agent ·

TradingAgents

TradingAgents: Multi-Agents LLM Financial Trading Framework

--- # TradingAgents: Multi-Agents LLM Financial Trading Framework ## News - [2026-10] **TradingAgents v0.7.0** released with runs on your own ChatGPT Plus or Pro plan, backtests scored from the first close after each decision, a note at start-up when a newer release is out, and a wheel attached to each GitHub release. - [2026-10] **TradingAgents v0.6.0** released with reports saved as one HTML page, a provider per model tier so the managers and analysts can run on different models, past decisions settled for every ticker while the analysts work, and company news read from Yahoo search while Yahoo's news feed is down. - [2026-09] **TradingAgents v0.5.2** released with parallel analysts for a faster analysis, a CLI that runs without prompts from flags such as `--ticker` and `--date`, the run's settings recorded in every report, and backtests that see only data published by each analysis date. Full release notes are in [CHANGELOG.md](CHANGELOG.md). Earlier news - [2026-09] **TradingAgents v0.5.1** released with a package layout organised by what each module holds (import paths moved), optional Jev screening of social posts, GPT-6 Sol and Luna as the default models, and fixes to run isolation and SEC EDGAR statements. - [2026-09] **TradingAgents v0.5.0** released with point-in-time integrity across every dated path, SEC EDGAR fundamentals served as filed, backtesting over a ticker and date grid, portfolio-aware runs, and current model lineups across every provider. - [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. - [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](https://arxiv.org/abs/2509.11420) released, with [Terminal](https://github.com/TauricResearch/Trading-R1) expected to land soon.

> 🎉 **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.](https://tauric.ai/disclaimer/) 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. The selected analysts work at the same time, each on its own tools, and the research debate starts once all of their reports are in.

### 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: ```bash 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: ```bash conda create -n tradingagents python=3.13 conda activate tradingagents ``` Or with [uv](https://docs.astral.sh/uv/): ```bash uv venv --python 3.13 source .venv/bin/activate ``` Install the package and its dependencies (`uv pip install .` with uv): ```bash pip install . ``` ### Docker Alternatively, run with Docker: ```bash 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: ```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) 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. To run on your ChatGPT Plus or Pro plan instead of an API key, choose "Subscription" in the CLI's provider menu, or set `llm_provider: "chatgpt"`. Sign in once with `tradingagents login chatgpt`: it prints an address to open in your browser, which returns to this machine on `127.0.0.1`. When the browser runs on another machine, or TradingAgents runs in Docker (`docker compose run --rm tradingagents login chatgpt`), paste the address the browser ends up on. The sign-in is kept in `~/.tradingagents/chatgpt-auth.json`, and `tradingagents logout chatgpt` revokes it. Runs count toward your plan's usage limits, and the plan does not accept `temperature` or `max_tokens`. 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 `, 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: ```bash cp .env.example .env ``` ### CLI Usage Launch the interactive CLI: ```bash 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. Your previous run's answers come back as the defaults, so pressing Enter accepts them. The `TRADINGAGENTS_*` variables in `.env` still skip their step entirely. To run without questions, for a scheduled job or a script, answer the per-run steps with flags and the rest with `TRADINGAGENTS_*` variables: ```bash 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 ``` Each flag skips only its own question. Run without a terminal, a missing answer stops the run before it starts and names the flag or variable to set. At start-up the interactive CLI mentions a newer release and notices from Tauric Research. It checks at most once a day, in the background, and sends nothing about your runs; set `TRADINGAGENTS_NO_UPDATE_CHECK=1` to turn it off. Runs without a terminal never check. 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. ### 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: `RELIANCE.NS`, `.BO` · Canada: `.TO` · Australia: `.AX` - China A-shares: Shanghai `.SS`, Shenzhen `.SZ` (e.g. `600519.SS` for Kweichow Moutai) - Crypto: `BTC-USD`, `ETH-USD`

An interface will appear showing results as they load, letting you track the agent's progress as it runs.

## TradingAgents Package ### Implementation Details We built TradingAgents with LangGraph to ensure flexibility and modularity. The framework supports multiple LLM providers: OpenAI, Google, Anthropic, xAI, DeepSeek, Qwen (Alibaba DashScope, international and China endpoints), GLM (Zhipu), MiniMax (global + China), OpenRouter, Ollama for local models, Azure OpenAI for enterprise, and your own ChatGPT plan. ### 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()) # forward propagate state, decision = ta.propagate("NVDA", "2026-09-01") print(decision) # 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. ```python from tradingagents.graph.trading_graph import TradingAgentsGraph from tradingagents.default_config import DEFAULT_CONFIG 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 portfoli

原始来源：https://github.com/TauricResearch/TradingAgents#readme