Black-Scholes
Closed-form analytics for vanilla options, benchmark pricing, and hedging workflows.
Pricing Library
Stochastic simulation and pricing infrastructure for serious quant workflows
QuantModels.ai includes a Python-based derivatives pricing engine built for volatility modeling, simulation-driven valuation, and portfolio risk metrics.
Engine Overview
The Pricing Library pairs a Python-first API with institutional modeling workflows, making it easier to move from market inputs to calibrated models, simulated paths, option prices, and risk diagnostics without fragmenting the stack. Heston and CIR++ simulation now run internally inside QuantModels.ai.
Heston stochastic volatility
Stochastic-volatility pricing and calibration routines for richer surface dynamics.
Monte Carlo simulation
Path-based simulation engines for scenario generation, exotic payoffs, and stress studies.
Greeks and sensitivities
Delta, gamma, vega, theta, and scenario-based sensitivities for risk oversight.
Calibration tools
Market-fit utilities to align models with observed implied volatility surfaces and term structures.
Python Preview
Heston workflow example
1from quantmodels.heston import HestonModel
2model = HestonModel(...)
3price = model.price_call(...)
Workflow
01
Market Data
02
Model Calibration
03
Simulation
04
Pricing
05
Risk Metrics
Contact
Speak with our team about live pricing and portfolio analytics
Whether you are evaluating a single model or replacing a fragmented workflow, we can scope the right rollout.
General
research@quantmodels.ai
Sales
enterprise@quantmodels.ai
Coverage
New York, London, Singapore
Enterprise clients can request controlled pilots, private deployment discussions, and solution workshops for treasury, derivatives, and risk teams.