Live dashboard · Documentation
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
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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: CEV, Dupire
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Pricing Engines: Provides a suite of numerical methods, allowing for technique comparison and validation:
- Analytic closed-form solutions
- Characteristic-function pricing: COS (Fang–Oosterlee), numerical integration, and Carr–Madan FFT
- Finite difference (PDE) solver using a Crank-Nicolson scheme
- Binomial and trinomial tree methods (CRR, TOPM, Leisen-Reimer) for European and American Black–Scholes options
- High-performance Monte Carlo engine for European and American options, accelerated with
numba, with antithetic variates
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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.
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Workflow Automation: High-level classes that orchestrate complex tasks like daily calibration runs and out-of-sample performance evaluation.
optpricing installs with pip (or uv) and supports Python 3.11 through 3.14.
pip install optpricing # library + CLI
pip install "optpricing[ui]" # also the Streamlit dashboardSome models require historical data (e.g., for calibration). Download data for a ticker like SPY:
optpricing data download --ticker SPYFor more details, see the Getting Started Guide.
Price an option directly from the terminal. The command below fetches the live option chain for AAPL and prices the contract with the spot, the 13-week T-bill rate, and the dividend yield implied by put-call parity. Heston has no closed form, so the default technique is COS (Fang–Oosterlee):
optpricing price --ticker AAPL --strike 630 --maturity 2027-06-18 --model Heston --param "rho=-0.7" --param "vol_of_vol=0.5"The same contract as an American option uses Longstaff–Schwartz Monte Carlo. Lattice techniques price American Black–Scholes only:
optpricing price -t AAPL -k 630 -T 2027-06-18 --style american --model Heston --param "rho=-0.7" --param "vol_of_vol=0.5"For more details, see the CLI Guide.
Visualize option chains and model outputs, and interact with a pricing calculator covering 12 models and 9 techniques (the full library exposes 17 pricing models plus parity/implied-rate models, and 10 techniques, including American Monte Carlo). Requires the ui extra.
optpricing dashboardFor more details, see the Dashboard Guide.
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.
The full documentation includes installation instructions, user guides, examples, and a complete API reference.
Contributions are welcome; see CONTRIBUTING for details.
This project is licensed under the MIT License. See LICENSE for details.