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<p align="center">
<img src="assets/TauricResearch.png" style="width: 60%; height: auto;">
</p>
<div align="center" style="line-height: 1;">
<a href="https://arxiv.org/abs/2412.20138" target="_blank"><img alt="arXiv" src="https://img.shields.io/badge/arXiv-2412.20138-B31B1B?logo=arxiv"/></a>
<a href="https://discord.com/invite/hk9PGKShPK" target="_blank"><img alt="Discord" src="https://img.shields.io/badge/Discord-TradingResearch-7289da?logo=discord&logoColor=white&color=7289da"/></a>
<a href="./assets/wechat.png" target="_blank"><img alt="WeChat" src="https://img.shields.io/badge/WeChat-TauricResearch-brightgreen?logo=wechat&logoColor=white"/></a>
<a href="https://x.com/TauricResearch" target="_blank"><img alt="X Follow" src="https://img.shields.io/badge/X-TauricResearch-white?logo=x&logoColor=white"/></a>
<br>
<a href="https://github.com/TauricResearch/" target="_blank"><img alt="Community" src="https://img.shields.io/badge/Join_GitHub_Community-TauricResearch-14C290?logo=discourse"/></a>
</div>
<div align="center">
<!-- Keep these links. Translations will automatically update with the README. -->
<a href="https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=de">Deutsch</a> |
<a href="https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=es">Español</a> |
<a href="https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=fr">français</a> |
<a href="https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=ja">日本語</a> |
<a href="https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=ko">한국어</a> |
<a href="https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=pt">Português</a> |
<a href="https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=ru">Русский</a> |
<a href="https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=zh">中文</a>
</div>
---
# TradingAgents: Multi-Agents LLM Financial Trading Framework
## News
- [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.
<div align="center">
<a href="https://www.star-history.com/#TauricResearch/TradingAgents&Date">
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=TauricResearch/TradingAgents&type=Date&theme=dark" />
<source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/svg?repos=TauricResearch/TradingAgents&type=Date" />
<img alt="TradingAgents Star History" src="https://api.star-history.com/svg?repos=TauricResearch/TradingAgents&type=Date" style="width: 80%; height: auto;" />
</picture>
</a>
</div>
> 🎉 **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!
<div align="center">
🚀 [TradingAgents](#tradingagents-framework) | ⚡ [Installation & CLI](#installation-and-cli) | 🎬 [Demo](https://www.youtube.com/watch?v=90gr5lwjIho) | 📦 [Package Usage](#tradingagents-package) | 🤝 [Contributing](#contributing) | 📄 [Citation](#citation)
</div>
## 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.
<p align="center">
<img src="assets/schema.png" style="width: 100%; height: auto;">
</p>
> 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. 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.
<p align="center">
<img src="assets/analyst.png" width="100%" style="display: inline-block; margin: 0 2%;">
</p>
### 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.
<p align="center">
<img src="assets/researcher.png" width="70%" style="display: inline-block; margin: 0 2%;">
</p>
### 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.
<p align="center">
<img src="assets/trader.png" width="70%" style="display: inline-block; margin: 0 2%;">
</p>
### 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.
<p align="center">
<img src="assets/risk.png" width="70%" style="display: inline-block; margin: 0 2%;">
</p>
## 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=3.13
conda activate tradingagents
```
Install dependencies:
```bash
pip install -r requirements.txt
```
### 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 OPENROUTER_API_KEY=... # OpenRouter
export ALPHA_VANTAGE_API_KEY=... # Alpha Vantage
```
For local models, configure Ollama with `llm_provider: "ollama"` in your config.
Alternatively, copy `.env.example` to `.env` and fill in your keys:
```bash
cp .env.example .env
```
### CLI Usage
You can also try out the CLI directly by running:
```bash
python -m cli.main
```
You will see a screen where you can select your desired tickers, date, LLMs, research depth, etc.
<p align="center">
<img src="assets/cli/cli_init.png" width="100%" style="display: inline-block; margin: 0 2%;">
</p>
An interface will appear showing results as they load, letting you track the agent's progress as it runs.
<p align="center">
<img src="assets/cli/cli_news.png" width="100%" style="display: inline-block; margin: 0 2%;">
</p>
<p align="center">
<img src="assets/cli/cli_transaction.png" width="100%" style="display: inline-block; margin: 0 2%;">
</p>
## TradingAgents Package
### Implementation Details
We built TradingAgents with LangGraph to ensure flexibility and modularity. The framework supports multiple LLM providers: OpenAI, Google, Anthropic, xAI, OpenRouter, and Ollama.
### 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
_, 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.
```python
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, openrouter, ollama
config["deep_think_llm"] = "gpt-5.2" # Model for complex reasoning
config["quick_think_llm"] = "gpt-5-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.
## 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](https://tauric.ai/).
## 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},
}
```

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2026-02-16: decided to use yfinance + Polygon instead of only Polygon because of rate limits

