QuantToGo MCP — 宏观因子量化信号源

English | 中文

A macro-factor quantitative signal source accessible via MCP (Model Context Protocol). 8 tools, 1 resource, zero config. AI Agents can self-register for a free trial, query live trading signals, and check subscription status — all within the conversation. All performance is forward-tracked from live signals — not backtested.

QuantToGo is not a trading platform, not an asset manager, not a copy-trading community. It is a quantitative signal source — like a weather forecast for financial markets. We publish systematic trading signals based on macroeconomic factors; you decide whether to act on them, in your own brokerage account.

📊 Live Strategy Performance

Last updated: 2026-03-16 · Auto-updated weekly via GitHub Actions · Verify in git history

All returns are cumulative since inception. Forward-tracked daily — every signal is timestamped at the moment it's published, immutable, including all losses and drawdowns. Git commit history provides an independent audit trail.

What is a Quantitative Signal Source?

Most quantitative services fall into three categories: self-build platforms (high technical barrier), asset management (you hand over your money), or copy-trading communities (unverifiable, opaque). A signal source is the fourth paradigm:

• A quant team runs strategy models and publishes trading signals
• You receive the signals and decide independently whether to act
• You execute in your own brokerage account — we never touch your funds
• All historical signals are forward-tracked with timestamps — fully auditable

Think of it as a weather forecast: it tells you there's an 80% chance of rain tomorrow. Whether you bring an umbrella is your decision.

How to evaluate any signal source — the QTGS Framework:

Quick Start

Claude Desktop / Claude Code

{
  "mcpServers": {
    "quanttogo": {
      "command": "npx",
      "args": ["-y", "quanttogo-mcp"]
    }
  }
}

Cursor

Add to .cursor/mcp.json:

{
  "mcpServers": {
    "quanttogo": {
      "command": "npx",
      "args": ["-y", "quanttogo-mcp"]
    }
  }
}

Coze（扣子）/ Remote SSE

{
  "mcpServers": {
    "quanttogo": {
      "url": "https://mcp.quanttogo.com/sse",
      "transportType": "sse"
    }
  }
}

Remote Streamable HTTP

https://mcp-us.quanttogo.com:8443/mcp

Tools

Discovery (free, no auth)

Signals (requires API Key — get one via register_trial)

Resource: quanttogo://strategies/overview — JSON overview of all strategies.

Try It Now

Ask your AI assistant:

"List all QuantToGo strategies and compare the top performers."

"I want to try QuantToGo signals. Register me with my-email@example.com."

"Show me the latest trading signals for the US panic dip-buying strategy."

"帮我注册 QuantToGo 试用，邮箱 xxx@gmail.com，然后看看美股策略的最新信号。"

🔗 Links

中文

什么是 QuantToGo？

QuantToGo 是一个宏观因子量化信号源——不是交易平台，不是资管产品，不是跟单社区。

我们运行基于宏观经济因子（汇率周期、流动性轮动、恐慌情绪、跨市场联动）的量化策略模型，持续发布交易信号。用户接收信号后，自主判断、自主执行、自主承担盈亏。我们不触碰用户的任何资金。

类比：天气预报告诉你明天大概率下雨，但不替你决定带不带伞。

核心特征

• 宏观因子驱动：每个策略的信号来源都有明确的经济学逻辑，不是数据挖掘
• 指数为主：80%以上标的为指数ETF/期货，规避个股风险
• 前置验证：所有信号从发出那一刻起不可篡改，完整展示回撤和亏损
• 零资金委托：你的钱始终在你自己的券商账户
• AI原生：通过MCP协议可被任何AI助手直接调用

快速体验

对你的AI助手说：

"帮我列出QuantToGo所有的量化策略，看看它们的表现。"

"帮我注册 QuantToGo 试用，邮箱 xxx@gmail.com，然后看看最新的交易信号。"

"有没有做A股的策略？最大回撤在30%以内的。"

🔗 链接

相关阅读

《量化信号源》系列文章：

1. 量化信号源：被低估的第四种量化服务范式（QTGS评估框架）
2. 宏观因子量化：为什么"硬逻辑"比"多因子"更适合信号源模式
3. 当AI学会调用量化策略：MCP协议与量化信号源的技术实现
4. 用AI助手获取实盘量化信号：一份实操指南

License

MIT