QuanTorch : High-Performance Torch Library for Derivatives Pricing

QuanTorch is a comprehensive derivatives pricing library built on top of PyTorch. It provides a range of tools for asset pricing, risk management, and model calibration based on PyTorch's GPU accelerateion and automatic differentiation.

Features

Asset Pricing:
- Option pricing models including Black-Scholes-Merton, binomial tree, and Monte Carlo simulations.
- Bond pricing models including callable, putable, and convertible bonds.
- Advanced options support for American, Bermudan, Asian, barrier and look-back options.
- Implied volatility calculation using "Let's be rational" algorithm.
- Futures and currency pricing.
Risk Management:
- Greeks calculation utilizing Malliavin calculus.
- Scenario analysis and stress testing.
- Market risk measures such as VaR and Expected Shortfall.
- Credit risk models including structural and reduced form models.
- Valuation adjustments (CVA, DVA, MVA, FVA).
Model Calibration:
- Calibration for stochastic models like Heston, Vasicek, SABR, and more.
- Local volatility models including Dupire.
Machine Learning and Reinforcement Learning:
- Regression and classification models using PyTorch.
- Reinforcement learning examples.

Installation

You can install QuanTorch via pip:

pip install -U quantorch

Usage (check out the examples folder for more information)

Exotic Options

American Option

import torch
from quantorch.core.asset_pricing.option_pricing.american_option import american_option

spot = torch.tensor(100.0)
strike = torch.tensor(105.0)
expiry = torch.tensor(1.0)
volatility = torch.tensor(0.2)
rate = torch.tensor(0.05)
steps = 100

price = american_option('call', spot, strike, expiry, volatility, rate, steps)
print(f'American Option Price: {price.item()}')

Bermudan Option

import torch
from quantorch.core.asset_pricing.option_pricing.bermudan_option import bermudan_option

spot = torch.tensor(100.0)
strike = torch.tensor(105.0)
expiry = torch.tensor(1.0)
volatility = torch.tensor(0.2)
rate = torch.tensor(0.05)
steps = 100
exercise_dates = torch.tensor([30, 60, 90])

price = bermudan_option('call', spot, strike, expiry, volatility, rate, steps, exercise_dates)
print(f'Bermudan Option Price: {price.item()}')

Asian Option

import torch
from quantorch.core.asset_pricing.option_pricing.asian_option import asian_option

spot = torch.tensor(100.0)
strike = torch.tensor(105.0)
expiry = torch.tensor(1.0)
volatility = torch.tensor(0.2)
rate = torch.tensor(0.05)
steps = 100

price = asian_option('call', spot, strike, expiry, volatility, rate, steps)
print(f'Asian Option Price: {price.item()}')

Greeks Calculation using Malliavin Calculus

import torch
from quantorch.risk_management.greeks.malliavin import malliavin_greek

option_price = torch.tensor(10.0)
underlying_price = torch.tensor(100.0)
volatility = torch.tensor(0.2)
expiry = torch.tensor(1.0)

greek = malliavin_greek(option_price, underlying_price, volatility, expiry)
print(f'Malliavin Greek: {greek.item()}')

Model Calibration

Heston Model Calibration

import torch
from quantorch.calibration.heston_calibration import calibrate_heston

market_prices = torch.tensor([10.0, 12.0, 14.0, 16.0])
strikes = torch.tensor([100.0, 105.0, 110.0, 115.0])
expiries = torch.tensor([1.0, 1.0, 1.0, 1.0])
spot = torch.tensor(100.0)
rate = torch.tensor(0.05)

params = calibrate_heston(market_prices, strikes, expiries, spot, rate)
print(f'Calibrated Heston Parameters: {params}')

Credit Risk Management Example

import torch
from quantorch.risk_management.credit_risk.structural_model import merton_model
from quantorch.risk_management.credit_risk.reduced_form_model import reduced_form_model

asset_value = torch.tensor(100.0)
debt = torch.tensor(80.0)
volatility = torch.tensor(0.2)
rate = torch.tensor(0.05)
expiry = torch.tensor(1.0)

# Merton Model
equity_value = merton_model(asset_value, debt, volatility, rate, expiry)
print(f'Equity Value (Merton Model): {equity_value.item()}')

lambda_0 = torch.tensor(0.02)
default_intensity = torch.tensor(0.05)
recovery_rate = torch.tensor(0.4)
time = torch.tensor(1.0)

# Reduced Form Model
expected_loss = reduced_form_model(lambda_0, default_intensity, recovery_rate, time)
print(f'Expected Loss (Reduced Form Model): {expected_loss.item()}')

Roadmap

Q4 2024

Develop an interactive dashboard for visualizing risk metrics and option pricing.

Q3 2025

Add support for more asset classes such as commodities and real estate.
Enhance the calibration module to support a broader range of models and optimization techniques.

Development

To contribute to QuanTorch, clone the repository and install the required dependencies:

git clone https://github.com/jialuechen/quantorch.git
cd quantorch
pip install -r requirements.txt

Run the tests to ensure everything is working:

pytest

License

This project is licensed under the Apache License 2.0. See the LICENSE file for details.

jialuechen / quantorch