pyqfin 1.0.1

Quantitative finance pricing library

pyqfin

A professional, vectorised quantitative finance library for pricing options, forwards, futures, and multi-asset derivatives.

Features

• Dictionary-based Quick API (Pricer): Easily price single instruments and portfolios.
• Multiple Engines: Black-Scholes (analytical), Monte Carlo, Heston (stochastic vol), and Cox-Ross-Rubinstein Binomial Trees.
• Exotics & Multi-Asset: Support for Asian, Barrier, Basket, Rainbow, and Spread options with Cholesky decompositions for correlated assets.
• American Options: Early exercise evaluation using Binomial Trees.
• Risk Management: Finite-difference Greeks calculation and implied volatility root-finding (Newton-Raphson, Bisection).
• Term Structure: Interpolated and flat yield curves.

Installation

pip install pyqfin

Quick Start

Single Option Pricing

The Pricer handles all the internal wiring (engines, yield curves, payoff classes).

from pyqfin import Pricer

# Vanilla Call (defaults to analytical BSM)
result = Pricer({
    'type': 'vanilla',
    'option_type': 'c',
    'S': 100, 'K': 105, 'T': 1.0, 
    'vol': 0.20, 'r': 0.05,
    'greeks': True
}).run()

print(f"Price: {result.price:.4f}")
print(f"Delta: {result.greeks['delta']:.4f}")

American Options

Automatically utilizes the binomial tree engine:

# American Put
result = Pricer({
    'type': 'american',
    'option_type': 'p',
    'S': 100, 'K': 105, 'T': 1.0, 
    'vol': 0.20, 'r': 0.05,
    'n_steps': 500  # Tree depth
}).run()
print(f"American Premium Price: {result.price:.4f}")

Multi-Asset Basket Option

Provide arrays for S and vol, and a correlation matrix. The library automatically uses Cholesky-based Monte Carlo.

import numpy as np

corr_matrix = np.array([
    [1.0, 0.6, 0.3], 
    [0.6, 1.0, 0.5], 
    [0.3, 0.5, 1.0]
])

result = Pricer({
    'type': 'basket',
    'option_type': 'c',
    'S': [100, 110, 90],
    'K': 100, 'T': 1.0,
    'vol': [0.20, 0.25, 0.30],
    'corr': corr_matrix,
    'weights': [1/3, 1/3, 1/3],
    'r': 0.05,
    'n_paths': 50000
}).run()

Portfolio Pricing

Pass a list of configurations (must include 'quantity') to price an entire book at once and aggregate risks.

portfolio = Pricer.portfolio([
    {'type': 'vanilla', 'option_type': 'c', 'S': 100, 'K': 105, 'T': 1.0, 'vol': 0.20, 'r': 0.05, 'quantity': 10},
    {'type': 'american', 'option_type': 'p', 'S': 100, 'K': 90, 'T': 0.5, 'vol': 0.25, 'r': 0.05, 'quantity': -5},
], greeks=True)

print(f"Total Portfolio Value: {portfolio.total_value:.2f}")
print(f"Net Delta: {portfolio.total_greeks['delta']:.2f}")

Heston Model (Stochastic Volatility)

If you select the heston engine, provide the specific variance parameters instead of standard volatility.

result = Pricer({
    'type': 'vanilla',
    'option_type': 'c',
    'S': 100, 'K': 105, 'T': 1.0, 'r': 0.05,
    'engine': 'heston',
    'v0': 0.04,        # initial variance
    'kappa': 2.0,      # mean-reversion speed
    'theta': 0.04,     # long-run variance
    'xi': 0.3,         # vol-of-vol
    'rho_heston': -0.7 # correlation
}).run()

Running Tests

If you cloned the repository and want to run the tests locally:

pip install pyqfin[dev]
pytest tests/ -v