Machine Learning and Data Science Blueprints for Finance: From Building Trading Strategies to Robo-A

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

By Hariom Tatsat, Sahil Puri, and Brad Lookabaugh

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).

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Book Information

Publisher: O'Reilly Media
Publish Date: 12/01/2020
Pages: 432
ISBN-13: 9781492073055
ISBN-10: 1492073059
Language: English

Full Description

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

About the Authors

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.

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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.

Learn More

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