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extrema MCP trading agent

MCP Server

基于MCP协议的自动化交易系统，利用LLM进行智能决策，支持多空仓位调整，适用于金融市场交易。

工具数

0

提示词数

0

GitHub Stars

4

资源数

0
Rust金融数据数据分析

安装说明

本站只整理中文说明和来源信息，不托管安装包，也不代用户安装。

GitHub

作者 / 组织

Lqz13Th

提供方

Lqz13Th

最后核验

2026/5/17 20:20

运行时

Python

快速接入

先看主来源和安装命令，再打开仓库或文档；下面只保留这个条目的关键接入事实。

命令预览

Python Trading Agent (LLM 决策)

详细介绍

MCP Trading Agent - 交易 Agent 系统

English | 中文

中文使用指南

📖 项目简介

这是一个基于 MCP (Model Context Protocol) 的自动化交易系统，由 Rust MCP Server 和 Python LLM Agent 组成。系统能够：

  • 实时接收市场数据（价格、持仓量等）
  • 使用 LLM (Gemini) 进行智能交易决策
  • 自动执行仓位调整
  • 支持做多和做空（-1 到 1 的仓位权重）

🏗️ 系统架构

Rust MCP Server (数据采集)
    ↓ (发送 AltTensor via ZMQ)
Python Trading Agent (LLM 决策)
    ↓ (解析响应，提取交易指令)
Rust MCP Mediator (执行交易)

📦 安装步骤

1. 安装 Rust 环境

# 安装 Rust (如果还没有)
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

2. 安装 Python 环境 (使用 uv)

# 安装 uv (如果还没有)
pip install uv

# 进入 Python 项目目录
cd python_mcp_host

# 安装依赖
uv sync

3. 配置 API Key

编辑 python_mcp_host/model_config.json:

Gemini 配置示例：

[
  {
    "port": 5001,
    "model_id": "gemini_one",
    "account_id": "okx_test",
    "llm_provider": "gemini",
    "api_key": "YOUR_GEMINI_API_KEY",
    "model_name": "gemini-2.5-flash-lite"
  }
]

DeepSeek 配置示例：

[
  {
    "port": 5002,
    "model_id": "deepseek_one",
    "account_id": "okx_test",
    "llm_provider": "deepseek",
    "api_key": "YOUR_DEEPSEEK_API_KEY",
    "model_name": "deepseek-chat",
    "base_url": "https://api.deepseek.com"
  }
]

同时使用多个 LLM：

[
  {
    "port": 5001,
    "model_id": "gemini_one",
    "account_id": "okx_test",
    "llm_provider": "gemini",
    "api_key": "YOUR_GEMINI_API_KEY",
    "model_name": "gemini-2.5-flash-lite"
  },
  {
    "port": 5002,
    "model_id": "deepseek_one",
    "account_id": "okx_test",
    "llm_provider": "deepseek",
    "api_key": "YOUR_DEEPSEEK_API_KEY",
    "model_name": "deepseek-chat"
  }
]

或者设置环境变量：

# Gemini
export GEMINI_API_KEY="YOUR_GEMINI_API_KEY"

# DeepSeek
export DEEPSEEK_API_KEY="YOUR_DEEPSEEK_API_KEY"

4. 配置交易账户

编辑 rust_mcp_server/account_config.json:

[
  {
    "account_id": "okx_test",
    "exchange": "okx",
    "api_key": "YOUR_OKX_API_KEY",
    "api_secret": "YOUR_OKX_SECRET",
    "passphrase": "YOUR_PASSPHRASE"
  }
]

🚀 快速开始

步骤 1: 启动 Python Trading Agent

cd python_mcp_host

# 方式 1: 交互式定义交易风格（推荐首次使用）
uv run mcp_host --port 5001

# 方式 2: 使用预定义的交易风格文件
uv run mcp_host --port 5001 --trading-style-file trading_style_example.json

# 方式 3: 直接在命令行输入交易风格
uv run mcp_host --port 5001 --trading-style "稳健型：优先控制风险，仓位30-50%"

交易风格示例文件：

  • trading_style_example.json - JSON 格式示例
  • trading_style_examples.txt - 多种风格示例（稳健型、激进型、趋势跟踪型等）

步骤 2: 启动 Rust MCP Server

cd rust_mcp_server
cargo run

📝 详细使用方法

Python Agent 参数说明

uv run mcp_host [选项]

选项：
  --port PORT               ZMQ 服务端口（必需，或设置 INFER_PORT 环境变量）
  --config PATH             模型配置文件路径（默认: model_config.json）
  --trading-style TEXT      直接输入交易风格文本
  --trading-style-file PATH 从文件加载交易风格（JSON 或文本文件）
  --prompt TEXT             测试模式：单次运行并显示结果（不启动服务器）
  --model-id ID             模型 ID（仅在 --prompt 模式下使用，默认: gemini_one）

