Machine Learning and Data Science Blueprints for Finance
From Building Trading Strategies to Robo-Advisors Using Python
Machine Learning and Data Science Blueprints for Finance shows how to use Python to model financial data. More than 20 case studies connect model choices to tasks such as estimating prices, comparing portfolios, and analyzing financial news.
- 409pages
- 10chapters
- 20+case studies
- 1stedition
This book is for you if you are
- a Python user exploring financial data
- a finance analyst comparing modeling techniques
- a data scientist learning how trading and portfolio problems are framed
Why this book
Suppose you want to estimate a price, group assets that behave similarly, or measure the tone of a news story. Each problem uses financial data, but each calls for a different kind of model. Hariom Tatsat, Sahil Puri, and Brad Lookabaugh use Python case studies to show how the question you ask shapes the data you need, the method you choose, and the way you evaluate the result.
Readers can follow a regression example to see how predictions are tested, then compare that approach with clustering, where the goal is to find patterns without a target value. Later chapters give reinforcement learning and natural language processing their own treatment. The book suits readers who already use Python and want to connect a familiar modeling technique to a concrete task in trading, portfolio analysis, or risk management.
What you'll learn
- Build a Python workflow for a financial dataset
- Compare regression and classification tasks
- Use clustering to group assets with similar behavior
- Explore reinforcement learning for trading decisions
- Analyze financial text with natural language processing
Not the right book for: Readers looking for a first programming course or specific investment recommendations.
Questions about this book
Which language do the examples use?
The book uses Python.
Does the book cover portfolio management?
Yes. The case studies include portfolio management and trading strategies.
Where should I start if I work with text?
Chapter 10 covers natural language processing.
Can I get it in another format?
Yes. You get the PDF right away. Need EPUB, Kindle or Word? Contact us with your order number and we send it in the format you need.
Does it work on Kindle?
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Contents
- 1Machine Learning in Finance: The Landscape
- 2Developing a Machine Learning Model in Python
- 3Artificial Neural Networks
- 4Supervised Learning: Models and Concepts
- 5Supervised Learning: Regression (Including Time Series Models)
- 6Supervised Learning: Classification
- 7Unsupervised Learning: Dimensionality Reduction
- 8Unsupervised Learning: Clustering
- 9Reinforcement Learning
- 10Natural Language Processing
Details
- Publisher
- O’Reilly Media
- Edition
- 1st edition, 2020
- Pages
- 409
- Language
- English
- Formats
- PDF (any other format on request)
- Licence
- Personal use, keep forever
