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Machine Learning for Finance

By : James Le , Jannes Klaas

4.1 (59)

Machine Learning for Finance

4.1 (59)

By: James Le , Jannes Klaas

Overview of this book

Machine Learning for Finance explores new advances in machine learning and shows how they can be applied across the financial sector, including insurance, transactions, and lending. This book explains the concepts and algorithms behind the main machine learning techniques and provides example Python code for implementing the models yourself. The book is based on Jannes Klaas’ experience of running machine learning training courses for financial professionals. Rather than providing ready-made financial algorithms, the book focuses on advanced machine learning concepts and ideas that can be applied in a wide variety of ways. The book systematically explains how machine learning works on structured data, text, images, and time series. You'll cover generative adversarial learning, reinforcement learning, debugging, and launching machine learning products. Later chapters will discuss how to fight bias in machine learning. The book ends with an exploration of Bayesian inference and probabilistic programming.

Machine Learning for Finance

Machine Learning for Finance

Contributors

Contributors

Preface

Preface

Other Books You May Enjoy

Other Books You May Enjoy

Free Chapter

Neural Networks and Gradient-Based Optimization

Neural Networks and Gradient-Based Optimization

Our journey in this book

What is machine learning?

Supervised learning

Unsupervised learning

Reinforcement learning

Setting up your workspace

Using Kaggle kernels

Using the AWS deep learning AMI

Approximating functions

A forward pass

A logistic regressor

Optimizing model parameters

Measuring model loss

A deeper network

A brief introduction to Keras

Tensors and the computational graph

Exercises

Summary

Applying Machine Learning to Structured Data

Applying Machine Learning to Structured Data

The data

Heuristic, feature-based, and E2E models

The machine learning software stack

The heuristic approach

The feature engineering approach

Preparing the data for the Keras library

Creating predictive models with Keras

A brief primer on tree-based methods

E2E modeling

Exercises

Summary

Utilizing Computer Vision

Utilizing Computer Vision

Convolutional Neural Networks

Filters on color images

The building blocks of ConvNets in Keras

More bells and whistles for our neural network

Working with big image datasets

Working with pretrained models

The modularity tradeoff

Computer vision beyond classification

Exercises

Summary

Understanding Time Series

Understanding Time Series

Visualization and preparation in pandas

Fast Fourier transformations

Autocorrelation

Establishing a training and testing regime

A note on backtesting

Median forecasting

ARIMA

Kalman filters

Forecasting with neural networks

Conv1D

Dilated and causal convolution

Simple RNN

LSTM

Recurrent dropout

Bayesian deep learning

Exercises

Summary

Parsing Textual Data with Natural Language Processing

Parsing Textual Data with Natural Language Processing

An introductory guide to spaCy

Named entity recognition

Part-of-speech (POS) tagging

Rule-based matching

Regular expressions

A text classification task

Preparing the data

Bag-of-words

Topic modeling

Word embeddings

Document similarity with word embeddings

A quick tour of the Keras functional API

Attention

Seq2seq models

Exercises

Summary

Using Generative Models

Using Generative Models

Understanding autoencoders

Visualizing latent spaces with t-SNE

Variational autoencoders

VAEs for time series

GANs

Using less data – active learning

SGANs for fraud detection

Exercises

Summary

Reinforcement Learning for Financial Markets

Reinforcement Learning for Financial Markets

Catch – a quick guide to reinforcement learning

Markov processes and the bellman equation – A more formal introduction to RL

Advantage actor-critic models

Evolutionary strategies and genetic algorithms

Practical tips for RL engineering

Frontiers of RL

Exercises

Summary

Privacy, Debugging, and Launching Your Products

Privacy, Debugging, and Launching Your Products

Debugging data

Debugging your model

Deployment

Performance tips

Exercises

Summary

Fighting Bias

Fighting Bias

Sources of unfairness in machine learning

Legal perspectives

Observational fairness

Training to be fair

Causal learning

Interpreting models to ensure fairness

Unfairness as complex system failure

A checklist for developing fair models

Exercises

Summary

Bayesian Inference and Probabilistic Programming

Bayesian Inference and Probabilistic Programming

An intuitive guide to Bayesian inference

Summary

Farewell

Further reading

Index

Index

Creating predictive models with Keras

Our data now contains the following columns:

amount, 
oldBalanceOrig, 
newBalanceOrig, 
oldBalanceDest, 
newBalanceDest, 
isFraud, 
isFlaggedFraud, 
type_CASH_OUT, 
type_TRANSFER, isNight

Now that we've got the columns, our data is prepared, and we can use it to create a model.

Extracting the target

To train the model, a neural network needs a target. In our case, isFraud is the target, so we have to separate it from the rest of the data. We can do this by running:

y_df = df['isFraud']
x_df = df.drop('isFraud',axis=1)

The first step only returns the isFraud column and assigns it to y_df.

The second step returns all columns except isFraud and assigns them to x_df.

We also need to convert our data from a pandas DataFrame to NumPy arrays. The pandas DataFrame is built on top of NumPy arrays but comes with lots of extra bells and whistles that make all the preprocessing we did earlier possible. To train a neural network, however, we just need the underlying data...

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