Marvin Gabler
GitHub
commit
70a9fc8963
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@ -1,6 +1,8 @@
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# Configuration Guide
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TradingAgents supports configuration through multiple methods with the following priority:
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**Copyright Notice:** Litadel is a successor of TradingAgents by TaurusResearch.
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Litadel supports configuration through multiple methods with the following priority:
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**Priority (highest to lowest):**
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1. Environment variables
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@ -200,7 +202,7 @@ The `→` lines show values being used from your config, skipping the prompts!
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## Troubleshooting
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**CLI still prompts for everything:**
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- Check that `config.ini` exists in the TradingAgents directory
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- Check that `config.ini` exists in the Litadel directory
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- Verify the file has the correct structure (see `config.example.ini`)
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- Check for typos in section names and keys
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63
README.md
63
README.md
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@ -1,5 +1,5 @@
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<p align="center">
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<img src="assets/TauricResearch.png" style="width: 60%; height: auto;">
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<img src="assets/litadel.png" style="width: 60%; height: auto;">
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</p>
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<div align="center" style="line-height: 1;">
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@ -25,37 +25,39 @@
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---
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# TradingAgents: Multi-Agents LLM Financial Trading Framework
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# Litadel: Multi-Agents LLM Financial Trading Framework
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> 🎉 **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.
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> **Copyright Notice:** Litadel is a successor of TradingAgents by TaurusResearch. This project builds upon and extends the original TradingAgents framework.
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>
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> 🎉 **Litadel** officially released! We have received numerous inquiries about the work, and we would like to express our thanks for the enthusiasm in our community.
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>
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> So we decided to fully open-source the framework. Looking forward to building impactful projects with you!
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<div align="center">
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<a href="https://www.star-history.com/#TauricResearch/TradingAgents&Date">
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<picture>
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<source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=TauricResearch/TradingAgents&type=Date&theme=dark" />
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<source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/svg?repos=TauricResearch/TradingAgents&type=Date" />
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<img alt="TradingAgents Star History" src="https://api.star-history.com/svg?repos=TauricResearch/TradingAgents&type=Date" style="width: 80%; height: auto;" />
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</picture>
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</a>
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</div>
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<div align="center">
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🚀 [TradingAgents](#tradingagents-framework) | ⚡ [Installation & CLI](#installation-and-cli) | 🎬 [Demo](https://www.youtube.com/watch?v=90gr5lwjIho) | 📦 [Package Usage](#tradingagents-package) | 🤝 [Contributing](#contributing) | 📄 [Citation](#citation)
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🚀 [Litadel](#litadel-framework) | ⚡ [Installation & CLI](#installation-and-cli) | 🎬 [Demo](https://www.youtube.com/watch?v=90gr5lwjIho) | 📦 [Package Usage](#litadel-package) | 🤝 [Contributing](#contributing) | 📄 [Citation](#citation)
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</div>
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## TradingAgents Framework
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## Litadel Framework
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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.
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Litadel 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.
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<p align="center">
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<img src="assets/schema.png" style="width: 100%; height: auto;">
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</p>
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> 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/)
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> Litadel 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/)
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### Additional Functionality in Litadel
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Litadel extends the original TradingAgents framework with enhanced capabilities:
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- ✅ **Commodities Support** - Full analysis and trading support for commodity markets
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- ✅ **Cryptocurrency Support** - Native support for crypto asset analysis and trading
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- 🚧 **Web Frontend** - Interactive web interface (in progress)
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- 🚧 **Automated Trading Mode** - Continuous automated trading execution (in progress)
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- 🚧 **Portfolio Management** - Multi-asset portfolio tracking and optimization (in progress)
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Our framework decomposes complex trading tasks into specialized roles. This ensures the system achieves a robust, scalable approach to market analysis and decision-making.
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@ -95,16 +97,16 @@ Our framework decomposes complex trading tasks into specialized roles. This ensu
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### Installation
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Clone TradingAgents:
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Clone Litadel:
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```bash
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git clone https://github.com/TauricResearch/TradingAgents.git
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cd TradingAgents
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git clone https://github.com/TauricResearch/Litadel.git
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cd Litadel
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```
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Create a virtual environment in any of your favorite environment managers:
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```bash
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conda create -n tradingagents python=3.13
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conda activate tradingagents
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conda create -n litadel python=3.13
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conda activate litadel
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```
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Install dependencies:
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@ -127,7 +129,7 @@ cp .env.example .env
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# Edit .env with your actual API keys
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```
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**Note:** We are happy to partner with Alpha Vantage to provide robust API support for TradingAgents. You can get a free AlphaVantage API [here](https://www.alphavantage.co/support/#api-key), TradingAgents-sourced requests also have increased rate limits to 60 requests per minute with no daily limits. Typically the quota is sufficient for performing complex tasks with TradingAgents thanks to Alpha Vantage’s open-source support program. If you prefer to use OpenAI for these data sources instead, you can modify the data vendor settings in `tradingagents/default_config.py`.
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**Note:** We are happy to partner with Alpha Vantage to provide robust API support for Litadel. You can get a free AlphaVantage API [here](https://www.alphavantage.co/support/#api-key), Litadel-sourced requests also have increased rate limits to 60 requests per minute with no daily limits. Typically the quota is sufficient for performing complex tasks with Litadel thanks to Alpha Vantage's open-source support program. If you prefer to use OpenAI for these data sources instead, you can modify the data vendor settings in `tradingagents/default_config.py`.
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### CLI Usage
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@ -151,15 +153,15 @@ An interface will appear showing results as they load, letting you track the age
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<img src="assets/cli/cli_transaction.png" width="100%" style="display: inline-block; margin: 0 2%;">
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</p>
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## TradingAgents Package
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## Litadel Package
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### Implementation Details
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We built TradingAgents with LangGraph to ensure flexibility and modularity. We utilize `o1-preview` and `gpt-4o` as our deep thinking and fast thinking LLMs for our experiments. However, for testing purposes, we recommend you use `o4-mini` and `gpt-4.1-mini` to save on costs as our framework makes **lots of** API calls.
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We built Litadel with LangGraph to ensure flexibility and modularity. We utilize `o1-preview` and `gpt-4o` as our deep thinking and fast thinking LLMs for our experiments. However, for testing purposes, we recommend you use `o4-mini` and `gpt-4.1-mini` to save on costs as our framework makes **lots of** API calls.
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### Python Usage
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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:
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To use Litadel 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:
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```python
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from tradingagents.graph.trading_graph import TradingAgentsGraph
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@ -204,13 +206,20 @@ print(decision)
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You can view the full list of configurations in `tradingagents/default_config.py`.
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## Roadmap
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### Upcoming Improvements
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- [ ] **Switch from LangChain/LangGraph to OpenAI Agents SDK** - For better maintainability and parallelization
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## Contributing
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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/).
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## Citation
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Please reference our work if you find *TradingAgents* provides you with some help :)
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Please reference our work if you find *Litadel* provides you with some help :)
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Original TradingAgents citation:
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```
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@misc{xiao2025tradingagentsmultiagentsllmfinancial,
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"""File handling and logging decorators for the Litadel CLI."""
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from functools import wraps
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from pathlib import Path
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def save_message_decorator(message_buffer, log_file):
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"""Decorator to save messages to log file after they're added to buffer."""
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original_add_message = message_buffer.add_message
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@wraps(original_add_message)
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def wrapper(*args, **kwargs):
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original_add_message(*args, **kwargs)
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timestamp, message_type, content = message_buffer.messages[-1]
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content = content.replace("\n", " ") # Replace newlines with spaces
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with open(log_file, "a") as f:
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f.write(f"{timestamp} [{message_type}] {content}\n")
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return wrapper
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def save_tool_call_decorator(message_buffer, log_file):
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"""Decorator to save tool calls to log file after they're added to buffer."""
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original_add_tool_call = message_buffer.add_tool_call
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@wraps(original_add_tool_call)
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def wrapper(*args, **kwargs):
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original_add_tool_call(*args, **kwargs)
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timestamp, tool_name, args = message_buffer.tool_calls[-1]
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args_str = ", ".join(f"{k}={v}" for k, v in args.items())
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with open(log_file, "a") as f:
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f.write(f"{timestamp} [Tool Call] {tool_name}({args_str})\n")
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return wrapper
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def save_report_section_decorator(message_buffer, report_dir):
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"""Decorator to save report sections to markdown files after they're updated."""
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original_update_report_section = message_buffer.update_report_section
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@wraps(original_update_report_section)
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def wrapper(section_name, content):
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original_update_report_section(section_name, content)
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if section_name in message_buffer.report_sections and message_buffer.report_sections[section_name] is not None:
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content = message_buffer.report_sections[section_name]
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if content:
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file_name = f"{section_name}.md"
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with open(report_dir / file_name, "w") as f:
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f.write(content)
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return wrapper
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def setup_file_handlers(message_buffer, results_dir: Path):
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"""
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Setup file handlers for logging messages, tool calls, and reports.
