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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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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/
11/11/201812/11/2018https://ipythonquant.wordpress.com/2018/11/11/fraud-detection-unsupervised-anomaly-detection-in-transactional-data-with-rnns-and-embeddings/
Matthias Gronckihttps://ipythonquant.wordpress.com/author/mgroncki/
Leave a commenthttps://ipythonquant.wordpress.com/2018/11/11/fraud-detection-unsupervised-anomaly-detection-in-transactional-data-with-rnns-and-embeddings/#respond
https://www.github.com/mgroncki/frauddetectionhttps://www.github.com/mgroncki/frauddetection
http://colah.github.io/posts/2015-08-Understanding-LSTMs/http://colah.github.io/posts/2015-08-Understanding-LSTMs/
https://www.tensorflow.org/tutorials/representation/word2vechttps://www.tensorflow.org/tutorials/representation/word2vec
https://web.stanford.edu/class/cs124/lec/languagemodeling.pdfhttps://web.stanford.edu/class/cs124/lec/languagemodeling.pdf
https://medium.com/@florijan.stamenkovic_99541/rnn-language-modelling-with-pytorch-packed-batching-and-tied-weights-9d8952db35a9https://medium.com/@florijan.stamenkovic_99541/rnn-language-modelling-with-pytorch-packed-batching-and-tied-weights-9d8952db35a9
https://github.com/mgroncki/FraudDetectionhttps://github.com/mgroncki/FraudDetection
https://www.github.com/pytorch/examples/tree/master/word_language_modelhttps://www.github.com/pytorch/examples/tree/master/word_language_model
gensimhttps://radimrehurek.com/gensim/
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/
26/08/201826/08/2018https://ipythonquant.wordpress.com/2018/08/26/signature-verification-with-deep-learning-transfer-learning-using-keras-and-knime/
Matthias Gronckihttps://ipythonquant.wordpress.com/author/mgroncki/
Leave a commenthttps://ipythonquant.wordpress.com/2018/08/26/signature-verification-with-deep-learning-transfer-learning-using-keras-and-knime/#respond
Machine Learning methodshttps://ipythonquant.wordpress.com/2018/05/08/from-logistic-regression-in-scikit-learn-to-deep-learning-with-tensorflow-a-fraud-detection-case-study-part-i/
Deep Learninghttps://ipythonquant.wordpress.com/2018/06/20/from-logistic-regression-in-scikit-learn-to-deep-learning-with-tensorflow-a-fraud-detection-case-study-part-iii/
KNIMEhttps://ipythonquant.wordpress.com/2018/08/05/fooling-around-with-knime-contd-deep-learning/
VGG16http://www.robots.ox.ac.uk/~vgg/research/very_deep/
Offline Signature Verification with Convolutional Neural Networkshttp://cs231n.stanford.edu/reports/2016/pdfs/276_Report.pdf
http://www.iapr-tc11.org/mediawiki/index.php/ICDAR_2011_Signature_Verification_Competition_(SigComp2011)http://www.iapr-tc11.org/mediawiki/index.php/ICDAR_2011_Signature_Verification_Competition_(SigComp2011)
From Logistic Regression in SciKit-Learn to Deep Learning with TensorFlow – A fraud detection case study – Part IIIhttps://ipythonquant.wordpress.com/2018/06/20/from-logistic-regression-in-scikit-learn-to-deep-learning-with-tensorflow-a-fraud-detection-case-study-part-iii/
20/06/201820/06/2018https://ipythonquant.wordpress.com/2018/06/20/from-logistic-regression-in-scikit-learn-to-deep-learning-with-tensorflow-a-fraud-detection-case-study-part-iii/
Matthias Gronckihttps://ipythonquant.wordpress.com/author/mgroncki/
2 Commentshttps://ipythonquant.wordpress.com/2018/06/20/from-logistic-regression-in-scikit-learn-to-deep-learning-with-tensorflow-a-fraud-detection-case-study-part-iii/#comments
Asian, Barriershttps://ipythonquant.wordpress.com/2018/05/22/tensorflow-meets-quantitative-finance-pricing-exotic-options-with-monte-carlo-simulations-in-tensorflow/
Bermudanshttps://ipythonquant.wordpress.com/2018/06/01/pricing-bermudan-options-in-tensorflow-learning-an-optimal-early-exercise-strategy/
using a LSTM network to learn a delta hedgehttps://ipythonquant.wordpress.com/2018/06/05/option-hedging-with-long-short-term-memory-recurrent-neural-networks-part-i/
parthttps://ipythonquant.wordpress.com/2018/05/18/from-logistic-regression-in-scikit-learn-to-deep-learning-with-tensorflow-a-fraud-detection-case-study-part-ii/
parthttps://ipythonquant.wordpress.com/2018/05/08/from-logistic-regression-in-scikit-learn-to-deep-learning-with-tensorflow-a-fraud-detection-case-study-part-i/
GitHub repositoryhttps://github.com/mgroncki/DataScienceNotebooks
http://www.texample.net/tikz/examples/neural-network/http://www.texample.net/tikz/examples/neural-network/
licensehttps://creativecommons.org/licenses/by/2.5/
GitHub repohttps://github.com/mgroncki/DataScienceNotebooks/blob/master/DataScienceNotebooks/LogisticRegression_PartIII.ipynb
Pricing Bermudan Options in TensorFlow – Learning an optimal Early Exercise Strategyhttps://ipythonquant.wordpress.com/2018/06/01/pricing-bermudan-options-in-tensorflow-learning-an-optimal-early-exercise-strategy/
