QuantToGo MCP
About
Macro-factor quantitative signal source — 8 live-tracked strategies (US + China), free 30-day trial, AI agent self-registration via MCP tools.
Details
- Author
- quanttogo
- Categories
- Other, Finance, AI
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Setup
Install QuantToGo MCP in your MCP client (Claude Desktop, Cursor, Windsurf, and others).
Repository: https://github.com/quanttogo/quanttogo-mcp
Follow the installation instructions in the repository README, then restart your MCP client.
Amacro-factor quantitative signal sourceaccessible 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 aquantitative 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.
Last updated: 2026-08-17· 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.
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). Asignal sourceis the fourth paradigm:
- A quant team runs strategy models and publishes trading signals
- You receive the signals anddecide independentlywhether to act
- You execute inyour own brokerage account— we never touch your funds
- All historical signals areforward-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:
{ "mcpServers": { "quanttogo": { "command": "npx", "args": ["-y", "quanttogo-mcp"] } } }
{ "mcpServers": { "quanttogo": { "command": "npx", "args": ["-y", "quanttogo-mcp"] } } }
{ "mcpServers": { "quanttogo": { "url": "https://mcp.quanttogo.com/sse", "transportType": "sse" } } }
Signals (requires API Key — get one viaregister_trial)
Resource:quanttogo://strategies/overview— JSON overview of all strategies.
"List all QuantToGo strategies and compare the top performers."
"I want to try QuantToGo signals. Register me withmy-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。"
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。
- 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
"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。"
- 量化信号源：被低估的第四种量化服务范式（QTGS评估框架）
- 宏观因子量化：为什么"硬逻辑"比"多因子"更适合信号源模式
- 当AI学会调用量化策略：MCP协议与量化信号源的技术实现
- 用AI助手获取实盘量化信号：一份实操指南
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