. 44075600
#> 6 AAPL   2025-09-03 13:30:00  237.  239.  234.  238.      238. 66427800
#> # ℹ 2 more variables: daily_return <dbl>, log_return <dbl>

Variations & Tips

Visualizing Return Distributions: Plot overlapping density curves to evaluate return dispersion and tail thickness:

ggplot(returns_df, aes(x = daily_return, fill = symbol)) +
  geom_density(alpha = 0.4) +
  scale_x_continuous(labels = label_percent(accuracy = 0.1)) +
  labs(
    title = "Daily Return Distributions",
    x     = "Daily Percentage Return",
    y     = "Density",
    fill  = "Ticker"
  ) +
  theme_minimal()

plot of chunk recipe-8-density-plot

Summary Statistics Table:

returns_summary <- returns_df |>
  group_by(symbol) |>
  summarise(
    trading_days  = n(),
    mean_daily    = mean(daily_return),
    sd_daily      = sd(daily_return),
    annual_return = mean_daily * 252,
    annual_vol    = sd_daily * sqrt(252)
  )
print(returns_summary)
#> # A tibble: 3 × 6
#>   symbol trading_days mean_daily sd_daily annual_return annual_vol
#>   <chr>         <int>      <dbl>    <dbl>         <dbl>      <dbl>
#> 1 AAPL            250  0.00139     0.0158        0.350       0.251
#> 2 GOOGL           250  0.00227     0.0206        0.573       0.327
#> 3 MSFT            250  0.0000991   0.0204        0.0250      0.323

9. Multi-Asset Return Correlation Matrix

Reshape multi-asset return series into a wide format to compute pairwise Pearson correlation coefficients, assess sector co-movement, and evaluate diversification benefits.

symbols <- c("AAPL", "MSFT", "NVDA", "GLD", "^GSPC")

prices <- yf_download_prices(symbols, period = "1y", interval = "1d")

returns_matrix <- prices |>
  group_by(symbol) |>
  arrange(date, .by_group = TRUE) |>
  mutate(daily_return = (adj_close / lag(adj_close)) - 1) |>
  filter(!is.na(daily_return)) |>
  ungroup() |>
  select(date, symbol, daily_return) |>
  pivot_wider(names_from = symbol, values_from = daily_return)

cor_matrix <- cor(select(returns_matrix, -date), use = "pairwise.complete.obs")
round(cor_matrix, 2)
#>       AAPL  GLD MSFT NVDA ^GSPC
#> AAPL  1.00 0.09 0.11 0.11  0.38
#> GLD   0.09 1.00 0.08 0.22  0.32
#> MSFT  0.11 0.08 1.00 0.27  0.38
#> NVDA  0.11 0.22 0.27 1.00  0.66
#> ^GSPC 0.38 0.32 0.38 0.66  1.00

Variations & Tips

Correlation Heatmap: Visualize asset correlation tiles using ggplot2:

cor_long <- as.data.frame(cor_matrix) |>
  mutate(asset1 = rownames(cor_matrix)) |>
  pivot_longer(-asset1, names_to = "asset2", values_to = "correlation")

ggplot(cor_long, aes(x = asset1, y = asset2, fill = correlation)) +
  geom_tile(color = "white") +
  geom_text(aes(label = round(correlation, 2)), color = "black", size = 4) +
  scale_fill_gradient2(
    low = "#d73027", mid = "#ffffbf", high = "#1a9850",
    midpoint = 0, limit = c(-1, 1), name = "Correlation"
  ) +
  labs(title = "Asset Return Correlation Matrix", x = NULL, y = NULL) +
  theme_minimal()

plot of chunk recipe-9-heatmap

Rolling 60-Day Pairwise Correlation: Track correlation stability over time:

rolling_cor <- returns_matrix |>
  mutate(
    roll_cor_aapl_gspc = rollapplyr(
      data      = cbind(AAPL, `^GSPC`),
      width     = 60,
      FUN       = function(x) cor(x[, 1], x[, 2], use = "complete.obs"),
      by.column = FALSE,
      fill      = NA
    )
  )

10. Compute Moving Averages & Trend Crossovers

Calculate 50-day and 200-day Simple Moving Averages (SMA) to classify market trend regimes and detect Golden Cross and Death Cross crossover signals.

