Python for Finance Cookbook: Over 50 recipes for applying modern Python libraries to financial data analysis
by: Eryk Lewinson
Book Description
Print Length 页数: 432 pages
Publisher finelybook 出版社: Packt Publishing (January 31,2020)
Language 语言: English
ISBN-10: 1789618517
ISBN-13: 9781789618518
Book Description
Solve common and not-so-common financial problems using Python libraries such as NumPy,SciPy,and pandas
Python is one of the most popular languages used with a huge set of libraries in the financial industry.
In this book,you’ll cover different ways of downloading financial data and preparing it for modeling. You’ll calculate popular indicators used in technical analysis,such as Bollinger Bands,MACD,and RSI,and backtest automatic trading strategies. Next,you’ll cover time series analysis and models such as exponential smoothing,ARIMA,and GARCH (including multivariate specifications),before exploring the popular CAPM and Fama-French’s Three-Factor Model. You’ll then discover how to optimize asset allocation and use Monte Carlo simulations for tasks such as calculating the price of American options and estimating the Value at Risk (VaR). In later chapters,you’ll work through an entire data science project in the finance domain. You’ll also learn how to solve credit card fraud and default problems using advanced classifiers such as random forest,XGBoost,LightGBM,and stacked models. You’ll then be able to tune the hyperparameters of models and handle class imbalance. Finally,you’ll focus on solving problems in finance with deep learning using PyTorch.
By the end of this book,you’ll have learned how to effectively analyze financial time series using a recipe-based approach.
What you will learn
Download and preprocess financial data from different sources
Backtest the performance of automatic trading strategies in a real-world setting
Create financial econometrics models in Python and interpret their results
Use Monte Carlo simulations for a variety of tasks
Improve the performance of financial models with the latest Python libraries
Apply machine learning and deep learning techniques to solve different financial problems
Understand the different approaches used to model financial time series data
Contents
Preface
Chapter 1: Financial Data and Preprocessing
Chapter 2: Technical Analysis in Python
Chapter 3: Time Series Modeling
Chapter 4: Multi-Factor Models
Chapter 5: Modeling Volatility with GARCH Class Models
Chapter 6: Monte Carlo Simulations in Finance
Chapter 7: Asset Allocation in Python
Chapter 8: Identifying Credit Default with Machine Learning
Chapter 9: Advanced Machine Learning Models in Finance
Chapter 10: Deep Learning in Finance
other Books You May Enjoy
Index
