Machine Learning and Data Science Blueprints for Finance - From Building Trading Strategies to Robo-Advisors Using Python

Machine learning and data science will significantly transform the finance industry in the next few years. With this practical guide, professionals at hedge funds, investment and retail banks, and fintech firms will learn how to build ML algorithms crucial to this industry. You'll examine ML concepts and over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP).

About the author

Hariom Tatsat currently works as a Vice President in the Quantitative Analytics division of an investment bank in New York. Hariom has extensive experience as a Quant in the areas of predictive modelling, financial instrument pricing, and risk management in several global investment banks and financial organizations. He completed his MS at UC Berkeley and his BE at IIT Kharagpur (India). Hariom has also completed FRM (Financial Risk Manager), CQF (Certificate in Quantitative Finance) and is a candidate for CFA Level 3.

Sahil Puri works as a Quantitative Researcher in the Analytics Division at P.I.M.C.O. His work involves testing model assumptions and finding strategies for multiple asset classes. Sahil has applied multiple statistical and machine learning based techniques to a wide variety of problems; examples include: generating text features, labeling curve anomalies, non-linear risk factor detection, and time series prediction. He completed his MS at UC Berkeley and his BE at Delhi College of Engineering (India).

Summary

Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry.

Product details

• Authors: Brad Lookabaugh, Sahil Puri, Hariom Tatsat
• Publisher: O'Reilly
• Languages: English
• Product format: Paperback / Softback
• Released: 28.02.2021
• EAN: 9781492073055
• ISBN: 978-1-4920-7305-5
• Dimensions: 178 mm x 233 mm x 24 mm
• Weight: 742 g
• Subjects: Natural sciences, medicine, IT, technology > IT, data processing > General, dictionaries; Social sciences, law, business > Business > Business administration; machine learning, Finance, BUSINESS & ECONOMICS / Finance / General, COMPUTERS / Machine Theory, COMPUTERS / Artificial Intelligence / General, Database design and theory, COMPUTERS / Data Science / Data Modeling & Design, COMPUTERS / Data Science / Machine Learning, COMPUTERS / Languages / Python, Finance and the finance industry, Database design & theory