prices <- yf_download_prices("AAPL", period = "2y", interval = "1d")

sma_df <- prices |>
  arrange(date) |>
  mutate(
    sma_50  = rollmeanr(adj_close, k = 50, fill = NA),
    sma_200 = rollmeanr(adj_close, k = 200, fill = NA),
    regime  = case_when(
      sma_50 > sma_200 ~ "Bullish (SMA50 > SMA200)",
      sma_50 < sma_200 ~ "Bearish (SMA50 < SMA200)",
      TRUE             ~ "Neutral"
    ),
    signal  = case_when(
      sma_50 > sma_200 & lag(sma_50) <= lag(sma_200) ~ "Golden Cross",
      sma_50 < sma_200 & lag(sma_50) >= lag(sma_200) ~ "Death Cross",
      TRUE                                           ~ NA_character_
    )
  )

tail(sma_df |> select(date, close, adj_close, sma_50, sma_200, regime, signal), 6)
#> # A tibble: 6 × 7
#>   date                close adj_close sma_50 sma_200 regime               signal
#>   <dttm>              <dbl>     <dbl>  <dbl>   <dbl> <chr>                <chr> 
#> 1 2026-08-17 13:30:00  306.      306.   309.    280. Bullish (SMA50 > SM… <NA>  
#> 2 2026-08-18 13:30:00  310.      310.   309.    280. Bullish (SMA50 > SM… <NA>  
#> 3 2026-08-19 13:30:00  317.      317.   309.    281. Bullish (SMA50 > SM… <NA>  
#> 4 2026-08-20 13:30:00  311.      311.   310.    281. Bullish (SMA50 > SM… <NA>  
#> 5 2026-08-21 13:30:00  309.      309.   310.    281. Bullish (SMA50 > SM… <NA>  
#> 6 2026-08-24 13:30:00  310.      310.   310.    281. Bullish (SMA50 > SM… <NA>

Variations & Tips

Moving Average Overlay Chart:

ggplot(filter(sma_df, !is.na(sma_200)), aes(x = date)) +
  geom_line(aes(y = adj_close), color = "gray60", alpha = 0.7, linewidth = 0.5) +
  geom_line(aes(y = sma_50, color = "50-day SMA"), linewidth = 0.9) +
  geom_line(aes(y = sma_200, color = "200-day SMA"), linewidth = 0.9) +
  scale_color_manual(
    name   = "Indicators",
    values = c("50-day SMA" = "#1f77b4", "200-day SMA" = "#d62728")
  ) +
  labs(
    title    = "AAPL Price Trend & Moving Averages",
    subtitle = "50-Day vs. 200-Day Simple Moving Average",
    x        = "Date",
    y        = "Adjusted Price (USD)"
  ) +
  theme_minimal() +
  theme(legend.position = "bottom")

plot of chunk recipe-10-ma-chart

Exponential Moving Average (EMA): Weight recent observations higher:

alpha <- 2 / (20 + 1)
sma_df <- sma_df |>
  mutate(ema_20 = stats::filter(adj_close * alpha, 1 - alpha, method = "recursive", sides = 1))

11. Calculate Historical & Maximum Drawdown (MDD)

Compute running peak prices and peak-to-trough percentage drawdowns to quantify historical capital loss risk, tail risk, and maximum drawdown limits.

prices <- yf_download_prices("NVDA", period = "5y", interval = "1d")

drawdown_df <- prices |>
  arrange(date) |>
  mutate(
    peak_price = cummax(adj_close),
    drawdown   = (adj_close - peak_price) / peak_price
  )

max_dd_val <- min(drawdown_df$drawdown, na.rm = TRUE)
worst_row  <- drawdown_df |> filter(drawdown == max_dd_val) |> slice(1)

cat("Maximum Drawdown (MDD):", sprintf("%.2f%%", max_dd_val * 100), "\n")
#> Maximum Drawdown (MDD): -66.34%
cat("Trough Date:           ", format(worst_row$date, "%Y-%m-%d"), "\n")
#> Trough Date:            2022-10-14
cat("Trough Price:          ", round(worst_row$adj_close, 2), "\n")
#> Trough Price:           11.2
cat("Previous Peak Price:   ", round(worst_row$peak_price, 2), "\n")
#> Previous Peak Price:    33.27

