quantitative

google/tf-quant-finance: High-performance TensorFlow library for quantitative finance.

Build Status

Table of contents

  1. Introduction
  2. Installation
  3. TensorFlow training
  4. Development roadmap
  5. Examples
  6. Contributing
  7. Development
  8. Community
  9. Disclaimers
  10. License

Introduction

This library provides high-performance components leveraging the hardware
acceleration support and automatic differentiation of TensorFlow. The
library will provide TensorFlow support for foundational mathematical methods,
mid-level methods, and specific pricing models. The coverage is being
expanded over the next few months.

The library is structured along three tiers:

  1. Foundational methods.
    Core mathematical methods – optimisation, interpolation, root finders,
    linear algebra, random and quasi-random number generation, etc.

  2. Mid-level methods.
    ODE & PDE solvers, Ito process framework, Diffusion Path Generators,
    Copula samplers etc.

  3. Pricing methods and other quant finance specific utilities.
    Specific Pricing models (e.g Local Vol (LV), Stochastic Vol (SV),
    Stochastic Local Vol (SLV), Hull-White (HW)) and their calibration.
    Rate curve building, payoff descriptions and schedule generation.

We aim for the library components to be easily accessible at each level. Each
layer

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