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Args:
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message_buffer: The MessageBuffer instance to decorate
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results_dir: Directory where results should be saved
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Returns:
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tuple: (log_file, report_dir) paths for reference
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"""
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# Create directories
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results_dir.mkdir(parents=True, exist_ok=True)
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report_dir = results_dir / "reports"
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report_dir.mkdir(parents=True, exist_ok=True)
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log_file = results_dir / "message_tool.log"
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log_file.touch(exist_ok=True)
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# Apply decorators
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message_buffer.add_message = save_message_decorator(message_buffer, log_file)
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message_buffer.add_tool_call = save_tool_call_decorator(message_buffer, log_file)
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message_buffer.update_report_section = save_report_section_decorator(message_buffer, report_dir)
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return log_file, report_dir
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@ -1,5 +1,19 @@
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"""Helper functions for the TradingAgents CLI."""
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"""Helper utilities and models for the TradingAgents CLI."""
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from enum import Enum
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# ===== Models =====
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class AnalystType(str, Enum):
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"""Enumeration of available analyst types."""
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MARKET = "market"
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SOCIAL = "social"
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NEWS = "news"
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FUNDAMENTALS = "fundamentals"
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# ===== Helper Functions =====
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def update_research_team_status(message_buffer, status):
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"""Update status for all research team members and trader."""
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@ -26,3 +40,4 @@ def extract_content_string(content):
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return ' '.join(text_parts)
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else:
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return str(content)
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|
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@ -0,0 +1,77 @@
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"""LLM provider and model configuration data for the Litadel CLI."""
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# LLM provider base URLs
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LLM_PROVIDERS = [
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("OpenAI", "https://api.openai.com/v1"),
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("Anthropic", "https://api.anthropic.com/"),
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("Google", "https://generativelanguage.googleapis.com/v1"),
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("Openrouter", "https://openrouter.ai/api/v1"),
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("Ollama", "http://localhost:11434/v1"),
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]
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# Quick-thinking (shallow) model options per provider
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SHALLOW_AGENT_OPTIONS = {
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"openai": [
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("GPT-4o-mini - Fast and efficient for quick tasks", "gpt-4o-mini"),
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("GPT-4.1-nano - Ultra-lightweight model for basic operations", "gpt-4.1-nano"),
|
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("GPT-4.1-mini - Compact model with good performance", "gpt-4.1-mini"),
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("GPT-4o - Standard model with solid capabilities", "gpt-4o"),
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("GPT-5 - Next generation model with enhanced capabilities", "gpt-5"),
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],
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"anthropic": [
|
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("Claude Haiku 3.5 - Fast inference and standard capabilities", "claude-3-5-haiku-latest"),
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("Claude Sonnet 3.5 - Highly capable standard model", "claude-3-5-sonnet-latest"),
|
||||
("Claude Sonnet 3.7 - Exceptional hybrid reasoning and agentic capabilities", "claude-3-7-sonnet-latest"),
|
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("Claude Sonnet 4 - High performance and excellent reasoning", "claude-sonnet-4-0"),
|
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],
|
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"google": [
|
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("Gemini 2.0 Flash-Lite - Cost efficiency and low latency", "gemini-2.0-flash-lite"),
|
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("Gemini 2.0 Flash - Next generation features, speed, and thinking", "gemini-2.0-flash"),
|
||||
("Gemini 2.5 Flash - Adaptive thinking, cost efficiency", "gemini-2.5-flash-preview-05-20"),
|
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],
|
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"openrouter": [
|
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("Meta: Llama 4 Scout", "meta-llama/llama-4-scout:free"),
|
||||
("Meta: Llama 3.3 8B Instruct - A lightweight and ultra-fast variant of Llama 3.3 70B", "meta-llama/llama-3.3-8b-instruct:free"),
|
||||
("google/gemini-2.0-flash-exp:free - Gemini Flash 2.0 offers a significantly faster time to first token", "google/gemini-2.0-flash-exp:free"),
|
||||
],
|
||||
"ollama": [
|
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("llama3.1 local", "llama3.1"),
|
||||
("llama3.2 local", "llama3.2"),
|
||||
]
|
||||
}
|
||||
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||||
# Deep-thinking model options per provider
|
||||
DEEP_AGENT_OPTIONS = {
|
||||
"openai": [
|
||||
("GPT-4.1-nano - Ultra-lightweight model for basic operations", "gpt-4.1-nano"),
|
||||
("GPT-4.1-mini - Compact model with good performance", "gpt-4.1-mini"),
|
||||
("GPT-4o - Standard model with solid capabilities", "gpt-4o"),
|
||||
("GPT-5 - Next generation model with enhanced capabilities", "gpt-5"),
|
||||
("o4-mini - Specialized reasoning model (compact)", "o4-mini"),
|
||||
("o3-mini - Advanced reasoning model (lightweight)", "o3-mini"),
|
||||
("o3 - Full advanced reasoning model", "o3"),
|
||||
("o1 - Premier reasoning and problem-solving model", "o1"),
|
||||
],
|
||||
"anthropic": [
|
||||
("Claude Haiku 3.5 - Fast inference and standard capabilities", "claude-3-5-haiku-latest"),
|
||||
("Claude Sonnet 3.5 - Highly capable standard model", "claude-3-5-sonnet-latest"),
|
||||
("Claude Sonnet 3.7 - Exceptional hybrid reasoning and agentic capabilities", "claude-3-7-sonnet-latest"),
|
||||
("Claude Sonnet 4 - High performance and excellent reasoning", "claude-sonnet-4-0"),
|
||||
("Claude Opus 4 - Most powerful Anthropic model", " claude-opus-4-0"),
|
||||
],
|
||||
"google": [
|
||||
("Gemini 2.0 Flash-Lite - Cost efficiency and low latency", "gemini-2.0-flash-lite"),
|
||||
("Gemini 2.0 Flash - Next generation features, speed, and thinking", "gemini-2.0-flash"),
|
||||
("Gemini 2.5 Flash - Adaptive thinking, cost efficiency", "gemini-2.5-flash-preview-05-20"),
|
||||
("Gemini 2.5 Pro", "gemini-2.5-pro-preview-06-05"),
|
||||
],
|
||||
"openrouter": [
|
||||
("DeepSeek V3 - a 685B-parameter, mixture-of-experts model", "deepseek/deepseek-chat-v3-0324:free"),
|
||||
("Deepseek - latest iteration of the flagship chat model family from the DeepSeek team.", "deepseek/deepseek-chat-v3-0324:free"),
|
||||
],
|
||||
"ollama": [
|
||||
("llama3.1 local", "llama3.1"),
|
||||
("qwen3", "qwen3"),
|
||||
]
|
||||
}
|
||||
|
||||
419
cli/main.py
419
cli/main.py
|
|
@ -2,7 +2,6 @@ from typing import Optional
|
|||
import datetime
|
||||
import typer
|
||||
from pathlib import Path
|
||||
from functools import wraps
|
||||
from rich.console import Console
|
||||
from dotenv import load_dotenv
|
||||
|
||||
|
|
@ -26,19 +25,20 @@ from rich.rule import Rule
|
|||
|
||||
from tradingagents.graph.trading_graph import TradingAgentsGraph
|
||||
from tradingagents.default_config import DEFAULT_CONFIG
|
||||
from cli.models import AnalystType
|
||||
from cli.utils import *
|
||||
from cli.helpers import AnalystType, update_research_team_status, extract_content_string
|
||||
from cli.prompts import *
|
||||
from cli.message_buffer import MessageBuffer
|
||||
from cli.ui_display import create_layout, update_display
|
||||
from cli.report_display import display_complete_report
|
||||
from cli.helper_functions import update_research_team_status, extract_content_string
|
||||
from cli.asset_detection import detect_asset_class, get_asset_class_display_name
|
||||
from cli.file_handlers import setup_file_handlers
|
||||
from cli.stream_processor import process_chunk
|
||||
|
||||
console = Console()
|
||||
|
||||
app = typer.Typer(
|
||||
name="TradingAgents",
|
||||
help="TradingAgents CLI: Multi-Agents LLM Financial Trading Framework",
|
||||
name="Litadel",
|
||||
help="Litadel CLI: Multi-Agents LLM Financial Trading Framework (successor of TradingAgents)",
|
||||
add_completion=True, # Enable shell completion
|
||||
)
|
||||
|
||||
|
|
@ -59,19 +59,17 @@ def get_user_selections():
|
|||
|
||||
# Create welcome box content
|
||||
welcome_content = f"{welcome_ascii}\n"
|
||||
welcome_content += "[bold green]TradingAgents: Multi-Agents LLM Financial Trading Framework - CLI[/bold green]\n\n"
|
||||
welcome_content += "[bold green]Litadel: Multi-Agents LLM Financial Trading Framework - CLI[/bold green]\n\n"
|
||||
welcome_content += "[dim]Successor of TradingAgents by TaurusResearch[/dim]\n\n"
|
||||
welcome_content += "[bold]Workflow Steps:[/bold]\n"
|
||||
welcome_content += "I. Analyst Team → II. Research Team → III. Trader → IV. Risk Management → V. Portfolio Management\n\n"
|
||||
welcome_content += (
|
||||
"[dim]Built by [Tauric Research](https://github.com/TauricResearch)[/dim]"
|
||||
)
|
||||
welcome_content += "I. Analyst Team → II. Research Team → III. Trader → IV. Risk Management → V. Portfolio Management"
|
||||
|
||||
# Create and center the welcome box
|
||||
welcome_box = Panel(
|
||||
welcome_content,
|
||||
border_style="green",
|
||||
padding=(1, 2),
|
||||
title="Welcome to TradingAgents",
|
||||
title="Welcome to Litadel",
|
||||
subtitle="Multi-Agents LLM Financial Trading Framework",
|
||||
)
|
||||
console.print(Align.center(welcome_box))
|
||||
|
|
@ -223,11 +221,8 @@ def get_analysis_date():
|
|||
|
||||
|
||||
|
||||
def run_analysis():
|
||||
# First get all user selections
|
||||
selections = get_user_selections()
|
||||
|
||||
# Create config with selected research depth
|
||||
def setup_config(selections):
|
||||
"""Create and configure the analysis config from user selections."""