Variations & Tips

Underwater Area Chart:

ggplot(drawdown_df, aes(x = date, y = drawdown)) +
  geom_area(fill = "#d9534f", alpha = 0.4) +
  geom_line(color = "#d9534f", linewidth = 0.7) +
  scale_y_continuous(labels = label_percent()) +
  labs(
    title    = "NVDA Historical Drawdown (Underwater Chart)",
    subtitle = paste0("Max Drawdown: ", sprintf("%.2f%%", max_dd_val * 100)),
    x        = "Date",
    y        = "Drawdown from Peak"
  ) +
  theme_minimal()

plot of chunk recipe-11-underwater-chart

Multi-Asset MDD Comparison: Compare worst-case drawdowns across securities:

multi_basket <- yf_download_prices(c("AAPL", "MSFT", "GOOGL", "^GSPC"), period = "5y")

mdd_comparison <- multi_basket |>
  group_by(symbol) |>
  arrange(date, .by_group = TRUE) |>
  mutate(
    peak = cummax(adj_close),
    dd   = (adj_close - peak) / peak
  ) |>
  summarise(
    max_drawdown     = min(dd, na.rm = TRUE),
    current_drawdown = last(dd)
  ) |>
  arrange(max_drawdown)

print(mdd_comparison)
#> # A tibble: 4 × 3
#>   symbol max_drawdown current_drawdown
#>   <chr>         <dbl>            <dbl>
#> 1 GOOGL        -0.443          -0.135 
#> 2 MSFT         -0.371          -0.0936
#> 3 AAPL         -0.334          -0.0867
#> 4 ^GSPC        -0.254          -0.0187

12. Calculate Stock Beta & CAPM Alpha

Fit a Capital Asset Pricing Model (CAPM) linear regression against a broad market index to estimate systematic market risk ($\beta$) and abnormal alpha ($\alpha$).

prices <- yf_download_prices(c("AAPL", "^GSPC"), period = "2y", interval = "1d")

returns_wide <- prices |>
  group_by(symbol) |>
  arrange(date, .by_group = TRUE) |>
  mutate(daily_return = (adj_close / lag(adj_close)) - 1) |>
  filter(!is.na(daily_return)) |>
  ungroup() |>
  select(date, symbol, daily_return) |>
  pivot_wider(names_from = symbol, values_from = daily_return) |>
  drop_na()

capm_fit <- lm(AAPL ~ `^GSPC`, data = returns_wide)
fit_summary <- summary(capm_fit)

alpha_daily <- coef(capm_fit)[1]
beta        <- coef(capm_fit)[2]
r_squared   <- fit_summary$r.squared

cat("Beta (Systematic Risk):", round(beta, 3), "\n")
#> Beta (Systematic Risk): 1.11
cat("Daily Alpha:           ", sprintf("%.4f%%", alpha_daily * 100), "\n")
#> Daily Alpha:            0.0060%
cat("Annualized Alpha:      ", sprintf("%.2f%%", alpha_daily * 252 * 100), "\n")
#> Annualized Alpha:       1.51%
cat("R-Squared:             ", round(r_squared, 3), "\n")
#> R-Squared:              0.39

Variations & Tips

CAPM Regression Scatter Plot:

ggplot(returns_wide, aes(x = `^GSPC`, y = AAPL)) +
  geom_point(alpha = 0.4, color = "#2c3e50") +
  geom_smooth(method = "lm", color = "#e74c3c", se = TRUE) +
  scale_x_continuous(labels = label_percent()) +
  scale_y_continuous(labels = label_percent()) +
  labs(
    title    = "AAPL vs. S&P 500 (CAPM Beta Regression)",
    subtitle = paste0("Beta = ", round(beta, 2), " | R² = ", round(r_squared, 2)),
    x        = "S&P 500 Daily Return",
    y        = "AAPL Daily Return"
  ) +
  theme_minimal()

plot of chunk recipe-12-regression-plot

Batch Beta Calculation for Indian Stocks:

in_basket  <- c("TCS.NS", "INFY.NS", "RELIANCE.NS", "HDFCBANK.NS", "^NSEI")
in_prices  <- yf_download_prices(in_basket, period = "2y", interval = "1d")

