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Repository Install

Multi-Agent AI Trading Floor Simulation using Model Context Protocol (MCP)

Обзор

Multi-Agent AI Trading Floor Simulation using Model Context Protocol (MCP) AIEquity Traders is a multi-agent trading floor simulation built with Python and powered by the Model Context Protocol (MCP). It features autonomous trading agents that interact with local and remote MCP servers to execute and optimize AI-driven trading strategies in real-time. 🤖 Multi-Agent System: 4 autonomous AI traders with unique decision-making logic. 🔗 MCP Integration: Seamless communication using the Model Context Protocol (MCP) for standardized tool interaction. 🧮 AI Trading Strategies: Adaptive algorithms that simulate market conditions and portfolio management. 🌐 Distributed Architecture: 6 MCP servers (local + remote) for scalable and asynchronous agent communication. ⚙️ Agentic Design: Each agent acts independently, sharing insights and coordinating via the MCP layer. Architecture: MCP Server/Client, Agentic Framework

README

🧠 AIEquity Traders

Multi-Agent AI Trading Floor Simulation using Model Context Protocol (MCP)

🚀 Overview

AIEquity Traders is a multi-agent trading floor simulation built with Python and powered by the Model Context Protocol (MCP). It features autonomous trading agents that interact with local and remote MCP servers to execute and optimize AI-driven trading strategies in real-time.

🧩 Key Features

🤖 Multi-Agent System: 4 autonomous AI traders with unique decision-making logic.

🔗 MCP Integration: Seamless communication using the Model Context Protocol (MCP) for standardized tool interaction.

🧮 AI Trading Strategies: Adaptive algorithms that simulate market conditions and portfolio management.

🌐 Distributed Architecture: 6 MCP servers (local + remote) for scalable and asynchronous agent communication.

⚙️ Agentic Design: Each agent acts independently, sharing insights and coordinating via the MCP layer.

🏗️ Tech Stack

Language: Python

Architecture: MCP Server/Client, Agentic Framework

Core Concepts: AI Decision-Making, Contextual Communication, Reinforcement Loop

Открыть полный README на GitHub

Установка

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