|
||||
config = DEFAULT_CONFIG.copy()
|
||||
config["max_debate_rounds"] = selections["research_depth"]
|
||||
config["max_risk_discuss_rounds"] = selections["research_depth"]
|
||||
|
|
@ -236,299 +231,125 @@ def run_analysis():
|
|||
config["backend_url"] = selections["backend_url"]
|
||||
config["llm_provider"] = selections["llm_provider"].lower()
|
||||
config["asset_class"] = selections["asset_class"]
|
||||
return config
|
||||
|
||||
|
||||
def setup_analysis(selections, config):
|
||||
"""Initialize the trading graph and file handlers."""
|
||||
# Initialize the graph
|
||||
graph = TradingAgentsGraph(
|
||||
[analyst.value for analyst in selections["analysts"]], config=config, debug=True
|
||||
)
|
||||
|
||||
# Create result directory
|
||||
results_dir = Path(config["results_dir"]) / selections["ticker"] / selections["analysis_date"]
|
||||
results_dir.mkdir(parents=True, exist_ok=True)
|
||||
report_dir = results_dir / "reports"
|
||||
report_dir.mkdir(parents=True, exist_ok=True)
|
||||
log_file = results_dir / "message_tool.log"
|
||||
log_file.touch(exist_ok=True)
|
||||
|
||||
def save_message_decorator(obj, func_name):
|
||||
func = getattr(obj, func_name)
|
||||
@wraps(func)
|
||||
def wrapper(*args, **kwargs):
|
||||
func(*args, **kwargs)
|
||||
timestamp, message_type, content = obj.messages[-1]
|
||||
content = content.replace("\n", " ") # Replace newlines with spaces
|
||||
with open(log_file, "a") as f:
|
||||
f.write(f"{timestamp} [{message_type}] {content}\n")
|
||||
return wrapper
|
||||
|
||||
def save_tool_call_decorator(obj, func_name):
|
||||
func = getattr(obj, func_name)
|
||||
@wraps(func)
|
||||
def wrapper(*args, **kwargs):
|
||||
func(*args, **kwargs)
|
||||
timestamp, tool_name, args = obj.tool_calls[-1]
|
||||
args_str = ", ".join(f"{k}={v}" for k, v in args.items())
|
||||
with open(log_file, "a") as f:
|
||||
f.write(f"{timestamp} [Tool Call] {tool_name}({args_str})\n")
|
||||
return wrapper
|
||||
# Create result directory and setup file handlers
|
||||
results_dir = Path(config["results_dir"]) / selections["ticker"] / selections["analysis_date"]
|
||||
log_file, report_dir = setup_file_handlers(message_buffer, results_dir)
|
||||
|
||||
return graph, log_file, report_dir
|
||||
|
||||
def save_report_section_decorator(obj, func_name):
|
||||
func = getattr(obj, func_name)
|
||||
@wraps(func)
|
||||
def wrapper(section_name, content):
|
||||
func(section_name, content)
|
||||
if section_name in obj.report_sections and obj.report_sections[section_name] is not None:
|
||||
content = obj.report_sections[section_name]
|
||||
if content:
|
||||
file_name = f"{section_name}.md"
|
||||
with open(report_dir / file_name, "w") as f:
|
||||
f.write(content)
|
||||
return wrapper
|
||||
|
||||
message_buffer.add_message = save_message_decorator(message_buffer, "add_message")
|
||||
message_buffer.add_tool_call = save_tool_call_decorator(message_buffer, "add_tool_call")
|
||||
message_buffer.update_report_section = save_report_section_decorator(message_buffer, "update_report_section")
|
||||
def initialize_display(layout, selections):
|
||||
"""Initialize the display with startup messages and agent statuses."""
|
||||
# Initial display
|
||||
update_display(layout, message_buffer)
|
||||
|
||||
# Add initial messages
|
||||
message_buffer.add_message("System", f"Selected ticker: {selections['ticker']}")
|
||||
message_buffer.add_message("System", f"Analysis date: {selections['analysis_date']}")
|
||||
message_buffer.add_message(
|
||||
"System",
|
||||
f"Selected analysts: {', '.join(analyst.value for analyst in selections['analysts'])}",
|
||||
)
|
||||
update_display(layout, message_buffer)
|
||||
|
||||
# Reset agent statuses
|
||||
for agent in message_buffer.agent_status:
|
||||
message_buffer.update_agent_status(agent, "pending")
|
||||
|
||||
# Reset report sections
|
||||
for section in message_buffer.report_sections:
|
||||
message_buffer.report_sections[section] = None
|
||||
message_buffer.current_report = None
|
||||
message_buffer.final_report = None
|
||||
|
||||
# Update agent status to in_progress for the first analyst
|
||||
first_analyst = f"{selections['analysts'][0].value.capitalize()} Analyst"
|
||||
message_buffer.update_agent_status(first_analyst, "in_progress")
|
||||
update_display(layout, message_buffer)
|
||||
|
||||
# Create spinner text
|
||||
spinner_text = f"Analyzing {selections['ticker']} on {selections['analysis_date']}..."
|
||||
update_display(layout, message_buffer, spinner_text)
|
||||
|
||||
# Now start the display layout
|
||||
|
||||
def run_stream_analysis(graph, selections, layout):
|
||||
"""Stream the analysis and process chunks in real-time."""
|
||||
# Initialize state and get graph args
|
||||
init_agent_state = graph.propagator.create_initial_state(
|
||||
selections["ticker"], selections["analysis_date"]
|
||||
)
|
||||
# CRITICAL: Add asset_class to state so market analyst can branch correctly
|
||||
init_agent_state["asset_class"] = selections["asset_class"]
|
||||
args = graph.propagator.get_graph_args()
|
||||
|
||||
# Stream the analysis
|
||||
trace = []
|
||||
for chunk in graph.graph.stream(init_agent_state, **args):
|
||||
# Process the chunk and update message buffer
|
||||
if process_chunk(chunk, message_buffer, selections["analysts"]):
|
||||
# Update the display after successful chunk processing
|
||||
update_display(layout, message_buffer)
|
||||
|
||||
trace.append(chunk)
|
||||
|
||||
return trace
|
||||
|
||||
|
||||
def finalize_analysis(trace, graph, selections, layout):
|
||||
"""Process final results and display the complete report."""
|
||||
# Get final state and decision
|
||||
final_state = trace[-1]
|
||||
decision = graph.process_signal(final_state["final_trade_decision"])
|
||||
|
||||
# Update all agent statuses to completed
|
||||
for agent in message_buffer.agent_status:
|
||||
message_buffer.update_agent_status(agent, "completed")
|
||||
|
||||
message_buffer.add_message(
|
||||
"Analysis", f"Completed analysis for {selections['analysis_date']}"
|
||||
)
|
||||
|
||||
# Update final report sections
|
||||
for section in message_buffer.report_sections.keys():
|
||||
if section in final_state:
|
||||
message_buffer.update_report_section(section, final_state[section])
|
||||
|
||||
# Display the complete final report
|
||||
display_complete_report(final_state)
|
||||
|
||||
update_display(layout, message_buffer)
|
||||
|
||||
|
||||
def run_analysis():
|
||||
"""Main analysis orchestrator - coordinates the entire trading analysis workflow."""