in_returns <- in_prices |>
  group_by(symbol) |>
  arrange(date, .by_group = TRUE) |>
  mutate(ret = (adj_close / lag(adj_close)) - 1) |>
  filter(!is.na(ret)) |>
  ungroup() |>
  select(date, symbol, ret) |>
  pivot_wider(names_from = symbol, values_from = ret) |>
  drop_na()

stocks <- setdiff(names(in_returns), c("date", "^NSEI"))
beta_table <- tibble(
  symbol = stocks,
  beta   = sapply(stocks, function(s) cov(in_returns[[s]], in_returns[["^NSEI"]]) / var(in_returns[["^NSEI"]]))
) |> arrange(desc(beta))

print(beta_table)
#> # A tibble: 4 × 2
#>   symbol       beta
#>   <chr>       <dbl>
#> 1 HDFCBANK.NS 1.08 
#> 2 RELIANCE.NS 1.04 
#> 3 INFY.NS     0.923
#> 4 TCS.NS      0.843

13. Portfolio Cumulative Returns & Wealth Index (Growth of $10,000)

Simulate a multi-asset portfolio, compound daily percentage returns over time, and compare the growth of a hypothetical $10,000 investment against the S&P 500 index.

tickers <- c("AAPL", "MSFT", "NVDA", "^GSPC")

prices <- yf_download_prices(tickers, period = "2y", interval = "1d")

returns_df <- prices |>
  group_by(symbol) |>
  arrange(date, .by_group = TRUE) |>
  mutate(daily_return = (adj_close / lag(adj_close)) - 1) |>
  filter(!is.na(daily_return)) |>
  ungroup()

# Equal-weighted tech basket
portfolio_returns <- returns_df |>
  filter(symbol != "^GSPC") |>
  group_by(date) |>
  summarise(daily_return = mean(daily_return), .groups = "drop") |>
  mutate(symbol = "Equal-Weight Tech Portfolio")

benchmark_returns <- returns_df |>
  filter(symbol == "^GSPC") |>
  select(date, symbol, daily_return)

wealth_df <- bind_rows(portfolio_returns, benchmark_returns) |>
  group_by(symbol) |>
  arrange(date, .by_group = TRUE) |>
  mutate(
    cum_return   = cumprod(1 + daily_return) - 1,
    wealth_index = 10000 * cumprod(1 + daily_return)
  ) |>
  ungroup()

wealth_df |>
  group_by(symbol) |>
  slice_tail(n = 1) |>
  select(symbol, date, cum_return, wealth_index)
#> # A tibble: 2 × 4
#> # Groups:   symbol [2]
#>   symbol                      date                cum_return wealth_index
#>   <chr>                       <dttm>                   <dbl>        <dbl>
#> 1 Equal-Weight Tech Portfolio 2026-08-24 13:30:00      0.474       14736.
#> 2 ^GSPC                       2026-08-24 13:30:00      0.362       13625.

Variations & Tips

Growth of $10,000 Visualization:

ggplot(wealth_df, aes(x = date, y = wealth_index, color = symbol)) +
  geom_line(linewidth = 0.9) +
  scale_y_continuous(labels = label_dollar(prefix = "$")) +
  labs(
    title = "Growth of $10,000: Tech Portfolio vs. S&P 500",
    x     = "Date",
    y     = "Portfolio Value ($)",
    color = "Strategy"
  ) +
  theme_minimal() +
  theme(legend.position = "bottom")

plot of chunk recipe-13-wealth-plot

Custom Asset Allocation Weights:

weights <- c("NVDA" = 0.50, "AAPL" = 0.30, "MSFT" = 0.20)

custom_port <- returns_df |>
  filter(symbol %in% names(weights)) |>
  mutate(weight = weights[symbol]) |>
  group_by(date) |>
  summarise(daily_return = sum(daily_return * weight), .groups = "drop") |>
  mutate(
    cum_return   = cumprod(1 + daily_return) - 1,
    wealth_index = 10000 * cumprod(1 + daily_return)
  )

14. Calculate Sharpe Ratio & Risk-Adjusted Metrics

Evaluate asset risk efficiency by computing annualized return, volatility, downside deviation, the Sharpe Ratio, and the Sortino Ratio relative to a risk-free benchmark rate ($R_f$).

