Practice/Meta/Design a Data Structure for Stock Data Retrieval

Design a Data Structure for Stock Data Retrieval

CodingMust

Problem

You need to build a system that stores historical stock price data and allows efficient retrieval. The system should support adding stock price entries and querying them based on the stock symbol and optionally a starting date.

Implement a class StockDataSystem with the following operations:

• add(stock_name, date, price): Store a stock price entry with the given stock symbol, date (in "YYYY-MM-DD" format), and price value.
• query(stock_name, start_date=None): Retrieve stock price records for the given symbol. If start_date is provided, return only records from that date onwards (inclusive). Results must be sorted by date in ascending chronological order. Return the data as a list of [date, price] pairs.

Requirements

1. The add method should store stock price data efficiently
2. The query method with only a stock name should return all records for that stock sorted by date
3. The query method with both stock name and start date should return only records on or after the start date, sorted by date
4. If no records match the query criteria, return an empty list
5. Dates are always in "YYYY-MM-DD" string format
6. Multiple price entries can exist for the same stock on the same date

Constraints

• Stock names are non-empty strings (e.g., "AAPL", "GOOGL")
• Dates follow the format "YYYY-MM-DD" and are valid calendar dates
• Prices are positive floating-point numbers
• Number of operations: up to 10^4
• Query operations should be efficient for large datasets
• Time complexity for query should ideally be O(k log k) where k is the number of matching records

Examples

Example 1:

` stocks = StockDataSystem() stocks.add("AAPL", "2023-01-15", 150.50) stocks.add("AAPL", "2023-01-10", 148.20) stocks.add("AAPL", "2023-01-20", 152.00) result = stocks.query("AAPL")

Output: [["2023-01-10", 148.20], ["2023-01-15", 150.50], ["2023-01-20", 152.00]] Explanation: All AAPL records are returned in ascending date order. `

Example 2:

` stocks = StockDataSystem() stocks.add("MSFT", "2023-02-01", 250.00) stocks.add("MSFT", "2023-02-05", 255.00) stocks.add("MSFT", "2023-02-10", 260.00) result = stocks.query("MSFT", "2023-02-05")

Output: [["2023-02-05", 255.00], ["2023-02-10", 260.00]] Explanation: Only records from 2023-02-05 onwards are returned. `

Example 3:

` stocks = StockDataSystem() stocks.add("TSLA", "2023-03-15", 200.00) result = stocks.query("NVDA")

Output: [] Explanation: No records exist for NVDA, so an empty list is returned. `