Browse all 243 finance AI skills for Claude

Parses thousands of specialized earning transcripts, industry forums, and social channels to build predictive sentiment scores.

Models optimal bid-ask spread placements under Avellaneda-Stoikov frameworks to optimize market-making profit profiles.

Calculates initial and variation margin requirements using SPAN models to optimize cross-margining across clearing houses.

Employs trinomial tree frameworks to model equity components, credit spreads, and embedded call/put features in convertible bonds.

Ingests raw satellite imagery embeddings and mobile location pings to forecast quarterly revenue numbers before earnings release.

Trains deep Q-networks (DQN) to split large parental block orders into optimal child routes minimizing market footprint.

Employs Generative Adversarial Networks to synthesize non-linear financial crisis scenarios without relying on historical repetition.

Run parametric and historical Value at Risk calculations, plus stress scenarios for equity, fixed income, and multi-asset portfolios.

Price barrier, Asian, lookback, digital, and rainbow options via Monte Carlo, PDE finite-difference, and closed-form solutions. Compare pricing methods and quantify model risk.

Calibrate Heston, SABR, and local volatility models to market option prices. Compute calibration error surfaces and produce parameters for exotic option pricing.

Compute CVA, DVA, FVA, MVA, and KVA for derivatives books under multiple netting/collateral regimes. Produce regulatory and IFRS 13 disclosures.

Classify current market regime (risk-on/off, volatility regime, trend/mean-reversion) using Hidden Markov Models and ML signals. Output regime-conditional portfolio positioning rules.