--- # Litadel: Multi-Agents LLM Financial Trading Framework > **Copyright Notice:** Litadel is a successor of TradingAgents. This project builds upon and extends the original TradingAgents framework.

## Overview Litadel is a comprehensive AI-powered trading analysis platform that delivers professional-grade market insights across **equities, commodities, and cryptocurrencies**. Get actionable BUY/SELL/HOLD recommendations backed by multi-agent analysis covering fundamentals, technicals, news sentiment, and risk assessment. > Litadel framework is designed for research and educational purposes. Trading performance may vary based on many factors. [It is not intended as financial, investment, or trading advice.](https://tauric.ai/disclaimer/) ### What You Get **Three Ways to Analyze:** - 🌐 **Web Dashboard** - Modern, real-time interface with live tracking, interactive charts, and comprehensive reports - 💻 **Interactive CLI** - Rich terminal experience with live agent progress and automatic report generation - 📦 **Python Package** - Integrate multi-agent analysis directly into your own applications **Multi-Asset Coverage:** - 📈 **Equities** - Full fundamental, technical, and sentiment analysis for stocks - 🛢️ **Commodities** - Specialized analysis for oil, metals, agricultural products, and more - ₿ **Cryptocurrencies** - Real-time crypto market analysis with sentiment tracking **Professional Analysis:** - Real-time market data with automatic caching - Multi-perspective analysis with bull vs. bear debates - Comprehensive reports covering all aspects of market conditions - Clear trading recommendations with confidence scores ## Dashboard ### Your Trading Command Center The web dashboard provides a complete control center for managing your trading analyses with real-time monitoring, interactive visualizations, and comprehensive reporting.

### Analysis Management Browse all your analyses with smart filtering and grouping. Track active analyses in real-time and review historical decisions with detailed statistics.

### Real-Time Analysis Tracking Watch your analysis unfold in real-time as AI agents collaborate to evaluate market conditions. See live progress updates, agent pipeline status, and streaming reports as they're generated.

### Comprehensive Analysis Reports Each completed analysis provides detailed insights with: - **Trading Decision** - Clear BUY/SELL/HOLD recommendation with confidence score - **Interactive Price Charts** - Candlestick charts with analysis date markers and 60-day history - **Market Metrics** - Current price, daily change, volume, and 52-week ranges - **Specialist Reports** - Detailed analysis from market, news, sentiment, and fundamental perspectives - **Research Debate** - Bull vs. bear perspectives with investment recommendations - **Risk Assessment** - Comprehensive risk evaluation and portfolio impact analysis

### Key Features - **Real-Time WebSocket Updates** - Live progress tracking without page refreshes - **Interactive Charts** - Visualize price action with candlestick or line charts - **Export Capabilities** - Download complete analysis data as JSON - **Analysis History** - Browse and compare past analyses by ticker and date - **Secure API Access** - API key authentication with configurable endpoints - **Responsive Design** - Works seamlessly on desktop and tablet devices ## Getting Started ### Installation Clone Litadel: ```bash git clone https://github.com/deepweather/Litadel.git cd Litadel ``` Create a virtual environment: ```bash conda create -n litadel python=3.13 conda activate litadel ``` Install dependencies: ```bash pip install -r requirements.txt ``` ### API Keys Setup You will need API keys for LLM providers and market data. The default configuration uses OpenAI for agents and [Alpha Vantage](https://www.alphavantage.co/support/#api-key) for market data. Create a `.env` file in the project root: ```bash cp .env.example .env # Edit .env with your actual API keys ``` Or export them directly: ```bash export OPENAI_API_KEY=$YOUR_OPENAI_API_KEY export ALPHA_VANTAGE_API_KEY=$YOUR_ALPHA_VANTAGE_API_KEY ``` **Note:** Litadel partners with Alpha Vantage to provide robust API support. Get a free API key [here](https://www.alphavantage.co/support/#api-key)—Litadel users receive increased rate limits (60 requests/minute, no daily limits) through Alpha Vantage's open-source support program. ## Usage ### Web Dashboard (Recommended) The web interface provides the most comprehensive experience with real-time tracking, interactive charts, and complete analysis history. **1. Start the API Server:** ```bash python -m api.main ``` On first run, the system will automatically create a database and generate an API key. **Save this key—you'll need it for the web interface.** **2. Start the Frontend:** ```bash cd frontend npm install npm run dev ``` **3. Access the Dashboard:** Open your browser to `http://localhost:5173` and enter your API key in Settings. You're ready to create your first analysis! ### Interactive CLI For a terminal-based experience with live agent progress tracking: ```bash python -m cli.main ``` Select your ticker, analysis date, analyst team, LLM models, and research depth through the interactive prompts.

