🔧 Built a multi-agent AI system for financial analysis
I developed a system where multiple AI agents collaborate to analyze markets and generate trading signals. Four specialized agents work together - research, analysis, trading, and validation - communicating through Redis message queues to reach consensus on trading decisions.
The system handles the full pipeline from data collection to signal generation. The research agent gathers market data, the analysis agent performs technical analysis with indicators like RSI and MACD, the trading agent generates signals using different strategies, and the validator ensures quality before final recommendations. Getting distributed consensus working reliably required implementing proper coordination mechanisms and handling various edge cases.
I built a real-time evaluation framework that tracks performance metrics, cost across different LLM providers, and quality assessments. The Streamlit dashboard provides live monitoring of everything from latency patterns to trading signal accuracy. The system includes comprehensive cost tracking since API usage can scale quickly with multiple agents running continuously.
The technical implementation uses FastAPI with async/await patterns, PostgreSQL for metrics storage, and Kubernetes deployment with auto-scaling. I implemented provider abstraction for multiple LLM services (OpenAI, Anthropic, Google) with intelligent routing and automatic failover. The infrastructure includes CI/CD pipelines with security scanning, plus monitoring with Prometheus and Grafana.
The financial capabilities cover technical analysis, risk assessment, and portfolio optimization. I focused on this domain because quantitative finance requires robust, scalable AI infrastructure, and I wanted to understand the technical challenges involved in deploying such systems in production environments.
The architecture emphasizes reliability and observability - circuit breaker patterns for fault tolerance, comprehensive logging and tracing, and automated alerting. The system is designed to handle real-world operational concerns like service degradation, cost management, and performance monitoring.
Full implementation available at https://lnkd.in/gh8u7YXq with documentation and deployment guides. The codebase demonstrates distributed systems design, async programming, and production deployment practices.