Python for Finance Cookbook E-Book

Eryk Lewinson

• Herausgeber: Packt Publishing
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 878

Veröffentlichungsjahr: 2022

Beschreibung

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.

Leseprobe

Python for Finance Cookbook

Second Edition

Over 80 powerful recipes for effective financial data analysis

Eryk Lewinson

BIRMINGHAM—MUMBAI

“Python” and the Python Logo are trademarks of the Python Software Foundation.

Python for Finance Cookbook

Second Edition

Copyright © 2022 Packt Publishing

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First published: Jan 2020

Second edition: Dec 2022

Production reference: 2291222

Published by Packt Publishing Ltd.

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B3 2PB, UK.

ISBN 978-1-80324-319-1

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Contributors

About the author

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.

Writing the second edition of my book was a unique experience. On the one hand, I knew what I should expect. On the other, it proved to be much more challenging in terms of both improving the existing content and expanding upon it. I must also admit that it was a very rewarding feeling to be contacted by readers with kind words about the first edition and valuable feedback on what to add and improve. Thanks to all of that, I have certainly learned a lot, and—in the end—I will remember those times fondly.

I would like to thank Agnieszka for her undeterred support and patience, my brother for once again being my first reader, and my mom for always having my back. I also greatly appreciated all the words of encouragement from my friends and colleagues. Without all of you, completing this book would not have been possible. Thank you.

About the reviewer

Roman Paolucci is a quantitative researcher specializing in the use of applied natural language processing and machine learning to extract equity trading signals. He is the course director and founder of Quant Guild (https://quantguild.com), an online community dedicated to education on topics pertaining to quantitative finance, data science, and software engineering. Roman is also the maintainer and sole contributor of the popular quantitative finance Python library QFin, available on GitHub and PyPi for use in the simulation of stochastic processes and various derivative pricing settings. His current research interests include natural language processing, machine learning for derivative pricing, randomized numerical linear algebra, and optimal portfolio hedging via reinforcement learning.

Thank you to my family and friends—without them none of my work would be possible.

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Contents

Preface

Who this book is for

What this book covers

To get the most out of this book

Get in touch

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

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

Summary

Visualizing Financial Time Series

Basic visualization of time series data

Visualizing seasonal patterns

Creating interactive visualizations

Creating a candlestick chart

Summary

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

Summary

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

Summary

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

Summary

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

Summary

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

Summary

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

Summary

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

Summary

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

Summary

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

Summary

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

Summary

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

Summary

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

Summary

Other Books You May Enjoy

Index

Landmarks

Cover

Index

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