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MCP工具

量化交易平台

基于 Python · 让 AI 助手直接操作你的系统与工具

英文名：QuantDinger

⭐ 7.0k Stars 🍴 1.5k Forks 💻 Python 📄 Apache-2.0 🏷 AI 8.0分

8.0AI 综合评分

ai量化交易回测加密货币股票外汇

⬇ 下载源码 ZIP ⚙️ 配置说明

✦ AI Skill Hub 推荐

经 AI Skill Hub 精选评估，量化交易平台获评「强烈推荐」。已获得 7.0k 颗 GitHub Star，这款MCP工具在功能完整性、社区活跃度和易用性方面表现出色，AI 评分 8.0 分，适合有一定技术背景的用户使用。

📚 深度解析

量化交易平台是一款基于 MCP（Model Context Protocol）标准协议的 AI 工具扩展。MCP 协议由 Anthropic 开发并开源，旨在建立 AI 模型与外部工具之间的标准化通信接口，目前已被 Claude Desktop、Claude Code、Cursor 等主流 AI 工具采纳。

通过安装量化交易平台，你的 AI 助手将获得额外的工具调用能力，可以用自然语言直接操控该工具的功能，无需学习复杂的命令行语法。MCP 工具的核心价值在于"一次配置，永久增强"——配置完成后，每次与 AI 对话时都可以无缝调用这些工具。

在技术实现上，MCP 工具通过标准的 JSON-RPC 协议与 AI 客户端通信，工具的功能以"工具列表"的形式暴露给 AI 模型，AI 可以按需调用。量化交易平台提供了结构化的工具调用接口，使 AI 模型能够精确地理解和使用每个功能点，显著降低 AI 在工具使用上的错误率。

与传统的 API 集成相比，MCP 工具的优势在于无需编写代码——用户只需在配置文件中添加几行 JSON，即可让 AI 获得全新能力。AI Skill Hub 将量化交易平台评为 AI 评分 8.0 分，属于同类工具中的优质选择。

📋 工具概览

量化交易平台是一款遵循 MCP（Model Context Protocol）标准协议的 AI 工具扩展。通过 MCP 协议，它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务，实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用，都可以通过自然语言在 AI 对话中直接触发，极大提升生产效率。

GitHub Stars

⭐ 7.0k

开发语言

Python

支持平台

Windows / macOS / Linux

维护状态

持续维护，定期更新

开源协议

Apache-2.0

AI 综合评分

8.0 分

工具类型

MCP工具

Forks

1.5k

📖 中文文档

以下内容由 AI Skill Hub 根据项目信息自动整理，如需查看完整原始文档请访问底部「原始来源」。

📌 核心特色

通过标准 MCP 协议与 Claude、Cursor 等主流 AI 客户端深度集成
提供结构化工具调用接口，显著降低 AI 集成复杂度
支持 Claude Desktop 和 Claude Code 无缝接入，开箱即用
可与其他 MCP 工具组合叠加，构建完整 AI 工作站
轻量无侵入设计，不影响现有系统架构

🎯 主要使用场景

在 Claude Desktop 对话中直接调用本地工具，实现 AI 与系统的深度联动
通过自然语言驱动复杂的多步骤自动化任务，代替繁琐手动操作
将多个 MCP 工具组合使用，构建个人专属 AI 工作站

以下安装命令基于项目开发语言和类型自动生成，实际以官方 README 为准。

安装命令

# 方式一：通过 Claude Code CLI 一键安装
claude skill install https://github.com/brokermr810/QuantDinger

# 方式二：手动配置 claude_desktop_config.json
{
  "mcpServers": {
    "------": {
      "command": "npx",
      "args": ["-y", "quantdinger"]
    }
  }
}

# 配置文件位置
# macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
# Windows: %APPDATA%/Claude/claude_desktop_config.json

📋 安装步骤说明

确认已安装 Node.js（v18 或以上版本）
打开 Claude Desktop 或 Claude Code 的 MCP 配置文件
按「交给 Agent 安装 → Claude Desktop」标签中的 JSON 配置填入 mcpServers 字段
保存配置文件并重启 Claude 客户端
重启后，在对话中即可使用本工具

