Stefan Jansen - Hands-on machine learning for algorithmic trading

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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.

Doug Ortiz is an experienced enterprise cloud, big data, data analytics, and solutions architect who has architected, designed, developed, re-engineered, and integrated enterprise solutions. His other expertise includes Amazon Web Services, Azure, Google Cloud platform, business intelligence, Hadoop, Spark, NoSQL databases, and SharePoint.

He is the founder of Illustris.

Sandipan Dey is a data scientist with a wide range of interests, including topics such as machine learning, deep learning, image processing, and computer vision. He has worked in numerous data science fields, including recommender systems, predictive models for the events industry, sensor localization models, sentiment analysis, and device prognostics. He earned his master's degree in computer science from the University of Maryland, Baltimore County, and has published in a few IEEE data mining conferences and journals.

He has earned certifications from 100+ MOOCs on data science, machine learning, deep learning, image processing, and related courses/specializations. He is a regular blogger on his blog (sandipanweb) and is a machine learning education enthusiast.

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The availability of diverse data has increased the demand for expertise in algorithmic trading strategies. With this book, you will select and apply machine learning (ML) to a broad range of data sources and create powerful algorithmic strategies.

This book will start by introducing you to essential elements, such as evaluating datasets, accessing data APIs using Python, using Quandl to access financial data, and managing prediction errors. We then cover various machine learning techniques and algorithms that can be used to build and train algorithmic models using pandas, Seaborn, StatsModels, and sklearn. We will then build, estimate, and interpret AR(p), MA(q), and ARIMA (p, d, q) models using StatsModels. You will apply Bayesian concepts of prior, evidence, and posterior, in order to distinguish the concept of uncertainty using PyMC3. We will then utilize NLTK, sklearn, and spaCy to assign sentiment scores to financial news and classify documents to extract trading signals. We will learn to design, build, tune, and evaluate feed forward neural networks,