交易风格定义

交易风格会影响 Agent 的所有交易决策。你可以：

  1. 使用示例文件
   uv run mcp_host --port 5001 --trading-style-file trading_style_example.json
  1. 自定义交易风格文件

创建 my_style.txt:

   稳健型交易风格：
   - 优先控制风险，单次交易风险不超过总资金的 20%
   - 仓位管理：正常市场条件下仓位控制在 30-50%
   - 注重止损，设置合理的止损点位
   - 基于 Z-Score 特征，当特征显著偏离（|z| > 2）时，谨慎操作

然后运行：

   uv run mcp_host --port 5001 --trading-style-file my_style.txt
  1. 交互式输入
   uv run mcp_host --port 5001
   # 程序会提示你输入交易风格

测试模式

测试 LLM 响应（不启动服务器）：

uv run mcp_host --port 5001 --prompt "当前市场如何？建议仓位多少？"

📊 运行日志示例

启动后，你会看到类似以下的日志：

[Agent] 🚀 Starting server on port 5001
[Agent] ✅ Loaded 1 model(s)
[Agent] 🔌 ZMQ bound to tcp://127.0.0.1:5001
[Agent] ⏳ Waiting for data from Rust MCP server...
[Agent] 📨 Received request | model_id=gemini_one
[Agent] 📊 Received | price=0.12345 | pos=0.5 | features=10
[Agent] 📝 Generated prompt (1234 chars)
[Agent] 🤖 Calling LLM...
[Agent] 💬 LLM Response: 根据当前市场数据，我建议...
[Agent] ✅ Decision | cmd=adjust_position | inst=DOGE_USDT_PERP | target_pos=0.6 | latency=3000ms

⚙️ 配置说明

仓位权重范围

  • 范围: -1 到 1
  • 1.0: 满仓做多
  • 0.0: 空仓
  • -1.0: 满仓做空
  • 0.5: 50% 做多
  • -0.5: 50% 做空

交易风格类型

参考 trading_style_examples.txt，包含：

  • 稳健型: 优先控制风险，仓位较小
  • 激进型: 追求高收益，可以承担较大风险
  • 趋势跟踪型: 跟随市场趋势，顺势而为
  • 均值回归型: 在价格偏离均值时反向操作
  • 平衡型: 在风险和收益之间寻求平衡

🔧 故障排查

问题 1: API Key 错误

ValueError: API key is required

解决:

  • Gemini: 检查 model_config.json 中的 api_key 字段，或设置 GEMINI_API_KEY 环境变量
  • DeepSeek: 检查 model_config.json 中的 api_key 字段，或设置 DEEPSEEK_API_KEY 环境变量

问题 1.1: DeepSeek 导入错误

ImportError: OpenAI package is required for DeepSeek

解决: 安装 OpenAI 包：pip install openai 或 uv sync（会自动安装依赖）

问题 2: 端口被占用

Error: Address already in use

解决: 使用 --port 参数指定其他端口，或确保 Rust MCP Server 使用相同端口

问题 3: 超时错误

Model prediction TIMEOUT - skipping this tick

解决:

  • Python Agent 已优化响应速度（减少 token 数量）
  • 如果仍然超时，可能需要修改 extrema_infra 库的超时设置
  • 参考 rust_mcp_server/TIMEOUT_CONFIG.md

问题 4: 价格数据未找到

Price for DOGE_USDT_PERP not available yet

解决: 等待几秒钟让 WebSocket 价格数据到达，这是正常的

📚 更多信息

  • Python Agent 详细文档: python_mcp_host/README_USAGE.md
  • 超时配置说明: rust_mcp_server/TIMEOUT_CONFIG.md
  • 交易风格示例: python_mcp_host/trading_style_examples.txt

English Usage Guide

📖 Project Overview

This is an automated trading system based on MCP (Model Context Protocol), consisting of a Rust MCP Server and a Python LLM Agent. The system can:

  • Receive real-time market data (prices, open interest, etc.)
  • Use LLM (Gemini / DeepSeek) for intelligent trading decisions
  • Automatically execute position adjustments
  • Support both long and short positions (-1 to 1 position weights)
  • Support multiple LLM providers running simultaneously

🏗️ System Architecture

Rust MCP Server (Data Collection)
    ↓ (Send AltTensor via ZMQ)
Python Trading Agent (LLM Decision)
    ↓ (Parse response, extract trading commands)
Rust MCP Mediator (Execute Trading)

