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Implement Stock Price Query APIs | Lead Bank Coding Question

Implement Stock Price Query APIs

Company: Lead Bank

Role: Software Engineer

Category: Coding & Algorithms

Difficulty: medium

Interview Round: Technical Screen

You are given an initial collection of stock price records. Each record contains: - `symbol`: a stock name or ticker string - `timestamp`: initially a calendar date at daily granularity - `price`: a decimal price Design and implement an in-memory `StockPriceStore`. You may define the exact function signatures and return types, but the behavior must be clear and testable. Support the following operations: 1. `get_price(symbol, date)`: return the price of the given stock on the given date, or an appropriate missing-value result if no price exists. 2. `get_change(symbol, start_date, end_date)`: return the price change for the given stock between two dates. Use a custom formula supplied by the interviewer, for example `formula(start_price, end_price)`. Handle cases where either price is missing or the formula cannot be applied. 3. `update_prices(symbol, start_date, end_date, new_prices)`: update the prices for the given stock over an inclusive date range and return whether the update succeeded. If any price in that range for that stock has already been updated before, the entire update should fail. The update should be atomic: either all requested prices are updated, or none are. 4. The timestamp granularity changes from daily to hourly. Add an operation `get_hourly_prices(symbol, day)` that returns all hourly prices for the given stock within that calendar day, ordered by time. Clarify and handle edge cases, including duplicate initial records, missing symbols, missing dates, invalid date ranges, repeated updates, partial update failures, and date/time parsing. Write representative test cases for your implementation.

Quick Answer: This question evaluates skills in time-series data modeling, API and data-structure design, transactional (atomic) batch updates, concurrency control, and date/time parsing for stock price records.

Part 1: Daily StockPriceStore API Processor

Implement a function `solution(initial_records, operations)` that simulates an in-memory daily stock price store. Each initial record is a tuple `(symbol, date_str, price)`, where `date_str` uses the format `YYYY-MM-DD`. Invalid initial dates must be ignored. If multiple valid initial records have the same `(symbol, date)`, the later record overrides the earlier one. Process the operations in order and return one result per operation. Supported operations are: `('get_price', symbol, date_str)` which returns the stored price or `None`; `('get_change', symbol, start_date, end_date, formula_name)` which returns either `end_price - start_price` for `formula_name == 'difference'` or `(end_price - start_price) / start_price` for `formula_name == 'percent_change'`; and `('update_prices', symbol, start_date, end_date, new_prices)` which tries to update every date in the inclusive range atomically. An update succeeds only if the date range is valid, the number of new prices matches the number of dates in the range, every date already exists for that symbol, and none of those dates was updated by any previous successful update. If any check fails, the entire update fails and no price is changed.

Constraints

0 <= len(initial_records), len(operations) <= 10^4
Date strings are intended to use format YYYY-MM-DD, but invalid strings may appear
Prices are numeric values
The sum of lengths of all update date ranges is at most 10^5

Examples

Input: ([('AAPL', '2024-01-01', 100.0), ('AAPL', '2024-01-02', 110.0), ('AAPL', '2024-01-03', 120.0)], [('get_price', 'AAPL', '2024-01-02'), ('get_change', 'AAPL', '2024-01-01', '2024-01-03', 'difference'), ('update_prices', 'AAPL', '2024-01-01', '2024-01-02', [101.0, 111.0]), ('get_price', 'AAPL', '2024-01-01'), ('update_prices', 'AAPL', '2024-01-02', '2024-01-03', [112.0, 121.0]), ('get_price', 'AAPL', '2024-01-03')])

Expected Output: [110.0, 20.0, True, 101.0, False, 120.0]

Explanation: The first update succeeds. The second update fails because 2024-01-02 was already updated before, so the update is rejected atomically and 2024-01-03 stays unchanged.

