Fetch and Cache Daily Stock Prices Across a Date Range

Problem: Stock Data Fetcher

You are tasked with implementing a StockDataClient that retrieves daily opening and closing prices for a given ticker symbol over a specific date range. You must interact with a mock external API that provides data in paginated JSON format.

Requirements

1. API Interface: Use a provided fetch_page(ticker, start_date, end_date, page_token) function which returns a dictionary: {'data': [...], 'next_page_token': str or None}.
2. Data Integrity: Ensure the returned list includes every calendar date in the requested range. If the API returns missing dates, identify them as 'missing'.
3. Resilience: Implement a bounded retry strategy (e.g., 3 attempts) with exponential backoff for transient network errors (HTTP 5xx, timeouts).
4. Caching: Implement a local cache (in-memory) to avoid redundant requests for the same ticker-date pairs.
5. Validation: Prices must be parsed as decimal.Decimal objects to maintain precision.

Function Signature

from decimal import Decimal
from typing import List, Dict, Optional
from datetime import date

class StockDataClient:
    def get_price_history(self, ticker: str, start_date: date, end_date: date) -> List[Dict]:
        """
        Returns a list of dicts: {'date': date, 'open': Decimal, 'close': Decimal}
        Sorted by date ascending.
        """
        pass

Example

Input: ticker='AAPL', start_date=2023-01-01, end_date=2023-01-02 Output:

[
  {"date": "2023-01-01", "open": "150.00", "close": "152.50"},
  {"date": "2023-01-02", "open": "152.50", "close": "151.00"}
]

Constraints

• The range can span up to 5 years.
• API results may contain duplicate date entries; ensure your client deduplicates them.
• Handle cases where the API returns an empty list or an error code.