Vollab (Volatility Laboratory) is a python package for testing out different approaches to volatility modelling within the field of mathematical finance.
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Updated
Apr 30, 2021 - Jupyter Notebook
Vollab (Volatility Laboratory) is a python package for testing out different approaches to volatility modelling within the field of mathematical finance.
Quantitative Finance Library & Option Trading Tool
Daily Volatility trading strategies on Index Equity Options
Closed-form solutions and fast calibration & simulation for SABR-based models with mean-reverting stochastic volatility
Jupyter notebooks implementing Finance projects
A package that utilises QT and OpenGL graphics to visualise realtime 3D volatility surfaces and analytics.
An interactive toolkit visualising options pricing and Greeks across Black-Scholes and Monte Carlo models with comparative analytics.
Live updating dynamic volatility surface constructed from options prices in C++
No-arbitrage SVI calibration is currently available.
active investing
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
Toolkit for option market research: SABR/SVI baseline calibration, neural network volatility surface models, fast Greeks inference, and reinforcement learning agents for dynamic hedging.
BEVL Toolkit is a Python library for constructing Break-Even Volatility (BEVL) surfaces — the volatility level that makes the expected P&L of a delta-hedged option equal to zero.
Implied Volatility Calibration via raw-SVI
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