Horus Flow Intelligence — Institutional Orderflow for AI Agents
@horustechltd
关于 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.
基本信息
配置
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"horus-flow": {
"command": "python",
"args": [
"horus_mcp_public.py",
"--transport",
"sse",
"--port",
"8011"
],
"env": {
"RAPIDAPI_KEY": "your_rapidapi_key_here"
}
}
}
}工具
未检测到工具
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 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
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