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

## ----load-libraries, message=FALSE, warning=FALSE-----------------------------
# # Install required packages if needed:
# # install.packages(c("yahoofinancer", "dplyr", "tidyr", "ggplot2", "scales"))
# 
# library(yahoofinancer)
# library(dplyr)
# library(tidyr)
# library(ggplot2)
# library(scales)

## ----fetch-income-------------------------------------------------------------
# peer_tickers <- c("AAPL", "MSFT", "GOOG")
# 
# income_df <- yf_get_financials(
#   tickers        = peer_tickers,
#   statement_type = "income",
#   frequency      = "annual"
# )
# 
# income_df |>
#   select(symbol, date, period_type, total_revenue, operating_income, net_income) |>
#   head(n = 6)
# #> # A tibble: 6 x 6
# #>   symbol date       period_type total_revenue operating_income   net_income
# #>   <chr>  <date>     <chr>               <dbl>            <dbl>        <dbl>
# #> 1 AAPL   2021-09-25 12M          365817000000     108949000000  94680000000
# #> 2 AAPL   2022-09-24 12M          394328000000     119437000000  99803000000
# #> 3 AAPL   2023-09-30 12M          383285000000     114301000000  96995000000
# #> 4 MSFT   2022-06-30 12M          198270000000      83383000000  72738000000
# #> 5 MSFT   2023-06-30 12M          211915000000      88523000000  72361000000
# #> 6 MSFT   2024-06-30 12M          245122000000     109433000000  88136000000

## ----fetch-cashflow-----------------------------------------------------------
# cashflow_df <- yf_get_financials(
#   tickers        = peer_tickers,
#   statement_type = "cash-flow",
#   frequency      = "annual"
# )
# 
# cashflow_df |>
#   select(symbol, date, operating_cash_flow, capital_expenditure, free_cash_flow) |>
#   head(n = 6)
# #> # A tibble: 6 x 5
# #>   symbol date       operating_cash_flow capital_expenditure free_cash_flow
# #>   <chr>  <date>                   <dbl>               <dbl>          <dbl>
# #> 1 AAPL   2021-09-25        104038000000        -11085000000    92953000000
# #> 2 AAPL   2022-09-24        122151000000        -10708000000   111443000000
# #> 3 AAPL   2023-09-30        110543000000        -10959000000    99584000000
# #> 4 MSFT   2022-06-30         89035000000        -23886000000    65149000000
# #> 5 MSFT   2023-06-30         87582000000        -28107000000    59475000000
# #> 6 MSFT   2024-06-30        118548000000        -44477000000    74071000000

## ----fetch-balancesheet-------------------------------------------------------
# balance_df <- yf_get_financials(
#   tickers        = peer_tickers,
#   statement_type = "balance-sheet",
#   frequency      = "annual"
# )
# 
# balance_df |>
#   select(symbol, date, total_assets, total_stockholder_equity, working_capital, total_debt) |>
#   head(n = 6)

## ----compute-ratios-----------------------------------------------------------
# fundamentals <- income_df |>
#   left_join(cashflow_df, by = c("symbol", "date", "period_type")) |>
#   left_join(balance_df, by = c("symbol", "date", "period_type")) |>
#   group_by(symbol) |>
#   arrange(date, .by_group = TRUE) |>
#   mutate(
#     operating_margin = operating_income / total_revenue,
#     net_margin       = net_income / total_revenue,
#     fcf_conversion   = free_cash_flow / operating_cash_flow,
#     debt_to_equity   = total_debt / total_stockholder_equity
#   ) |>
#   ungroup()
# 
# # View the most recent period for each peer
# latest_fundamentals <- fundamentals |>
#   group_by(symbol) |>
#   slice_tail(n = 1) |>
#   ungroup() |>
#   select(symbol, date, total_revenue, operating_margin, net_margin, fcf_conversion, debt_to_equity)
# 
# latest_fundamentals

## ----fetch-valuation----------------------------------------------------------
# stocks <- Tickers$new(peer_tickers)
# val_measures <- stocks$valuation_measures
# 
# val_measures |>
#   group_by(symbol) |>
#   slice_tail(n = 1) |>
#   ungroup() |>
#   select(symbol, date, market_cap, pe_ratio, forward_pe_ratio, peg_ratio, enterprise_value_ebitda_ratio)
# #> # A tibble: 3 x 7
# #>   symbol date       market_cap pe_ratio forward_pe_ratio peg_ratio enterprise_value_ebitda_ratio
# #>   <chr>  <date>          <dbl>    <dbl>            <dbl>     <dbl>                         <dbl>
# #> 1 AAPL   2024-06-30    3.45e12     33.2             28.5      2.10                          24.2
# #> 2 GOOG   2024-06-30    2.25e12     25.4             21.8      1.45                          17.8
# #> 3 MSFT   2024-06-30    3.30e12     36.1             30.2      2.35                          25.6

