AI Market Analysis System

This project presents a sophisticated, multi-agent AI system designed for comprehensive financial market analysis. It targets engineers, researchers, and power users seeking advanced trading insights, portfolio optimization, and robust risk management. The system leverages a suite of specialized AI agents, real-time data integration, and advanced machine learning techniques to provide data-driven decision support and continuous system improvement.

How It Works

The core architecture comprises 10 specialized AI agents, each focusing on distinct analytical tasks such as momentum detection, sentiment analysis, correlation tracking, risk assessment, volatility prediction, event impact analysis, and forecasting. These agents collaborate through an intelligent routing system, dynamically selecting and weighting agents based on market conditions and performance. Key technologies include LLM-RAG for context-aware event analysis, Reinforcement Learning (RL) for strategy optimization, and an ensemble signal blender that combines agent outputs using weighted voting algorithms. Data is managed via a robust PostgreSQL database, with a modern Angular frontend and a FastAPI backend providing real-time access and visualization.

Quick Start & Requirements

• Primary Install: Docker Compose is the recommended deployment method (docker-compose up -d). Local development requires Python 3.11+ and PostgreSQL 15+.
• Prerequisites: Docker, Docker Compose, Python 3.11+, PostgreSQL 15+. Recommended: 8GB+ RAM, 10GB+ disk space.
• Access: Frontend: http://localhost:4200, API Docs: http://localhost:8001/docs.
• Documentation: CHANGELOG.md, DEVELOPMENT.md, QUICK_REFERENCE.md are available within the repository.

Highlighted Details

• Multi-Agent Framework: Features 10 specialized agents (Momentum, Sentiment, Correlation, Risk, Volatility, Volume, Event Impact, Forecast, Strategy, Meta) for comprehensive analysis.
• LLM-RAG Powered Event Analysis: Utilizes Large Language Models and Retrieval-Augmented Generation for deep, context-aware analysis of market events.
• Reinforcement Learning Strategy Optimization: Employs RL algorithms (PPO, DQN, A2C) for adaptive trading strategies and dynamic trade allocation.
• Ensemble Signal Blending: Combines signals from multiple agents using weighted voting, dynamic weighting, and quality assessment for robust predictions.
• Real-Time Data & Analytics: Integrates live market data (Yahoo Finance, NewsAPI, Alpha Vantage) and provides extensive real-time analytics, including regime detection, portfolio management, and risk assessment.

Maintenance & Community

The project includes a detailed CHANGELOG.md and ROADMAP.md outlining development history and future plans. Specific community links (Discord, Slack) or notable contributors are not explicitly mentioned in the provided README.

Licensing & Compatibility

The project is licensed under the MIT License. This license is permissive and generally compatible with commercial use and closed-source linking, allowing broad adoption and modification.

Limitations & Caveats

While highly comprehensive, the project roadmap indicates ongoing development for key features such as real-time execution APIs, advanced multi-asset support (cryptocurrency, forex, commodities), and enterprise features like user management. Some advanced ML models and RL capabilities are listed under "Future" or "Next Steps," suggesting they may not be fully production-ready or may require further integration and testing. The system relies heavily on external APIs, which could be subject to rate limits or changes.