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Title: Python | Jupyter notebooks – a Swiss Army Knife for Quants

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Jupyter notebooks – a Swiss Army Knife for Quantshttps://ipythonquant.wordpress.com/
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From Logistic Regression in SciKit-Learn to Deep Learning with TensorFlow – A fraud detection case study – Part Ihttps://ipythonquant.wordpress.com/2018/05/08/from-logistic-regression-in-scikit-learn-to-deep-learning-with-tensorflow-a-fraud-detection-case-study-part-i/
08/05/2018https://ipythonquant.wordpress.com/2018/05/08/from-logistic-regression-in-scikit-learn-to-deep-learning-with-tensorflow-a-fraud-detection-case-study-part-i/
Matthias Gronckihttps://ipythonquant.wordpress.com/author/mgroncki/
7 Commentshttps://ipythonquant.wordpress.com/2018/05/08/from-logistic-regression-in-scikit-learn-to-deep-learning-with-tensorflow-a-fraud-detection-case-study-part-i/#comments
GitHubhttps://github.com/mgroncki/IPythonScripts/blob/master/LogisticRegression_Part1.ipynb
kagglehttps://www.kaggle.com/mgroncki/logistic-regression-in-sk-learn
kagglehttps://www.kaggle.com/mlg-ulb/creditcardfraud
Anaconda Cloudhttps://anaconda.org
tutorialshttp://pandas.pydata.org/pandas-docs/stable/tutorials.html
https://github.com/scikit-learn-contrib/imbalanced-learnhttps://github.com/scikit-learn-contrib/imbalanced-learn
http://scikit-learn.org/stable/modules/linear_model.html#logistic-regressionhttp://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
Exposure Simulation Part III / CVA for Bermudan Swaptionshttps://ipythonquant.wordpress.com/2016/06/26/exposure-simulation-part-iii-cva-for-bermudan-swaptions/
26/06/201626/06/2016https://ipythonquant.wordpress.com/2016/06/26/exposure-simulation-part-iii-cva-for-bermudan-swaptions/
Matthias Gronckihttps://ipythonquant.wordpress.com/author/mgroncki/
1 Commenthttps://ipythonquant.wordpress.com/2016/06/26/exposure-simulation-part-iii-cva-for-bermudan-swaptions/#comments
previous postshttps://ipythonquant.wordpress.com/2015/04/13/cva-calculation-with-quantlib-and-python/
posthttps://ipythonquant.wordpress.com/2016/05/29/exposure-simulation-cva-and-pfe-for-multi-callable-swaps-part-ii/
herehttp://nbviewer.jupyter.org/github/mgroncki/IPythonScripts/blob/master/MonteCarloPricing-Swaption-Part_III.ipynb
Exposure Simulation / CVA and PFE for multi-callable swaps Part IIhttps://ipythonquant.wordpress.com/2016/05/29/exposure-simulation-cva-and-pfe-for-multi-callable-swaps-part-ii/
29/05/201629/05/2016https://ipythonquant.wordpress.com/2016/05/29/exposure-simulation-cva-and-pfe-for-multi-callable-swaps-part-ii/
Matthias Gronckihttps://ipythonquant.wordpress.com/author/mgroncki/
3 Commentshttps://ipythonquant.wordpress.com/2016/05/29/exposure-simulation-cva-and-pfe-for-multi-callable-swaps-part-ii/#comments
last post https://ipythonquant.wordpress.com/2015/05/02/exposure-simulation-pfe-and-cva-for-multi-callable-swaps-bermudan-swaptions-part-1-of-3/
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nbViewerhttp://nbviewer.jupyter.org/github/mgroncki/IPythonScripts/blob/master/MonteCarloPricing-Swaption-Part_II.ipynb
Exposure simulation / PFE and CVA for multi-callable swaps / Bermudan swaptions… Part 1 of 3https://ipythonquant.wordpress.com/2015/05/02/exposure-simulation-pfe-and-cva-for-multi-callable-swaps-bermudan-swaptions-part-1-of-3/
02/05/201502/05/2015https://ipythonquant.wordpress.com/2015/05/02/exposure-simulation-pfe-and-cva-for-multi-callable-swaps-bermudan-swaptions-part-1-of-3/
Matthias Gronckihttps://ipythonquant.wordpress.com/author/mgroncki/
3 Commentshttps://ipythonquant.wordpress.com/2015/05/02/exposure-simulation-pfe-and-cva-for-multi-callable-swaps-bermudan-swaptions-part-1-of-3/#comments
potential future exposurehttps://ipythonquant.wordpress.com/2015/04/08/expected-exposure-and-pfe-simulation-with-quantlib-and-python/
credit value adjustmenthttps://ipythonquant.wordpress.com/2015/04/13/cva-calculation-with-quantlib-and-python/
QuantLib forkhttps://github.com/mgroncki/quantlib/tree/SwigSwapExtension
nbviewerhttp://nbviewer.ipython.org/github/mgroncki/IPythonScripts/blob/master/MonteCarloPricing-Swaption.ipynb
GitHubhttps://github.com/mgroncki/IPythonScripts
Expected Exposure and PFE simulation with QuantLib and Pythonhttps://ipythonquant.wordpress.com/2015/04/08/expected-exposure-and-pfe-simulation-with-quantlib-and-python/
08/04/201516/04/2015https://ipythonquant.wordpress.com/2015/04/08/expected-exposure-and-pfe-simulation-with-quantlib-and-python/
Matthias Gronckihttps://ipythonquant.wordpress.com/author/mgroncki/
8 Commentshttps://ipythonquant.wordpress.com/2015/04/08/expected-exposure-and-pfe-simulation-with-quantlib-and-python/#comments
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A brief introduction to the QuantLib in Python…https://ipythonquant.wordpress.com/2015/04/04/a-brief-introduction-to-the-quantlib-in-python/
04/04/201508/04/2015https://ipythonquant.wordpress.com/2015/04/04/a-brief-introduction-to-the-quantlib-in-python/
Matthias Gronckihttps://ipythonquant.wordpress.com/author/mgroncki/
7 Commentshttps://ipythonquant.wordpress.com/2015/04/04/a-brief-introduction-to-the-quantlib-in-python/#comments
swighttp://www.swig.org
websitehttp://www.lfd.uci.edu/~gohlke/pythonlibs/
GitHubhttps://github.com/lballabio/quantlib
boost libraryhttp://www.boost.org
swighttp://swig.org
herehttp://quantlib.org/install/linux.shtml
herehttp://quantlib.org/install/macosx.shtml
IPythonScriptshttp://github.com/mgroncki/ipythonscripts
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Expected Exposurehttps://ipythonquant.wordpress.com/tag/expected-exposure/
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Fast Monte-Carlo Pricing and Greeks for Barrier Options using GPU computing on Google Cloud Platform in Pythonhttps://ipythonquant.wordpress.com/2018/11/18/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 learninghttps://ipythonquant.wordpress.com/2018/11/11/fraud-detection-unsupervised-anomaly-detection-in-transactional-data-with-rnns-and-embeddings/
Signature Verification with deep learning / transfer learning using Keras and KNIMEhttps://ipythonquant.wordpress.com/2018/08/26/signature-verification-with-deep-learning-transfer-learning-using-keras-and-knime/
Fooling Around with KNIME cont’d: Deep Learninghttps://ipythonquant.wordpress.com/2018/08/05/fooling-around-with-knime-contd-deep-learning/
Fooling around with KNIMEhttps://ipythonquant.wordpress.com/2018/07/19/fooling-around-with-knime/
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