OIPD computes the market's expectations about the probable future prices of an asset, based on information contained in options data.

A package that utilises QT and OpenGL graphics to visualise realtime 3D volatility surfaces and analytics.

Deep‑Hedging in PyTorch (MCPG): europäische & amerikanische Optionen mit RSQP‑Risiko, GJR‑GARCH‑Pfade, IV‑Features und Chebyshev‑Pricing inkl. Baselines.

Closed-form solutions and fast calibration & simulation for SABR-based models with mean-reverting stochastic volatility

python pacakge for volatility models with automatic differentiation

Mini project showcasing option pricing, Greeks, and implied volatility in Python for quantitative finance.

Fast and vectorised pricer and implied volatility fitters for Black-Scholes and Merton models

A professional Black-Scholes Option Pricing & Risk Analysis Dashboard built by Aurokrishnaa R L using Python and Streamlit. Includes Greeks, P&L, Sensitivity Heatmaps and Implied Volatility.

Determine implied volatility according to Black-Scholes dynamics.

Pricing & calibration engine (15+ models; API + CLI + Streamlit UI)

Python tool that analyzes market sentiment based on option metrics

Black-Scholes Model - Implementation of the Black-Scholes Model for European option call/put pricing with features including calculating option prices based on market parameters, estimating implied volatility, live data using Yahoo Finance API, heatmap visualisation and visualising option prices against different factors...

A Python library for pricing options under various risk-neutral density assumptions, computing option-implied densities, and extracting model parameters from market data.

Implied volatility surfaces from SPX option chains data (both calls and puts), interpolation for continuous querying, and GUI to visualize surfaces and calculate Black-Scholes prices and IVs

A Python Script To Fetch The Government Securities T-Bills Interest Rates From RBI Website.

A vectorized implementation of py_vollib, that supports numpy arrays and pandas Series and DataFrames.

A Python project that visualizes a 3D implied volatility surface for options on any ticker symbol. Configurable inputs include risk-free rate, dividend yield, and strike price range. Ideal for analyzing how volatility varies with time to expiry, moneyness, and strike price.

Computing implied volatility by Newton-Raphson method

VolSplinesLib is a Python library for interpolating implied volatility surfaces using various volatility models. The library provides tools for fitting and interpolating models to market data, supporting popular methods like RFV, SLV, SABR, and SVI.

OptionsPricerLib is a Python library for pricing financial options using various european and american models. The library provides options pricing, implied volatility calculation, and the Greeks for options, covering models such as Barone-Adesi Whaley, Black-Scholes, Leisen-Reimer, Jarrow-Rudd, and Cox-Ross-Rubinstein.