Watch as agents collaborate in real-time, with live updates showing their reasoning and tool usage:

Results are automatically saved to `results///` with detailed logs and markdown reports. ### Python Package Integrate Litadel's multi-agent analysis directly into your own applications, trading bots, or research pipelines. **Basic Usage:** ```python from tradingagents.graph.trading_graph import TradingAgentsGraph from tradingagents.default_config import DEFAULT_CONFIG # Initialize the trading agents ta = TradingAgentsGraph(debug=True, config=DEFAULT_CONFIG.copy()) # Run analysis and get trading decision _, decision = ta.propagate("NVDA", "2024-05-10") print(decision) ``` **Custom Configuration:** Customize LLM models, debate rounds, and data sources to match your needs: ```python from tradingagents.graph.trading_graph import TradingAgentsGraph from tradingagents.default_config import DEFAULT_CONFIG # Create custom configuration config = DEFAULT_CONFIG.copy() config["deep_think_llm"] = "o1-mini" # Deep reasoning model config["quick_think_llm"] = "gpt-4o-mini" # Fast operations model config["max_debate_rounds"] = 3 # More thorough research debates # Configure data sources config["data_vendors"] = { "core_stock_apis": "yfinance", # Price data "technical_indicators": "yfinance", # Technical analysis "fundamental_data": "alpha_vantage", # Company fundamentals "news_data": "alpha_vantage", # News and sentiment } # Run with custom config ta = TradingAgentsGraph(debug=True, config=config) _, decision = ta.propagate("AAPL", "2024-05-10") ``` **Cost Optimization:** For testing and development, we recommend using `gpt-4o-mini` and `o1-mini` to minimize costs, as the multi-agent framework makes numerous API calls during analysis. For production use with higher accuracy requirements, consider `gpt-4o` and `o1-preview`. **Data Sources:** The default configuration uses YFinance for price/technical data and Alpha Vantage for fundamentals/news. You can switch to OpenAI for web-based data fetching or use local cached data for offline experimentation. See `tradingagents/default_config.py` for all available options. ## How It Works Litadel uses a multi-agent architecture that mirrors the structure of professional trading firms. Specialized AI agents collaborate to provide comprehensive market analysis.

### Analyst Team Four specialized analysts evaluate different aspects of market conditions: - **Technical Analyst** - Analyzes price patterns, trends, and technical indicators (MACD, RSI, moving averages) - **Fundamentals Analyst** - Evaluates company financials, earnings, balance sheets, and intrinsic value - **News Analyst** - Monitors global news, macroeconomic indicators, and their market impact - **Sentiment Analyst** - Analyzes social media and public sentiment to gauge market mood

### Researcher Team Bull and bear researchers critically assess analyst insights through structured debates, balancing potential gains against risks.

### Trader Agent Synthesizes all reports and research to make informed trading decisions with clear timing and position sizing recommendations.

### Risk Management and Portfolio Manager Evaluates portfolio risk by assessing market volatility, liquidity, and other risk factors. The risk team provides final assessment and approval for trading decisions.

## What's New in Litadel ### Completed Features - ✅ **Web Dashboard** - Full-featured web interface with real-time analysis tracking - ✅ **REST API** - Complete API for programmatic access with WebSocket support - ✅ **Multi-Asset Support** - Equities, commodities, and cryptocurrencies - ✅ **Interactive Charts** - Real-time candlestick and line charts with market data - ✅ **Analysis History** - Persistent storage and browsing of all analyses - ✅ **Export Capabilities** - Download complete analysis data as JSON ### Roadmap - 🚧 **Automated Trading Mode** - Continuous automated trading execution - 🚧 **Portfolio Management** - Multi-asset portfolio tracking and optimization - 🚧 **Backtesting Engine** - Historical performance analysis with TauricDB - 🚧 **OpenAI Agents SDK Migration** - Enhanced parallelization and maintainability ## 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. ## Citation Please reference our work if you find *Litadel* provides you with some help :) Litadel citation: ``` @software{gabler2025litadel, title={Litadel: Multi-Agents LLM Financial Trading Framework}, author={Marvin Gabler}, year={2025}, url={https://github.com/deepweather/Litadel}, note={Extended framework based on TradingAgents} } ``` Original TradingAgents citation: ``` @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}, } ```