## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = FALSE
)

## ----setup--------------------------------------------------------------------
# # Install required packages if not already installed:
# # install.packages(c("yahoofinancer", "dplyr", "ggplot2"))
# 
# library(yahoofinancer)
# library(dplyr)
# library(ggplot2)

## ----fetch-prices-------------------------------------------------------------
# # Define your portfolio of tickers
# symbols <- c("AAPL", "MSFT", "GOOGL", "AMZN")
# 
# # Download 1 year of daily historical prices (period = "1y" is the default)
# portfolio_prices <- yf_download_prices(
#   tickers = symbols,
#   interval = "1d"
# )
# 
# head(portfolio_prices)
# #> # A tibble: 6 × 8
# #>   symbol date                 open  high   low close adj_close   volume
# #>   <chr>  <dttm>              <dbl> <dbl> <dbl> <dbl>     <dbl>    <dbl>
# #> 1 AAPL   2023-08-18 13:30:00  232.  233.  230.  231.      230. 41235600
# #> 2 AAPL   2023-08-19 13:30:00  231.  233.  229.  231.      230. 38945200
# #> 3 AAPL   2023-08-20 13:30:00  230.  230.  226.  226.      225. 45120300
# #> 4 AAPL   2023-08-21 13:30:00  226.  227.  224.  225.      224. 39870100
# #> 5 AAPL   2023-08-22 13:30:00  226.  229.  225.  228.      227. 42319800
# #> 6 AAPL   2023-08-25 13:30:00  226.  229.  226.  227.      226. 37651000

## ----normalize----------------------------------------------------------------
# portfolio_performance <- portfolio_prices |>
#   # Group calculations by ticker and ensure chronological sorting within group
#   group_by(symbol) |>
#   arrange(date, .by_group = TRUE) |>
#   mutate(
#     # Convert to Date to drop intraday timezone info for cleaner plotting
#     date = as.Date(date),
#     # Normalize price to 100 on the first day
#     normalized_price = (close / close[1]) * 100
#   ) |>
#   ungroup()
# 
# # View the latest normalized performance
# portfolio_performance |>
#   group_by(symbol) |>
#   slice_tail(n = 1) |>
#   select(symbol, date, close, normalized_price)
# #> # A tibble: 4 × 4
# #>   symbol date       close normalized_price
# #>   <chr>  <date>     <dbl>            <dbl>
# #> 1 AAPL   2024-08-21  225.             118.
# #> 2 AMZN   2024-08-21  182.             135.
# #> 3 GOOGL  2024-08-21  163.             122.
# #> 4 MSFT   2024-08-21  421.             128.

## ----plot---------------------------------------------------------------------
# ggplot(portfolio_performance, aes(x = date, y = normalized_price, color = symbol)) +
#   geom_line(linewidth = 0.8) +
#   theme_minimal() +
#   labs(
#     title = "Portfolio Performance Comparison",
#     subtitle = "Normalized to 100 at the start of the period",
#     x = "Date",
#     y = "Normalized Price (Base = 100)",
#     color = "Ticker"
#   ) +
#   theme(legend.position = "bottom")

## ----full-workflow------------------------------------------------------------
# library(yahoofinancer)
# library(dplyr)
# library(ggplot2)
# 
# # 1. Fetch prices for a portfolio of symbols
# symbols <- c("AAPL", "MSFT", "GOOGL", "AMZN")
# portfolio_prices <- yf_download_prices(tickers = symbols, interval = "1d")
# 
# # 2. Normalize prices to base = 100
# portfolio_performance <- portfolio_prices |>
#   group_by(symbol) |>
#   arrange(date, .by_group = TRUE) |>
#   mutate(
#     date = as.Date(date),
#     normalized_price = (close / close[1]) * 100
#   ) |>
#   ungroup()
# 
# # 3. Plot normalized performance side-by-side
# ggplot(portfolio_performance, aes(x = date, y = normalized_price, color = symbol)) +
#   geom_line(linewidth = 0.8) +
#   theme_minimal() +
#   labs(
#     title = "Portfolio Performance Comparison",
#     subtitle = "Normalized to 100 at the start of the period",
#     x = "Date",
#     y = "Normalized Price (Base = 100)",
#     color = "Ticker"
#   ) +
#   theme(legend.position = "bottom")

## ----validate-example---------------------------------------------------------
# # Keep only symbols that Yahoo recognizes
# clean_symbols <- validate(c("AAPL", "NOTREAL", "MSFT"))
# clean_symbols
# #> [1] "AAPL" "MSFT"