|
||||
# Get user selections
|
||||
selections = get_user_selections()
|
||||
|
||||
# Setup configuration and initialize components
|
||||
config = setup_config(selections)
|
||||
graph, log_file, report_dir = setup_analysis(selections, config)
|
||||
|
||||
# Create display layout and run analysis
|
||||
layout = create_layout()
|
||||
|
||||
with Live(layout, refresh_per_second=4) as live:
|
||||
# Initial display
|
||||
update_display(layout, message_buffer)
|
||||
|
||||
# Add initial messages
|
||||
message_buffer.add_message("System", f"Selected ticker: {selections['ticker']}")
|
||||
message_buffer.add_message(
|
||||
"System", f"Analysis date: {selections['analysis_date']}"
|
||||
)
|
||||
message_buffer.add_message(
|
||||
"System",
|
||||
f"Selected analysts: {', '.join(analyst.value for analyst in selections['analysts'])}",
|
||||
)
|
||||
update_display(layout, message_buffer)
|
||||
|
||||
# Reset agent statuses
|
||||
for agent in message_buffer.agent_status:
|
||||
message_buffer.update_agent_status(agent, "pending")
|
||||
|
||||
# Reset report sections
|
||||
for section in message_buffer.report_sections:
|
||||
message_buffer.report_sections[section] = None
|
||||
message_buffer.current_report = None
|
||||
message_buffer.final_report = None
|
||||
|
||||
# Update agent status to in_progress for the first analyst
|
||||
first_analyst = f"{selections['analysts'][0].value.capitalize()} Analyst"
|
||||
message_buffer.update_agent_status(first_analyst, "in_progress")
|
||||
update_display(layout, message_buffer)
|
||||
|
||||
# Create spinner text
|
||||
spinner_text = (
|
||||
f"Analyzing {selections['ticker']} on {selections['analysis_date']}..."
|
||||
)
|
||||
update_display(layout, message_buffer, spinner_text)
|
||||
|
||||
# Initialize state and get graph args
|
||||
init_agent_state = graph.propagator.create_initial_state(
|
||||
selections["ticker"], selections["analysis_date"]
|
||||
)
|
||||
# CRITICAL: Add asset_class to state so market analyst can branch correctly
|
||||
init_agent_state["asset_class"] = selections["asset_class"]
|
||||
args = graph.propagator.get_graph_args()
|
||||
|
||||
# Stream the analysis
|
||||
trace = []
|
||||
for chunk in graph.graph.stream(init_agent_state, **args):
|
||||
if len(chunk["messages"]) > 0:
|
||||
# Get the last message from the chunk
|
||||
last_message = chunk["messages"][-1]
|
||||
|
||||
# Extract message content and type
|
||||
if hasattr(last_message, "content"):
|
||||
content = extract_content_string(last_message.content) # Use the helper function
|
||||
msg_type = "Reasoning"
|
||||
else:
|
||||
content = str(last_message)
|
||||
msg_type = "System"
|
||||
|
||||
# Add message to buffer
|
||||
message_buffer.add_message(msg_type, content)
|
||||
|
||||
# If it's a tool call, add it to tool calls
|
||||
if hasattr(last_message, "tool_calls"):
|
||||
for tool_call in last_message.tool_calls:
|
||||
# Handle both dictionary and object tool calls
|
||||
if isinstance(tool_call, dict):
|
||||
message_buffer.add_tool_call(
|
||||
tool_call["name"], tool_call["args"]
|
||||
)
|
||||
else:
|
||||
message_buffer.add_tool_call(tool_call.name, tool_call.args)
|
||||
|
||||
# Update reports and agent status based on chunk content
|
||||
# Analyst Team Reports - use a mapping to reduce repetition
|
||||
analyst_mappings = [
|
||||
("market_report", "Market Analyst", "social", "Social Analyst"),
|
||||
("sentiment_report", "Social Analyst", "news", "News Analyst"),
|
||||
("news_report", "News Analyst", "fundamentals", "Fundamentals Analyst"),
|
||||
("fundamentals_report", "Fundamentals Analyst", None, None),
|
||||
]
|
||||
|
||||
for report_key, analyst_name, next_type, next_analyst in analyst_mappings:
|
||||
if report_key in chunk and chunk[report_key]:
|
||||
message_buffer.update_report_section(report_key, chunk[report_key])
|
||||
message_buffer.update_agent_status(analyst_name, "completed")
|
||||
|
||||
if report_key == "fundamentals_report":
|
||||
# Special case: set all research team to in_progress
|
||||
update_research_team_status(message_buffer, "in_progress")
|
||||
elif next_type and next_type in [a.value for a in selections["analysts"]]:
|
||||
message_buffer.update_agent_status(next_analyst, "in_progress")
|
||||
|
||||
# Research Team - Handle Investment Debate State
|
||||
if (
|
||||
"investment_debate_state" in chunk
|
||||
and chunk["investment_debate_state"]
|
||||
):
|
||||
debate_state = chunk["investment_debate_state"]
|
||||
|
||||
# Update Bull Researcher status and report
|
||||
if "bull_history" in debate_state and debate_state["bull_history"]:
|
||||
# Keep all research team members in progress
|
||||
update_research_team_status(message_buffer, "in_progress")
|
||||
# Extract latest bull response
|
||||
bull_responses = debate_state["bull_history"].split("\n")
|
||||
latest_bull = bull_responses[-1] if bull_responses else ""
|
||||
if latest_bull:
|
||||
message_buffer.add_message("Reasoning", latest_bull)
|
||||
# Update research report with bull's latest analysis
|
||||
message_buffer.update_report_section(
|
||||
"investment_plan",
|
||||
f"### Bull Researcher Analysis\n{latest_bull}",
|
||||
)
|
||||
|
||||
# Update Bear Researcher status and report
|
||||
if "bear_history" in debate_state and debate_state["bear_history"]:
|
||||
# Keep all research team members in progress
|
||||
update_research_team_status(message_buffer, "in_progress")
|
||||
# Extract latest bear response
|
||||
bear_responses = debate_state["bear_history"].split("\n")
|
||||
latest_bear = bear_responses[-1] if bear_responses else ""
|
||||
if latest_bear:
|
||||
message_buffer.add_message("Reasoning", latest_bear)
|
||||
# Update research report with bear's latest analysis
|
||||
message_buffer.update_report_section(
|
||||
"investment_plan",
|
||||
f"{message_buffer.report_sections['investment_plan']}\n\n### Bear Researcher Analysis\n{latest_bear}",
|
||||
)
|
||||
|
||||
# Update Research Manager status and final decision
|
||||
if (
|
||||
"judge_decision" in debate_state
|
||||
and debate_state["judge_decision"]
|
||||
):
|
||||
# Keep all research team members in progress until final decision
|
||||
update_research_team_status(message_buffer, "in_progress")
|
||||
message_buffer.add_message(
|
||||
"Reasoning",
|
||||
f"Research Manager: {debate_state['judge_decision']}",
|
||||
)
|
||||
# Update research report with final decision
|
||||
message_buffer.update_report_section(
|
||||
"investment_plan",
|
||||
f"{message_buffer.report_sections['investment_plan']}\n\n### Research Manager Decision\n{debate_state['judge_decision']}",
|
||||
)
|
||||
# Mark all research team members as completed
|
||||
update_research_team_status(message_buffer, "completed")
|
||||
# Set first risk analyst to in_progress
|
||||
message_buffer.update_agent_status(
|
||||
"Risky Analyst", "in_progress"
|
||||
)
|
||||
|
||||
# Trading Team
|
||||
if (
|
||||
"trader_investment_plan" in chunk
|
||||
and chunk["trader_investment_plan"]
|
||||
):
|
||||
message_buffer.update_report_section(
|
||||
"trader_investment_plan", chunk["trader_investment_plan"]
|
||||
)
|
||||
# Set first risk analyst to in_progress
|
||||
message_buffer.update_agent_status("Risky Analyst", "in_progress")
|
||||
|
||||
# Risk Management Team - Handle Risk Debate State
|
||||
if "risk_debate_state" in chunk and chunk["risk_debate_state"]:
|
||||
risk_state = chunk["risk_debate_state"]
|
||||
|
||||
# Handle all risk analysts with a mapping
|
||||
risk_analysts = [
|
||||
("current_risky_response", "Risky Analyst"),
|
||||
("current_safe_response", "Safe Analyst"),
|
||||
("current_neutral_response", "Neutral Analyst"),
|
||||
]
|
||||
|
||||
for response_key, analyst_name in risk_analysts:
|
||||
if response_key in risk_state and risk_state[response_key]:
|
||||
message_buffer.update_agent_status(analyst_name, "in_progress")
|
||||
message_buffer.add_message(
|
||||
"Reasoning",
|
||||
f"{analyst_name}: {risk_state[response_key]}",
|
||||
)
|
||||
message_buffer.update_report_section(
|
||||
"final_trade_decision",
|
||||
f"### {analyst_name} Analysis\n{risk_state[response_key]}",
|
||||
)
|
||||
|
||||
# Update Portfolio Manager status and final decision
|
||||
if "judge_decision" in risk_state and risk_state["judge_decision"]:
|
||||
message_buffer.update_agent_status(
|
||||
"Portfolio Manager", "in_progress"
|
||||
)
|
||||
message_buffer.add_message(
|
||||
"Reasoning",
|
||||
f"Portfolio Manager: {risk_state['judge_decision']}",
|
||||
)
|
||||
# Update risk report with final decision only
|
||||
message_buffer.update_report_section(
|
||||
"final_trade_decision",
|
||||
f"### Portfolio Manager Decision\n{risk_state['judge_decision']}",
|
||||
)
|
||||
# Mark risk analysts as completed
|
||||
message_buffer.update_agent_status("Risky Analyst", "completed")
|
||||
message_buffer.update_agent_status("Safe Analyst", "completed")
|
||||
message_buffer.update_agent_status(
|
||||
"Neutral Analyst", "completed"
|
||||
)
|
||||
message_buffer.update_agent_status(
|
||||
"Portfolio Manager", "completed"
|
||||
)
|
||||
|
||||
# Update the display
|
||||
update_display(layout, message_buffer)
|
||||
|
||||
trace.append(chunk)
|
||||
|
||||
# Get final state and decision
|
||||
final_state = trace[-1]
|
||||
decision = graph.process_signal(final_state["final_trade_decision"])
|
||||
|
||||
# Update all agent statuses to completed
|
||||
for agent in message_buffer.agent_status:
|
||||
message_buffer.update_agent_status(agent, "completed")
|
||||
|
||||
message_buffer.add_message(
|
||||
"Analysis", f"Completed analysis for {selections['analysis_date']}"
|
||||
)
|
||||
|
||||
# Update final report sections
|
||||
for section in message_buffer.report_sections.keys():
|
||||
if section in final_state:
|
||||
message_buffer.update_report_section(section, final_state[section])
|
||||
|
||||
# Display the complete final report
|
||||
display_complete_report(final_state)
|
||||
|
||||
update_display(layout, message_buffer)
|
||||
# Initialize display with startup messages
|
||||
initialize_display(layout, selections)
|
||||
|
||||
# Run the streaming analysis
|
||||
trace = run_stream_analysis(graph, selections, layout)
|
||||
|
||||
# Process and display final results
|
||||
finalize_analysis(trace, graph, selections, layout)
|
||||
|
||||
|
||||
@app.command()
|
||||
|
|
|
|||
|
|
@ -1,10 +0,0 @@
|
|||
from enum import Enum
|
||||
from typing import List, Optional, Dict
|
||||
from pydantic import BaseModel
|
||||
|
||||
|
||||
class AnalystType(str, Enum):
|
||||
MARKET = "market"
|
||||
SOCIAL = "social"
|
||||
NEWS = "news"
|
||||
FUNDAMENTALS = "fundamentals"
|
||||
|
|
@ -1,7 +1,10 @@
|
|||
"""Interactive user prompts for the Litadel CLI."""