以下用法示例由 AI Skill Hub 整理，涵盖最常见的使用场景。

常用命令 / 代码示例

# 安装后在 Claude 对话中直接使用
# 示例：
用户: 请帮我用 量化交易平台 执行以下任务...
Claude: [自动调用 量化交易平台 MCP 工具处理请求]

# 查看可用工具列表
# 在 Claude 中输入："列出所有可用的 MCP 工具"

以下配置示例基于典型使用场景生成，具体参数请参照官方文档调整。

配置示例

// claude_desktop_config.json 配置示例
{
  "mcpServers": {
    "______": {
      "command": "npx",
      "args": ["-y", "quantdinger"],
      "env": {
        // "API_KEY": "your-api-key-here"
      }
    }
  }
}

// 保存后重启 Claude Desktop 生效

📑 README 深度解析真实文档完整度 82/100 含工作流图查看 GitHub 原文 →

以下内容由系统直接从 GitHub README 解析整理，保留代码块、表格与列表结构。

简介

QuantDinger

The open-source AI infrastructure layer for quant trading

Turn trading ideas into Python strategies, backtests, paper trading, and live execution — all in one self-hosted stack.

AI research → Strategy code → Backtest → Paper/Live execution → Monitoring

English · 简体中文 · 日本語 · 한국어 · ไทย · Tiếng Việt · العربية

SaaS · API Docs · Video Demo · Website · AWS Marketplace

---

Product overview

Audience: independent quants, Python strategy authors, prop/small teams, and operators building white-label quant products on private infrastructure — without handing API keys to a black-box SaaS.

Technical highlights

	What makes QuantDinger different
Full-stack quant OS	Charting, indicator IDE, AI research, backtests, live bots, quick trade, and broker account management — one product, one Postgres state store.
Agent-native	First-class Agent Gateway (`/api/agent/v1`) + [`quantdinger-mcp`](https://pypi.org/project/quantdinger-mcp/) on PyPI — Cursor, Claude Code, and Codex can read markets, run backtests, and trade (paper by default) with full audit logs.
Dual strategy runtimes	`IndicatorStrategy` (vectorized dataframe signals + chart overlays) and `ScriptStrategy` (event-driven `on_bar`, explicit orders) — research and production in the same codebase.
Multi-venue execution	CCXT crypto (Binance, OKX, Bybit, …), IBKR stocks, MT5 forex, Alpaca US equities/ETFs/crypto — unified Broker Accounts page with isolated multi-tenant sessions.
Production-grade infra	PostgreSQL 16 + Redis 7, connection pooling, background workers (orders, portfolio monitor, reflection), idempotent schema bootstrap, GHCR multi-arch images (amd64/arm64).
Security by default	Refuses default `SECRET_KEY`, agent tokens hashed at rest, paper-only trading unless explicitly unlocked server-side, every agent call audit-logged.
Operator-ready	OAuth, multi-user roles, credits/membership/USDT billing toggles, AWS Marketplace AMI, 7-language docs — build a commercial quant product on top, not just a hobby bot.

<details> <summary>More install paths (GHCR-only, build notes)</summary>

Lightest — two files only (no git clone):

curl -O https://raw.githubusercontent.com/brokermr810/QuantDinger/main/docker-compose.ghcr.yml
curl -o backend.env https://raw.githubusercontent.com/brokermr810/QuantDinger/main/backend_api_python/env.example
docker compose -f docker-compose.ghcr.yml pull
docker compose -f docker-compose.ghcr.yml up -d

Do not use docker compose up --build for a normal install — the main compose file only declares image: for the frontend; --build affects the backend only. Rebuild backend after code changes: docker compose up -d --build backend. For Vue source builds, use docker-compose.build.yml (see Installation).

</details>

Features at a glance

Research & AI — Multi-LLM ensemble analysis, watchlists, opportunity radar, NL→indicator/strategy, post-backtest AI hints; optional confidence calibration. Agent Gateway + MCP for Cursor / Claude Code / Codex with scoped tokens and SSE job streaming.
Build — Professional KLine chart UI; IndicatorStrategy (dataframe buy/sell signals) and ScriptStrategy (on_bar, ctx.buy() / ctx.sell()); AI code generation as a starting point, Python as source of truth.
Validate — Server-side backtests with equity curves, drawdown metrics, trade logs, and strategy snapshots — no client-side-only backtest theater.
Operate — Live strategy bots, quick trade, 10+ crypto exchanges via CCXT, IBKR / MT5 / Alpaca (US stocks, ETFs, crypto); unified Broker Accounts page; notifications (Telegram, email, SMS, Discord, webhooks).
Platform — Docker Compose + GHCR images, PostgreSQL 16, Redis 7, OAuth, multi-user RBAC, credits / membership / USDT billing toggles, AWS Marketplace AMI, 7-language documentation.

