commodity-pricinglisted

Analyze commodity pricing and trading systems including forward curves, option models, position management, risk metrics, and regulatory reporting. Use when: 'review pricing models', 'audit trading system', 'evaluate VaR implementation', 'check commodity risk management', 'assess ETRM system', 'review derivatives valuation', 'analyze energy trading platform', 'evaluate hedge accounting'.
tinh2/skills-hub-registry · ★ 4 · AI & Automation · score 73

Install: claude install-skill tinh2/skills-hub-registry

You are in AUTONOMOUS MODE. Do NOT ask questions. Evaluate every component of the commodity pricing and trading system systematically. ## INPUT $ARGUMENTS (optional). If no arguments provided, analyze the entire commodity pricing codebase in the current working directory. --- ## PHASE 0: SYSTEM DISCOVERY Auto-detect the commodity trading system architecture. ### Tech Stack - `requirements.txt` / `pyproject.toml` -> Python (QuantLib, NumPy, SciPy, pandas, arch) - `pom.xml` / `build.gradle` -> Java/Scala (Spark, Flink, enterprise ETRM systems) - `package.json` -> Node.js (API layer, dashboard, reporting frontend) - `go.mod` / `Cargo.toml` -> Go/Rust (low-latency pricing engines, market data feeds) - `docker-compose.yml` / `k8s/` -> Container orchestration - `.proto` files -> gRPC for inter-service communication ### Trading Components - Identify pricing models: Black-Scholes, Monte Carlo, binomial trees, finite difference. - Identify market data: real-time feeds (ICE, CME, NYMEX), historical databases, curve construction. - Identify position management: trade capture, portfolio aggregation, P&L calculation. - Identify risk systems: VaR engines, stress testing, Greeks calculation, limit monitoring. - Identify settlement: physical delivery tracking, financial settlement, netting, invoicing. - Identify regulatory: EMIR/Dodd-Frank reporting, REMIT surveillance, position limits. - Identify deal capture: trade entry, confirmation, lifecycle events (amendments, novations). Prod