|
||||
|
||||
import questionary
|
||||
from typing import List, Optional, Tuple, Dict
|
||||
|
||||
from cli.models import AnalystType
|
||||
from cli.helpers import AnalystType
|
||||
from cli.llm_config import SHALLOW_AGENT_OPTIONS, DEEP_AGENT_OPTIONS, LLM_PROVIDERS
|
||||
|
||||
ANALYST_ORDER = [
|
||||
("Market Analyst", AnalystType.MARKET),
|
||||
|
|
@ -131,38 +134,6 @@ def select_research_depth() -> int:
|
|||
|
||||
def select_shallow_thinking_agent(provider) -> str:
|
||||
"""Select shallow thinking llm engine using an interactive selection."""
|
||||
|
||||
# Define shallow thinking llm engine options with their corresponding model names
|
||||
SHALLOW_AGENT_OPTIONS = {
|
||||
"openai": [
|
||||
("GPT-4o-mini - Fast and efficient for quick tasks", "gpt-4o-mini"),
|
||||
("GPT-4.1-nano - Ultra-lightweight model for basic operations", "gpt-4.1-nano"),
|
||||
("GPT-4.1-mini - Compact model with good performance", "gpt-4.1-mini"),
|
||||
("GPT-4o - Standard model with solid capabilities", "gpt-4o"),
|
||||
("GPT-5 - Next generation model with enhanced capabilities", "gpt-5"),
|
||||
],
|
||||
"anthropic": [
|
||||
("Claude Haiku 3.5 - Fast inference and standard capabilities", "claude-3-5-haiku-latest"),
|
||||
("Claude Sonnet 3.5 - Highly capable standard model", "claude-3-5-sonnet-latest"),
|
||||
("Claude Sonnet 3.7 - Exceptional hybrid reasoning and agentic capabilities", "claude-3-7-sonnet-latest"),
|
||||
("Claude Sonnet 4 - High performance and excellent reasoning", "claude-sonnet-4-0"),
|
||||
],
|
||||
"google": [
|
||||
("Gemini 2.0 Flash-Lite - Cost efficiency and low latency", "gemini-2.0-flash-lite"),
|
||||
("Gemini 2.0 Flash - Next generation features, speed, and thinking", "gemini-2.0-flash"),
|
||||
("Gemini 2.5 Flash - Adaptive thinking, cost efficiency", "gemini-2.5-flash-preview-05-20"),
|
||||
],
|
||||
"openrouter": [
|
||||
("Meta: Llama 4 Scout", "meta-llama/llama-4-scout:free"),
|
||||
("Meta: Llama 3.3 8B Instruct - A lightweight and ultra-fast variant of Llama 3.3 70B", "meta-llama/llama-3.3-8b-instruct:free"),
|
||||
("google/gemini-2.0-flash-exp:free - Gemini Flash 2.0 offers a significantly faster time to first token", "google/gemini-2.0-flash-exp:free"),
|
||||
],
|
||||
"ollama": [
|
||||
("llama3.1 local", "llama3.1"),
|
||||
("llama3.2 local", "llama3.2"),
|
||||
]
|
||||
}
|
||||
|
||||
choice = questionary.select(
|
||||
"Select Your [Quick-Thinking LLM Engine]:",
|
||||
choices=[
|
||||
|
|
@ -190,42 +161,6 @@ def select_shallow_thinking_agent(provider) -> str:
|
|||
|
||||
def select_deep_thinking_agent(provider) -> str:
|
||||
"""Select deep thinking llm engine using an interactive selection."""
|
||||
|
||||
# Define deep thinking llm engine options with their corresponding model names
|
||||
DEEP_AGENT_OPTIONS = {
|
||||
"openai": [
|
||||
("GPT-4.1-nano - Ultra-lightweight model for basic operations", "gpt-4.1-nano"),
|
||||
("GPT-4.1-mini - Compact model with good performance", "gpt-4.1-mini"),
|
||||
("GPT-4o - Standard model with solid capabilities", "gpt-4o"),
|
||||
("GPT-5 - Next generation model with enhanced capabilities", "gpt-5"),
|
||||
("o4-mini - Specialized reasoning model (compact)", "o4-mini"),
|
||||
("o3-mini - Advanced reasoning model (lightweight)", "o3-mini"),
|
||||
("o3 - Full advanced reasoning model", "o3"),
|
||||
("o1 - Premier reasoning and problem-solving model", "o1"),
|
||||
],
|
||||
"anthropic": [
|
||||
("Claude Haiku 3.5 - Fast inference and standard capabilities", "claude-3-5-haiku-latest"),
|
||||
("Claude Sonnet 3.5 - Highly capable standard model", "claude-3-5-sonnet-latest"),
|
||||
("Claude Sonnet 3.7 - Exceptional hybrid reasoning and agentic capabilities", "claude-3-7-sonnet-latest"),
|
||||
("Claude Sonnet 4 - High performance and excellent reasoning", "claude-sonnet-4-0"),
|
||||
("Claude Opus 4 - Most powerful Anthropic model", " claude-opus-4-0"),
|
||||
],
|
||||
"google": [
|
||||
("Gemini 2.0 Flash-Lite - Cost efficiency and low latency", "gemini-2.0-flash-lite"),
|
||||
("Gemini 2.0 Flash - Next generation features, speed, and thinking", "gemini-2.0-flash"),
|
||||
("Gemini 2.5 Flash - Adaptive thinking, cost efficiency", "gemini-2.5-flash-preview-05-20"),
|
||||
("Gemini 2.5 Pro", "gemini-2.5-pro-preview-06-05"),
|
||||
],
|
||||
"openrouter": [
|
||||
("DeepSeek V3 - a 685B-parameter, mixture-of-experts model", "deepseek/deepseek-chat-v3-0324:free"),
|
||||
("Deepseek - latest iteration of the flagship chat model family from the DeepSeek team.", "deepseek/deepseek-chat-v3-0324:free"),
|
||||
],
|
||||
"ollama": [
|
||||
("llama3.1 local", "llama3.1"),
|
||||
("qwen3", "qwen3"),
|
||||
]
|
||||
}
|
||||
|
||||
choice = questionary.select(
|
||||
"Select Your [Deep-Thinking LLM Engine]:",
|
||||
choices=[
|
||||
|
|
@ -250,20 +185,11 @@ def select_deep_thinking_agent(provider) -> str:
|
|||
|
||||
def select_llm_provider() -> tuple[str, str]:
|
||||
"""Select the OpenAI api url using interactive selection."""
