Explore effective trading strategies in real-world markets using NumPy, spaCy, pandas, scikit-learn, 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 workflow 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 fine-tune supervised, unsupervised, and reinforcement learning models
• Optimize portfolio risk and performance using pandas, NumPy, and scikit-learn
• 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

Hands-On 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

Chapter 1: Machine Learning for Trading

Chapter 2: Market and Fundamental Data

Chapter 3: Alternative Data for Finance

Chapter 4: Alpha Factor Research

Chapter 5: Strategy Evaluation

Chapter 6: The Machine Learning Process

Chapter 7: Linear Models

Chapter 8: Time Series Models

Chapter 9: Bayesian Machine Learning

Chapter 10: Decision Trees and Random Forests

Chapter 11: Gradient Boosting Machines

Chapter 12: Unsupervised Learning

Chapter 13: Working with Text Data

Chapter 14: Topic Modeling

Chapter 15: Word Embeddings

Chapter 16: Next Steps

About the Author

Stefan Jansen, CFA is Founder and Lead Data Scientist at Applied AI where he advises Fortune 500 companies and startups across industries on translating business goals into a data and AI strategy, builds data science teams and develops ML solutions. Before his current venture, he was Managing Partner and Lead Data Scientist at an international investment firm where he built the predictive analytics and investment research practice. He was also an executive at a global fintech startup operating in 15 markets, worked for the World Bank, advised Central Banks in emerging markets, and has worked in 6 languages on four continents. Stefan holds Master's from Harvard and Berlin University and teaches data science at General Assembly and Datacamp.