Prerequisites

Item	Notes
[Docker](https://docs.docker.com/get-docker/) + Docker Compose v2	Used for Postgres, Redis, API, and static UI.
`git`	To clone this repository.
Ports (defaults)	`8888` (web), `5000` (API, bound to 127.0.0.1), `5432` / `6379` (DB/Redis, loopback by default). Change via root `.env` if they collide.
Disk	Postgres volume grows with users, strategies, and logs; plan a few GB minimum for serious use.

Installation & first-time setup (Docker Compose)

Already ran Try in 2 minutes? Skip this section — it's the same outcome, just expanded into a step-by-step checklist for first-time deployers and operations folks who want to understand every knob.

This section mirrors a typical “local deploy” path: prepare the host → obtain the code → configure secrets → start the stack → verify → harden → optionally wire AI. Node.js is not required: the frontend service pulls ghcr.io/brokermr810/quantdinger-frontend directly, so Nginx serves the SPA without any local build step.

Common Docker commands

docker compose ps
docker compose logs -f backend
docker compose restart backend
docker compose pull
docker compose up -d
docker compose up -d --build backend   # backend code changes only
docker compose down

Minimal Example: Python Indicator Strategy

This is the kind of Python-native strategy logic QuantDinger is designed for:

```python

2) Create backend configuration (mandatory)

cp backend_api_python/env.example backend_api_python/.env

Almost all runtime behavior is driven by backend_api_python/.env (database URL, admin user, LLM keys, workers, billing toggles, etc.). The optional repository root .env only adjusts Compose-level concerns such as ports and image mirrors (IMAGE_PREFIX).

BACKEND_IMAGE=ghcr.io/<your-fork>/quantdinger-backend # optional, for forks

6) Optional: enable AI features

AI analysis, NL→code, and related flows need at least one LLM provider configured. Open backend_api_python/env.example, find the AI / LLM block, copy the relevant keys into your .env (for example LLM_PROVIDER + OPENROUTER_API_KEY, or another supported provider). Restart the backend after edits.

Optional root `.env` (Compose only)

For custom ports or mirror/prefix for base images (slow Docker Hub pulls), create a file named .env in the repository root (same directory as docker-compose.yml):

FRONTEND_PORT=3000
BACKEND_PORT=127.0.0.1:5001
IMAGE_PREFIX=docker.m.daocloud.io/library/

Production-style TLS, domain, and reverse-proxy placement are covered in Cloud deployment.

Configuration Areas

Use backend_api_python/env.example as the primary template. Key areas include:

Area	Examples
Authentication	`SECRET_KEY`, `ADMIN_USER`, `ADMIN_PASSWORD`
Database	`DATABASE_URL`
LLM / AI	`LLM_PROVIDER`, `OPENROUTER_API_KEY`, `OPENAI_API_KEY`
OAuth	`GOOGLE_CLIENT_ID`, `GITHUB_CLIENT_ID`
Security	`TURNSTILE_SITE_KEY`, `ENABLE_REGISTRATION`
Billing	`BILLING_ENABLED`, `BILLING_COST_AI_ANALYSIS`
Membership	`MEMBERSHIP_MONTHLY_PRICE_USD`, `MEMBERSHIP_MONTHLY_CREDITS`
USDT Payment	`USDT_PAY_ENABLED`, `USDT_TRC20_XPUB`, `TRONGRID_API_KEY`
Optional data APIs	`TWELVE_DATA_API_KEY`, `FINNHUB_API_KEY`, `TIINGO_API_KEY`, `ADANOS_API_KEY`