|
||||
# Define OpenAI api options with their corresponding endpoints
|
||||
BASE_URLS = [
|
||||
("OpenAI", "https://api.openai.com/v1"),
|
||||
("Anthropic", "https://api.anthropic.com/"),
|
||||
("Google", "https://generativelanguage.googleapis.com/v1"),
|
||||
("Openrouter", "https://openrouter.ai/api/v1"),
|
||||
("Ollama", "http://localhost:11434/v1"),
|
||||
]
|
||||
|
||||
choice = questionary.select(
|
||||
"Select your LLM Provider:",
|
||||
choices=[
|
||||
questionary.Choice(display, value=(display, value))
|
||||
for display, value in BASE_URLS
|
||||
for display, value in LLM_PROVIDERS
|
||||
],
|
||||
instruction="\n- Use arrow keys to navigate\n- Press Enter to select",
|
||||
style=questionary.Style(
|
||||
|
|
@ -283,3 +209,4 @@ def select_llm_provider() -> tuple[str, str]:
|
|||
print(f"You selected: {display_name}\tURL: {url}")
|
||||
|
||||
return display_name, url
|
||||
|
||||
|
|
@ -1,4 +1,4 @@
|
|||
"""Report display functions for the TradingAgents CLI."""
|
||||
"""Report display functions for the Litadel CLI."""
|
||||
|
||||
from rich.console import Console
|
||||
from rich.panel import Panel
|
||||
|
|
|
|||
|
|
@ -1,7 +1,5 @@
|
|||
|
||||
______ ___ ___ __
|
||||
/_ __/________ _____/ (_)___ ____ _/ | ____ ____ ____ / /______
|
||||
/ / / ___/ __ `/ __ / / __ \/ __ `/ /| |/ __ `/ _ \/ __ \/ __/ ___/
|
||||
/ / / / / /_/ / /_/ / / / / / /_/ / ___ / /_/ / __/ / / / /_(__ )
|
||||
/_/ /_/ \__,_/\__,_/_/_/ /_/\__, /_/ |_\__, /\___/_/ /_/\__/____/
|
||||
/____/ /____/
|
||||
██ ██ ████████ █████ ██████ ███████ ██
|
||||
██ ██ ██ ██ ██ ██ ██ ██ ██
|
||||
██ ██ ██ ███████ ██ ██ █████ ██
|
||||
██ ██ ██ ██ ██ ██ ██ ██ ██
|
||||
███████ ██ ██ ██ ██ ██████ ███████ ███████
|
||||
|
|
@ -0,0 +1,229 @@
|
|||
"""Stream processing logic for handling agent analysis chunks in the Litadel CLI."""
|
||||
|
||||
from cli.helpers import extract_content_string, update_research_team_status
|
||||
|
||||
|
||||
def process_message_chunk(chunk, message_buffer):
|
||||
"""
|
||||
Process message content and tool calls from a chunk.
|
||||
|
||||
Args:
|
||||
chunk: The chunk dictionary from the graph stream
|
||||
message_buffer: The MessageBuffer instance to update
|
||||
"""
|
||||
if len(chunk["messages"]) == 0:
|
||||
return
|
||||
|
||||
# Get the last message from the chunk
|
||||
last_message = chunk["messages"][-1]
|
||||
|
||||
# Extract message content and type
|
||||
if hasattr(last_message, "content"):
|
||||
content = extract_content_string(last_message.content)
|
||||
msg_type = "Reasoning"
|
||||
else:
|
||||
content = str(last_message)
|
||||
msg_type = "System"
|
||||
|
||||
# Add message to buffer
|
||||
message_buffer.add_message(msg_type, content)
|
||||
|
||||
# If it's a tool call, add it to tool calls
|
||||
if hasattr(last_message, "tool_calls"):
|
||||
for tool_call in last_message.tool_calls:
|
||||
# Handle both dictionary and object tool calls
|
||||
if isinstance(tool_call, dict):
|
||||
message_buffer.add_tool_call(
|
||||
tool_call["name"], tool_call["args"]
|
||||
)
|
||||
else:
|
||||
message_buffer.add_tool_call(tool_call.name, tool_call.args)
|
||||
|
||||
|
||||
def process_analyst_reports(chunk, message_buffer, selected_analysts):
|
||||
"""
|
||||
Process analyst team reports from chunk.
|
||||
|
||||
Args:
|
||||
chunk: The chunk dictionary from the graph stream
|
||||
message_buffer: The MessageBuffer instance to update
|
||||
selected_analysts: List of selected analyst types
|
||||
"""
|
||||
# Mapping of report keys to analyst info
|
||||
analyst_mappings = [
|
||||
("market_report", "Market Analyst", "social", "Social Analyst"),
|
||||
("sentiment_report", "Social Analyst", "news", "News Analyst"),
|
||||
("news_report", "News Analyst", "fundamentals", "Fundamentals Analyst"),
|
||||
("fundamentals_report", "Fundamentals Analyst", None, None),
|
||||
]
|
||||
|
||||
for report_key, analyst_name, next_type, next_analyst in analyst_mappings:
|
||||
if report_key in chunk and chunk[report_key]:
|
||||
message_buffer.update_report_section(report_key, chunk[report_key])
|
||||
message_buffer.update_agent_status(analyst_name, "completed")
|
||||
|
||||
if report_key == "fundamentals_report":
|
||||
# Special case: set all research team to in_progress
|
||||
update_research_team_status(message_buffer, "in_progress")
|
||||
elif next_type and next_type in [a.value for a in selected_analysts]:
|
||||
message_buffer.update_agent_status(next_analyst, "in_progress")
|
||||
|
||||
|
||||
def process_research_debate(chunk, message_buffer):
|
||||
"""
|
||||
Process research team investment debate state from chunk.
|
||||
|
||||
Args:
|
||||
chunk: The chunk dictionary from the graph stream
|
||||
message_buffer: The MessageBuffer instance to update
|
||||
"""
|
||||
if "investment_debate_state" not in chunk or not chunk["investment_debate_state"]:
|
||||
return
|
||||
|
||||
debate_state = chunk["investment_debate_state"]
|
||||
|
||||
# Update Bull Researcher status and report
|
||||
if "bull_history" in debate_state and debate_state["bull_history"]:
|
||||
# Keep all research team members in progress
|
||||
update_research_team_status(message_buffer, "in_progress")
|
||||
# Extract latest bull response
|
||||
bull_responses = debate_state["bull_history"].split("\n")
|
||||
latest_bull = bull_responses[-1] if bull_responses else ""
|
||||
if latest_bull:
|
||||
message_buffer.add_message("Reasoning", latest_bull)
|
||||
# Update research report with bull's latest analysis
|
||||
message_buffer.update_report_section(
|
||||
"investment_plan",
|
||||
f"### Bull Researcher Analysis\n{latest_bull}",
|
||||
)
|
||||
|
||||
# Update Bear Researcher status and report
|
||||
if "bear_history" in debate_state and debate_state["bear_history"]:
|
||||
# Keep all research team members in progress
|
||||
update_research_team_status(message_buffer, "in_progress")
|
||||
# Extract latest bear response
|
||||
bear_responses = debate_state["bear_history"].split("\n")
|
||||
latest_bear = bear_responses[-1] if bear_responses else ""
|
||||
if latest_bear:
|
||||
message_buffer.add_message("Reasoning", latest_bear)
|
||||
# Update research report with bear's latest analysis
|
||||
message_buffer.update_report_section(
|
||||
"investment_plan",
|
||||
f"{message_buffer.report_sections['investment_plan']}\n\n### Bear Researcher Analysis\n{latest_bear}",
|
||||
)
|
||||
|
||||
# Update Research Manager status and final decision
|
||||
if "judge_decision" in debate_state and debate_state["judge_decision"]:
|
||||
# Keep all research team members in progress until final decision
|
||||
update_research_team_status(message_buffer, "in_progress")
|
||||
message_buffer.add_message(
|
||||
"Reasoning",
|
||||
f"Research Manager: {debate_state['judge_decision']}",
|
||||
)
|
||||
# Update research report with final decision
|
||||
message_buffer.update_report_section(
|
||||
"investment_plan",
|
||||
f"{message_buffer.report_sections['investment_plan']}\n\n### Research Manager Decision\n{debate_state['judge_decision']}",
|
||||
)
|
||||
# Mark all research team members as completed
|
||||
update_research_team_status(message_buffer, "completed")
|
||||
# Set first risk analyst to in_progress
|
||||
message_buffer.update_agent_status("Risky Analyst", "in_progress")
|
||||
|
||||
|
||||
def process_trader_report(chunk, message_buffer):
|
||||
"""
|
||||
Process trader investment plan from chunk.
