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MCP Order Flow Server

@fintools-ai

代码仓库

基本信息

分类

其他

传输方式

stdio

作者

fintools-ai

关于 MCP Order Flow Server

This is the mcp to fetch and get order flow information

#mcp-order-flow#order-flow-analysis#options-trading

MCP Order Flow Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "mcp-order-flow-server": {
      "command": "python",
      "args": [
        "-m",
        "venv",
        "venv"
      ]
    }
  }
}

MCP Order Flow Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is MCP Order Flow Server?

MCP Order Flow Server provides real-time order flow analysis for options trading decisions. It consumes quote data from Redis and delivers structured analysis through MCP tools, including momentum indicators, size dynamics, pattern detection, support/resistance levels, and market behavior flags.

How to use MCP Order Flow Server?

Install dependencies, configure Redis environment variables (REDIS_HOST, REDIS_PORT, etc.), and run the server via python -m src.mcp_server or the dev_start.sh script. The primary tool is analyze_order_flow(ticker, history, include_patterns), which returns XML-formatted order flow data.

Key features of MCP Order Flow Server

  • Real-time momentum indicators (bid/ask lifts, drops, price changes)
  • Size dynamics analysis (volume patterns, large order detection)
  • Pattern detection (absorption, stacking, sweeps, momentum shifts)
  • Support/resistance level identification from order flow
  • Market behavior flags (YES/NO) for LLM interpretation
  • Background processing every 1 second for updated metrics

Use cases of MCP Order Flow Server

  • Enabling LLM agents to make informed options trading decisions
  • Detecting institutional accumulation or distribution via absorption patterns
  • Identifying trend initiation or reversal through momentum shifts
  • Recognizing aggressive execution or stop hunting with sweep detection
  • Providing structured order flow context for automated trading assistants

常见问题

1

What are the runtime dependencies?

Python 3.10 or later, a running Redis server, and the `mcp-trading-data-broker` publishing quotes into Redis.

2

How is the data stored and for how long?

Raw quotes and detected patterns are stored in Redis with a default TTL of 3600 seconds. Calculated metrics have TTLs ranging from 60 to 600 seconds depending on the window.

3

What patterns does the server detect?

It detects absorption, stacking, momentum shifts, and sweeps. Each pattern has specific trigger conditions defined in the README (e.g., >15k size change for sweeps).

4

How do I configure the server?

Set environment variables such as `REDIS_HOST`, `REDIS_PORT`, `REDIS_DB`, `LOG_LEVEL`, and `PROCESSOR_INTERVAL` (default 1 second). A Claude Desktop configuration example is provided for integration.

5

Is authentication or authorization supported?

The README does not mention any authentication or authorization mechanisms for the MCP server or its Redis connection. An optional `REDIS_PASSWORD` environment variable exists for Redis authentication.

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