Proxy	`PROXY_URL`
Workers	`ENABLE_PENDING_ORDER_WORKER`, `ENABLE_PORTFOLIO_MONITOR`, `ENABLE_REFLECTION_WORKER`
AI tuning	`ENABLE_AI_ENSEMBLE`, `ENABLE_CONFIDENCE_CALIBRATION`, `AI_ENSEMBLE_MODELS`

API documentation

Resource	Link
Human Web API (OpenAPI)	[`docs/api/openapi.yaml`](docs/api/openapi.yaml)
ReDoc viewer (serve over HTTP)	[`docs/api/index.html`](docs/api/index.html) — run `python -m http.server` from `docs/api/`
Conventions (auth, envelopes)	[`docs/API_CONVENTIONS.md`](docs/API_CONVENTIONS.md)
Agent Gateway	[`docs/agent/agent-openapi.json`](docs/agent/agent-openapi.json)

---

QuantDinger quick demo: install, sign in, charting, AI analysis, and strategy workflow

_{From zero to running stack — charting, AI research, and strategy workflow in minutes.}

QuantDinger system architecture: Data Sources → Indicator / Signal / Strategy / Backtesting / AI Analysis layers → Execution, with the closed-loop quant workflow (Idea → Indicator → Strategy → Backtest → Optimize → Execute → Monitor)

_{Closed loop: AI research → Strategy code → Backtest → Paper/Live execution → Monitoring — market data in, audited orders out.}

Crypto exchanges (API keys)

Exchange	Signup Link
Binance	[Register](https://www.bsmkweb.cc/register?ref=QUANTDINGER)
Bitget	[Register](https://partner.hdmune.cn/bg/7r4xz8kd)
Bybit	[Register](https://partner.bybit.com/b/DINGER)
OKX	[Register](https://www.xqmnobxky.com/join/QUANTDINGER)
Gate.io	[Register](https://www.gateport.business/share/DINGER)
HTX	[Register](https://www.htx.com/invite/zh-cn/1f?invite_code=dinger)

Troubleshooting (first boot)

Symptom	What to check
`QuantDinger-Vue` not found	You added `-f docker-compose.build.yml` without cloning Vue source. Drop the override (plain `docker compose up -d`) or clone into `./QuantDinger-Vue/` first.
`redis` / `python` / `node` pull fails, `content size of zero`	Docker Hub unreachable from Docker Desktop. Set root `.env` `IMAGE_PREFIX=docker.m.daocloud.io/library/` and/or configure Docker Desktop → Proxies (system VPN alone is often not enough).
Backend exits immediately	`SECRET_KEY` still default, or invalid `.env` syntax. Read `docker compose logs backend`.
Blank page or API errors from browser	`FRONTEND_URL` / origins mismatch; API not reachable from the host you opened.
Port already in use	Another Postgres, Redis, or local service on `5432` / `6379` / `5000` / `8888`. Adjust variables in root `.env` per `docker-compose.yml`.
Many live strategies, “start denied”	Raise `STRATEGY_MAX_THREADS` in `backend_api_python/.env` and restart API (see comments in `env.example`).

FAQ

🎯 aiskill88 AI 点评 A 级 2026-05-31

高质量的开源MCP工具，支持多种交易平台和资产

⚡ 核心功能

通过标准 MCP 协议与 Claude、Cursor 等主流 AI 客户端深度集成
提供结构化工具调用接口，显著降低 AI 集成复杂度
支持 Claude Desktop 和 Claude Code 无缝接入，开箱即用
可与其他 MCP 工具组合叠加，构建完整 AI 工作站
轻量无侵入设计，不影响现有系统架构

👥 适合人群

Claude Desktop / Claude Code 用户AI 工具开发者需要扩展 AI 能力的专业人士自动化工程师

🎯 使用场景

在 Claude Desktop 对话中直接调用本地工具，实现 AI 与系统的深度联动
通过自然语言驱动复杂的多步骤自动化任务，代替繁琐手动操作
将多个 MCP 工具组合使用，构建个人专属 AI 工作站

⚖️ 优点与不足

✅ 优点

+GitHub 7.0k Star，社区高度认可
+Apache-2.0 协议，可免费商用
+标准化 MCP 协议，生态互联性强
+与 Claude 官方生态无缝对接
+即插即用，配置简单快捷

⚠️ 不足

−依赖 Claude 客户端，非 Claude 用户无法使用
