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TradingAgents

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TradingAgents是基于多智能体与大语言模型（LLM）的金融交易框架，模拟真实交易机构的分工协作流程，输出交易决策参考。

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AI 依据上游资料解读 · 2026/8/31

TradingAgents是基于多智能体与大语言模型（LLM）的金融交易框架，模拟真实交易机构的分工协作流程，输出交易决策参考。

解决什么问题
传统金融交易决策要么依赖人工多岗协作，人力成本高、效率低，要么单模型策略维度单一、风险考虑不全；自行搭建智能化多角色交易分析系统研发成本高，落地周期长。
适合什么团队
适合金融机构量化交易研究团队、独立交易研究者、金融科技研发团队，用于交易策略验证、智能化交易决策辅助等场景。
使用前注意
本框架仅用于研究用途，不构成任何金融投资建议；交易效果受所用大模型、数据源质量等多种因素影响，使用需自行配置对应API密钥。

本页用于缩短初步筛选时间，不构成技术、采购或法律结论。 正式使用前请在真实业务数据上验证，并以官方说明与许可证为准。

项目导读

从官方资料看清能力、部署与采用边界

AI 翻译整理 · 保留官方来源

以下内容依据项目公开 README 或模型卡翻译整理，代码、命令和产品名保持原样。

TradingAgents 项目导读

一、项目定位

TradingAgents是一款开源的多智能体（多个具备独立专业能力的智能程序，可分工协作完成复杂任务）大语言模型（LLM，可理解、生成自然语言的人工智能模型）金融交易框架，完全模拟真实交易公司的组织架构与决策流程，将复杂的交易决策任务拆解为不同专业角色的智能体协作完成，相关技术细节可参考官方技术报告。本框架仅用于研究用途，不构成任何金融、投资或交易建议。 框架架构

二、核心能力

框架将交易决策流程拆分为四大协作模块，各模块智能体各司其职，还可通过动态讨论确定最优策略：

  1. 分析师团队：包含四类细分角色：

分析师团队工作逻辑

  • 基本面分析师：评估企业财务数据与绩效指标，判断标的内在价值与潜在风险
  • 情绪分析师：聚合新闻、StockTwits、Reddit等平台的讨论内容，输出市场短期情绪判断
  • 新闻分析师：监控全球新闻与宏观经济指标，解读事件对市场的影响
  • 技术分析师：基于MACD、RSI等技术指标识别交易规律，预测价格走势

研究员团队工作逻辑

交易员工作逻辑

风控与投资组合经理工作逻辑

  1. 研究员团队：由看多、看空两类研究员组成，对分析师团队输出的结论进行交叉验证，通过结构化辩论平衡潜在收益与风险。
  2. 交易员智能体：整合分析师与研究员的全部报告，输出交易决策，确定交易的时间与仓位。
  3. 风控与投资组合经理：风控团队持续评估组合的波动率、流动性等风险指标，调整交易策略并输出评估报告，最终由投资组合经理审批交易申请，审批通过后发送到模拟交易所执行。

此外，框架支持雅虎财经覆盖的所有主流市场标的，包括美股（如AAPL、SPY）、港股（如0700.HK）、东京证券交易所（如7203.T）、伦敦证券交易所（如AZN.L）等。

三、典型使用方式

框架提供交互式CLI操作入口，启动后即可按需选择要分析的标的代码、分析日期、所用LLM服务商、研究深度等配置，即可获得完整的多维度分析报告与交易决策建议，可用于交易策略回测、策略有效性验证等场景，你也可以查看官方演示了解操作流程。

启动命令如下：

tradingagents          # 安装后直接调用命令
python -m cli.main     # 从源码直接运行

四、部署要求

框架提供两种部署方式，可按需选择：

本地Python环境部署

要求Python 3.12版本，操作步骤如下：

# 克隆代码
git clone https://github.com/TauricResearch/TradingAgents.git
cd TradingAgents
# 创建虚拟环境
conda create -n tradingagents python=3.12
conda activate tradingagents
# 安装依赖
pip install .

Docker部署

无需配置本地环境，直接运行容器即可：

cp .env.example .env  # 配置对应的API密钥
docker compose run --rm tradingagents

如果要对接本地Ollama运行的开源大模型，可使用以下命令：

docker compose --profile ollama run --rm tradingagents-ollama

接口配置要求

框架支持国内外主流LLM服务商，包括OpenAI、谷歌、Anthropic、DeepSeek、通义千问、智谱GLM、MiniMax、AWS Bedrock等，也支持对接任意OpenAI兼容的自定义大模型接口（如vLLM、LM Studio、llama.cpp等）。使用前需要配置对应服务商的API密钥，本地部署的开源模型无需API密钥。