📦 Installation

1. Install Rust Environment

# Install Rust (if not already installed)
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

2. Install Python Environment (using uv)

# Install uv (if not already installed)
pip install uv

# Navigate to Python project directory
cd python_mcp_host

# Install dependencies
uv sync

3. Configure API Key

Edit python_mcp_host/model_config.json:

Gemini Configuration Example:

[
  {
    "port": 5001,
    "model_id": "gemini_one",
    "account_id": "okx_test",
    "llm_provider": "gemini",
    "api_key": "YOUR_GEMINI_API_KEY",
    "model_name": "gemini-2.5-flash-lite"
  }
]

DeepSeek Configuration Example:

[
  {
    "port": 5002,
    "model_id": "deepseek_one",
    "account_id": "okx_test",
    "llm_provider": "deepseek",
    "api_key": "YOUR_DEEPSEEK_API_KEY",
    "model_name": "deepseek-chat",
    "base_url": "https://api.deepseek.com"
  }
]

Using Multiple LLMs:

[
  {
    "port": 5001,
    "model_id": "gemini_one",
    "account_id": "okx_test",
    "llm_provider": "gemini",
    "api_key": "YOUR_GEMINI_API_KEY",
    "model_name": "gemini-2.5-flash-lite"
  },
  {
    "port": 5002,
    "model_id": "deepseek_one",
    "account_id": "okx_test",
    "llm_provider": "deepseek",
    "api_key": "YOUR_DEEPSEEK_API_KEY",
    "model_name": "deepseek-chat"
  }
]

Or set environment variables:

# Gemini
export GEMINI_API_KEY="YOUR_GEMINI_API_KEY"

# DeepSeek
export DEEPSEEK_API_KEY="YOUR_DEEPSEEK_API_KEY"

4. Configure Trading Account

Edit rust_mcp_server/account_config.json:

[
  {
    "account_id": "okx_test",
    "exchange": "okx",
    "api_key": "YOUR_OKX_API_KEY",
    "api_secret": "YOUR_OKX_SECRET",
    "passphrase": "YOUR_PASSPHRASE"
  }
]

🚀 Quick Start

Step 1: Start Python Trading Agent

cd python_mcp_host

# Method 1: Interactive trading style definition (recommended for first use)
uv run mcp_host --port 5001

# Method 2: Use predefined trading style file
uv run mcp_host --port 5001 --trading-style-file trading_style_example.json

# Method 3: Enter trading style directly in command line
uv run mcp_host --port 5001 --trading-style "Conservative: prioritize risk control, position 30-50%"

Trading Style Example Files:

  • trading_style_example.json - JSON format example
  • trading_style_examples.txt - Multiple style examples (conservative, aggressive, trend-following, etc.)

Step 2: Start Rust MCP Server

cd rust_mcp_server
cargo run

📝 Detailed Usage

Python Agent Parameters

uv run mcp_host [OPTIONS]

Options:
  --port PORT              ZMQ service port (required, or set INFER_PORT env var)
  --config PATH            Model config file path (default: model_config.json)
  --trading-style TEXT     Enter trading style text directly
  --trading-style-file PATH Load trading style from file (JSON or text file)
  --prompt TEXT            Test mode: single run and display results (doesn't start server)
  --model-id ID            Model ID (only used in --prompt mode, default: gemini_one)

Trading Style Definition

Trading style affects all trading decisions of the Agent. You can:

  1. Use Example File
   uv run mcp_host --port 5001 --trading-style-file trading_style_example.json
  1. Custom Trading Style File

Create my_style.txt:

   Conservative Trading Style:
   - Prioritize risk control, single trade risk not exceeding 20% of capital
   - Position management: 30-50% under normal market conditions
   - Focus on stop-loss, set reasonable stop-loss points
   - Based on Z-Score features, be cautious when features deviate significantly (|z| > 2)

Then run:

   uv run mcp_host --port 5001 --trading-style-file my_style.txt
  1. Interactive Input
   uv run mcp_host --port 5001
   # Program will prompt you to enter trading style

Test Mode

Test LLM response (without starting server):

uv run mcp_host --port 5001 --prompt "How is the current market? What position do you recommend?"

