Finance Agent

GreenAgentFinance is the core evaluation framework for Phase 1 of the AgentBeats Finance Competition. It acts as a deterministic "Green Agent" designed to objectively assess participant "Purple Agents" on their ability to retrieve, analyze, and synthesize financial data from authoritative sources. Key Highlights: Purpose: To evaluate financial AI agents across 50 curated questions involving market analysis, trend recognition, and quantitative guidance comparisons. System Architecture: Operates within an isolated Docker network using the A2A (Agent-to-Agent) Protocol via JSON-RPC. It ensures reproducibility by using fixed seeds and offline data tools (SEC EDGAR, web search caches). Scoring Methodology: Employs a rubric-based system focused on two main pillars: Correctness: Validating factual criteria. Contradiction: Ensuring semantic consistency. Citation Integrity: Verifying that all referenced sources are valid and traceable. Performance Metrics: Outputs a final results.json containing the average score, pass rate, citation validity, and execution duration.

A vulnerable financial analyst agent designed for benchmarking and attack simulation. It exposes intentionally weak tools for document reading, database querying (with no input sanitization), and report writing. The agent is used as a target for red-teaming and security evaluation

Enterprise finance agent with real DCF valuation, Monte Carlo GBM simulation, Black-Scholes option pricing with Greeks, IRR via scipy solver, and parametric VaR/CVaR/Sharpe/Sortino. Backed by the Solstice Plutus engine.