TradingAgents Agent工作流

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 MISTRAL_API_KEY=...         # Mistral
export MOONSHOT_API_KEY=...        # Kimi (Moonshot)
export GROQ_API_KEY=...            # Groq
export NVIDIA_API_KEY=...          # NVIDIA NIM
export FRED_API_KEY=...            # FRED macro data (free, optional)
export ALPHA_VANTAGE_API_KEY=...   # Alpha Vantage
export TYPESAFE_API_KEY=...        # Jev social-post screening (optional)

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 llm_provider: "bedrock", configure AWS credentials (environment variables, ~/.aws/credentials, or an IAM role) and AWS_DEFAULT_REGION, and use a Bedrock model ID, e.g. us.anthropic.claude-opus-5-5.

For local models, configure Ollama with llm_provider: "ollama". The default endpoint is http://localhost:11434/v1; set OLLAMA_BASE_URL to point at a remote ollama-serve. Pull models with ollama pull <name>, 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 llm_provider: "openai_compatible" and set the endpoint via backend_url (or TRADINGAGENTS_LLM_BACKEND_URL), 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 OPENAI_COMPATIBLE_API_KEY when the endpoint requires one.

With TYPESAFE_API_KEY set, the Sentiment Analyst screens StockTwits and Reddit posts with TypeSafe's Jev before reading them. Posts that are not about the company are dropped, and each source opens with a count of the remaining posts by stance: bullish, bearish, neutral, or unclear. Without the key, posts pass through unscreened. jev-latest moves with new releases; set TYPESAFE_DEFAULT_MODEL to a versioned ID such as jev-1.13.0 to hold it fixed across runs. To reach Jev through OpenRouter, put an OpenRouter key in TYPESAFE_API_KEY and set TYPESAFE_BASE_URL=https://openrouter.ai/api.

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

cp .env.example .env

Installation and CLI

Installation

Clone TradingAgents:

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

TradingAgents needs Python 3.11 or later. Create a virtual environment in any of your favorite environment managers:

conda create -n tradingagents python=3.13
conda activate tradingagents

Or with uv:

uv venv --python 3.13
source .venv/bin/activate

Install the package and its dependencies (uv pip install . with uv):

pip install .

Docker

Alternatively, run with Docker:

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

After updating the repository, rebuild the image with docker compose build.

Results, reports, the memory log and the cache live in the tradingagents_data volume. To keep them in a folder on the host instead, create the folder and point TRADINGAGENTS_DATA_DIR at it, in .env or the shell: mkdir -p data && TRADINGAGENTS_DATA_DIR=./data docker compose run --rm tradingagents.

For local models with Ollama:

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

CLI Usage

Launch the interactive CLI:

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

To run without questions, for a scheduled job or a script, answer the per-run steps with flags and the rest with TRADINGAGENTS_* variables:

export TRADINGAGENTS_LLM_PROVIDER=openai TRADINGAGENTS_QUICK_THINK_LLM=gpt-6-luna TRADINGAGENTS_DEEP_THINK_LLM=gpt-6-sol
export TRADINGAGENTS_OUTPUT_LANGUAGE=English TRADINGAGENTS_MAX_DEBATE_ROUNDS=1 TRADINGAGENTS_MAX_RISK_ROUNDS=1
tradingagents --ticker NVDA --date 2026-09-23 --analysts market,news,fundamentals --save --no-show

A saved report also includes complete_report.html, the report as one page with its sections listed beside the text, for reading in a browser, on a phone or in print. Answering the save question at the prompt also asks about the page and can open it in your browser; --no-html skips it, and so does ta.save_reports(state, "NVDA", html=False) from Python.

Python Usage

To use TradingAgents inside your code, you can import the tradingagents module and initialize a TradingAgentsGraph() object. The .propagate() function will return a decision. You can run main.py, here's also a quick example:

```python from tradingagents.graph.trading_graph import TradingAgentsGraph from tradingagents.default_config import DEFAULT_CONFIG

ta = TradingAgentsGraph(debug=True, config=DEFAULT_CONFIG.copy())

the same report tree the CLI saves, under results_dir/reports

ta.save_reports(state, "NVDA")

You can also adjust the default configuration to set your own choice of LLMs, debate rounds, etc.

config = DEFAULT_CONFIG.copy() config["llm_provider"] = "openai" # e.g. openai, google, anthropic, deepseek, groq, ollama; openai_compatible covers any OpenAI-compatible endpoint (vLLM, LM Studio, llama.cpp, ...) config["deep_think_llm"] = "gpt-6-sol" # Model for complex reasoning config["quick_think_llm"] = "gpt-6-luna" # Model for quick tasks config["max_debate_rounds"] = 2

ta = TradingAgentsGraph(debug=True, config=config) _, decision = ta.propagate("NVDA", "2026-09-01") print(decision)

The quick model serves the analysts, researchers, debaters and trader; the deep model serves the research and portfolio managers. Each can run on its own provider, for example the managers on Claude while the rest run on OpenAI:

A tier on its own provider uses that provider's key and default endpoint; set quick_think_backend_url or deep_think_backend_url for a local or relay endpoint. The TRADINGAGENTS_DEEP_THINK_PROVIDER and TRADINGAGENTS_QUICK_THINK_PROVIDER variables set them for the CLI, together with the tier's model variable.

See tradingagents/default_config.py for all configuration options.

TradingAgents Package