prices <- yf_download_prices(c("AAPL", "MSFT", "NVDA", "^GSPC"), period = "2y", interval = "1d")

rf_annual <- 0.04
rf_daily  <- rf_annual / 252

performance_metrics <- prices |>
  group_by(symbol) |>
  arrange(date, .by_group = TRUE) |>
  mutate(
    daily_return  = (adj_close / lag(adj_close)) - 1,
    excess_return = daily_return - rf_daily
  ) |>
  filter(!is.na(daily_return)) |>
  summarise(
    trading_days      = n(),
    annual_return     = mean(daily_return) * 252,
    annual_volatility = sd(daily_return) * sqrt(252),
    sharpe_ratio      = (annual_return - rf_annual) / annual_volatility,
    downside_vol      = sqrt(mean(pmin(excess_return, 0)^2)) * sqrt(252),
    sortino_ratio     = (annual_return - rf_annual) / downside_vol,
    .groups           = "drop"
  ) |>
  arrange(desc(sharpe_ratio))

print(performance_metrics)
#> # A tibble: 4 × 7
#>   symbol trading_days annual_return annual_volatility sharpe_ratio downside_vol
#>   <chr>         <int>         <dbl>             <dbl>        <dbl>        <dbl>
#> 1 ^GSPC           499         0.169             0.162        0.797        0.111
#> 2 NVDA            499         0.355             0.449        0.700        0.314
#> 3 AAPL            499         0.203             0.289        0.565        0.197
#> 4 MSFT            499         0.132             0.288        0.318        0.188
#> # ℹ 1 more variable: sortino_ratio <dbl>

Variations & Tips

Risk vs. Return Bubble Chart:

ggplot(performance_metrics, aes(x = annual_volatility, y = annual_return, color = symbol)) +
  geom_point(aes(size = sharpe_ratio), alpha = 0.8) +
  geom_text(aes(label = symbol), vjust = -1.2, fontface = "bold") +
  geom_hline(yintercept = rf_annual, linetype = "dashed", color = "gray50") +
  scale_x_continuous(labels = label_percent()) +
  scale_y_continuous(labels = label_percent()) +
  scale_size_continuous(range = c(4, 10), name = "Sharpe Ratio") +
  labs(
    title    = "Risk vs. Return Profile",
    subtitle = "Bubble size represents Sharpe Ratio",
    x        = "Annualized Risk / Volatility",
    y        = "Annualized Return"
  ) +
  theme_minimal() +
  theme(legend.position = "right")

plot of chunk recipe-14-bubble-plot

15. Compute Bollinger Bands & Volatility Envelopes

Construct 20-day volatility envelopes ($\text{SMA}_{20} \pm 2\sigma$), compute $\%B$ and Bandwidth indicators, and screen a universe of stocks for volatility breakouts.

# 1. Download prices for a single security
prices <- yf_download_prices("MSFT", period = "1y", interval = "1d")

# 2. Compute Bollinger Bands, %B, and Bandwidth
bb_df <- prices |>
  filter(!is.na(adj_close)) |>
  arrange(date) |>
  mutate(
    bb_middle = rollmeanr(adj_close, k = 20, fill = NA),
    bb_sd     = rollapplyr(adj_close, width = 20, FUN = sd, fill = NA),
    bb_upper  = bb_middle + (2 * bb_sd),
    bb_lower  = bb_middle - (2 * bb_sd),
    bb_pct_b  = (adj_close - bb_lower) / (bb_upper - bb_lower),
    bandwidth = (bb_upper - bb_lower) / bb_middle
  )

tail(bb_df |> select(date, adj_close, bb_lower, bb_middle, bb_upper, bb_pct_b, bandwidth), 6)
#> # A tibble: 6 × 7
#>   date                adj_close bb_lower bb_middle bb_upper bb_pct_b bandwidth
#>   <dttm>                  <dbl>    <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
#> 1 2026-08-17 13:30:00      479.     352.      453.     554.    0.630     0.446
#> 2 2026-08-18 13:30:00      481.     359.      457.     556.    0.619     0.429
#> 3 2026-08-19 13:30:00      483.     369.      462.     555.    0.614     0.404
#> 4 2026-08-20 13:30:00      481.     382.      467.     552.    0.582     0.365
#> 5 2026-08-21 13:30:00      483.     397.      472.     547.    0.573     0.318
#> 6 2026-08-24 13:30:00      487.     413.      477.     541.    0.578     0.269