📊 Example Log Output

After starting, you'll see logs like:

[Agent] 🚀 Starting server on port 5001
[Agent] ✅ Loaded 1 model(s)
[Agent] 🔌 ZMQ bound to tcp://127.0.0.1:5001
[Agent] ⏳ Waiting for data from Rust MCP server...
[Agent] 📨 Received request | model_id=gemini_one
[Agent] 📊 Received | price=0.12345 | pos=0.5 | features=10
[Agent] 📝 Generated prompt (1234 chars)
[Agent] 🤖 Calling LLM...
[Agent] 💬 LLM Response: Based on current market data, I recommend...
[Agent] ✅ Decision | cmd=adjust_position | inst=DOGE_USDT_PERP | target_pos=0.6 | latency=3000ms

⚙️ Configuration

Position Weight Range

  • Range: -1 to 1
  • 1.0: Full long position
  • 0.0: No position (flat)
  • -1.0: Full short position
  • 0.5: 50% long
  • -0.5: 50% short

Trading Style Types

Refer to trading_style_examples.txt, includes:

  • Conservative: Prioritize risk control, smaller positions
  • Aggressive: Pursue high returns, can bear larger risks
  • Trend Following: Follow market trends, go with the flow
  • Mean Reversion: Reverse operation when price deviates from mean
  • Balanced: Seek balance between risk and return

🔧 Troubleshooting

Issue 1: API Key Error

ValueError: API key is required

Solution: Check api_key field in model_config.json, or set GEMINI_API_KEY environment variable

Issue 2: Port Already in Use

Error: Address already in use

Solution: Use --port parameter to specify another port, or ensure Rust MCP Server uses the same port

Issue 3: Timeout Error

Model prediction TIMEOUT - skipping this tick

Solution:

  • Python Agent has been optimized for faster response (reduced token count)
  • If still timing out, may need to modify timeout settings in extrema_infra library
  • Refer to rust_mcp_server/TIMEOUT_CONFIG.md

Issue 4: Price Data Not Found

Price for DOGE_USDT_PERP not available yet

Solution: Wait a few seconds for WebSocket price data to arrive, this is normal

🔌 Supported LLM Providers

  • Gemini: Google's Gemini models

- Default model: gemini-2.5-flash-lite - API Key env var: GEMINI_API_KEY

  • DeepSeek: DeepSeek models (OpenAI compatible API)

- Default model: deepseek-chat - API Key env var: DEEPSEEK_API_KEY - Requires: pip install openai or uv sync

📚 More Information

  • Python Agent detailed docs: python_mcp_host/README_USAGE.md
  • Timeout configuration: rust_mcp_server/TIMEOUT_CONFIG.md
  • Trading style examples: python_mcp_host/trading_style_examples.txt
  • DeepSeek config example: python_mcp_host/model_config.deepseek.example.json

🎯 Quick Reference

Common Commands

# Start Python Agent (interactive style)
cd python_mcp_host
uv run mcp_host --port 5001

# Start Python Agent (with style file)
uv run mcp_host --port 5001 --trading-style-file trading_style_example.json

# Test LLM response
uv run mcp_host --port 5001 --prompt "What position do you recommend?"

# Start Rust Server
cd rust_mcp_server
cargo run

File Structure

.
├── python_mcp_host/          # Python LLM Agent
│   ├── src/
│   │   └── python_mcp_host/
│   │       ├── __main__.py   # Main entry point
│   │       ├── infer_host.py # ZMQ server & prompt generation
│   │       └── model_operator.py # LLM & response parsing
│   ├── model_config.json     # Model configuration
│   ├── trading_style_example.json # Trading style example
│   └── README_USAGE.md       # Detailed usage guide
│
└── rust_mcp_server/          # Rust MCP Server
    ├── src/
    │   ├── main.rs           # Main entry point
    │   └── arch/
    │       ├── server_module/ # MCP server logic
    │       └── account_module/ # Account management
    ├── account_config.json   # Account configuration
    └── TIMEOUT_CONFIG.md     # Timeout configuration guide

📞 Support

如有问题，请检查：

  1. API Key 是否正确配置
  2. 端口号是否一致（默认 5001）
  3. 网络连接是否正常
  4. 查看日志中的错误信息

For issues, please check:

  1. API Key is correctly configured
  2. Port numbers match (default 5001)
  3. Network connection is normal
  4. Check error messages in logs

目录标签

目录标签

Rust金融数据数据分析自动化交易本地部署金融市场LLM决策多空策略实时数据

接入字段

传输方式（transport，传输协议）

stdio

鉴权方式（authType，认证方式）

api-key

运行时（runtime，运行环境）

Python

工具数量（toolCount，工具数）

0

资源数量（resourceCount，资源数）

0

提示词数量（promptCount，提示词数）

0

权限和风险

stdioapi-key部署方式未说明

接入前请确认传输方式、认证方式和部署位置，并根据实际工具能力限制访问范围。

安装前确认

不要直接授予不必要的文件、网络或账号权限；先核对安装命令和配置内容。

来源信息

extrema_mcp_trading_agent logo

MCP World

mcpworld.com

/detail/7d415dea227b75a7b78e59e40d908ba9

基于MCP协议的自动化交易系统，利用LLM进行智能决策，支持多空仓位调整，适用于金融市场交易。

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