We are building a derivative pricing library with a strong focus on the differentiable programming paradigm. We implement efficient algorithms for sensitivity analysis of various pricing models suitable for fast calibration in realtime trading environments, and benefiting from GPU acceleration for risk evaluation of large trading books. Yet our aim is to avoid as much as possible compromises on computational accuracy and underlying dynamics expressiveness needed in complex modelling set-ups.
We hope also that our approach will make it easier to integrate pricing components directly into algorithmic trading systems and machine learning pipelines, as well as portfolio and margin optimisation platforms.
Hands-on tutorials:
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Introduction: Arbitrage and Risk-Neutral Pricing - a great place to start providing even newcomers with the necessary option pricing background.
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Basic numerical techniques for the Black-Scholes-Merton model
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Calibrating the volatility smile & surface with stochastic volatility models
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American Monte-Carlo with AAD with the LSM algorithm
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Monte-Carlo pricing with the Heston model
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Modern Interest Rates modelling
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Machine Learning applications with Deep Hedging
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