Book Image

Hands-On Machine Learning for Algorithmic Trading

By : Stefan Jansen
Book Image

Hands-On Machine Learning for Algorithmic Trading

By: Stefan Jansen

Overview of this book

The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This book enables you to use a broad range of supervised and unsupervised algorithms to extract signals from a wide variety of data sources and create powerful investment strategies. This book shows how to access market, fundamental, and alternative data via API or web scraping and offers a framework to evaluate alternative data. You’ll practice the ML work?ow from model design, loss metric definition, and parameter tuning to performance evaluation in a time series context. You will understand ML algorithms such as Bayesian and ensemble methods and manifold learning, and will know how to train and tune these models using pandas, statsmodels, sklearn, PyMC3, xgboost, lightgbm, and catboost. This book also teaches you how to extract features from text data using spaCy, classify news and assign sentiment scores, and to use gensim to model topics and learn word embeddings from financial reports. You will also build and evaluate neural networks, including RNNs and CNNs, using Keras and PyTorch to exploit unstructured data for sophisticated strategies. Finally, you will apply transfer learning to satellite images to predict economic activity and use reinforcement learning to build agents that learn to trade in the OpenAI Gym.

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Table of Contents (23 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Machine Learning for Trading
Machine Learning for Trading
How to read this book
The rise of ML in the investment industry
Design and execution of a trading strategy
ML and algorithmic trading strategies
Summary
2
Market and Fundamental Data
Market and Fundamental Data
How to work with market data
How to work with fundamental data
Efficient data storage with pandas
Summary
3
Alternative Data for Finance
Alternative Data for Finance
The alternative data revolution
Evaluating alternative datasets
The market for alternative data
Working with alternative data
Summary
4
Alpha Factor Research
Alpha Factor Research
Engineering alpha factors
Seeking signals – how to use zipline
Separating signal and noise – how to use alphalens
Alpha factor resources
Summary
5
Strategy Evaluation
Strategy Evaluation
How to build and test a portfolio with zipline
How to measure performance with pyfolio
How to avoid the pitfalls of backtesting
How to manage portfolio risk and return
Summary
6
The Machine Learning Process
The Machine Learning Process
Learning from data
The machine learning workflow
Summary
7
Linear Models
Linear Models
Linear regression for inference and prediction
The multiple linear regression model
How to build a linear factor model
Shrinkage methods – regularization for linear regression
How to use linear regression to predict returns
Linear classification
Summary
8
Time Series Models
Time Series Models
Analytical tools for diagnostics and feature extraction
Univariate time series models
Multivariate time series models
Summary
9
Bayesian Machine Learning
Bayesian Machine Learning
How Bayesian machine learning works
Probabilistic programming with PyMC3
Summary
10
Decision Trees and Random Forests
Decision Trees and Random Forests
Decision trees
Random forests
Summary
11
Gradient Boosting Machines
Gradient Boosting Machines
Adaptive boosting
Gradient boosting machines
Fast scalable GBM implementations
How to interpret GBM results
Summary
12
Unsupervised Learning
Unsupervised Learning
Dimensionality reduction
Clustering
Summary
13
Working with Text Data
Working with Text Data
How to extract features from text data
From text to tokens – the NLP pipeline
From tokens to numbers – the document-term matrix
Text classification and sentiment analysis
Summary
14
Topic Modeling
Topic Modeling
Learning latent topics: goals and approaches
Latent semantic indexing
Probabilistic latent semantic analysis
Latent Dirichlet allocation
Summary
15
Word Embeddings
Word Embeddings
How word embeddings encode semantics
Word vectors from SEC filings using gensim
Sentiment analysis with Doc2vec
Bonus – Word2vec for translation
Summary
16
Deep Learning
Deep Learning
Deep learning and AI
How to design a neural network
How to build a neural network using Python
How to train a neural network
How to use DL libraries
How to optimize neural network architectures
Summary
17
Convolutional Neural Networks
Convolutional Neural Networks
How ConvNets work
How to design and train a CNN using Python
Transfer learning – faster training with less data
How to detect objects
Recent developments
Summary
18
Recurrent Neural Networks
Recurrent Neural Networks
How RNNs work
How to build and train RNNs using Python
Summary
19
Autoencoders and Generative Adversarial Nets
Autoencoders and Generative Adversarial Nets
How autoencoders work
Designing and training autoencoders using Python
How GANs work
Summary
20
Reinforcement Learning
Reinforcement Learning
Key elements of RL
How to solve RL problems
Dynamic programming – Value and Policy iteration
Q-learning
Deep reinforcement learning
Reinforcement learning for trading
Summary
21
Next Steps
Next Steps
Key takeaways and lessons learned
ML for trading in practice
Conclusion
22
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Shrinkage methods – regularization for linear regression

The least squares methods to train a linear regression model will produce the best, linear, and unbiased coefficient estimates when the Gauss-Markov assumptions are met. Variations like GLS fare similarly well even when OLS assumptions about the error covariance matrix are violated. However, there are estimators that produce biased coefficients to reduce the variance to achieve a lower generalization error overall.

When a linear regression model contains many correlated variables, their coefficients will be poorly determined because the effect of a large positive coefficient on the RSS can be canceled by a similarly large negative coefficient on a correlated variable. Hence, the model will have a tendency for high variance due to this wiggle room of the coefficients that increases the risk that the model overfits...

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