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Python for Finance Cookbook: Over 80 powerful recipes for effective financial da
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eBay item number:395251037990
Item specifics
- Condition
- ISBN-13
- 9781803243191
- Book Title
- Python for Finance Cookbook
- ISBN
- 9781803243191
- Publication Year
- 2022
- Type
- Textbook
- Format
- Trade Paperback
- Language
- English
- Publication Name
- Python for Finance Cookbook : over 60 Powerful Recipes for Effective Financial Data Analysis
- Publisher
- Packt Publishing, The Limited
- Number of Pages
- 740 Pages
About this product
Product Information
Use modern Python libraries such as pandas, NumPy, and scikit-learn and popular machine learning and deep learning methods to solve financial modeling problems Purchase of the print or Kindle book includes a free eBook in the PDF format Key Features: Explore unique recipes for financial data processing and analysis with Python Apply classical and machine learning approaches to financial time series analysis Calculate various technical analysis indicators and backtesting backtest trading strategies Book Description: Python is one of the most popular programming languages in the financial industry, with a huge collection of accompanying libraries. In this new edition of the Python for Finance Cookbook, you'll explore classical quantitative finance approaches to data modeling, such as GARCH, CAPM, factor models, and modern machine learning and deep learning solutions. You will use popular Python libraries that, in a few lines of code, provide the means to quickly process, analyze, and draw conclusions from financial data. In this new edition, more emphasis was put on exploratory data analysis to help you visualize and better understand financial data. While doing so, you'll also learn how to use Streamlit to create an elegant, interactive web applications to present the results of technical analyses. Finally, you'll become familiar with modern machine learning and deep learning models which you can use for tasks such as credit default prediction, time series forecasting, and more. Using the recipes in this book, you will become proficient in financial data analysis, be it for personal or professional projects. You will also understand which potential issues to expect with such analyses and, more importantly, how to overcome them. What You Will Learn: Preprocess, analyze, and visualize financial data Explore time series modeling with statistical (exponential smoothing, ARIMA) and machine learning models Uncover advanced time series forecasting algorithms such as Meta's Prophet Use Monte Carlo simulations for derivatives valuation and risk assessment Explore volatility modeling using univariate and multivariate GARCH models Investigate various approaches to asset allocation Learn how to approach ML-projects on with an example of default prediction Explore modern deep learning models such as Google's TabNet, Amazon's DeepAR and NeuralProphet Who this book is for: This book is intended for financial analysts, data analysts and scientists, and Python developers with a familiarity with financial concepts. You'll learn how to correctly use advanced approaches for analysis, avoid potential pitfalls and common mistakes, and reach correct conclusions for a broad range of finance problems. Working knowledge of the Python programming language (particularly libraries such as pandas and NumPy) is necessary.
Product Identifiers
Publisher
Packt Publishing, The Limited
ISBN-10
1803243198
ISBN-13
9781803243191
eBay Product ID (ePID)
2330057565
Product Key Features
Publication Name
Python for Finance Cookbook : over 60 Powerful Recipes for Effective Financial Data Analysis
Format
Trade Paperback
Language
English
Publication Year
2022
Type
Textbook
Number of Pages
740 Pages
Additional Product Features
Lc Classification Number
Hg106
Edition Number
2
Topic
Data Modeling & Design, Finance / General, Programming Languages / Python
Dewey Decimal
332.02855133
Dewey Edition
23
Genre
Computers, Business & Economics
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Premier Books LLC
David Taylor
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19806-3356 Wilmington, DE
United States
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eBay item number:395251037990
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