# awesome-trading-agents — Trading Agents + MCP List > Curated list of trading agents, market-data MCPs, and skills, with “If you only read three” starters and bilingual docs. Verified 114★; pushed 2026-05-11. ## Install Copy the content below into your project: ## Quick Use ```bash git clone https://github.com/LLMQuant/awesome-trading-agents.git cd awesome-trading-agents # Start from the “If you only read three” section rg "If you only read three" README.md ``` ## Intro Curated list of trading agents, market-data MCPs, and skills, with “If you only read three” starters and bilingual docs. Verified 114★; pushed 2026-05-11. **Best for:** Builders exploring LLM trading workflows who want a curated map of agents, MCPs, and skills **Works with:** Any stack; use the list to pick projects and follow each project's docs **Setup time:** 4-10 minutes ### Key facts (verified) - GitHub: 114 stars · 8 forks · pushed 2026-05-11. - License: CC0-1.0 · owner avatar + repo URL verified via GitHub API. - README-backed entrypoint: `Open README`. ## Main - Treat this as a map, not a recommendation: trading is risky—evaluate each project’s safety and constraints before running money flows. - Start with data-only MCPs and research agents; only consider execution connectors after you have monitoring and limits. - Use the “Agents / MCPs / Skills” split to build a modular stack: one agent, one data MCP, one skill pack. - Keep everything paper-trading first: log decisions, compare to baselines, and only then consider live execution. ### README (excerpt)

Awesome Trading Agents — The Trading Agentic Stack

English · 简体中文

Awesome Trading Agents collects open-source projects where LLMs help research markets, make trading decisions, or connect agents to market data and execution tools. The list focuses on three building blocks: Agents, MCPs, and Skills. It does not try to cover classic quant libraries, time-series models, or reinforcement-learning trading bots; those are better served by [`georgezouq/awesome-ai-in-finance`](https://github.com/georgezouq/awesome-ai-in-finance) and [`wilsonfreitas/awesome-quant`](https://github.com/wilsonfreitas/awesome-quant). Entries are selected for public code or artifacts, clear LLM-driven behavior, recent activity, useful documentation, a distinct role, and visible adoption. Stewarded by the [LLMQuant](https://llmquant.com) community. > [!TIP] > **If you only read three:** > > - **Agents** — [TauricResearch/TradingAgents](#agents-tradingagents) · [virattt/ai-hedge-fund](#agents-ai-hedge-fund) · [HKUDS/AI-Trader](#agents-ai-trader) > - **MCPs** — [alpacahq/alpaca-mcp-server](#mcps-alpaca) · [krakenfx/kraken-cli](#mcps-kraken-cli) · [financial-datasets/mcp-server](#mcps-financial-datasets) ### Source-backed notes - README explains the list focuses on three building blocks: Agents, MCPs, and Skills, and includes a “If you only read three” starter section. - README states the repo is bilingual (English + Simplified Chinese) and links a Chinese README. - GitHub metadata verifies CC0-1.0 license, stars, and last push date for attribution. ### FAQ - **Is this financial advice?**: No—it's a curated list of open-source projects; use at your own risk and follow local laws. - **What should I read first?**: The “If you only read three” section, then pick one agent + one data MCP to test. - **Can I use it with non-trading tasks?**: Yes—many components (data MCPs, skills) can be reused for research workflows. ## Source & Thanks > Created by [LLMQuant](https://github.com/LLMQuant). Licensed under CC0-1.0. > > [LLMQuant/awesome-trading-agents](https://github.com/LLMQuant/awesome-trading-agents) — ⭐ 114 Thanks to the upstream maintainers and contributors for publishing this work under an open license. --- ## Quick Use ```bash git clone https://github.com/LLMQuant/awesome-trading-agents.git cd awesome-trading-agents # Start from the “If you only read three” section rg "If you only read three" README.md ``` ## Intro awesome-trading-agents 汇总交易相关的开源 agent、行情/交易 MCP 与技能包，并提供“先看这三个”入口与中英双语文档，便于快速入门；已验证 114★，更新于 2026-05-11。 **Best for:** 想了解 LLM 交易工作流，并需要 agent/MCP/skills 选型地图的开发者 **Works with:** 不限定栈；按清单选择项目并遵循各自文档与风险提示 **Setup time:** 4-10 minutes ### Key facts (verified) - GitHub：114 stars · 8 forks；最近更新 2026-05-11。 - 许可证：CC0-1.0；作者头像与仓库链接均已通过 GitHub API 复核。 - README 中可对照的入口：`Open README`。 ## Main - 把它当地图而不是背书：交易风险高，先评估每个项目的边界/安全性再运行任何资金相关流程。 - 先从数据与研究类 MCP/agent 开始；只有在监控与限制完善后再考虑执行连接器。 - 按“Agents/MCPs/Skills”三分法搭模块化栈：一个 agent + 一个数据 MCP + 一个技能包。 - 先纸上交易：记录决策与基线对比，再考虑是否接真实下单。 ### README (excerpt)

English · 简体中文