Goldman Sachs Trading Aggregator System Design

Background

Goldman Sachs technical interviews often wrap a deep test of your real engineering skills in a FinTech scenario. What you receive might not be an online platform link, but a project framework you need to complete, test, and upload locally. This is a comprehensive test of your code robustness, test completeness, and understanding of financial scenarios.

Recently, a student experienced this entire process from "business requirement to engineering delivery" in a mock Goldman Sachs remote interview. With oavoservice's "Real-time API Design + TDD Concept + Risk Scenario Prediction" service, he not only solved the problem perfectly but also submitted a solution full of defensive programming thoughts that impressed the interviewer.

Level 1: Basic Functionality - Can You Implement a Ticker Converter?

📜 Essence of the Problem

In Goldman Sachs' trading system, different exchanges might use different Ticker symbols to represent the same company (e.g., 'AAPL' vs 'AAPL.US').

Task:

• Implement a function normalize_ticker(ticker) to convert various stock ticker formats into a standard internal format.
• You need to write your own test cases to verify your logic in doTestsPass().

The focus of this question is not how difficult string manipulation is, but whether you can foresee various "dirty data" possibilities in the real world and build a firewall for your function.

oavoservice's Mindset Injection

Before the student started writing if/else regex, we first guided him to think about test cases to drive development:

1. Happy Path

• 'AAPL', 'GOOG.US', 'MSFT'

2. Format Variations

• 'aapl' (lowercase) should be converted to uppercase
• ' GOOG ' (spaces) should be trimmed
• 'BRK.A', 'BRK-B' (special characters) clarify handling rules

3. Invalid/Malicious Inputs

• None, '' (empty input)
• '12345' (pure numbers)
• An extremely long string (potential performance or security issues)

Result

Guided by oavoservice, the student defined clear test cases first, then started writing code to pass these tests. This Test-Driven Development (TDD) pattern not only ensured code robustness but also demonstrated a highly professional software development process to the interviewer.

Level 2: Classic Upgrade - Can You Design a "Trade Stream Aggregator"? (Meeting Rooms II)

📜 The Real Challenge

Imagine you need to monitor all trading streams of an exchange within a day. Each trading stream can be seen as a time interval [start_time, end_time]. You need to design a system to calculate the maximum number of trading streams happening simultaneously at any given moment. This can be used to assess peak server capacity requirements.

This is the application of the classic LeetCode "Meeting Rooms II" in a financial scenario, testing your understanding of interval scheduling problems and optimal algorithms.

oavoservice's Mindset Injection

We immediately identified two core approaches for the student and analyzed the pros and cons:

Approach 1: Sweep-line / Difference Array

Idea: Split each interval [start, end] into two events: (+1, start_time) and (-1, end_time).

Steps:

1. Sort all events by timestamp.
2. Iterate through events, maintaining a concurrent_streams counter.
3. Add 1 for +1 events, subtract 1 for -1 events.
4. Record the maximum count encountered.

Pros: Clear logic, easy to understand, O(N log N) complexity.

Approach 2: Min-Heap

Idea: Sort all trade streams by start time. Maintain a min-heap storing the end times of currently active trade streams.

Steps:

1. Iterate through sorted streams.
2. For each new stream, check if the heap top (earliest ending one) has finished.
3. If finished, pop it.
4. Push the new stream's end time.
5. The heap size at any moment represents the concurrency count.

Pros: Also O(N log N), potentially better space efficiency in some scenarios.

Test-Driven Thinking

While coding, we guided him again to think in TDD:

1. No Overlap: [[1,2], [3,4], [5,6]] (Peak 1)
2. Complete Overlap: [[1,5], [2,4], [3,3]] (Peak 3)
3. Partial Overlap: Complex examples
4. Edge Cases:
   • Empty input []
   • Start time equals end time [[1,1]]

Result

The student chose the more intuitive Sweep-line method and clearly explained the ideas and complexities of both approaches. More importantly, he wrote a comprehensive set of test cases for this complex algorithm problem, completing the loop from requirement to delivery.

🎯 Summary: oavoservice Helps You Show "Engineering Qualities" Goldman Sachs Values

In this Goldman Sachs interview, which seemed like an algorithm test but was actually an engineering test, oavoservice's value lay in:

1. Switching Mindset from "Solving" to "Defensive Programming & Testing"

Helping you design robust code capable of handling real-world dirty data, and proving it with professional test cases.

2. Enhancing "Problem Transformation" Ability

Guiding you to transform vague financial scenarios (peak capacity) into classic, quantifiable algorithmic models ("Meeting Rooms II").

3. Injecting "Rigor"

Making you actively consider various abnormal inputs and edge cases, demonstrating the high level of rigor required for a FinTech engineer.

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