OptPricing: A Quantitative Finance Library for Derivative Pricing and Analysis

optpricing is a Python library for pricing, calibrating, and analyzing financial derivatives. It is built with a focus on architectural clarity, model breadth, and practical usability through a robust API, command-line interface, and an interactive dashboard.

Diljit Singh LinkedIn

Core Features

• Model Library: Implements a comprehensive set of models, including:

  • Stochastic Volatility: Heston, SABR
  • Jump-Diffusion: Merton, Bates, Kou, SABR with Jumps
  • Pure Levy Processes: Variance Gamma (VG), Normal Inverse Gaussian (NIG), CGMY, Hyperbolic
  • Interest Rate Models: Vasicek, Cox-Ingersoll-Ross (CIR), Put-Call Parity Implied Rate
  • Local Volatility: Dupire's Equation

• Pricing Engines: Provides a suite of numerical methods, allowing for technique comparison and validation:

  • Analytic closed-form solutions
  • Numerical integration and FFT-based pricing via characteristic functions
  • Finite difference (PDE) solver using a Crank-Nicolson scheme
  • Binomial and trinomial tree methods (CRR, TOPM, Leisen-Reimer) for European and American options
  • High-performance Monte Carlo engine for European and American options, accelerated with numba, featuring variance reduction techniques (e.g., antithetic variates, control variates, importance sampling)

• Interfaces:

  • Programmatic API: Use the package as a Python library to build custom financial models in your scripts. Define options, stocks, rates, and models programmatically to compute prices and other metrics.
  • Command-Line Interface (CLI): A robust CLI for live pricing, data management, model calibration, and historical backtesting.
  • Interactive Dashboard (UI): A Streamlit application for visual analysis of option chains, implied volatility surfaces, and model calibrations.

• Workflow Automation: High-level classes that orchestrate complex tasks like daily calibration runs and out-of-sample performance evaluation.

Quick Start

optpricing is designed for a straightforward installation using pip and is compatible with Python 3.10 and higher.

1. Install the Library

pip install optpricing

2. Download Historical Data

Some models require historical data (e.g., for calibration). Download data for a ticker like SPY:

optpricing data download --ticker SPY

For more details, see the Getting Started Guide.

3. Use the CLI

Price an option directly from the terminal. The command below fetches the live option chain for AAPL, retrieves the current dividend rate, calculates the implied risk-free rate from at-the-money contracts, and prices the contract with Heston’s model using its default pricing technique (FFT):

optpricing price --ticker AAPL --strike 630 --maturity 2025-12-19 --model Heston --param "rho=-0.7" --param "vol_of_vol=0.5"

To price the same contract as an American Option use:

optpricing price -t AAPL -k 210 -T 2025-12-19 --style american --model Heston --param "rho=-0.7" --param "vol_of_vol=0.5"

For more details, see the CLI Guide.

4. Launch the Dashboard

Visualize option chains and model outputs, interact with a pricing calculator featuring 15 models and 10 techniques.

optpricing dashboard

For more details, see the Dashboard Guide.

5. Use the Programmatic API

The most powerful way to use the package is via the API, which provides customization of nearly every aspect of pricing:

from optpricing import Option, OptionType, Rate, Stock, ZeroCouponBond
from optpricing.models import BSMModel, CIRModel, VasicekModel
from optpricing.techniques import ClosedFormTechnique

# Define an option, underlying and rate
option = Option(strike=105, maturity=1.0, option_type=OptionType.CALL)
stock = Stock(spot=100, dividend=0.01)
rate = Rate(rate=0.05)

# Choose a model and technique
bsm_model = BSMModel(params={"sigma": 0.20})
cf_technique = ClosedFormTechnique()

result = cf_technique.price(option, stock, bsm_model, rate)
print(f"The option price is: {result.price:.4f}")


delta = cf_technique.delta(option, stock, bsm_model, rate)
gamma = cf_technique.gamma(option, stock, bsm_model, rate)
vega = cf_technique.vega(option, stock, bsm_model, rate)

print(f"Delta: {delta:.4f}")
print(f"Gamma: {gamma:.4f}")
print(f"Vega:  {vega:.4f}")

target_price = 7.50
iv = cf_technique.implied_volatility(
    option, stock, bsm_model, rate, target_price=target_price
)
print(f"Implied volatility for price ${target_price:.2f}: {iv:.4%}")


# Zero Coupon Bond
bond = ZeroCouponBond(maturity=1.0)
r0_stock = Stock(spot=0.05)  # initial short rate
dummy_rate = Rate(rate=0.0)  # ignored by rate models

vasicek = VasicekModel(params={"kappa": 0.86, "theta": 0.09, "sigma": 0.02})
cir = CIRModel(params={"kappa": 0.86, "theta": 0.09, "sigma": 0.02})

p_vasi = cf_technique.price(bond, r0_stock, vasicek, dummy_rate).price
p_cir = cf_technique.price(bond, r0_stock, cir, dummy_rate).price

print(f"Vasicek ZCB Price: {p_vasi:.4f}")
print(f"CIR ZCB Price:     {p_cir:.4f}")

For more details, see the API Guide.

Documentation

The full documentation includes installation instructions, user guides, examples, and a complete API reference.

• View the Official Documentation

Guides

• Introduction
• Installation
• Getting Started
• CLI
• Dashboard
• API
• Examples
• API Reference

Contributing

Contributions are welcome; see CONTRIBUTING for details.

License

This project is licensed under the MIT License. See LICENSE for details.