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James Ma Weiming - Mastering Python for Finance: Implement advanced state-of-the-art financial statistical applications using Python

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Take your financial skills to the next level by mastering cutting-edge mathematical and statistical financial applicationsKey FeaturesExplore advanced financial models used by the industry and ways of solving them using PythonBuild state-of-the-art infrastructure for modeling, visualization, trading, and moreEmpower your financial applications by applying machine learning and deep learningBook DescriptionThe second edition of Mastering Python for Finance will guide you through carrying out complex financial calculations practiced in the industry of finance by using next-generation methodologies. You will master the Python ecosystem by leveraging publicly available tools to successfully perform research studies and modeling, and learn to manage risks with the help of advanced examples.You will start by setting up your Jupyter notebook to implement the tasks throughout the book. You will learn to make efficient and powerful data-driven financial decisions using popular libraries such as TensorFlow, Keras, Numpy, SciPy, and sklearn. You will also learn how to build financial applications by mastering concepts such as stocks, options, interest rates and their derivatives, and risk analytics using computational methods. With these foundations, you will learn to apply statistical analysis to time series data, and understand how time series data is useful for implementing an event-driven backtesting system and for working with high-frequency data in building an algorithmic trading platform. Finally, you will explore machine learning and deep learning techniques that are applied in finance.By the end of this book, you will be able to apply Python to different paradigms in the financial industry and perform efficient data analysis.What you will learnSolve linear and nonlinear models representing various financial problemsPerform principal component analysis on the DOW index and its componentsAnalyze, predict, and forecast stationary and non-stationary time series processesCreate an event-driven backtesting tool and measure your strategiesBuild a high-frequency algorithmic trading platform with PythonReplicate the CBOT VIX index with SPX options for studying VIX-based strategiesPerform regression-based and classification-based machine learning tasks for predictionUse TensorFlow and Keras in deep learning neural network architectureWho this book is forIf you are a financial or data analyst or a software developer in the financial industry who is interested in using advanced Python techniques for quantitative methods in finance, this is the book you need! You will also find this book useful if you want to extend the functionalities of your existing financial applications by using smart machine learning techniques. Prior experience in Python is required.Table of ContentsOverview of Financial Analysis with PythonThe Importance of Linearity in FinanceNonlinearity in FinanceNumerical Methods for Pricing OptionsModeling Interest Rates and DerivatesStatistical Analysis of Time Series DataInteractive Financial Analytics with VIXBuilding an Algorithmic Trading PlatformImplementing a Backtesting SystemMachine Learning for FinanceDeep Learning for Finance

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Mastering Python for Finance Second Edition Implement advanced - photo 1
Mastering Python for Finance
Second Edition
Implement advanced state-of-the-art financial statistical applications using Python
James Ma Weiming

BIRMINGHAM - MUMBAI Mastering Python for FinanceSecond Edition Copyright - photo 2

BIRMINGHAM - MUMBAI
Mastering Python for FinanceSecond Edition

Copyright 2019 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.

Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

Commissioning Editor: Sunith Shetty
Acquisition Editor: Devika Battike
Content Development Editor: Snehal Kolte
Technical Editor: Manikandan Kurup
Copy Editor: Safis Editing
Project Coordinator: Manthan Patel
Proofreader: Safis Editing
Indexer: Tejal Daruwale Soni
Graphics: Jisha Chirayil
Production Coordinator: Arvindkumar Gupta

First published: April 2015
Second edition: April 2019

Production reference: 1300419

Published by Packt Publishing Ltd.
Livery Place
35 Livery Street
Birmingham
B3 2PB, UK.

ISBN 978-1-78934-646-6

www.packtpub.com


To the team at Skyline Trading on the CBOT trading floor - Mr. Milt Robinson, Brian Hickman, and Frank.
To my family, friends, and colleagues.
And, of course, to Paik Yen.
Contributors
About the author

James Ma Weiming is a software engineer based in Singapore. His studies and research are focused on financial technology, machine learning, data sciences, and computational finance. James started his career in financial services working with treasury fixed income and foreign exchange products, and fund distribution. His interests in derivatives led him to Chicago, where he worked with veteran traders of the Chicago Board of Trade to devise high-frequency, low-latency strategies to game the market. He holds an MS degree in finance from Illinois Tech's Stuart School of Business in the United States and a bachelor's degree in computer engineering from Nanyang Technological University.

About the reviewers

AnilOmanwar has over 11 years' experience in researching cognitive computing, while natural language processing (NLP), machine learning, information visualization, and text analytics are a few key areas of his research interests. He is proficient in sentiment analysis, questionnaire-based feedback, text clustering, and phrase extraction in diverse domains such as banking, oil and gas, life sciences, manufacturing, retail, social media, and others. He is currently associated with IBM Australia for NLP and IBM Watson in the oil and gas domain. The objective of his research is to automate critical manual decisions and assist domain experts to optimize human-machine capabilities. He holds multiple patents on emerging technologies, including NLP automation and device intelligence.

Rahul Shendge has a bachelor's degree in computer engineering from University of Pune and is certified in multiple technologies. He is an open source enthusiast and works as a senior software engineer. He has worked in multiple domains, including finance, healthcare, and education. He has hands-on experience of cloud, designing, and trading algorithms with machine learning. He is constantly exploring technical novelties and he is open-minded and eager to learn about new technologies. He is passionate about helping clients to make valuable business decisions using analytics in respective areas. His interests include working with and exploring data analytics solutions.

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What this book covers

, Overview of Financial Analysis with Python , goes briefly through setting up a Python environment, including a Jupyter Notebook, so that you can proceed with the rest of the chapters in this book. Within Jupyter, we will perform some time series analysis with pandas, using plots for analysis.

, The Importance of Linearity in Finance, uses Python to solve systems of linear equations, perform integer programming, and apply matrix algebra to the linear optimization of portfolio allocation.

, Nonlinearity in Finance , explores some methods that will help us extract information from nonlinear models. You will learn root-finding methods in nonlinear volatility modeling. The optimize module of SciPy contains the root and fsolve functions, which can also help us to perform root finding on non-linear models.

, Numerical Methods for Pricing Options , explores trees, lattices, and finite differencing schemes for the valuation of options.

, Modeling Interest Rates and Derivatives , discusses the bootstrapping process of the yield curve and covers some short-rate models for pricing interest rate derivatives with Python.

, Statistical Analysis of Time Series Data , introduces principal component analysis for identifying principal components. The Dicker-Fuller test is used for testing whether a time series is stationary.

, Interactive Financial Analytics with VIX , discusses volatility indexes. We will perform analytics on a US stock index and VIX data, and replicate the main index using the options prices of the sub-indexes.

, Building an Algorithmic Trading Platform , takes a step-by-step approach to developing a mean-reverting and trend-following live trading infrastructure using a broker API.

, Implementing a Backtesting System , discusses how to design and implement an event-driven backtesting system and helps you to visualize the performance of our simulated trading strategy.

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