Horus Flow Intelligence — Institutional Orderflow for AI Agents

Name: Horus Flow Intelligence — Institutional Orderflow for AI Agents
Author: horustechltd

@horustechltd

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

Basic information

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.