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https://github.com/luweizheng/finax
High Performance Quantative Finance on JAX
https://github.com/luweizheng/finax
gpu high-performance-computing quantitative-finance
Last synced: 4 months ago
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High Performance Quantative Finance on JAX
- Host: GitHub
- URL: https://github.com/luweizheng/finax
- Owner: luweizheng
- License: mit
- Created: 2022-04-06T09:18:18.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2022-06-28T03:23:27.000Z (over 2 years ago)
- Last Synced: 2024-10-08T02:40:04.992Z (4 months ago)
- Topics: gpu, high-performance-computing, quantitative-finance
- Language: Python
- Homepage:
- Size: 257 KB
- Stars: 9
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Finax: High Performance Quantative Finance on JAX
## Introduction
We developed a quantative finance library based on [JAX](https://github.com/google/jax). This library provides high-performance financial computation leveraging the hardware
acceleration, parallel scientific computing and automatic differentiation of JAX.
Finax is still a research project that is currently under development. Some APIs may change in the future. We welcome any suggestions and contributions.
We can:
* run financial workloads on CPU/GPU/TPU with XLA acceleration
* calculate mathematical derivative of financial models, i.e. Greeks
* distribute workloads on multiple devices
`examples` directory contains several demonstrations of using our Finax library.
## Install
### JAX installation
You must first follow [JAX's installation guide](https://github.com/google/jax/#installation) to install JAX based on your device architecture (CPU/GPU/TPU).
### Finax
```
pip install finax --upgrade
```
## Usage
### Getting-started
Here is an example for option pricing using [Black-Scholes model](https://en.wikipedia.org/wiki/Black-Scholes_model).
```
import numpy as np
from jax import jit
from jax.config import config
config.update("jax_enable_x64", True)
from finax.black_sholes.vanilla_prices import option_price
option_price_fn = jit(option_price)
dtype = jnp.float64
forwards = jnp.array([1.0, 2.0, 3.0, 4.0, 5.0], dtype=dtype)
strikes = jnp.array([3.0, 3.0, 3.0, 3.0, 3.0], dtype=dtype)
volatilities = jnp.array([0.0001, 102.0, 2.0, 0.1, 0.4], dtype=dtype)
expiries = jnp.array(1.0, dtype=dtype)
computed_prices = option_price_fn(
volatilities=volatilities,
strikes=strikes,
expiries=expiries,
forwards=forwards)
```
### 64-bit precision
To enable 64-bit precision, set the respective JAX flag _before_ importing `finax` (see the JAX [guide](https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision)), for example:
```python
from jax.config import config
config.update("jax_enable_x64", True)
```