Variations & Tips

Bollinger Bands Ribbon Plot:

ggplot(filter(bb_df, !is.na(bb_upper)), aes(x = date)) +
  geom_ribbon(aes(ymin = bb_lower, ymax = bb_upper), fill = "#e0e7ff", alpha = 0.6) +
  geom_line(aes(y = bb_upper), color = "#4f46e5", linetype = "dashed", linewidth = 0.5) +
  geom_line(aes(y = bb_middle), color = "#3b82f6", linewidth = 0.8) +
  geom_line(aes(y = bb_lower), color = "#4f46e5", linetype = "dashed", linewidth = 0.5) +
  geom_line(aes(y = adj_close), color = "#1e293b", linewidth = 0.7) +
  labs(
    title    = "MSFT Price with 20-Day Bollinger Bands (±2σ)",
    subtitle = "Shaded channel represents standard volatility envelope",
    x        = "Date",
    y        = "Adjusted Price (USD)"
  ) +
  theme_minimal()

plot of chunk recipe-15-ribbon-plot

Multi-Ticker Volatility Breakout Screener:

watchlist <- c("AAPL", "MSFT", "NVDA", "GOOGL", "AMZN")

screener_results <- yf_download_prices(watchlist, period = "6mo", interval = "1d") |>
  filter(!is.na(adj_close) & !is.na(close)) |>
  group_by(symbol) |>
  arrange(date, .by_group = TRUE) |>
  mutate(
    mb    = rollmeanr(adj_close, k = 20, fill = NA),
    sd    = rollapplyr(adj_close, width = 20, FUN = sd, fill = NA),
    ub    = mb + (2 * sd),
    lb    = mb - (2 * sd),
    pct_b = (adj_close - lb) / (ub - lb)
  ) |>
  filter(!is.na(pct_b)) |>
  slice_tail(n = 1) |>
  ungroup() |>
  mutate(
    status = case_when(
      pct_b > 1.0 ~ "Above Upper Band (Overbought/Breakout)",
      pct_b < 0.0 ~ "Below Lower Band (Oversold/Breakdown)",
      TRUE        ~ "Within Normal Bands"
    )
  ) |>
  select(symbol, date, close = adj_close, ub, lb, pct_b, status)

print(screener_results)
#> # A tibble: 5 × 7
#>   symbol date                close    ub    lb pct_b status             
#>   <chr>  <dttm>              <dbl> <dbl> <dbl> <dbl> <chr>              
#> 1 AAPL   2026-08-24 13:30:00  310.  335.  291. 0.442 Within Normal Bands
#> 2 AMZN   2026-08-24 13:30:00  262.  294.  232. 0.486 Within Normal Bands
#> 3 GOOGL  2026-08-24 13:30:00  348.  373.  326. 0.470 Within Normal Bands
#> 4 MSFT   2026-08-24 13:30:00  487.  541.  413. 0.578 Within Normal Bands
#> 5 NVDA   2026-08-24 13:30:00  208.  235.  192. 0.377 Within Normal Bands

yahoofinancer Cookbook: 15 Practical Recipes

Overview

Required Packages

1. Download Historical Equity Prices

Variations & Tips

2. Batch Download Multiple Tickers

Variations & Tips

3. Intraday Price Series & Timeframes

Variations & Tips

4. Real-Time Market Overview & Regional Filtering

Variations & Tips

5. Download Benchmark Index History

Variations & Tips

6. Historical Currency & Forex Conversions

Variations & Tips

7. Validate Ticker Symbols Before Pipelines

Variations & Tips

8. Calculate Daily Percentage Returns

Variations & Tips

9. Multi-Asset Return Correlation Matrix

Variations & Tips

10. Compute Moving Averages & Trend Crossovers

Variations & Tips

11. Calculate Historical & Maximum Drawdown (MDD)

Variations & Tips

12. Calculate Stock Beta & CAPM Alpha

Variations & Tips

13. Portfolio Cumulative Returns & Wealth Index (Growth of $10,000)

Variations & Tips

14. Calculate Sharpe Ratio & Risk-Adjusted Metrics

Variations & Tips

15. Compute Bollinger Bands & Volatility Envelopes

Variations & Tips