01/06/201801/06/2018https://ipythonquant.wordpress.com/2018/06/01/pricing-bermudan-options-in-tensorflow-learning-an-optimal-early-exercise-strategy/
Matthias Gronckihttps://ipythonquant.wordpress.com/author/mgroncki/
2 Commentshttps://ipythonquant.wordpress.com/2018/06/01/pricing-bermudan-options-in-tensorflow-learning-an-optimal-early-exercise-strategy/#comments
posthttps://ipythonquant.wordpress.com/2018/05/22/tensorflow-meets-quantitative-finance-pricing-exotic-options-with-monte-carlo-simulations-in-tensorflow/
GitHubhttps://github.com/mgroncki/DataScienceNotebooks/blob/master/BermudanTensorFlow.ipynb
Bermudan Swaptionshttps://ipythonquant.wordpress.com/2016/05/29/exposure-simulation-cva-and-pfe-for-multi-callable-swaps-part-ii/
American Monte Carlo Simulation for CVA calculationshttps://ipythonquant.wordpress.com/2016/06/26/exposure-simulation-part-iii-cva-for-bermudan-swaptions/
https://ipythonquant.wordpress.com/2018/06/01/pricing-bermudan-options-in-tensorflow-learning-an-optimal-early-exercise-strategy/approx_0/
https://ipythonquant.wordpress.com/2018/06/01/pricing-bermudan-options-in-tensorflow-learning-an-optimal-early-exercise-strategy/approx_1/
https://ipythonquant.wordpress.com/2018/06/01/pricing-bermudan-options-in-tensorflow-learning-an-optimal-early-exercise-strategy/approx_2/
GitHubhttps://github.com/mgroncki/DataScienceNotebooks/blob/master/BermudanTensorFlow.ipynb
TensorFlow meets Quantitative Finance: Pricing Exotic Options with Monte Carlo Simulations in TensorFlowhttps://ipythonquant.wordpress.com/2018/05/22/tensorflow-meets-quantitative-finance-pricing-exotic-options-with-monte-carlo-simulations-in-tensorflow/
22/05/2018https://ipythonquant.wordpress.com/2018/05/22/tensorflow-meets-quantitative-finance-pricing-exotic-options-with-monte-carlo-simulations-in-tensorflow/
Matthias Gronckihttps://ipythonquant.wordpress.com/author/mgroncki/
4 Commentshttps://ipythonquant.wordpress.com/2018/05/22/tensorflow-meets-quantitative-finance-pricing-exotic-options-with-monte-carlo-simulations-in-tensorflow/#comments
posthttps://ipythonquant.wordpress.com/2018/05/18/from-logistic-regression-in-scikit-learn-to-deep-learning-with-tensorflow-a-fraud-detection-case-study-part-ii/
notebookhttps://github.com/mgroncki/DataScienceNotebooks/blob/master/BarriersTensorFlow.ipynb
https://people.maths.ox.ac.uk/howison/barriers.pdfhttps://people.maths.ox.ac.uk/howison/barriers.pdf
https://warwick.ac.uk/fac/soc/wbs/subjects/finance/research/wpaperseries/1994/94-54.pdfhttps://warwick.ac.uk/fac/soc/wbs/subjects/finance/research/wpaperseries/1994/94-54.pdf
https://people.maths.ox.ac.uk/gilesm/talks/quant08.pdfhttps://people.maths.ox.ac.uk/gilesm/talks/quant08.pdf
GitHubhttps://github.com/mgroncki/DataScienceNotebooks/blob/master/BarriersTensorFlow.ipynb
From Logistic Regression in SciKit-Learn to Deep Learning with TensorFlow – A fraud detection case study – Part IIhttps://ipythonquant.wordpress.com/2018/05/18/from-logistic-regression-in-scikit-learn-to-deep-learning-with-tensorflow-a-fraud-detection-case-study-part-ii/
18/05/2018https://ipythonquant.wordpress.com/2018/05/18/from-logistic-regression-in-scikit-learn-to-deep-learning-with-tensorflow-a-fraud-detection-case-study-part-ii/
Matthias Gronckihttps://ipythonquant.wordpress.com/author/mgroncki/
5 Commentshttps://ipythonquant.wordpress.com/2018/05/18/from-logistic-regression-in-scikit-learn-to-deep-learning-with-tensorflow-a-fraud-detection-case-study-part-ii/#comments
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GitHubhttps://github.com/mgroncki/DataScienceNotebooks/tree/master/DataScienceNotebooks
kagglehttps://www.kaggle.com/mgroncki/logistic-regression-in-tensorflow
http://ruder.io/optimizing-gradient-descent/index.htmlhttp://ruder.io/optimizing-gradient-descent/index.html
GitHubhttps://github.com/mgroncki/DataScienceNotebooks/blob/master/DataScienceNotebooks/LogisticRegression_Part2.ipynb
kagglehttps://www.kaggle.com/mgroncki/logistic-regression-in-tensorflow
https://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdfhttps://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf
Wikipediahttps://en.wikipedia.org/wiki/Gradient_descent
repositoryhttps://github.com/mgroncki/DataScienceNotebooks
repohttps://github.com/mgroncki/IPythonScripts
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/
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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/
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statsmodelshttp://statsmodels.sourceforge.net
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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
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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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