Input: ([('MSFT', '2024-02-01', 200.0), ('MSFT', '2024-02-01', 210.0), ('MSFT', '2024-02-02', 252.0)], [('get_price', 'MSFT', '2024-02-01'), ('get_change', 'MSFT', '2024-02-01', '2024-02-02', 'percent_change')])

Expected Output: [210.0, 0.2]

Explanation: Duplicate initial records are resolved by keeping the later valid one, so the price on 2024-02-01 is 210.0.

Input: ([('TSLA', '2024-03-01', 0.0), ('TSLA', '2024-03-02', 50.0)], [('get_change', 'TSLA', '2024-03-01', '2024-03-02', 'percent_change'), ('get_change', 'TSLA', '2024-03-03', '2024-03-04', 'difference'), ('update_prices', 'TSLA', '2024-03-02', '2024-03-01', [1.0, 2.0]), ('get_price', 'TSLA', 'bad-date')])

Expected Output: [None, None, False, None]

Explanation: Percent change cannot be computed when the start price is zero. Missing dates and invalid date strings also produce failure or `None`.

Input: ([('IBM', '2024-04-01', 10.0), ('IBM', '2024-04-03', 30.0)], [('update_prices', 'IBM', '2024-04-01', '2024-04-03', [11.0, 20.0, 31.0]), ('get_price', 'IBM', '2024-04-01'), ('update_prices', 'IBM', '2024-04-01', '2024-04-01', [15.0, 16.0])])

Expected Output: [False, 10.0, False]

Explanation: The first update fails because 2024-04-02 does not exist, so no prices are changed. The last update fails because the number of replacement prices does not match the date range length.

Hints

Use a nested dictionary like `store[symbol][date]` so point lookups are fast.
For atomic updates, first validate the whole date range, and only then write the new prices.

Part 2: Hourly StockPriceStore and Daily Hour List Query

Implement a function `solution(initial_records, operations)` for an in-memory stock price store with hourly timestamps. Each initial record is a tuple `(symbol, timestamp_str, price)`, where `timestamp_str` uses the format `YYYY-MM-DD HH:MM`. Invalid initial timestamps must be ignored. If multiple valid initial records have the same `(symbol, timestamp)`, the later record overrides the earlier one. Process operations in order and return one result per operation. Supported operations are: `('get_price', symbol, timestamp_str)` for exact timestamp lookup, and `('get_hourly_prices', symbol, day_str)` which returns all prices for that symbol within the given calendar day as a list of `(timestamp_str, price)` pairs sorted by time. For a valid day with no data, return an empty list. For an invalid query date or timestamp string, return `None` for that operation.

Constraints

0 <= len(initial_records), len(operations) <= 10^5
Timestamp strings are intended to use format YYYY-MM-DD HH:MM, but invalid strings may appear
Day query strings are intended to use format YYYY-MM-DD
Prices are numeric values

Examples

Input: ([('AAPL', '2024-01-01 10:00', 101.0), ('AAPL', '2024-01-01 09:00', 100.0), ('AAPL', '2024-01-01 15:00', 105.0), ('MSFT', '2024-01-01 09:00', 200.0)], [('get_hourly_prices', 'AAPL', '2024-01-01'), ('get_price', 'AAPL', '2024-01-01 10:00'), ('get_price', 'MSFT', '2024-01-01 10:00')])

Expected Output: [[('2024-01-01 09:00', 100.0), ('2024-01-01 10:00', 101.0), ('2024-01-01 15:00', 105.0)], 101.0, None]

Explanation: Hourly prices for a day must be returned in time order even if the input records were unsorted.

Input: ([('AAPL', '2024-02-01 09:00', 50.0), ('AAPL', '2024-02-01 09:00', 55.0), ('AAPL', '2024-02-02 09:00', 60.0)], [('get_price', 'AAPL', '2024-02-01 09:00'), ('get_hourly_prices', 'AAPL', '2024-02-01'), ('get_hourly_prices', 'AAPL', '2024-02-02')])

Expected Output: [55.0, [('2024-02-01 09:00', 55.0)], [('2024-02-02 09:00', 60.0)]]

Explanation: Duplicate initial records are resolved by keeping the later valid timestamp entry.