|
||||
|
||||
Args:
|
||||
chunk: The chunk dictionary from the graph stream
|
||||
message_buffer: The MessageBuffer instance to update
|
||||
"""
|
||||
if "trader_investment_plan" in chunk and chunk["trader_investment_plan"]:
|
||||
message_buffer.update_report_section(
|
||||
"trader_investment_plan", chunk["trader_investment_plan"]
|
||||
)
|
||||
# Set first risk analyst to in_progress
|
||||
message_buffer.update_agent_status("Risky Analyst", "in_progress")
|
||||
|
||||
|
||||
def process_risk_debate(chunk, message_buffer):
|
||||
"""
|
||||
Process risk management team debate state from chunk.
|
||||
|
||||
Args:
|
||||
chunk: The chunk dictionary from the graph stream
|
||||
message_buffer: The MessageBuffer instance to update
|
||||
"""
|
||||
if "risk_debate_state" not in chunk or not chunk["risk_debate_state"]:
|
||||
return
|
||||
|
||||
risk_state = chunk["risk_debate_state"]
|
||||
|
||||
# Handle all risk analysts with a mapping
|
||||
risk_analysts = [
|
||||
("current_risky_response", "Risky Analyst"),
|
||||
("current_safe_response", "Safe Analyst"),
|
||||
("current_neutral_response", "Neutral Analyst"),
|
||||
]
|
||||
|
||||
for response_key, analyst_name in risk_analysts:
|
||||
if response_key in risk_state and risk_state[response_key]:
|
||||
message_buffer.update_agent_status(analyst_name, "in_progress")
|
||||
message_buffer.add_message(
|
||||
"Reasoning",
|
||||
f"{analyst_name}: {risk_state[response_key]}",
|
||||
)
|
||||
message_buffer.update_report_section(
|
||||
"final_trade_decision",
|
||||
f"### {analyst_name} Analysis\n{risk_state[response_key]}",
|
||||
)
|
||||
|
||||
# Update Portfolio Manager status and final decision
|
||||
if "judge_decision" in risk_state and risk_state["judge_decision"]:
|
||||
message_buffer.update_agent_status(
|
||||
"Portfolio Manager", "in_progress"
|
||||
)
|
||||
message_buffer.add_message(
|
||||
"Reasoning",
|
||||
f"Portfolio Manager: {risk_state['judge_decision']}",
|
||||
)
|
||||
# Update risk report with final decision only
|
||||
message_buffer.update_report_section(
|
||||
"final_trade_decision",
|
||||
f"### Portfolio Manager Decision\n{risk_state['judge_decision']}",
|
||||
)
|
||||
# Mark risk analysts as completed
|
||||
message_buffer.update_agent_status("Risky Analyst", "completed")
|
||||
message_buffer.update_agent_status("Safe Analyst", "completed")
|
||||
message_buffer.update_agent_status("Neutral Analyst", "completed")
|
||||
message_buffer.update_agent_status("Portfolio Manager", "completed")
|
||||
|
||||
|
||||
def process_chunk(chunk, message_buffer, selected_analysts):
|
||||
"""
|
||||
Process a single chunk from the graph stream, updating the message buffer
|
||||
with messages, reports, and agent statuses.
|
||||
|
||||
Args:
|
||||
chunk: The chunk dictionary from the graph stream
|
||||
message_buffer: The MessageBuffer instance to update
|
||||
selected_analysts: List of selected analyst types
|
||||
|
||||
Returns:
|
||||
bool: True if chunk was processed, False if chunk had no messages
|
||||
"""
|
||||
if len(chunk["messages"]) == 0:
|
||||
return False
|
||||
|
||||
# Process messages and tool calls
|
||||
process_message_chunk(chunk, message_buffer)
|
||||
|
||||
# Process different report types
|
||||
process_analyst_reports(chunk, message_buffer, selected_analysts)
|
||||
process_research_debate(chunk, message_buffer)
|
||||
process_trader_report(chunk, message_buffer)
|
||||
process_risk_debate(chunk, message_buffer)
|
||||
|
||||
return True
|
||||
|
||||
|
|
@ -1,4 +1,4 @@
|
|||
"""UI display functions for the TradingAgents CLI using Rich library."""
|
||||
"""UI display functions for the Litadel CLI using Rich library."""
|
||||
|
||||
from rich.panel import Panel
|
||||
from rich.spinner import Spinner
|
||||
|
|
@ -30,9 +30,8 @@ def update_display(layout, message_buffer, spinner_text=None):
|
|||
# Header with welcome message
|
||||
layout["header"].update(
|
||||
Panel(
|
||||
"[bold green]Welcome to TradingAgents CLI[/bold green]\n"
|
||||
"[dim]© [Tauric Research](https://github.com/TauricResearch)[/dim]",
|
||||
title="Welcome to TradingAgents",
|
||||
"[bold green]Welcome to Litadel CLI[/bold green]",
|
||||
title="Welcome to Litadel",
|
||||
border_style="green",
|
||||
padding=(1, 2),
|
||||
expand=True,
|
||||
|
|
|
|||
|
|
@ -1,4 +1,5 @@
|
|||
# TradingAgents Configuration
|
||||
# Litadel Configuration
|
||||
# Copyright Notice: Litadel is a successor of TradingAgents by TaurusResearch
|
||||
# Copy this file to config.ini and customize your settings
|
||||
|
||||
[llm]
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
[project]
|
||||
name = "tradingagents"
|
||||
name = "litadel"
|
||||
version = "0.1.0"
|
||||
description = "Add your description here"
|
||||
description = "Multi-Agents LLM Financial Trading Framework (successor of TradingAgents by TaurusResearch)"
|
||||
readme = "README.md"
|
||||
requires-python = ">=3.10"
|
||||
dependencies = [
|
||||
|
|
|
|||
13
setup.py
13
setup.py
|
|
@ -1,14 +1,17 @@
|
|||
"""
|
||||
Setup script for the TradingAgents package.
|
||||
Setup script for the Litadel package.
|
||||
|
||||
Copyright Notice: Litadel is a successor of TradingAgents by TaurusResearch.
|
||||
This project builds upon and extends the original TradingAgents framework.
|
||||
"""
|
||||
|
||||
from setuptools import setup, find_packages
|
||||
|
||||
setup(
|
||||
name="tradingagents",
|
||||
name="litadel",
|
||||
version="0.1.0",
|
||||
description="Multi-Agents LLM Financial Trading Framework",
|
||||
author="TradingAgents Team",
|
||||
description="Multi-Agents LLM Financial Trading Framework (successor of TradingAgents)",