五、近期更新状态

项目更新活跃，最新版本为2026年7月发布的v0.3.1，修复了稳定性问题，新增Alpha Vantage前视过滤、图形路由器 crash 安全防护、可配置LLM重试预算等能力，支持Claude Sonnet 5、Fable 5等新模型。 此前的版本也陆续新增了多LLM厂商支持、多语言适配、回测日期保真、结构化输出智能体、决策日志持久化等能力，功能迭代节奏稳定。

六、许可证与采用建议

本项目使用Apache-2.0许可证，可自由修改、商用。采用前请注意：

  1. 本框架仅用于研究用途，所有输出不构成投资建议，实际交易决策需由专业人员判断
  2. 交易效果受所选大模型、数据源质量、参数配置等多种不确定因素影响，正式使用前需经过充分的回测验证
  3. 如需对接敏感业务数据，建议优先选择本地部署开源大模型的方案，避免数据泄露风险
可核对的事实层

官方资料与来源

查看来源 →
  • agent
  • finance
  • llm
  • multiagent
  • trading
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近期更新2026/8/30
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上游部署线索
## Installation and CLI

### Installation

Clone TradingAgents:
```bash
git clone https://github.com/TauricResearch/TradingAgents.git
cd TradingAgents
```

Create a virtual environment in any of your favorite environment managers:
```bash
conda create -n tradingagents python=3.12
conda activate tradingagents
```

Install the package and its dependencies:
```bash
pip install .
```

### Docker

Alternatively, run with Docker:
```bash
cp .env.example .env  # add your API keys
docker compose run --rm tradingagents
```

For local models with Ollama:
```bash
docker compose --profile ollama run --rm tradingagents-ollama
```

