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Horus Flow Intelligence — Institutional Orderflow for AI Agents

@horustechltd

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About Horus Flow Intelligence — Institutional Orderflow for AI Agents

Official MCP server for Horus Flow Intelligence: Institutional-grade market microstructure and orderflow physics for AI trading agents.

#crypto#orderflow#trading#finance#institutional#binance#equities#alpaca#orderbook#spoofing#whale#quantitative#algorithmic-trading#mcp#python

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "horus-flow": {
      "command": "python",
      "args": [
        "horus_mcp_public.py",
        "--transport",
        "sse",
        "--port",
        "8011"
      ],
      "env": {
        "RAPIDAPI_KEY": "your_rapidapi_key_here"
      }
    }
  }
}

Tools

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Overview

What is Horus Flow Intelligence — Institutional Orderflow for AI Agents?

An MCP server that provides sub-second institutional orderflow intelligence for autonomous AI agents and HFT traders. It ingests Level 2 orderbook data from Binance (crypto) and Alpaca (US equities) via WebSocket, then uses a physics-based engine to measure tick imbalances, flow deltas, and liquidity events. The server is designed for AI agents such as Claude and Cursor, and for trading bots that need real-time market microstructure signals.

How to use Horus Flow Intelligence — Institutional Orderflow for AI Agents?

Install dependencies (pip install mcp httpx), set the RAPIDAPI_KEY environment variable, and run the SSE server with python horus_mcp_public.py --transport sse --port 8011. Then pull live flow data via the API endpoint (e.g., https://flow.horustek.pro/v1/flow/crypto/BTCUSDT) using your API key in the X-API-Key header.

Key features of Horus Flow Intelligence — Institutional Orderflow for AI Agents

  • Sub-second institutional orderflow detection.
  • Physics-based decision matrix (bid/ask ratio, delta acceleration).
  • L2 orderbook, tick imbalance, and flow delta analysis.
  • Sub-millisecond response time (local) with 29ms latency.
  • Zero-hallucination logic for AI agent data gathering.
  • Native MCP integration for plug-and-play AI agent connection.

Use cases of Horus Flow Intelligence — Institutional Orderflow for AI Agents

  • AI agents detecting liquidity events and spoofing in real time.
  • HFT traders using orderflow physics for sub-second decision making.
  • Trading bots executing bailout strategies based on market state signals.
  • Developers visualizing live market edge proof via the developer portal.

FAQ from Horus Flow Intelligence — Institutional Orderflow for AI Agents

What is Horus Flow Intelligence and how does it differ from traditional indicators?

Horus uses Level 2 orderbook physics, tick imbalances, and 5-second flow deltas to measure institutional whale activity in real time, unlike lagging indicators like RSI or MACD.

What are the runtime dependencies and how do I get an API key?

Requires Python 3.12+, the mcp and httpx libraries, and a RapidAPI key (available on RapidAPI) or direct API key. Set the RAPIDAPI_KEY environment variable.

Where does the data come from and how is it processed?

Data is ingested from Binance (crypto) and Alp

Frequently asked questions

What is Horus Flow Intelligence and how does it differ from traditional indicators?

Horus uses Level 2 orderbook physics, tick imbalances, and 5-second flow deltas to measure institutional whale activity in real time, unlike lagging indicators like RSI or MACD.

What are the runtime dependencies and how do I get an API key?

Requires Python 3.12+, the `mcp` and `httpx` libraries, and a RapidAPI key (available on RapidAPI) or direct API key. Set the `RAPIDAPI_KEY` environment variable.

Where does the data come from and how is it processed?

Data is ingested from Binance (crypto) and Alp

Comments

Basic information

Category

Productivity

License

MIT

Transports

stdio

Author

horustechltd

Submitted by

HORUS TECH LTD

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