google/tf-quant-finance
Tf Quant Finance
This is a quantitative finance library built on TensorFlow for financial engineering, asset pricing, and risk management. It serves as a financial derivative pricing engine, a model calibration tool, and a hardware-accelerated math library for numerical tasks.
The library provides specialized capabilities for pricing financial assets using standard models and American option logic, as well as calibrating pricing models to market data through local volatility. It includes tools for constructing yield curves via bootstrapping algorithms and monotone convex interpolation.
The framework covers a broad range of quantitative modeling tasks, including the simulation of stochastic processes, sampling from copulas to model dependency structures, and solving ordinary and partial differential equations. It also provides numerical analysis tools for root finding and mathematical optimization.
Features
- Quantitative Finance & Trading - A comprehensive high-performance library for financial engineering, asset pricing, and risk management built on TensorFlow.
- Automatic Differentiation Engines - Provides a computational graph engine for calculating precise gradients used in model calibration and optimization.
- TensorFlow Graph Execution - Executes mathematical operations via TensorFlow computational graphs to leverage hardware acceleration.
- Diffusion Path Generators - Generates sample paths for financial variables using diffusion generators to model market uncertainty.
- Derivative Pricing Models - Implements quantitative models for computing option values and managing financial derivatives.
- Model Calibration Frameworks - Fits model parameters to real-world market data using local volatility and optimization algorithms.
- Asset Price Path Simulators - Generates sample paths for financial assets using diffusion-based stochastic process simulators.
- Volatility Calibration - Fits pricing model parameters to market data using local volatility and calibration algorithms.
- Model Calibration Algorithms - Implements algorithms to fit pricing model parameters to market data using local volatility and bootstrapping.
- Vectorized Stochastic Simulations - Simulates multiple stochastic process trajectories simultaneously using vectorized tensor operations on GPUs and CPUs.
- Copula Models - Uses copula-based sampling to generate correlated random variables for dependency modeling.
- Differential Equation Solvers - Implements numerical solvers for ordinary and partial differential equations using multi-dimensional methods.
- General PDE Solvers - Provides a framework for solving ordinary and partial differential equations using multi-dimensional numerical methods.
- Hardware-Accelerated Numerical Libraries - Offers hardware-accelerated numerical tools for root finding and optimization using GPUs and automatic differentiation.
- Mathematical Optimization Solving - Provides numerical solvers for mathematical optimization, root finding, and interpolation tasks.
- Numerical Integration Tools - Computes multi-dimensional definite integrals and numerical approximations to solve differential equations.
- Financial Analysis Tools - Provides tools for complex financial modeling, including solving differential equations and root finding.
- Hardware-Accelerated Implementations - Offloads iterative numerical searches for function zeros to parallel processing hardware to accelerate pricing.
- Yield Curve Construction - Implements bootstrapping algorithms and monotone convex interpolation to construct financial yield curves.
- AI and Machine Learning - High-performance TensorFlow library for quantitative finance.
- Financial Analysis - High-performance quantitative finance using TensorFlow.
- Financial Analytics - Quantitative finance tools built on TensorFlow.
- Financial Analytics Tools - High-performance library for quantitative finance.
- Financial Instruments and Pricing - High-performance library for quantitative finance using machine learning frameworks.
