| # Autonomous Trading Agents | |
| Autonomous Trading Agents is a multi-agent AI system where four independent AI traders — each modelled on a legendary investor — autonomously research financial news, make portfolio decisions, and execute real stock trades every hour. Built with the OpenAI Agents SDK, MCP (Model Context Protocol), and Gemini 2.5 Flash. | |
| ## What It Does | |
| Four agents run concurrently using Python asyncio. Each agent reads its current portfolio, calls a Researcher sub-agent to search the web for relevant financial news, checks live stock prices via Polygon.io, and executes buy or sell trades. Every decision and trade is logged to a SQLite database and displayed in a live Gradio dashboard. | |
| The trading is simulated — each agent starts with $10,000 — but the stock prices are real, sourced from Polygon.io. | |
| ## The Four Traders | |
| Each agent has a distinct strategy inspired by its namesake investor. Warren follows value investing — patient, long-term, focused on fundamentals. George is a macro trader — bold and contrarian. Ray is systematic and risk-balanced across asset classes. Cathie focuses on disruptive innovation and crypto ETFs. Agents can also rewrite their own strategy over time using a dedicated tool. | |
| ## What Makes It Interesting | |
| The system uses a three-tier agent hierarchy — an orchestrator coordinates four trader agents, each of which spins up a Researcher sub-agent on demand. MCP servers handle tool access for web search, market data, memory persistence, and push notifications. A custom TracingProcessor hooks into the SDK's internal observability pipeline to surface real-time agent activity in the dashboard. Agents alternate between a trade mode (find new opportunities) and a rebalance mode (review existing holdings) on successive runs, keeping each LLM call focused. | |
| ## Tech Stack | |
| Python, OpenAI Agents SDK, Gemini 2.5 Flash, MCP, Polygon.io, Tavily, SQLite, LibSQL, Gradio, Plotly, Pydantic. |