0fda24515f
Three related changes that take the rating pipeline from heuristic-only to type-safe at the source. 1) Research Manager prompt now uses the same 5-tier scale (Buy / Overweight / Hold / Underweight / Sell) as the Portfolio Manager, signal_processing, and the memory log. The prior 3-tier wording (Buy / Sell / Hold) was the only remaining inconsistency in the pipeline. 2) Centralise the 5-tier vocabulary and the heuristic prose-rating parser into tradingagents/agents/utils/rating.py. Both the memory log and the signal processor now share the same parser instead of duplicating regex and word-walker logic. 3) Make structured output a first-class part of the Portfolio Manager's primary call. The PM uses llm.with_structured_output(PortfolioDecision) so each provider's native structured-output mode (json_schema for OpenAI/xAI, response_schema for Gemini, tool-use for Anthropic, function_calling for OpenAI-compatible providers) yields a typed Pydantic instance directly. A render helper turns that instance back into the same markdown shape downstream consumers (memory log, CLI display, saved reports) already expect, so no other code has to know the PM now produces structured output. Providers without structured support fall back gracefully to free-text + the deterministic heuristic. The previous SignalProcessor had been making a second LLM call to re-extract the rating from the PM's prose; that round-trip is now eliminated. SignalProcessor is a thin adapter over parse_rating(), makes zero LLM calls, and stays for backwards compatibility with process_signal() callers. Schema (PortfolioDecision) captures rating + executive_summary + investment_thesis + optional price_target + time_horizon, with field descriptions doubling as output instructions. Agent prose remains the primary artifact; structured output is layered onto the PM only because it is the one agent whose output has machine-readable downstream consumers. 15 new tests cover the heuristic parser (markdown-bold edge cases that had no coverage before), the structured PM happy path, the free-text fallback path, and that SignalProcessor never invokes the LLM. Full suite: 77 tests pass in ~2s without API keys.
TradingAgents: Multi-Agents LLM Financial Trading Framework
News
- [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 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 | ⚡ Installation & CLI | 🎬 Demo | 📦 Package Usage | 🤝 Contributing | 📄 Citation
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. This ensures the system achieves a robust, scalable approach to market analysis and decision-making.
Analyst Team
- Fundamentals Analyst: Evaluates company financials and performance metrics, identifying intrinsic values and potential red flags.
- Sentiment Analyst: Analyzes social media and public sentiment using sentiment scoring algorithms 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. It determines the timing and magnitude of trades based on comprehensive market insights.
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=3.13
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 (Alibaba DashScope)
export ZHIPU_API_KEY=... # GLM (Zhipu)
export OPENROUTER_API_KEY=... # OpenRouter
export ALPHA_VANTAGE_API_KEY=... # Alpha Vantage
For enterprise providers (e.g. Azure OpenAI, AWS Bedrock), copy .env.enterprise.example to .env.enterprise and fill in your credentials.
For local models, configure Ollama with llm_provider: "ollama" in your config.
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.
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), GLM (Zhipu), OpenRouter, Ollama for local models, and Azure OpenAI for enterprise.
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:
from tradingagents.graph.trading_graph import TradingAgentsGraph
from tradingagents.default_config import DEFAULT_CONFIG
ta = TradingAgentsGraph(debug=True, config=DEFAULT_CONFIG.copy())
# forward propagate
_, decision = ta.propagate("NVDA", "2026-01-15")
print(decision)
You can also adjust the default configuration to set your own choice of LLMs, debate rounds, etc.
from tradingagents.graph.trading_graph import TradingAgentsGraph
from tradingagents.default_config import DEFAULT_CONFIG
config = DEFAULT_CONFIG.copy()
config["llm_provider"] = "openai" # openai, google, anthropic, xai, deepseek, qwen, glm, openrouter, ollama, azure
config["deep_think_llm"] = "gpt-5.4" # Model for complex reasoning
config["quick_think_llm"] = "gpt-5.4-mini" # Model for quick tasks
config["max_debate_rounds"] = 2
ta = TradingAgentsGraph(debug=True, config=config)
_, decision = ta.propagate("NVDA", "2026-01-15")
print(decision)
See tradingagents/default_config.py for all configuration options.
Persistence and Recovery
TradingAgents persists two kinds of state across runs.
Decision log
The decision log is always on. Each completed run appends its decision to ~/.tradingagents/memory/trading_memory.md. On the next run for the same ticker, TradingAgents fetches the realised return (raw and alpha vs SPY), generates a one-paragraph reflection, and injects the most recent same-ticker decisions plus recent cross-ticker lessons into the Portfolio Manager prompt, so each analysis carries forward what worked and what didn't.
Override the path with TRADINGAGENTS_MEMORY_LOG_PATH.
Checkpoint resume
Checkpoint resume is opt-in via --checkpoint. When enabled, LangGraph saves state after each node so a crashed or interrupted run resumes from the last successful step instead of starting over. On a resume run you will see Resuming from step N for <TICKER> on <date> in the logs; on a new run you will see Starting fresh. Checkpoints are cleared automatically on successful completion.
Per-ticker SQLite databases live at ~/.tradingagents/cache/checkpoints/<TICKER>.db (override the base with TRADINGAGENTS_CACHE_DIR). Use --clear-checkpoints to reset all of them before a run.
tradingagents analyze --checkpoint # enable for this run
tradingagents analyze --clear-checkpoints # reset before running
config = DEFAULT_CONFIG.copy()
config["checkpoint_enabled"] = True
ta = TradingAgentsGraph(config=config)
_, decision = ta.propagate("NVDA", "2026-01-15")
Contributing
We welcome contributions from the community! Whether it's fixing a bug, improving documentation, or suggesting a new feature, your input helps make this project better. If you are interested in this line of research, please consider joining our open-source financial AI research community Tauric Research.
Citation
Please reference our work if you find TradingAgents provides you with some help :)
@misc{xiao2025tradingagentsmultiagentsllmfinancial,
title={TradingAgents: Multi-Agents LLM Financial Trading Framework},
author={Yijia Xiao and Edward Sun and Di Luo and Wei Wang},
year={2025},
eprint={2412.20138},
archivePrefix={arXiv},
primaryClass={q-fin.TR},
url={https://arxiv.org/abs/2412.20138},
}