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Jupyter notebooks – a Swiss Army Knife for Quants

A blog about quantitative finance, data science in fraud detection, machine and deep learning by Matthias Groncki

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Disclaimer

Disclaimer:

This blog is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.

The posts on this blog are my own and don’t represent my employer’s opinions.

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About Me

Matthias Groncki

Matthias Groncki

I am a quantitative developer with more than 10 years of experience in the field of quantitative risk management. Throughout my career, I have gained a strong background in derivatives pricing across multiple asset classes, honing my skills in market and model risk management. My proficiency in programming languages such as C++ and Python has been instrumental in developing robust and efficient risk management solutions, allowing me to leverage my quantitative expertise and technical acumen to drive actionable insights and mitigate potential risks. Additionally, I have gained valuable experience in non-financial and credit risk management, making me a versatile professional capable of tackling diverse risk challenges.

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Categories

  • Allgemein (8)
  • Classification (5)
  • Data Science (5)
  • Deep Learning (3)
  • Fraud Detection (6)
  • Machine Learning (4)
  • Monte Carlo Simulation (7)
  • Pricing (5)
  • Python (6)
  • Quantitative Finance (3)
  • QuantLib (5)
  • TensorFlow (4)
  • xVA (1)

Keywords

American Monte Carlo Automatic Differentiation Bermudan Swaption Classification Counterparty Credit Risk CVA Data Science Deep Learning Dimension reduction Embedding European Swaption Exotic Options Expected Exposure Fraud Fraud Detection Image Classification Interest rate derivates IPython Keras KNIME Logistic Regression LSTM Machine Learning Monte Carlo Simulation NLP Options Ordinary least squares PFE Pricing Python PyTorch QuantLib Recurrent Neural Network Representation Learning Scikit-Learn Short rate model Signature Verification Swap Swaption Swig TensorFlow Transfer Learning Unsupervised Learning

Last Posts

  • Fast Monte-Carlo Pricing and Greeks for Barrier Options using GPU computing on Google Cloud Platform in Python
  • Fraud detection: Behavioural modeling and unsupervised anomaly detection with deep learning
  • Signature Verification with deep learning / transfer learning using Keras and KNIME
  • Fooling Around with KNIME cont’d: Deep Learning
  • Fooling around with KNIME
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