## ----create-screener----------------------------------------------------------
# # Extract latest valuation metrics
# latest_valuation <- val_measures |>
#   group_by(symbol) |>
#   slice_tail(n = 1) |>
#   ungroup() |>
#   select(symbol, pe_ratio, forward_pe_ratio, peg_ratio, enterprise_value_ebitda_ratio)
# 
# # Combine fundamentals and valuation
# screener_table <- latest_fundamentals |>
#   left_join(latest_valuation, by = "symbol") |>
#   mutate(
#     # Screening criteria flags
#     pass_margin    = operating_margin >= 0.25,
#     pass_fcf       = fcf_conversion >= 0.70,
#     pass_peg       = peg_ratio < 2.5,
#     pass_valuation = forward_pe_ratio < 35,
#     screen_passed  = pass_margin & pass_fcf & pass_peg & pass_valuation
#   )
# 
# screener_table |>
#   select(symbol, operating_margin, fcf_conversion, forward_pe_ratio, peg_ratio, screen_passed)

## ----plot-margin-trends-------------------------------------------------------
# ggplot(fundamentals, aes(x = date, y = operating_margin, color = symbol)) +
#   geom_line(linewidth = 1.1) +
#   geom_point(size = 2.5) +
#   scale_y_continuous(labels = percent_format(accuracy = 1)) +
#   scale_x_date(date_labels = "%Y", date_breaks = "1 year") +
#   labs(
#     title    = "Annual Operating Margin Comparison",
#     subtitle = "Operating income as a percentage of total revenue",
#     x        = "Fiscal Year",
#     y        = "Operating Margin",
#     color    = "Company",
#     caption  = "Source: Yahoo Finance via yahoofinancer"
#   ) +
#   theme_minimal(base_size = 12)

## ----plot-val-vs-margin-------------------------------------------------------
# ggplot(screener_table, aes(x = operating_margin, y = enterprise_value_ebitda_ratio, label = symbol)) +
#   geom_point(aes(color = symbol), size = 4) +
#   geom_text(vjust = -1, fontface = "bold") +
#   scale_x_continuous(labels = percent_format(accuracy = 1)) +
#   labs(
#     title    = "EV / EBITDA Multiple vs. Operating Margin",
#     subtitle = "Evaluating valuation premium relative to operating efficiency",
#     x        = "Operating Margin",
#     y        = "Enterprise Value / EBITDA Multiple",
#     caption  = "Source: Yahoo Finance via yahoofinancer"
#   ) +
#   theme_minimal(base_size = 12) +
#   theme(legend.position = "none")

## ----full-workflow------------------------------------------------------------
# library(yahoofinancer)
# library(dplyr)
# library(ggplot2)
# library(scales)
# 
# # 1. Define peer universe
# tickers <- c("AAPL", "MSFT", "GOOG")
# 
# # 2. Fetch income statement and cash flow statements
# income_df <- yf_get_financials(tickers, statement_type = "income", frequency = "annual")
# cashflow_df <- yf_get_financials(tickers, statement_type = "cash-flow", frequency = "annual")
# 
# # 3. Retrieve quarterly valuation measures
# stocks <- Tickers$new(tickers)
# val_measures <- stocks$valuation_measures
# 
# # 4. Join and calculate key metrics
# latest_income <- income_df |>
#   group_by(symbol) |>
#   slice_tail(n = 1) |>
#   ungroup() |>
#   mutate(operating_margin = operating_income / total_revenue) |>
#   select(symbol, date, total_revenue, operating_margin)
# 
# latest_cf <- cashflow_df |>
#   group_by(symbol) |>
#   slice_tail(n = 1) |>
#   ungroup() |>
#   mutate(fcf_conversion = free_cash_flow / operating_cash_flow) |>
#   select(symbol, fcf_conversion)
# 
# latest_val <- val_measures |>
#   group_by(symbol) |>
#   slice_tail(n = 1) |>
#   ungroup() |>
#   select(symbol, pe_ratio, forward_pe_ratio, peg_ratio, enterprise_value_ebitda_ratio)
# 
# screener_summary <- latest_income |>
#   left_join(latest_cf, by = "symbol") |>
#   left_join(latest_val, by = "symbol")
# 
# # 5. Display screener table
# print(screener_summary)