Agent-Based Investment Portfolio Simulation and Risk Explorer

Added Jun 2025 3 design docs

Learning to invest by trial and error is expensive, and most students and retail investors never get a safe environment to test strategies against realistic market behavior. Simulation platforms close that gap, but only if they go beyond static backtests and actually explain the reasoning behind each outcome.

The intern builds a finance education platform where agentic AI acts as the analysis engine: agents ingest market data feeds, simulate user-defined investment strategies, stress-test portfolios across scenarios, and generate personalized recommendations with the rationale spelled out. A Python backend runs the simulations, PostgreSQL stores portfolios, scenario runs, and results, and a Next.js frontend lets users compose scenarios, visualize risk and return, and compare strategies side by side. Docker packages the services for consistent deployment, while secure authentication and exportable reports round out a product users could genuinely learn from.

Completing the platform demonstrates skills employers notice immediately: orchestrating AI agents around quantitative simulations, communicating risk honestly through visualization, and shipping a full-stack fintech application from data feed to dashboard.

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