Python for Finance Cookbook - Second Edition : Over 80 Powerful Recipes for Effective Financial Data Analysis / Eryk Lewinson.

Format:

Book

Author/Creator:

Lewinson, Eryk, author.

Language:

English

Subjects (All):

Finance--Data processing.

Finance.

Python (Computer program language).

Physical Description:

1 online resource (741 pages)

Edition:

Second edition.

Place of Publication:

Birmingham, England : Packt Publishing Ltd., [2022]

Biography/History:

Lewinson Eryk: Eryk Lewinson received his master's degree in Quantitative Finance from Erasmus University Rotterdam. In his professional career, he has gained experience in the practical application of data science methods while working in risk management and data science departments of two "big 4" companies, a Dutch neo-broker and most recently the Netherlands' largest online retailer. Outside of work, he has written over a hundred articles about topics related to data science, which have been viewed more than 3 million times. In his free time, he enjoys playing video games, reading books, and traveling with his girlfriend.

Summary:

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 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 will explore classical quantitative finance approaches to data modeling, such as GARCH, CAPM, factor models, as well as 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 will also learn how to use Streamlit to create elegant, interactive web applications to present the results of technical analyses. 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 using 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.

Contents:

Cover

Copyright

Contributors

Table of Contents

Preface

Chapter 1: Acquiring Financial Data

Getting data from Yahoo Finance

Getting data from Nasdaq Data Link

Getting data from Intrinio

Getting data from Alpha Vantage

Getting data from CoinGecko

Summary

Chapter 2: Data Preprocessing

Converting prices to returns

Adjusting the returns for inflation

Changing the frequency of time series data

Different ways of imputing missing data

Converting currencies

Different ways of aggregating trade data

Chapter 3: Visualizing Financial Time Series

Basic visualization of time series data

Visualizing seasonal patterns

Creating interactive visualizations

Creating a candlestick chart

Chapter 4: Exploring Financial Time Series Data

Outlier detection using rolling statistics

Outlier detection with the Hampel filter

Detecting changepoints in time series

Detecting trends in time series

Detecting patterns in a time series using the Hurst exponent

Investigating stylized facts of asset returns

Chapter 5: Technical Analysis and Building Interactive Dashboards

Calculating the most popular technical indicators

Downloading the technical indicators

Recognizing candlestick patterns

Building an interactive web app for technical analysis using Streamlit

Deploying the technical analysis app

Chapter 6: Time Series Analysis and Forecasting

Time series decomposition

Testing for stationarity in time series

Correcting for stationarity in time series

Modeling time series with exponential smoothing methods

Modeling time series with ARIMA class models

Finding the best-fitting ARIMA model with auto-ARIMA

Chapter 7: Machine Learning-Based Approaches to Time Series Forecasting.

Validation methods for time series

Feature engineering for time series

Time series forecasting as reduced regression

Forecasting with Meta's Prophet

AutoML for time series forecasting with PyCaret

Chapter 8: Multi-Factor Models

Estimating the CAPM

Estimating the Fama-French three-factor model

Estimating the rolling three-factor model on a portfolio of assets

Estimating the four- and five-factor models

Estimating cross-sectional factor models using the Fama-MacBeth regression

Chapter 9: Modeling Volatility with GARCH Class Models

Modeling stock returns' volatility with ARCH models

Modeling stock returns' volatility with GARCH models

Forecasting volatility using GARCH models

Multivariate volatility forecasting with the CCC-GARCH model

Forecasting the conditional covariance matrix using DCC-GARCH

Chapter 10: Monte Carlo Simulations in Finance

Simulating stock price dynamics using a geometric Brownian motion

Pricing European options using simulations

Pricing American options with Least Squares Monte Carlo

Pricing American options using QuantLib

Pricing barrier options

Estimating Value-at-Risk using Monte Carlo

Chapter 11: Asset Allocation

Evaluating an equally-weighted portfolio's performance

Finding the efficient frontier using Monte Carlo simulations

Finding the efficient frontier using optimization with SciPy

Finding the efficient frontier using convex optimization with CVXPY

Finding the optimal portfolio with Hierarchical Risk Parity

Chapter 12: Backtesting Trading Strategies

Vectorized backtesting with pandas

Event-driven backtesting with backtrader

Backtesting a long/short strategy based on the RSI

Backtesting a buy/sell strategy based on Bollinger bands.

Backtesting a moving average crossover strategy using crypto data

Backtesting a mean-variance portfolio optimization

Chapter 13: Applied Machine Learning: Identifying Credit Default

Loading data and managing data types

Exploratory data analysis

Splitting data into training and test sets

Identifying and dealing with missing values

Encoding categorical variables

Fitting a decision tree classifier

Organizing the project with pipelines

Tuning hyperparameters using grid searches and cross-validation

Chapter 14: Advanced Concepts for Machine Learning Projects

Exploring ensemble classifiers

Exploring alternative approaches to encoding categorical features

Investigating different approaches to handling imbalanced data

Leveraging the wisdom of the crowds with stacked ensembles

Bayesian hyperparameter optimization

Investigating feature importance

Exploring feature selection techniques

Exploring explainable AI techniques

Chapter 15: Deep Learning in Finance

Exploring fastai's Tabular Learner

Exploring Google's TabNet

Time series forecasting with Amazon's DeepAR

Time series forecasting with NeuralProphet

Packtpage

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Index.

Notes:

Includes index.

Description based on publisher supplied metadata and other sources.

Description based on print version record.

ISBN:

9781803238838

1803238836

OCLC:

1358413516