This book is for financial analysts, data analysts/scientists, and Python developers who want to learn how to implement a broad range of tasks in the financial domain. This book should also be helpful to data scientists who want to devise intelligent financial strategies in order to perform efficient financial analytics. Working knowledge of the Python programming language is mandatory.
- Title Page
- Copyright and Credits
- Dedication
- About Packt
- Contributors
- Preface
- Financial Data and Preprocessing
- Technical Analysis in Python
- Time Series Modeling
- Multi-Factor Models
- Modeling Volatility with GARCH Class Models
- Monte Carlo Simulations in Finance
- Asset Allocation in Python
- Identifying Credit Default with Machine Learning
- Loading data and managing data types
- Exploratory data analysis
- Splitting data into training and test sets
- Dealing with missing values
- Encoding categorical variables
- Fitting a decision tree classifier
- Implementing scikit-learn's pipelines
- Tuning hyperparameters using grid searches and cross-validation
- Advanced Machine Learning Models in Finance
- Deep Learning in Finance
- Other Books You May Enjoy
Who this book is for
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