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TradingAgents

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TradingAgents是基于大语言模型（LLM，具备自然语言理解与推理能力的人工智能模型）的多智能体金融交易框架，模拟真实交易团队分工协作输出交易决策参考。

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AI 依据上游资料解读 · 2026/9/7

TradingAgents是基于大语言模型（LLM，具备自然语言理解与推理能力的人工智能模型）的多智能体金融交易框架，模拟真实交易团队分工协作输出交易决策参考。

解决什么问题
金融交易决策需整合基本面、舆情、技术面、宏观政策等多维度信息，个人或小团队很难做到全面覆盖、高效权衡风险收益，且易受主观判断偏差影响，缺乏标准化的分析协作流程。
适合什么团队
适合券商、量化投资机构、金融科技团队的交易研究部门，用于搭建AI辅助交易分析系统、开展交易策略回测研究。
使用前注意
本项目仅用于研究用途，不构成任何投资建议；使用需配置对应大语言模型及行情数据源的API密钥，交易效果受所选模型、数据质量等因素影响。

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。

项目定位

TradingAgents是TauricResearch开源的多智能体（Multi-Agent，由多个具备独立职能的智能单元分工协作完成复杂任务的技术架构）金融交易框架，完全模拟真实交易公司的团队分工模式，通过不同职能的大语言模型（LLM，具备自然语言理解、推理和生成能力的人工智能模型）驱动的智能体协作，完成全流程交易分析并输出决策参考。本项目仅用于研究用途，不构成任何金融、投资或交易建议。

框架架构

核心能力

项目将复杂的交易分析任务拆解为多个专业角色，各角色分工明确、相互协作：

包含四类细分分析师：基本面分析师负责评估企业财务数据、业绩指标，判断标的内在价值与潜在风险；舆情分析师整合新闻、社交平台讨论内容，输出市场情绪判断；新闻分析师监控全球新闻、宏观经济指标，解读事件对市场的影响；技术分析师通过MACD、RSI等技术指标识别交易规律，预测价格走势。

  1. 分析师团队

分析师团队职能

由看涨、看跌两类研究员组成，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。

  1. 研究员团队

研究员团队协作逻辑

整合分析师、研究员的输出报告，判断交易的时机与仓位规模，输出交易提案。

  1. 交易员智能体

交易员决策逻辑

风控团队持续评估投资组合的波动率、流动性等风险指标，调整交易策略并输出风险评估报告；投资组合经理最终审批交易提案，审批通过后订单将发送至模拟交易所执行。

  1. 风控与投资组合经理

风控与投资组合管理逻辑

典型使用方式

项目提供交互式命令行工具，启动后可按需选择要分析的标的代码、分析日期、LLM服务商、研究深度等参数，系统将自动运行完整的多智能体分析流程，输出最终交易决策参考。此外框架支持历史时点数据回测，可用于验证策略在过往行情中的表现。

你可以通过官方演示视频了解完整操作流程。

部署要求

项目支持两种部署方式，可按需选择：

  1. 本地环境部署：需准备Python 3.12运行环境，克隆项目仓库后创建虚拟环境，安装依赖包即可使用。
  2. Docker部署：可直接通过容器启动，只需将示例环境变量文件复制为.env，填入对应API密钥即可运行。如果需要使用本地部署的大模型，可配合Ollama通过指定profile启动容器。

项目兼容多家LLM服务商，包括OpenAI、Google、Anthropic、DeepSeek、通义千问、智谱GLM、MiniMax等，同时支持AWS Bedrock、OpenRouter等聚合服务，也兼容所有符合OpenAI接口规范的开源大模型服务（如vLLM、LM Studio等）。使用前需配置所选LLM服务商的API密钥，以及Alpha Vantage等行情数据源的API密钥。

近期迭代状态

项目更新迭代频率较高，近期核心版本更新内容如下：

  • 2026年8月发布v0.4.0版本，修复了宏观数据、舆情、决策日志的时点偏差问题，优化决策信号清晰度，支持命令行断点恢复，新增GPT-5.6、GLM-5.3模型支持。
  • 2026年7月发布v0.3.1版本，修复了数据正确性与稳定性问题，新增加密货币舆情数据源、可配置的LLM重试机制，支持Claude Sonnet 5等模型。
  • 2026年6月发布v0.3.0版本，新增数据访问合规校验，扩展了NVIDIA、Kimi、Groq等LLM服务商接入，新增FRED、Polymarket等数据供应商支持。

许可证与采用建议

项目采用Apache-2.0许可证，允许商业使用、修改、二次分发，无强制开源衍生代码的要求。

建议券商、量化投资机构的交易研究团队基于本框架快速搭建AI辅助交易分析系统，无需从零开发多智能体协作流程。使用前建议先通过历史回测验证适配性，不要直接用于实盘交易，可根据自身交易策略调整各智能体的决策规则与权重，适配业务需求。

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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关注仓库776
复刻次数19.8k
开放议题363
近期更新2026/9/6
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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。

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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

  • [2026-08] TradingAgents v0.4.0 released with look-ahead / point-in-time fixes across FRED macro, social sentiment, and the decision-log memory; clearer decision signals; working CLI checkpoint resume; Trader price grounding; and the GPT-5.6 and GLM-5.3 models. See CHANGELOG.md for the full list.
  • [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.
  • [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 ticke