Machine Learning and Data Science Blueprints for Finance
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A01=Brad Lookabaugh
A01=Hariom Tatsat
A01=Sahil Puri
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Author_Brad Lookabaugh
Author_Hariom Tatsat
Author_Sahil Puri
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Machine Learning Deep Learning Data Science Reinforcement Learning Natural Language Processing supervised learning unsupervised learning regression classification principal component analysis clustering techniques trading strategy cryptocurrency Python Pa
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softlaunch
Product details
- ISBN 9781492073055
- Dimensions: 178 x 233mm
- Publication Date: 30 Nov 2020
- Publisher: O'Reilly Media
- Publication City/Country: US
- Product Form: Paperback
- Language: English
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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. You'll examine ML concepts and over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP).
Ideal for professionals working at hedge funds, investment and retail banks, and fintech firms, this book also delves deep into portfolio management, algorithmic trading, derivative pricing, fraud detection, asset price prediction, sentiment analysis, and chatbot development. You'll explore real-life problems faced by practitioners and learn scientifically sound solutions supported by code and examples.
This book covers:
Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio management
Supervised learning classification-based models for credit default risk prediction, fraud detection, and trading strategies
Dimensionality reduction techniques with case studies in portfolio management, trading strategy, and yield curve construction
Algorithms and clustering techniques for finding similar objects, with case studies in trading strategies and portfolio management
Reinforcement learning models and techniques used for building trading strategies, derivatives hedging, and portfolio management
NLP techniques using Python libraries such as NLTK and scikit-learn for transforming text into meaningful representations
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).