Input: ([('TSLA', '2024-03-05 12:00', 700.0)], [('get_hourly_prices', 'TSLA', '2024-03-06'), ('get_hourly_prices', 'TSLA', 'bad-day'), ('get_price', 'TSLA', '2024-03-05'), ('get_price', 'NONE', '2024-03-05 12:00')])

Expected Output: [[], None, None, None]

Explanation: A valid day with no data returns an empty list. Invalid query strings return `None`.

Input: ([], [('get_hourly_prices', 'AAPL', '2024-01-01'), ('get_price', 'AAPL', '2024-01-01 09:00')])

Expected Output: [[], None]

Explanation: With no initial data, day queries return empty lists and exact timestamp lookups return `None`.

Hints

A hash map keyed by `(symbol, timestamp)` makes exact lookups simple.
Group records by `(symbol, day)` and sort each daily bucket once so `get_hourly_prices` can return results quickly.

Implement Stock Price Query APIs

Company: Lead Bank

Role: Software Engineer

Category: Coding & Algorithms

Difficulty: medium

Interview Round: Technical Screen

Part 1: Daily StockPriceStore API Processor

Constraints

0 <= len(initial_records), len(operations) <= 10^4
Date strings are intended to use format YYYY-MM-DD, but invalid strings may appear
Prices are numeric values
The sum of lengths of all update date ranges is at most 10^5

Examples

Expected Output: [110.0, 20.0, True, 101.0, False, 120.0]

Explanation: The first update succeeds. The second update fails because 2024-01-02 was already updated before, so the update is rejected atomically and 2024-01-03 stays unchanged.

Expected Output: [210.0, 0.2]

Explanation: Duplicate initial records are resolved by keeping the later valid one, so the price on 2024-02-01 is 210.0.

Expected Output: [None, None, False, None]

Explanation: Percent change cannot be computed when the start price is zero. Missing dates and invalid date strings also produce failure or `None`.

Expected Output: [False, 10.0, False]

Explanation: The first update fails because 2024-04-02 does not exist, so no prices are changed. The last update fails because the number of replacement prices does not match the date range length.

Hints

Use a nested dictionary like `store[symbol][date]` so point lookups are fast.
For atomic updates, first validate the whole date range, and only then write the new prices.

Part 2: Hourly StockPriceStore and Daily Hour List Query

Constraints

0 <= len(initial_records), len(operations) <= 10^5
Timestamp strings are intended to use format YYYY-MM-DD HH:MM, but invalid strings may appear
Day query strings are intended to use format YYYY-MM-DD
Prices are numeric values

Examples

Expected Output: [[('2024-01-01 09:00', 100.0), ('2024-01-01 10:00', 101.0), ('2024-01-01 15:00', 105.0)], 101.0, None]

Explanation: Hourly prices for a day must be returned in time order even if the input records were unsorted.

Expected Output: [55.0, [('2024-02-01 09:00', 55.0)], [('2024-02-02 09:00', 60.0)]]

Explanation: Duplicate initial records are resolved by keeping the later valid timestamp entry.

Expected Output: [[], None, None, None]

Explanation: A valid day with no data returns an empty list. Invalid query strings return `None`.

Input: ([], [('get_hourly_prices', 'AAPL', '2024-01-01'), ('get_price', 'AAPL', '2024-01-01 09:00')])

Expected Output: [[], None]

Explanation: With no initial data, day queries return empty lists and exact timestamp lookups return `None`.

Hints

A hash map keyed by `(symbol, timestamp)` makes exact lookups simple.
Group records by `(symbol, day)` and sort each daily bucket once so `get_hourly_prices` can return results quickly.

Quick Overview

Quick Overview