|
||||
author="Litadel Team",
|
||||
author_email="yijia.xiao@cs.ucla.edu",
|
||||
url="https://github.com/TauricResearch",
|
||||
packages=find_packages(),
|
||||
|
|
@ -29,7 +32,7 @@ setup(
|
|||
python_requires=">=3.10",
|
||||
entry_points={
|
||||
"console_scripts": [
|
||||
"tradingagents=cli.main:app",
|
||||
"litadel=cli.main:app",
|
||||
],
|
||||
},
|
||||
classifiers=[
|
||||
|
|
|
|||
|
|
@ -1,4 +1,6 @@
|
|||
# TradingAgents/graph/__init__.py
|
||||
# Litadel/graph/__init__.py
|
||||
# Copyright Notice: Litadel is a successor of TradingAgents by TaurusResearch.
|
||||
# This project builds upon and extends the original TradingAgents framework.
|
||||
|
||||
from .trading_graph import TradingAgentsGraph
|
||||
from .conditional_logic import ConditionalLogic
|
||||
|
|
|
|||
|
|
@ -1,4 +1,6 @@
|
|||
# TradingAgents/graph/propagation.py
|
||||
# Litadel/graph/propagation.py
|
||||
# Copyright Notice: Litadel is a successor of TradingAgents by TaurusResearch.
|
||||
# This project builds upon and extends the original TradingAgents framework.
|
||||
|
||||
from typing import Dict, Any
|
||||
from tradingagents.agents.utils.agent_states import (
|
||||
|
|
|
|||
|
|
@ -1,4 +1,6 @@
|
|||
# TradingAgents/graph/reflection.py
|
||||
# Litadel/graph/reflection.py
|
||||
# Copyright Notice: Litadel is a successor of TradingAgents by TaurusResearch.
|
||||
# This project builds upon and extends the original TradingAgents framework.
|
||||
|
||||
from typing import Dict, Any
|
||||
from langchain_openai import ChatOpenAI
|
||||
|
|
|
|||
|
|
@ -1,4 +1,6 @@
|
|||
# TradingAgents/graph/setup.py
|
||||
# Litadel/graph/setup.py
|
||||
# Copyright Notice: Litadel is a successor of TradingAgents by TaurusResearch.
|
||||
# This project builds upon and extends the original TradingAgents framework.
|
||||
|
||||
from typing import Dict, Any
|
||||
from langchain_openai import ChatOpenAI
|
||||
|
|
|
|||
|
|
@ -1,4 +1,6 @@
|
|||
# TradingAgents/graph/signal_processing.py
|
||||
# Litadel/graph/signal_processing.py
|
||||
# Copyright Notice: Litadel is a successor of TradingAgents by TaurusResearch.
|
||||
# This project builds upon and extends the original TradingAgents framework.
|
||||
|
||||
from langchain_openai import ChatOpenAI
|
||||
|
||||
|
|
|
|||
|
|
@ -1,4 +1,6 @@
|
|||
# TradingAgents/graph/trading_graph.py
|
||||
# Litadel/graph/trading_graph.py
|
||||
# Copyright Notice: Litadel is a successor of TradingAgents by TaurusResearch.
|
||||
# This project builds upon and extends the original TradingAgents framework.
|
||||
|
||||
import os
|
||||
from pathlib import Path
|
||||
|
|
|
|||
132
uv.lock
132
uv.lock
|
|
@ -1,5 +1,5 @@
|
|||
version = 1
|
||||
revision = 3
|
||||
revision = 2
|
||||
requires-python = ">=3.10"
|
||||
resolution-markers = [
|
||||
"python_full_version >= '3.13'",
|
||||
|
|
@ -1951,6 +1951,71 @@ wheels = [
|
|||
{ url = "https://files.pythonhosted.org/packages/03/a5/866b44697cee47d1cae429ed370281d937ad4439f71af82a6baaa139d26a/Lazify-0.4.0-py2.py3-none-any.whl", hash = "sha256:c2c17a7a33e9406897e3f66fde4cd3f84716218d580330e5af10cfe5a0cd195a", size = 3107, upload-time = "2018-06-14T13:12:22.273Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "litadel"
|
||||
version = "0.1.0"
|
||||
source = { virtual = "." }
|
||||
dependencies = [
|
||||
{ name = "akshare" },
|
||||
{ name = "backtrader" },
|
||||
{ name = "chainlit" },
|
||||
{ name = "chromadb" },
|
||||
{ name = "eodhd" },
|
||||
{ name = "feedparser" },
|
||||
{ name = "finnhub-python" },
|
||||
{ name = "grip" },
|
||||
{ name = "langchain-anthropic" },
|
||||
{ name = "langchain-experimental" },
|
||||
{ name = "langchain-google-genai" },
|
||||
{ name = "langchain-openai" },
|
||||
{ name = "langgraph" },
|
||||
{ name = "pandas" },
|
||||
{ name = "parsel" },
|
||||
{ name = "praw" },
|
||||
{ name = "pytz" },
|
||||
{ name = "questionary" },
|
||||
{ name = "redis" },
|
||||
{ name = "requests" },
|
||||
{ name = "rich" },
|
||||
{ name = "setuptools" },
|
||||
{ name = "stockstats" },
|
||||
{ name = "tqdm" },
|
||||
{ name = "tushare" },
|
||||
{ name = "typing-extensions" },
|
||||
{ name = "yfinance" },
|
||||
]
|
||||
|
||||
[package.metadata]
|
||||
requires-dist = [
|
||||
{ name = "akshare", specifier = ">=1.16.98" },
|
||||
{ name = "backtrader", specifier = ">=1.9.78.123" },
|
||||
{ name = "chainlit", specifier = ">=2.5.5" },
|
||||
{ name = "chromadb", specifier = ">=1.0.12" },
|
||||
{ name = "eodhd", specifier = ">=1.0.32" },
|
||||
{ name = "feedparser", specifier = ">=6.0.11" },
|
||||
{ name = "finnhub-python", specifier = ">=2.4.23" },
|
||||
{ name = "grip", specifier = ">=4.6.2" },
|
||||
{ name = "langchain-anthropic", specifier = ">=0.3.15" },
|
||||
{ name = "langchain-experimental", specifier = ">=0.3.4" },
|
||||
{ name = "langchain-google-genai", specifier = ">=2.1.5" },
|
||||
{ name = "langchain-openai", specifier = ">=0.3.23" },
|
||||
{ name = "langgraph", specifier = ">=0.4.8" },
|
||||
{ name = "pandas", specifier = ">=2.3.0" },
|
||||
{ name = "parsel", specifier = ">=1.10.0" },
|
||||
{ name = "praw", specifier = ">=7.8.1" },
|
||||
{ name = "pytz", specifier = ">=2025.2" },
|
||||
{ name = "questionary", specifier = ">=2.1.0" },
|
||||
{ name = "redis", specifier = ">=6.2.0" },
|
||||
{ name = "requests", specifier = ">=2.32.4" },
|
||||
{ name = "rich", specifier = ">=14.0.0" },
|
||||
{ name = "setuptools", specifier = ">=80.9.0" },
|
||||
{ name = "stockstats", specifier = ">=0.6.5" },
|
||||
{ name = "tqdm", specifier = ">=4.67.1" },
|
||||
{ name = "tushare", specifier = ">=1.4.21" },
|
||||
{ name = "typing-extensions", specifier = ">=4.14.0" },
|
||||
{ name = "yfinance", specifier = ">=0.2.63" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "literalai"
|
||||
version = "0.1.201"
|
||||
|
|
@ -4762,71 +4827,6 @@ wheels = [
|
|||
{ url = "https://files.pythonhosted.org/packages/67/53/d840eff3b6cc64f40f115b8de9a17a100973be3679f14b66056ab5a4615e/traceloop_sdk-0.40.9-py3-none-any.whl", hash = "sha256:c061a278f40062986379bc2df76635cb545c3f81301f6b38d51b3a6f9c288f54", size = 31486, upload-time = "2025-06-10T09:55:57.458Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "tradingagents"
|
||||
version = "0.1.0"
|
||||
source = { virtual = "." }
|
||||
dependencies = [
|
||||
{ name = "akshare" },
|
||||
{ name = "backtrader" },
|
||||
{ name = "chainlit" },
|
||||
{ name = "chromadb" },
|
||||
{ name = "eodhd" },
|
||||
{ name = "feedparser" },
|
||||
{ name = "finnhub-python" },
|
||||
{ name = "grip" },
|
||||
{ name = "langchain-anthropic" },
|
||||
{ name = "langchain-experimental" },
|
||||
{ name = "langchain-google-genai" },
|
||||
{ name = "langchain-openai" },
|
||||
{ name = "langgraph" },
|
||||
{ name = "pandas" },
|
||||
{ name = "parsel" },
|
||||
{ name = "praw" },
|
||||
{ name = "pytz" },
|
||||
{ name = "questionary" },
|
||||
{ name = "redis" },
|
||||
{ name = "requests" },
|
||||
{ name = "rich" },
|
||||
{ name = "setuptools" },
|
||||
{ name = "stockstats" },
|
||||
{ name = "tqdm" },
|
||||
{ name = "tushare" },
|
||||
{ name = "typing-extensions" },
|
||||
{ name = "yfinance" },
|
||||
]
|
||||
|
||||
[package.metadata]
|
||||
requires-dist = [
|
||||
{ name = "akshare", specifier = ">=1.16.98" },
|
||||
{ name = "backtrader", specifier = ">=1.9.78.123" },
|
||||
{ name = "chainlit", specifier = ">=2.5.5" },
|
||||
{ name = "chromadb", specifier = ">=1.0.12" },
|
||||
{ name = "eodhd", specifier = ">=1.0.32" },
|
||||
{ name = "feedparser", specifier = ">=6.0.11" },
|
||||
{ name = "finnhub-python", specifier = ">=2.4.23" },
|
||||
{ name = "grip", specifier = ">=4.6.2" },
|
||||
{ name = "langchain-anthropic", specifier = ">=0.3.15" },
|
||||
{ name = "langchain-experimental", specifier = ">=0.3.4" },
|
||||
{ name = "langchain-google-genai", specifier = ">=2.1.5" },
|
||||
{ name = "langchain-openai", specifier = ">=0.3.23" },
|
||||
{ name = "langgraph", specifier = ">=0.4.8" },
|
||||
{ name = "pandas", specifier = ">=2.3.0" },
|
||||
{ name = "parsel", specifier = ">=1.10.0" },
|
||||
{ name = "praw", specifier = ">=7.8.1" },
|
||||
{ name = "pytz", specifier = ">=2025.2" },
|
||||
{ name = "questionary", specifier = ">=2.1.0" },
|
||||
{ name = "redis", specifier = ">=6.2.0" },
|
||||
{ name = "requests", specifier = ">=2.32.4" },
|
||||
{ name = "rich", specifier = ">=14.0.0" },
|
||||
{ name = "setuptools", specifier = ">=80.9.0" },
|
||||
{ name = "stockstats", specifier = ">=0.6.5" },
|
||||
{ name = "tqdm", specifier = ">=4.67.1" },
|
||||
{ name = "tushare", specifier = ">=1.4.21" },
|
||||
{ name = "typing-extensions", specifier = ">=4.14.0" },
|
||||
{ name = "yfinance", specifier = ">=0.2.63" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "tushare"
|
||||
version = "1.4.21"
|
||||
|
|
|
|||
Loading…
Reference in New Issue