IPython_notebooks

Set of Jupyter (iPython) notebooks (and few pdf-presentations) about things that I am interested on, like Computer Science, Statistics and Machine-Learning, Artificial Intelligence (AI), Financial Engineering, Optimization, Stochastic Modelling, Time-Series forecasting, Science in general... and more.

NEW: Neural Networks (RNN/LSTM/GRU) for time-series forecasting in Commodities Markets using TensorFlow/Keras (Here, just the simplest of the univariate versions. If you are interested in the deep-LSTM multivariate version, contact me)
NEW: Auto-Machine Learning Model Selection using GridSearchCV: It has some interesting ideas I am working on... hope you like. If you want to know how to go further, please contact me

My favourites?

Algorithmic Problems in Python: (Computer Science) It contains more than 60 algorithm problems about generators, list-comprehension, map/reduce/filter problems, decorators, regular expressions, simulations, classes... This is still a work-in-progress, but feel free to have a look, and enjoy.
Doing Science with Python: (Python programming) this one is huge, almost a book... and it is still 'work-in-progress' but it contains not only the basics about data-containers, or basic algorithms, but also advance hash-functions and functional programming... but more specifically, or advanced in Python, maybe you find intriguing the notebooks about Decorators, Closures and Wrappers or the one about Iterables, Generetors and Yield, or related with High Performance Computation (HPC) check HPC Cython vs. Multiprocessing out. Hope you like.
Doing Statistics with Python: (Statisitics and Machine Learning) Another huge notebook, in fact, I wrote it thinking in a free eBook, but it is quite general (and basic) in terms of statistics.
- If you look for some more advanced statistics, but always with Python implementations, have a look to the notebooks of Volatility Forecasting, Outliers detection for multivariate datasets, K-means without Scikit or EM-Algorithm for clustering and missing values
- If you want to go deeper in Statistics Techniques for Big (distributed) Data, maybe you should have a look into the PCA for Big Data or into Regression Techniques for Big Data notebooks, where I am using Apache Spark with PySpark.
- If you are working in Machine Learning and Deep Learning, have a look into one of the Support Vector Machines (SVM) notebooks (e.g. Cross Validation in SVM, or Classification Metrics with SVM), and more specifically into Deep Learning in Generative Models: Autoencoders, VANs and GANs notebook with TensorFlow and Keras.
- If you are interested in knowing how to work in modelling with MongoDB in order to store your input/output or model results, have a look to the code of MongoDB, Numpy and Scikit-learn notebook.
Two-Factor Model: (Stochastic Modelling) this one is more technical, from the mathematical point of view, but quite interesting if you are working in commodities modelling, derivatives pricing, or risk-management. This one is the simplest possible version, that made the equivalence between Black-76 and the Two-Factor Model for forward contracts. I will update it soon, with a more object-oriented version. Stay tuned. If you want to have the full object-oriented version, with Robust Calibration of the SDE using Semidefinite Programming and unit-testing, contact me.
Just for FUN: If you like Astronomy like me, check this out: Web-scrapping NASA Astronomy Picture of the day
... and if you are interested in Financial Engineering, you will find interesting the notebooks about:

Financial Engineering
European Option Class in Python
American Options pricing using LSMC
Asian Options pricing using MC with Control Variate
JumpDiffusion Option pricing
Spread Options pricing
Market-Risk and VaR
Swing options

among others... pls, be my guest, and serve yourself.

Have fun!

jpc

This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License.

Name		Name	Last commit message	Last commit date
Latest commit History 559 Commits
.ipynb_checkpoints		.ipynb_checkpoints
.DS_Store		.DS_Store
.gitignore		.gitignore
A JumpDiffusion class for simulation.ipynb		A JumpDiffusion class for simulation.ipynb
Algorithmic Problems in Python.ipynb		Algorithmic Problems in Python.ipynb
Algorithmic Problems in Python3.ipynb		Algorithmic Problems in Python3.ipynb
Asian Option valuation with MC and Control Variate.ipynb		Asian Option valuation with MC and Control Variate.ipynb
Auto-ML Unsupervised.ipynb		Auto-ML Unsupervised.ipynb
Automatic Machine Learning.ipynb		Automatic Machine Learning.ipynb
Classification Metrics with SVM.ipynb		Classification Metrics with SVM.ipynb
Convex Optimization and Quadratic Smoothing Example.ipynb		Convex Optimization and Quadratic Smoothing Example.ipynb
Cross-Validation in SVM.ipynb		Cross-Validation in SVM.ipynb
Decorators, Closures and Wrappers.ipynb		Decorators, Closures and Wrappers.ipynb
Deep_Q_Learning_Network.ipynb		Deep_Q_Learning_Network.ipynb
Doing Science with Python.ipynb		Doing Science with Python.ipynb
Doing Science with Python.pdf		Doing Science with Python.pdf
Doing Statistics with Python.ipynb		Doing Statistics with Python.ipynb
EM_Algorithm.ipynb		EM_Algorithm.ipynb
European Option Class in Python.ipynb		European Option Class in Python.ipynb
Examples Reactive Programming.ipynb		Examples Reactive Programming.ipynb
Generative_Models_Autoencoders_and_GANs.ipynb		Generative_Models_Autoencoders_and_GANs.ipynb
HPC Cython vs Multiprocessing.ipynb		HPC Cython vs Multiprocessing.ipynb
Iterables, Generetors and Yield in Python.ipynb		Iterables, Generetors and Yield in Python.ipynb
K-Means without Scikit-Learn.ipynb		K-Means without Scikit-Learn.ipynb
LICENSE		LICENSE
Least Square Monte Carlo Implementation in a Python Class.ipynb		Least Square Monte Carlo Implementation in a Python Class.ipynb
Market Risk and VaR Estimation Methodologies.ipynb		Market Risk and VaR Estimation Methodologies.ipynb
MongoDB, Numpy and Scikit-learn.ipynb		MongoDB, Numpy and Scikit-learn.ipynb
Multiprocessing MonteCarlo Simulation.ipynb		Multiprocessing MonteCarlo Simulation.ipynb
Multivariate Outliers.ipynb		Multivariate Outliers.ipynb
PCA with BigData.ipynb		PCA with BigData.ipynb
README.md		README.md
RNN_LSTM_GRU_for_Hedge_Funds _flows.ipynb		RNN_LSTM_GRU_for_Hedge_Funds _flows.ipynb
Reactive Programming presentation.pdf		Reactive Programming presentation.pdf
Regression Techniques with BigData.ipynb		Regression Techniques with BigData.ipynb
SQL queries from Python.ipynb		SQL queries from Python.ipynb
SQLite Notebook.ipynb		SQLite Notebook.ipynb
Scikit-Learn_Examples.ipynb		Scikit-Learn_Examples.ipynb
Spread Options.ipynb		Spread Options.ipynb
Swing options valuation with jumps.pdf		Swing options valuation with jumps.pdf
The 3 by 3 Puzzle Problem.ipynb		The 3 by 3 Puzzle Problem.ipynb
Time-Series Analysis and Volatility Forecast.ipynb		Time-Series Analysis and Volatility Forecast.ipynb
Two_Factor_Model-SimpleVersion.ipynb		Two_Factor_Model-SimpleVersion.ipynb
Web Scrapping .ipynb		Web Scrapping .ipynb
Web-scrapping NASA Astronomy Picture.ipynb		Web-scrapping NASA Astronomy Picture.ipynb
Working with Excel from Python.ipynb		Working with Excel from Python.ipynb
ai1_jesus_colino.pdf		ai1_jesus_colino.pdf
playing_with_pandas.ipynb		playing_with_pandas.ipynb

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