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**Simplest starting system (GitHub Issues + Milestones)**
1. Create **Milestones** (these are your big phases):
- v0.1 – Minimal end-to-end loop (manual trigger, 1 ticker, basic buy + guardian sell)
- v0.2 – Scheduler + Profit Guardian polish
- v0.3 – Multi-ticker + Dashboard
- Backtesting foundation
- Experiments & Research
2. Create **Issue labels** (keep to 6–8 max):
- priority:high / priority:medium
- area:graph / area:agents / area:tools / area:data / area:ui / area:config
- type:bug / type:feature / type:refactor / type:experiment
- status:blocked (use when waiting on API key, etc.)
3. Workflow for a typical work session:
- Open repo → look at Milestone “v0.1”
- Pick 1–3 small issues (ideally < 2–4 hours each)
- Create branch: `git checkout -b 47-add-profit-guardian-node`
- Work → commit often with conventional messages:
```
feat: add initial profit guardian node with take-profit logic
refactor: extract current_price helper to tools/
fix: handle division by zero in profit pct calc
```
- When done → create Pull Request → link the issue (`closes #47`)
- Merge (you can squash or rebase — solo so whatever feels clean)
### Daily / Weekly Rituals (prevents chaos)
- **Start session (5 min)**: Open GitHub → see open issues in current milestone → pick 1 thing
- **End session (5 min)**:
- Commit & push even if incomplete
- Write quick note in issue or `docs/log.md`: “Today: implemented trailing stop calc. Tomorrow: test with SHOP data gap.”
- **Weekly (Sunday? 15–30 min)**:
- Review open issues → close stale ones or move to “parking lot” milestone
- Update `README.md` status section (very motivating)
- Run quick manual test / backtest → screenshot result → drop in issue or docs/screenshots/

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<p align="center">
<img src="assets/TauricResearch.png" style="width: 60%; height: auto;">
</p>
<div align="center" style="line-height: 1;">
<a href="https://arxiv.org/abs/2412.20138" target="_blank"><img alt="arXiv" src="https://img.shields.io/badge/arXiv-2412.20138-B31B1B?logo=arxiv"/></a>
<a href="https://discord.com/invite/hk9PGKShPK" target="_blank"><img alt="Discord" src="https://img.shields.io/badge/Discord-TradingResearch-7289da?logo=discord&logoColor=white&color=7289da"/></a>
<a href="./assets/wechat.png" target="_blank"><img alt="WeChat" src="https://img.shields.io/badge/WeChat-TauricResearch-brightgreen?logo=wechat&logoColor=white"/></a>
<a href="https://x.com/TauricResearch" target="_blank"><img alt="X Follow" src="https://img.shields.io/badge/X-TauricResearch-white?logo=x&logoColor=white"/></a>
<br>
<a href="https://github.com/TauricResearch/" target="_blank"><img alt="Community" src="https://img.shields.io/badge/Join_GitHub_Community-TauricResearch-14C290?logo=discourse"/></a>
</div>
<div align="center">
<!-- Keep these links. Translations will automatically update with the README. -->
<a href="https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=de">Deutsch</a> |
<a href="https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=es">Español</a> |
<a href="https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=fr">français</a> |
<a href="https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=ja">日本語</a> |
<a href="https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=ko">한국어</a> |
<a href="https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=pt">Português</a> |
<a href="https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=ru">Русский</a> |
<a href="https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=zh">中文</a>
</div>
---
# TradingAgents: Multi-Agents LLM Financial Trading Framework
## News
- [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.
<div align="center">
<a href="https://www.star-history.com/#TauricResearch/TradingAgents&Date">
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=TauricResearch/TradingAgents&type=Date&theme=dark" />
<source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/svg?repos=TauricResearch/TradingAgents&type=Date" />
<img alt="TradingAgents Star History" src="https://api.star-history.com/svg?repos=TauricResearch/TradingAgents&type=Date" style="width: 80%; height: auto;" />
</picture>
</a>
</div>
> 🎉 **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!
<div align="center">
🚀 [TradingAgents](#tradingagents-framework) | ⚡ [Installation & CLI](#installation-and-cli) | 🎬 [Demo](https://www.youtube.com/watch?v=90gr5lwjIho) | 📦 [Package Usage](#tradingagents-package) | 🤝 [Contributing](#contributing) | 📄 [Citation](#citation)
</div>
## 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.
<p align="center">
<img src="assets/schema.png" style="width: 100%; height: auto;">
</p>
> 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. 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.
<p align="center">
<img src="assets/analyst.png" width="100%" style="display: inline-block; margin: 0 2%;">
</p>
### 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.
<p align="center">
<img src="assets/researcher.png" width="70%" style="display: inline-block; margin: 0 2%;">
</p>
### 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.
<p align="center">
<img src="assets/trader.png" width="70%" style="display: inline-block; margin: 0 2%;">
</p>
### 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.
<p align="center">
<img src="assets/risk.png" width="70%" style="display: inline-block; margin: 0 2%;">
</p>
## 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=3.13
conda activate tradingagents
```
Install dependencies:
```bash
pip install -r requirements.txt
```
### 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 OPENROUTER_API_KEY=... # OpenRouter
export ALPHA_VANTAGE_API_KEY=... # Alpha Vantage
```
For local models, configure Ollama with `llm_provider: "ollama"` in your config.
Alternatively, copy `.env.example` to `.env` and fill in your keys:
```bash
cp .env.example .env
```
### CLI Usage
You can also try out the CLI directly by running:
```bash
python -m cli.main
```
You will see a screen where you can select your desired tickers, date, LLMs, research depth, etc.
<p align="center">
<img src="assets/cli/cli_init.png" width="100%" style="display: inline-block; margin: 0 2%;">
</p>
An interface will appear showing results as they load, letting you track the agent's progress as it runs.
<p align="center">
<img src="assets/cli/cli_news.png" width="100%" style="display: inline-block; margin: 0 2%;">
</p>
<p align="center">
<img src="assets/cli/cli_transaction.png" width="100%" style="display: inline-block; margin: 0 2%;">
</p>
## TradingAgents Package
### Implementation Details
We built TradingAgents with LangGraph to ensure flexibility and modularity. The framework supports multiple LLM providers: OpenAI, Google, Anthropic, xAI, OpenRouter, and Ollama.
### 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
_, 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.
```python
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, openrouter, ollama
config["deep_think_llm"] = "gpt-5.2" # Model for complex reasoning
config["quick_think_llm"] = "gpt-5-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.
## 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](https://tauric.ai/).
## 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},
}
```