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Three-Layer Architecture for AI Trading Systems

source post: Video by max_kelleyy

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Original post

Video by max_kelleyy

Source: instagram · Max Kelley Saved: 20260617 Tags: instagram, ai, finance, trading Display: Three-Layer Architecture for AI Trading Systems — Separates AI trading systems into analyst, orchestration, and data layers for modularity and safer paper-trade validation.

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 the post showed

Caption: Don't build an AI trading bot.

Build a research desk connected to a data layer first, then build the execution layer.

Comment AI and I'll send you over the links

#ai #finance #trading

Key claims from transcript: If you're trying to use AI to trade, don't think of it as building one trading bot. Think of it as three layers that need to integrate and work together. Layer 1 is the analyst. It's going to be this repo. Layer 2 is going to be the agentic framework that says repo. And third layer is going to be the data layer. And that's this repo. I would start with these three tools in paper trading. Test out

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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

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.