### Required APIs

TradingAgents supports multiple LLM providers. Set the API key for your chosen provider:

```bash
export OPENAI_API_KEY=...          # OpenAI (GPT)
export GOOGLE_API_KEY=...          # Google (Gemini)
export ANTHROPIC_API_KEY=...       # Anthropic (Claude

该片段来自项目 README，仅用于初步判断；实际部署请以官方文档为准。

项目官网查看 Releases

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核对上游原始说明节选

TradingAgents: Multi-Agents LLM Financial Trading Framework

Deutsch | Español | français | 日本語 | 한국어 | Português | Русский | 中文

---

TradingAgents: Multi-Agents LLM Financial Trading Framework

News

  • [2026-07] TradingAgents v0.3.1 released with correctness and stability fixes: Alpha Vantage look-ahead filtering, graph-router crash-safety, graph-shape-aware checkpoint resume, working crypto sentiment sources, a configurable LLM retry budget, Bedrock API-key auth, and Claude Sonnet 5 / Fable 5 support. See CHANGELOG.md for the full list.
  • [2026-06] TradingAgents v0.3.0 released with a verified data-access contract, an expanded provider registry (NVIDIA, Kimi, Groq, Mistral, Bedrock, and any OpenAI-compatible endpoint), FRED and Polymarket data vendors, a current-generation model catalog, and a CI gate.
  • [2026-05] TradingAgents v0.2.5 released with the grounded Sentiment Analyst, GPT-5.5 etc. model coverage, Qwen/GLM/MiniMax dual-region support, TRADINGAGENTS env-var configurability with API-key auto-detection, remote Ollama support, non-US alpha benchmarks, and ticker path-traversal hardening.
  • [2026-04] TradingAgents v0.2.4 released with structured-output agents (Research Manager, Trader, Portfolio Manager), LangGraph checkpoint resume, persistent decision log, DeepSeek/Qwen/GLM/Azure provider support, Docker, and a Windows UTF-8 encoding fix.
  • [2026-03] TradingAgents v0.2.3 released with multi-language support, GPT-5.4 family models, unified model catalog, backtesting date fidelity, and proxy support.
  • [2026-03] TradingAgents v0.2.2 released with GPT-5.4/Gemini 3.1/Claude 4.6 model coverage, five-tier rating scale, OpenAI Responses API, Anthropic effort control, and cross-platform stability.
  • [2026-02] TradingAgents v0.2.0 released with multi-provider LLM support (GPT-5.x, Gemini 3.x, Claude 4.x, Grok 4.x) and improved system architecture.
  • [2026-01] Trading-R1 Technical Report released, with Terminal expected to land soon.

🚀 TradingAgents | ⚡ Installation & CLI | 🎬 Demo | 📦 Package Usage | 🤝 Contributing | 📄 Citation

🎉 TradingAgents officially released! We have received numerous inquiries about the work, and we would like to express our thanks for the enthusiasm in our community.
So we decided to fully open-source the framework. Looking forward to building impactful projects with you!

TradingAgents Framework

TradingAgents is a multi-agent trading framework that mirrors the dynamics of real-world trading firms. By deploying specialized LLM-powered agents: from fundamental analysts, sentiment experts, and technical analysts, to trader, risk management team, the platform collaboratively evaluates market conditions and informs trading decisions. Moreover, these agents engage in dynamic discussions to pinpoint the optimal strategy.

TradingAgents framework is designed for research purposes. Trading performance may vary based on many factors, including the chosen backbone language models, model temperature, trading periods, the quality of data, and other non-deterministic factors. It is not intended as financial, investment, or trading advice.

Our framework decomposes complex trading tasks into specialized roles.

Analyst Team

  • Fundamentals Analyst: Evaluates company financials and performance metrics, identifying intrinsic values and potential red flags.
  • Sentiment Analyst: Aggregates news headlines, StockTwits, and Reddit chatter into a single sentiment read to gauge short-term market mood.
  • News Analyst: Monitors global news and macroeconomic indicators, interpreting the impact of events on market conditions.
  • Technical Analyst: Utilizes technical indicators (like MACD and RSI) to detect trading patterns and forecast price movements.

Researcher Team

  • Comprises both bullish and bearish researchers who critically assess the insights provided by the Analyst Team. Through structured debates, they balance potential gains against inherent risks.

Trader Agent

  • Composes reports from the analysts and researchers to make informed trading decisions, determining the timing and magnitude of trades.

Risk Management and Portfolio Manager

  • Continuously evaluates portfolio risk by assessing market volatility, liquidity, and other risk factors. The risk management team evaluates and adjusts trading strategies, providing assessment reports to the Portfolio Manager for final decision.
  • The Portfolio Manager approves/rejects the transaction proposal. If approved, the order will be sent to the simulated exchange and executed.

Installation and CLI

Installation

Clone TradingAgents:

git clone https://github.com/TauricResearch/TradingAgents.git
cd TradingAgents

Create a virtual environment in any of your favorite environment managers:

conda create -n tradingagents python=3.12
conda activate tradingagents

Install the package and its dependencies:

pip install .

Docker

Alternatively, run with Docker:

cp .env.example .env  # add your API keys
docker compose run --rm tradingagents

For local models with Ollama:

docker compose --profile ollama run --rm tradingagents-ollama

Required APIs

TradingAgents supports multiple LLM providers. Set the API key for your chosen provider:

export OPENAI_API_KEY=...          # OpenAI (GPT)
export GOOGLE_API_KEY=...          # Google (Gemini)
export ANTHROPIC_API_KEY=...       # Anthropic (Claude)
export XAI_API_KEY=...             # xAI (Grok)
export DEEPSEEK_API_KEY=...        # DeepSeek
export DASHSCOPE_API_KEY=...       # Qwen — International (dashscope-intl.aliyuncs.com)
export DASHSCOPE_CN_API_KEY=...    # Qwen — China (dashscope.aliyuncs.com)
export ZHIPU_API_KEY=...           # GLM via Z.AI (international)
export ZHIPU_CN_API_KEY=...        # GLM via BigModel (China, open.bigmodel.cn)
export MINIMAX_API_KEY=...         # MiniMax — Global (api.minimax.io)
export MINIMAX_CN_API_KEY=...      # MiniMax — China (api.minimaxi.com)
export OPENROUTER_API_KEY=...      # OpenRouter
export ALPHA_VANTAGE_API_KEY=...   # Alpha Vantage

For Azure OpenAI, copy .env.enterprise.example to .env.enterprise and fill in your credentials.

For AWS Bedrock, install the extra with pip install ".[bedrock]", set llmprovider: "bedrock", configure AWS credentials (environment variables, /.aws/credentials, or an IAM role) and AWSDEFAULTREGION, and use a Bedrock model ID, e.g. us.anthropic.claude-opus-4-8-v1:0.

For local models, configure Ollama with llmprovider: "ollama". The default endpoint is http://localhost:11434/v1; set OLLAMABASEURL to point at a remote ollama-serve. Pull models with ollama pull , and pick "Custom model ID" in the CLI for any model not listed by default.

For any other OpenAI-compatible server (vLLM, LM Studio, llama.cpp, or a custom relay), use llmprovider: "openaicompatible" and set the endpoint via backendurl (or TRADINGAGENTSLLMBACKENDURL), e.g. http://localhost:8000/v1 for vLLM or http://localhost:1234/v1 for LM Studio. The model is whatever your server serves. No key is needed for local servers; set OPENAICOMPATIBLEAPIKEY when the endpoint requires one.

Alternatively, copy .env.example to .env and fill in your keys:

cp .env.example .env

CLI Usage

Launch the interactive CLI:

tradingagents          # installed command
python -m cli.main     # alternative: run directly from source

You will see a screen where you can select your desired tickers, analysis date, LLM provider, research depth, and more.

Markets and tickers

TradingAgents works with any market Yahoo Finance covers, using the exchange-suffixed ticker. Company identity and the alpha benchmark resolve automatically per market.

  • US: AAPL, SPY
  • Hong Kong: 0700.HK · Tokyo: 7203.T · London: AZN.L
  • India: `RELI