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https://github.com/PacktPublishing/Machine-Learning-for-Finance

Machine Learning for Finance, published by Packt
https://github.com/PacktPublishing/Machine-Learning-for-Finance

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Machine Learning for Finance, published by Packt

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README 

        



# Machine Learning for Finance

This is the code repository for [Machine Learning for Finance](https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-finance), published by [Packt](https://www.packtpub.com/?utm_source=github). It contains all the supporting project files necessary to work through the book from start to finish.



## About the book

Machine Learning for Finance explores new advances in machine learning and shows how they can be applied in the financial sector. It explains the concepts and algorithms behind the main machine learning techniques and provides example Python code for implementing the models yourself.



## How to run this code 

The code in this repository is quite compute heavy and best run on a GPU enabled machine. The datascience platform [Kaggle](http://kaggle.com/) offers free GPU recourses together with free online Jupyter notebooks. To make edits on the Kaggle notebooks, click 'Fork' to create a new copy of the notebook. You will need a Kaggle account for this.



Alternatively you can just view the notebooks on [NB Viewer](http://nbviewer.jupyter.org/github/PacktPublishing/Machine-Learning-for-Finance/tree/master/) or download the code and run it locally.



## Chapter 1 - A neural Network from Scratch

A neural network from Scratch & Intro to Keras: [Run on Kaggle](https://www.kaggle.com/jannesklaas/machine-learning-for-finance-chapter-1-code), [View Online](http://nbviewer.jupyter.org/github/PacktPublishing/Machine-Learning-for-Finance/blob/master/1%20A%20neural%20network%20from%20scratch%20%26%20Intro%20to%20Keras.ipynb)



Excercise excel sheet: [Download](https://github.com/PacktPublishing/Machine-Learning-for-Finance/blob/master/1%20Excel%20Exercise.xlsx)



## Chapter 2 - Structured Data



Credit card fraud detection: [Run On Kaggle](https://www.kaggle.com/jannesklaas/structured-data-code), [View Online](http://nbviewer.jupyter.org/github/PacktPublishing/Machine-Learning-for-Finance/blob/master/2%20structured%20data.ipynb)



## Chapter 3 - Computer Vision Building Blocks

Classifying MNIST digits: [Run On Kaggle](https://www.kaggle.com/jannesklaas/classifying-mnist-digits), [View Online](http://nbviewer.jupyter.org/github/PacktPublishing/Machine-Learning-for-Finance/blob/master/3.1%20MNIST.ipynb)



## Chapter 4 - Practical Computer Vision

Classifying Plants: [View Online](http://nbviewer.jupyter.org/github/PacktPublishing/Machine-Learning-for-Finance/blob/master/3.2%20Plant%20Classification.ipynb), [Run On Colab](https://colab.research.google.com/drive/1fwO5ETB7HsYRHb8k5mU9oU3JtyBGOcML)



Intro to Python Generators: [Run On Kaggle](https://www.kaggle.com/jannesklaas/intro-to-python-generators)



Keras Generator with Logistic Regression: [Run On Kaggle](https://www.kaggle.com/jannesklaas/keras-generator-with-logistic-regression)



Stacking VGG: [Run On Kaggle](https://www.kaggle.com/jannesklaas/stacking-vgg)



Preprocessing and Saving VGG Outputs: [Run On Kaggle](https://www.kaggle.com/jannesklaas/preprocessing-saving-vgg-outputs)



Rule Based Preprocessing and Augmentation: [Run On Kaggle](https://www.kaggle.com/jannesklaas/rule-based-pre-processing-augmentation)



Visualizing ConvNets: [Run On Kaggle](https://www.kaggle.com/jannesklaas/visualizing-convnets)



## Chapter 5 - Time Series 

Forecasting Web Traffic: Classic Methods: [Run On Kaggle](https://www.kaggle.com/jannesklaas/time-series-eda), [View Online](http://nbviewer.jupyter.org/github/PacktPublishing/Machine-Learning-for-Finance/blob/master/4.1%20EDA%20%26%20Classic%20methods.ipynb)



Forecasting Web Traffic: Time Series Neural Nets: [Run On Kaggle](https://www.kaggle.com/jannesklaas/nns-on-time-series), [View Online](http://nbviewer.jupyter.org/github/PacktPublishing/Machine-Learning-for-Finance/blob/master/4.2%20NN%20on%20time%20series.ipynb)



Expressing Uncertainty with Bayesian Deep Learning: [Run On Kaggle](https://www.kaggle.com/jannesklaas/bayesian), [View Online](http://nbviewer.jupyter.org/github/PacktPublishing/Machine-Learning-for-Finance/blob/master/4.3%20Bayesian%20Deep%20Learning.ipynb)



## Chapter 6 - Natural Language processing

Analyzing the News: [Run On Kaggle](https://www.kaggle.com/jannesklaas/analyzing-the-news), [View Online](http://nbviewer.jupyter.org/github/PacktPublishing/Machine-Learning-for-Finance/blob/master/5.1%20Analyzing%20the%20news.ipynb)



