Machine Learning and Data Science Blueprints for Finance: From Building Trading Strategies to Robo-Advisors Using Python
Tatsat, Hariom, Puri, Sahil, Lookabaugh, Brad
- 出版商: O'Reilly
- 出版日期: 2020-12-01
- 定價: $2,170
- 售價: 9.5 折 $2,062
- 貴賓價: 9.0 折 $1,953
- 語言: 英文
- 頁數: 432
- 裝訂: Quality Paper - also called trade paper
- ISBN: 1492073059
- ISBN-13: 9781492073055
Python、Machine Learning 機器學習 、資料科學
金融機器學習與資料科學藍圖 (Machine Learning and Data Science Blueprints for Finance: From Building Trading Strategies to Robo-Advisors Using Python) (繁中版)
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).