Process a Mutable Stock-Price Log

Problem Statement

Financial ledgers frequently ingest updates, corrections, and queries for market data. Design a class StockPriceTracker that maintains a mutable log of stock closing prices across various dates.

Dates are provided as opaque string identifiers (e.g., "2023-10-01", "D1"). Do not assume any chronological ordering based on lexicographical string order; each date should be treated strictly as a unique identifier.

Implement the following methods:

• record_price(ticker: str, date: str, price: float) -> None Records a price for a given ticker on a specified date. If a price already exists for that (ticker, date) pair, update it to the new price and preserve the record of previous values (update provenance).

• get_price(ticker: str, date: str) -> float | None Returns the current price for the given ticker on the specified date, or None if no price has been recorded.

• get_history(ticker: str, date: str) -> list[float] Returns the complete history of values recorded for the given (ticker, date) pair in the order they were submitted. If no entry exists, return an empty list.

• get_pct_change(ticker: str, date_from: str, date_to: str) -> str | None Calculates the percentage change from date_from to date_to using the latest recorded prices: ((price_to - price_from) / price_from) * 100. Return the result formatted as a string rounded to two decimal places with a % suffix (e.g., "+5.25%" for positive, "-3.10%" for negative, or "0.00%" for zero). If either date has no recorded price or if price_from is 0, return None.

• compare_tickers(ticker_a: str, ticker_b: str, date_from: str, date_to: str) -> str | None Compares the percentage change of ticker_a against ticker_b across the same two dates.

  • Returns ticker_a if ticker_a had a strictly greater percentage change.
  • Returns ticker_b if ticker_b had a strictly greater percentage change.
  • Returns "TIE" if both percentage changes are equal.
  • Returns None if either ticker cannot calculate a valid percentage change.

Example

tracker = StockPriceTracker()

tracker.record_price("AAPL", "2023-01-01", 100.0)
tracker.record_price("AAPL", "2023-01-02", 110.0)
tracker.record_price("MSFT", "2023-01-01", 200.0)
tracker.record_price("MSFT", "2023-01-02", 210.0)

# AAPL changed +10.00%, MSFT changed +5.00%
assert tracker.get_pct_change("AAPL", "2023-01-01", "2023-01-02") == "+10.00%"
assert tracker.get_pct_change("MSFT", "2023-01-01", "2023-01-02") == "+5.00%"
assert tracker.compare_tickers("AAPL", "MSFT", "2023-01-01", "2023-01-02") == "AAPL"

# Update AAPL price for 2023-01-02 to 95.0
tracker.record_price("AAPL", "2023-01-02", 95.0)
assert tracker.get_history("AAPL", "2023-01-02") == [110.0, 95.0]
assert tracker.get_pct_change("AAPL", "2023-01-01", "2023-01-02") == "-5.00%"
assert tracker.compare_tickers("AAPL", "MSFT", "2023-01-01", "2023-01-02") == "MSFT"

Constraints

• Number of total operations: up to 200,000.
• price >= 0.0 for all record operations.
• Ticker names and date identifiers are non-empty ASCII strings of length 1 <= len <= 32.
• All query operations (get_price, get_history, get_pct_change, compare_tickers) should run in O(1) average time.