Classifying Tweets: [Run On Kaggle](https://www.kaggle.com/jannesklaas/nlp-disasters), [View Online](http://nbviewer.jupyter.org/github/PacktPublishing/Machine-Learning-for-Finance/blob/master/5.2%20Classifying%20Tweets.ipynb)



Topic modeling with LDA: [Run On Kaggle](https://www.kaggle.com/jannesklaas/topic-modeling-with-lda), [View Online](http://nbviewer.jupyter.org/github/PacktPublishing/Machine-Learning-for-Finance/blob/master/5.3%20Topic%20Modeling.ipynb)



Sequence to Sequence models: [Run On Kaggle](https://www.kaggle.com/jannesklaas/a-simple-seq2seq-translator), [View Online](http://nbviewer.jupyter.org/github/PacktPublishing/Machine-Learning-for-Finance/blob/master/5.4%20Translation.ipynb)



## Chapter 7 - Generative Models 

(Variational) Autoencoder for MNIST: [Run On Kaggle](https://www.kaggle.com/jannesklaas/mnist-autoencoder-vae), [View Online](http://nbviewer.jupyter.org/github/PacktPublishing/Machine-Learning-for-Finance/blob/master/6.1%20MNIST%20examples.ipynb)



(Variational) Autoencoder for Fraud Detection: [Run On Kaggle](https://www.kaggle.com/jannesklaas/credit-vae), [View Online](http://nbviewer.jupyter.org/github/PacktPublishing/Machine-Learning-for-Finance/blob/master/6.2%20Fraud%20examples.ipynb)



MNIST DCGAN: [Run On Kaggle](https://www.kaggle.com/jannesklaas/mnist-gan), [View Online](http://nbviewer.jupyter.org/github/PacktPublishing/Machine-Learning-for-Finance/blob/master/6.3%20MNIST%20DCGAN.ipynb)



Semi Supervised Generative Adversarial Network for Fraud Detection: [Run On Kaggle](https://www.kaggle.com/jannesklaas/semi-supervised-gan-for-fraud-detection), [View Online](http://nbviewer.jupyter.org/github/PacktPublishing/Machine-Learning-for-Finance/blob/master/6.4%20SGAN.ipynb)



## Chapter 8 - Reinforcement Learning 

Q-Learning: [View Online](http://nbviewer.jupyter.org/github/PacktPublishing/Machine-Learning-for-Finance/blob/master/7.1%20Q-Learning.ipynb)



A2C Pole Balancing: [View Online](http://nbviewer.jupyter.org/github/PacktPublishing/Machine-Learning-for-Finance/blob/master/7.1%20Q-Learning.ipynb)



A2C for Trading: [Run On Kaggle](https://www.kaggle.com/jannesklaas/a2c-stock-trading) [View Online](http://nbviewer.jupyter.org/github/PacktPublishing/Machine-Learning-for-Finance/blob/master/7.3%20A2C%20Trading.ipynb)



## Chapter 9 - Debugging ML Systems

Unit Testing Data: [Run On Kaggle](https://www.kaggle.com/jannesklaas/marbles-test), [View Online](http://nbviewer.jupyter.org/github/PacktPublishing/Machine-Learning-for-Finance/blob/master/8.1%20Unit%20Testing%20Data.ipynb)



Hyperparameter Optimization: [View Online](http://nbviewer.jupyter.org/github/PacktPublishing/Machine-Learning-for-Finance/blob/master/8.2%20Hyperopt.ipynb)



Learning Rate Search: [View Online](http://nbviewer.jupyter.org/github/PacktPublishing/Machine-Learning-for-Finance/blob/master/8.4%20LR_Search.ipynb)



Using Tensorboard: [View Online](http://nbviewer.jupyter.org/github/PacktPublishing/Machine-Learning-for-Finance/blob/master/8.5%20Tensorboard.ipynb)



Converting Keras Models to TF Estimators: [View Online](http://nbviewer.jupyter.org/github/PacktPublishing/Machine-Learning-for-Finance/blob/master/8.6%20TF%20Estimator.ipynb)



Faster Python with Cython: [Download Part 1](https://github.com/PacktPublishing/Machine-Learning-for-Finance/blob/master/cython_fib_8_7.pyx), [Download Part 2](https://github.com/PacktPublishing/Machine-Learning-for-Finance/blob/master/8_7_cython_setup.py)



## Chapter 10 - Fighting Bias in Machine Learning 



Understanding Parity in Excel: [Download](https://github.com/PacktPublishing/Machine-Learning-for-Finance/blob/master/9.1_parity.xlsx)



Learning How to Pivot: [View Online](http://nbviewer.jupyter.org/github/PacktPublishing/Machine-Learning-for-Finance/blob/master/9.2_Learning_to_be_fair.ipynb)



Interpretability with SHAP: [View Online](http://nbviewer.jupyter.org/github/PacktPublishing/Machine-Learning-for-Finance/blob/master/9.3_SHAP.ipynb)