AlphaLabs: AI Trading Platform Built with Kiro Specs

The Problem That Kept Me Up at Night

Harshit Aggarwal’s AlphaLabs project aimed to build a platform for AI-driven trading, integrating real-time market data, WebSocket streams, and financial calculations. The system required 20+ backend services and 22+ technical indicators to function cohesively.

Why This Matters

Traditional trading algorithms rely on rigid rules, while AlphaLabs uses AI for contextual decision-making. However, integrating AI with real-time data posed risks: inconsistent model responses, synchronization failures, and the complexity of managing 4–5 LLMs in Council Mode. Without structured development, the project risked becoming a “Frankenstein architecture” of loosely coupled components.

Key Insights

• “20+ backend services in AlphaLabs, 2025”: The project’s scale required strict modularization.
• “Council Mode uses 4–5 LLMs for consensus”: Combines multiple models to reduce bias, as detailed in the article.
• “Kiro specs used by AlphaLabs for structured development”: Enabled progress tracking and consistent architecture.

Practical Applications

• Use Case: AlphaLabs allows AI-driven trading with real-time data and 22+ indicators.
• Pitfall: Over-reliance on AI without fallback mechanisms may lead to unhandled edge cases.

References:

• https://dev.to/harshit_aggarwal_2e641193/from-chaos-to-code-alphalabs-103e