−MCP 协议仍在持续演进，接口可能变更
−需要一定的配置步骤

⚠️ 使用须知

AI Skill Hub 为第三方内容聚合平台，本页面信息基于公开数据整理，不对工具功能和质量作任何法律背书。

建议在沙箱或测试环境中充分验证后，再部署至生产环境，并做好必要的安全评估。

📄 License 说明

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natively-cluely-ai-assistant — Claude Skill 中文使用文档

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❓ 常见问题 FAQ

如何使用QuantDinger进行回测？−

参考官方文档和示例代码

支持哪些交易平台？+

MCP 是什么？和普通 API 有什么区别？+

我需要编程基础才能使用这个 MCP 工具吗？+

这个工具支持 Claude Code 吗？还是只有 Claude Desktop？+

MCP 工具是否会访问我的本地文件或数据？+

多个 MCP 工具可以同时使用吗？+

💡 AI Skill Hub 点评

AI Skill Hub 点评：量化交易平台的核心功能完整，质量优秀。对于Claude Desktop / Claude Code 用户来说，这是一个值得纳入个人工具库的选择。建议先在非生产环境试用，再逐步推广。

⬇️ 获取与下载

⬇ 下载源码 ZIP

✅ Apache-2.0 协议 · 可免费商用 · 直接从 aiskill88 服务器下载，无需跳转 GitHub

📚 深入学习量化交易平台

查看分步骤安装教程和完整使用指南，快速上手这款工具

⚙️ 安装教程 📚 使用教程

🌐 原始信息

原始名称	`QuantDinger`
Topics	`ai量化交易回测加密货币股票外汇`
GitHub	https://github.com/brokermr810/QuantDinger
License	Apache-2.0
语言	Python

🔗 原始来源

🐙 GitHub 仓库 https://github.com/brokermr810/QuantDinger 🌐 官方网站 https://www.quantdinger.com

收录时间：2026-05-31 · 更新时间：2026-05-31 · License：Apache-2.0 · AI Skill Hub 不对第三方内容的准确性作法律背书。

	What makes QuantDinger different
Full-stack quant OS	Charting, indicator IDE, AI research, backtests, live bots, quick trade, and broker account management — one product, one Postgres state store.
Agent-native	First-class Agent Gateway (`/api/agent/v1`) + [`quantdinger-mcp`](https://pypi.org/project/quantdinger-mcp/) on PyPI — Cursor, Claude Code, and Codex can read markets, run backtests, and trade (paper by default) with full audit logs.
Dual strategy runtimes	`IndicatorStrategy` (vectorized dataframe signals + chart overlays) and `ScriptStrategy` (event-driven `on_bar`, explicit orders) — research and production in the same codebase.
Multi-venue execution	CCXT crypto (Binance, OKX, Bybit, …), IBKR stocks, MT5 forex, Alpaca US equities/ETFs/crypto — unified Broker Accounts page with isolated multi-tenant sessions.
Production-grade infra	PostgreSQL 16 + Redis 7, connection pooling, background workers (orders, portfolio monitor, reflection), idempotent schema bootstrap, GHCR multi-arch images (amd64/arm64).
Security by default	Refuses default `SECRET_KEY`, agent tokens hashed at rest, paper-only trading unless explicitly unlocked server-side, every agent call audit-logged.
Operator-ready	OAuth, multi-user roles, credits/membership/USDT billing toggles, AWS Marketplace AMI, 7-language docs — build a commercial quant product on top, not just a hobby bot.

量化交易平台

📚 深度解析

📋 工具概览

📖 中文文档

简介

QuantDinger

The open-source AI infrastructure layer for quant trading

Product overview

Technical highlights

Features at a glance

Prerequisites

Installation & first-time setup (Docker Compose)

Common Docker commands

Minimal Example: Python Indicator Strategy

2) Create backend configuration (mandatory)

BACKEND_IMAGE=ghcr.io/<your-fork>/quantdinger-backend # optional, for forks

6) Optional: enable AI features

Optional root `.env` (Compose only)

Configuration Areas

API documentation

Crypto exchanges (API keys)

Troubleshooting (first boot)

FAQ

⚡ 核心功能

👥 适合人群

🎯 使用场景

⚖️ 优点与不足

🔗 相关工具推荐

❓ 常见问题 FAQ

🤖 交给 Agent 安装 · 量化交易平台