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Three-Layer Architecture for AI Trading Systems

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TL;DR

A three-layer architectural framework for building AI-assisted trading systems, separating concerns into an analyst layer (AI research), an agentic orchestration layer, and a data layer. The post advocates starting with paper trading before live execution. Building a monolithic AI trading bot is fragile; separating the system into analyst, agentic framework, and data layers makes it modular, testable, and safer — especially when validated first through paper trading.

What it actually is

  • What: A three-layer architectural framework for building AI-assisted trading systems, separating concerns into an analyst layer (AI research), an agentic orchestration layer, and a data layer. The post advocates starting with paper trading before live execution.
  • Who built it / maintained by: Max Kelley (Instagram: max_kelleyy), an independent creator/educator in the AI finance space
  • Status: unknown
  • Why it matters: Building a monolithic AI trading bot is fragile; separating the system into analyst, agentic framework, and data layers makes it modular, testable, and safer — especially when validated first through paper trading.
  • How it compares to alternatives:
  • ai-hedge-fund (virattt)
  • FinRobot
  • Zipline
  • Backtrader
  • LangChain trading agents
  • QuantConnect
  • GitHub stars: 60,400 · License: MIT · Archived: no

Links

  • Repo: https://github.com/virattt/ai-hedge-fund

Kickstarter guide

The creator recommends assembling three open-source repos: one for the AI analyst layer, one for the agentic orchestration framework, and one for the market data layer. Start by connecting them in a paper trading environment to test strategies with no real capital at risk. Follow Max Kelley on Instagram and comment 'AI' on the post to receive the specific repo links and a setup guide.