Book description
Explore effective trading strategies in realworld markets using NumPy, spaCy, pandas, scikitlearn, and Keras
Key Features
 Implement machine learning algorithms to build, train, and validate algorithmic models
 Create your own algorithmic design process to apply probabilistic machine learning approaches to trading decisions
 Develop neural networks for algorithmic trading to perform time series forecasting and smart analytics
Book Description
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.
What you will learn
 Implement machine learning techniques to solve investment and trading problems
 Leverage market, fundamental, and alternative data to research alpha factors
 Design and finetune supervised, unsupervised, and reinforcement learning models
 Optimize portfolio risk and performance using pandas, NumPy, and scikitlearn
 Integrate machine learning models into a live trading strategy on Quantopian
 Evaluate strategies using reliable backtesting methodologies for time series
 Design and evaluate deep neural networks using Keras, PyTorch, and TensorFlow
 Work with reinforcement learning for trading strategies in the OpenAI Gym
Who this book is for
HandsOn Machine Learning for Algorithmic Trading is for data analysts, data scientists, and Python developers, as well as investment analysts and portfolio managers working within the finance and investment industry. If you want to perform efficient algorithmic trading by developing smart investigating strategies using machine learning algorithms, this is the book for you. Some understanding of Python and machine learning techniques is mandatory.
Table of contents
 Title Page
 Copyright and Credits
 About Packt
 Contributors
 Preface

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
 Market and Fundamental Data
 Alternative Data for Finance
 Alpha Factor Research

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

The Machine Learning Process
 Learning from data

The machine learning workflow
 Basic walkthrough – knearest neighbors
 Frame the problem – goals and metrics
 Collecting and preparing the data
 Explore, extract, and engineer features
 Selecting an ML algorithm
 Design and tune the model
 How to use crossvalidation for model selection
 Parameter tuning with scikitlearn
 Challenges with crossvalidation in finance
 Summary

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

Time Series Models
 Analytical tools for diagnostics and feature extraction
 Univariate time series models
 Multivariate time series models
 Summary

Bayesian Machine Learning
 How Bayesian machine learning works
 Probabilistic programming with PyMC3
 Summary
 Decision Trees and Random Forests
 Gradient Boosting Machines
 Unsupervised Learning

Working with Text Data
 How to extract features from text data
 From text to tokens – the NLP pipeline
 From tokens to numbers – the documentterm matrix
 Text classification and sentiment analysis
 Summary

Topic Modeling
 Learning latent topics: goals and approaches
 Latent semantic indexing
 Probabilistic latent semantic analysis
 Latent Dirichlet allocation
 Summary

Word Embeddings
 How word embeddings encode semantics
 Word vectors from SEC filings using gensim
 Sentiment analysis with Doc2vec
 Bonus – Word2vec for translation
 Summary
 Deep Learning

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

Recurrent Neural Networks
 How RNNs work
 How to build and train RNNs using Python
 Summary
 Autoencoders and Generative Adversarial Nets

Reinforcement Learning
 Key elements of RL
 How to solve RL problems
 Dynamic programming – Value and Policy iteration
 Qlearning
 Deep reinforcement learning
 Reinforcement learning for trading
 Summary

Next Steps
 Key takeaways and lessons learned
 ML for trading in practice
 Conclusion
 Other Books You May Enjoy
Product information
 Title: HandsOn Machine Learning for Algorithmic Trading
 Author(s):
 Release date: December 2018
 Publisher(s): Packt Publishing
 ISBN: 9781789346411
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