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Gemini Code Assistant Workspace Configuration

This document provides a comprehensive guide for the Gemini Code Assistant to effectively understand and contribute to the TradingAgents project.

Project Overview

TradingAgents is a multi-agent LLM framework for financial trading that simulates a real-world trading firm. It leverages a team of specialized LLM-powered agents, including analysts, researchers, and a trader, to collaboratively analyze market conditions and make informed trading decisions. The framework is built on LangGraph, which enables a modular and flexible agent-based architecture.

The core of the project is the TradingAgentsGraph, which orchestrates the interactions between the different agents. The agents are organized into teams:

Analyst Team: Gathers and analyzes different types of data (e.g., fundamentals, news, social media).
Researcher Team: Debates the findings of the analyst team to form an investment plan.
Trader Agent: Makes the final trading decision based on the input from the other teams.
Risk Management Team: Assesses the risk of the proposed trade.

Getting Started

Installation

Clone the repository:

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

Create and activate a virtual environment:

conda create -n tradingagents python=3.13
conda activate tradingagents

Install the required dependencies:
```
pip install -r requirements.txt
```

API Keys

The project requires the following API keys to be set as environment variables:

FinnHub: For financial data.

export FINNHUB_API_KEY=$YOUR_FINNHUB_API_KEY

OpenAI: For the LLM agents.

export OPENAI_API_KEY=$YOUR_OPENAI_API_KEY

OpenRouter (optional): If you want to use OpenRouter as your LLM provider.
```
export OPENROUTER_API_KEY=$YOUR_OPENROUTER_API_KEY
```

Project Structure

C:\Users\kevin\repo\TradingAgents\
├───.gitignore
├───.python-version
├───ERROR_HANDLING_GUIDE.md
├───LICENSE
├───main.py
├───openrouter_status.py
├───pyproject.toml
├───README.md
├───requirements.txt
├───setup.py
├───uv.lock
├───.git\...
├───.venv\
│   ├───include\...
│   ├───Lib\...
│   ├───Scripts\...
│   └───share\...
├───assets\
│   ├───analyst.png
│   ├───researcher.png
│   ├───risk.png
│   ├───schema.png
│   ├───TauricResearch.png
│   ├───trader.png
│   ├───wechat.png
│   └───cli\
│       ├───cli_init.png
│       ├───cli_news.png
│       ├───cli_technical.png
│       └───cli_transaction.png
├───cli\
│   ├───__init__.py
│   ├───main.py
│   ├───models.py
│   ├───utils.py
│   ├───__pycache__\
│   └───static\
│       └───welcome.txt
├───results\
│   ├───AAPL\...
│   └───TSLA\...
└───tradingagents\
    ├───default_config.py
    ├───__pycache__\
    ├───agents\
    │   ├───__init__.py
    │   ├───__pycache__\
    │   ├───analysts\
    │   │   ├───fundamentals_analyst.py
    │   │   ├───market_analyst.py
    │   │   ├───news_analyst.py
    │   │   ├───social_media_analyst.py
    │   │   └───__pycache__\
    │   ├───managers\
    │   │   ├───research_manager.py
    │   │   ├───risk_manager.py
    │   │   └───__pycache__\
    │   ├───researchers\
    │   │   ├───bear_researcher.py
    │   │   ├───bull_researcher.py
    │   │   └───__pycache__\
    │   ├───risk_mgmt\
    │   │   ├───aggresive_debator.py
    │   │   ├───conservative_debator.py
    │   │   ├───neutral_debator.py
    │   │   └───__pycache__\
    │   ├───trader\
    │   │   ├───trader.py
    │   │   └───__pycache__\
    │   └───utils\
    │       ├───agent_states.py
    │       ├───agent_utils.py
    │       ├───memory.py
    │       └───__pycache__\
    ├───dataflows\
    │   ├───__init__.py
    │   ├───config.py
    │   ├───finnhub_utils.py
    │   ├───googlenews_utils.py
    │   ├───interface.py
    │   ├───reddit_utils.py
    │   ├───stockstats_utils.py
    │   ├───utils.py
    │   ├───yfin_utils.py
    │   ├───__pycache__\
    │   └───data_cache\
    └───graph\
        ├───__init__.py
        ├───conditional_logic.py
        ├───propagation.py
        ├───reflection.py
        ├───setup.py
        ├───signal_processing.py
        ├───trading_graph.py
        └───__pycache__\

main.py: A simple script demonstrating how to use the TradingAgentsGraph as a Python package.
cli/main.py: The main entry point for the command-line interface.
tradingagents/: The core Python package.
- agents/: Contains the implementation of the different agents.
- dataflows/: Handles data acquisition from various APIs.
- graph/: Defines the LangGraph graph and the overall orchestration of the agents.
- default_config.py: The default configuration for the project.
requirements.txt: A list of the Python dependencies.
pyproject.toml: Project metadata and dependencies.
README.md: The main README file for the project.

Core Concepts

TradingAgentsGraph

The TradingAgentsGraph class in tradingagents/graph/trading_graph.py is the central orchestrator of the framework. It initializes the LLMs, tools, and agents, and then constructs a LangGraph graph to define the workflow. The propagate method executes the graph for a given company and date.

Agents

Agents are defined in the tradingagents/agents/ directory. Each agent is a "node" in the LangGraph graph. The agents are created by functions that take the LLM and other necessary components as input. These functions return a node function that processes the current state of the graph and returns an updated state.

Dataflows

The tradingagents/dataflows/ directory contains the logic for fetching data from various financial data APIs, such as Finnhub, Yahoo Finance, and Reddit. The Toolkit class in tradingagents/agents/__init__.py provides a unified interface for the agents to access these data sources.

Development Guidelines

Adding a New Agent

Create a new Python file in the appropriate subdirectory of tradingagents/agents/.
Define a create_<agent_name>_agent function that takes the LLM and any other necessary components as input.
Inside this function, define a node function that takes the current state of the graph as input.
Prepare a prompt for the LLM and invoke it.
Return an updated state dictionary.
Add the new agent to the TradingAgentsGraph in tradingagents/graph/trading_graph.py.

Adding a New Tool

Add a new function to the appropriate file in tradingagents/dataflows/.
Add the new function to the Toolkit class in tradingagents/agents/__init__.py.
Add the new tool to the appropriate ToolNode in the _create_tool_nodes method of the TradingAgentsGraph class.

Running Tests

This project does not currently have a dedicated test suite. When adding new features, it is recommended to add unit tests to ensure the correctness of the code.

Key Commands

Run the CLI:
```
python -m cli.main
```
Run the package example:
```
python main.py
```

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