HW8(2016–2024)

library(tidyverse)
library(tidyquant)
library(lubridate)
library(scales)

# Stock tickers
tickers <- c("AAPL","AMD","MU","NIO","NVDA","PRPL","SNAP","TSLA")

# Date range
start_date <- as.Date("2016-01-01")
end_date   <- as.Date("2024-12-31")

# Download daily adjusted prices
prices_daily <- tq_get(
  tickers,
  from = start_date,
  to   = end_date,
  get  = "stock.prices"
) %>%
  select(symbol, date, adjusted) %>%
  arrange(symbol, date)

# Sample every 5 trading days
sample_every <- 5

prices_sampled <- prices_daily %>%
  group_by(symbol) %>%
  mutate(row_id = row_number()) %>%
  filter(row_id %% sample_every == 1) %>%
  ungroup() %>%
  select(-row_id)

# Plot 1: Sampled stock prices
ggplot(prices_sampled, aes(x = date, y = adjusted, color = symbol)) +
  geom_line(linewidth = 0.9) +
  labs(
    title = "Sampled Stock Prices (2016–2024)",
    x = "Date",
    y = "Adjusted Close Price",
    color = "Stock"
  ) +
  theme_minimal()

# Normalize prices (base = 1)
prices_normalized <- prices_sampled %>%
  group_by(symbol) %>%
  mutate(norm_price = adjusted / first(adjusted)) %>%
  ungroup()

# Plot 2: Normalized sampled prices
ggplot(prices_normalized, aes(x = date, y = norm_price, color = symbol)) +
  geom_line(linewidth = 0.9) +
  labs(
    title = "Normalized Stock Prices (2016–2024)",
    x = "Date",
    y = "Normalized Price (Base = $1)",
    color = "Stock"
  ) +
  theme_minimal()