What caused the sudden spike in error rates for Vise's tax-loss harvesting algorithm last week?

Introduction

The sudden spike in error rates for Vise's tax-loss harvesting algorithm last week is a critical issue that demands immediate attention. As we delve into this product execution problem, I'll employ a systematic approach to identify, validate, and address the root cause while considering both short-term fixes and long-term implications for our product strategy.

To tackle this challenge, I'll begin by asking clarifying questions to gather essential context. Then, I'll rule out basic external factors before diving deep into product understanding, metric breakdown, and data analysis. This will lead us to form data-driven hypotheses, conduct root cause analysis, and ultimately develop a comprehensive resolution plan.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking there might have been a recent deployment. Has there been any code release or system update in the days leading up to the error spike?

Why it matters: Recent changes often correlate with performance issues. Expected answer: Yes, there was a deployment two days prior. Impact on approach: If confirmed, we'd prioritize reviewing recent changes.

• Considering the nature of tax-loss harvesting, I'm curious about market conditions. Have there been any significant market fluctuations or unusual trading patterns recently?

Why it matters: Market volatility can stress-test algorithms and expose weaknesses. Expected answer: The market has been relatively stable. Impact on approach: If stable, we'd focus more on internal factors rather than external market conditions.

• Given the specificity of "error rates," I'm wondering about our monitoring setup. Can you clarify what specific errors we're seeing and how they're being measured?

Why it matters: Understanding the error types helps narrow down potential causes. Expected answer: Increased API timeouts and data inconsistencies. Impact on approach: This would guide our technical investigation towards specific components.

• Thinking about user impact, I'm curious about the scale. What percentage of users or transactions are affected by these errors?

Why it matters: Helps prioritize the issue and understand its scope. Expected answer: Approximately 15% of transactions are failing. Impact on approach: A high percentage would escalate the urgency and broaden our investigation.

• Considering potential data issues, has there been any change in our data sources or providers for market information or user portfolios?

Why it matters: Data quality and consistency are crucial for algorithm performance. Expected answer: No recent changes in data providers. Impact on approach: If unchanged, we'd focus more on internal data processing rather than external data sources.