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Python for Finance Second Edition
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Python for Finance Second Edition Financial modeling and quantitative analysis explained
Python for Finance
Financial modeling and quantitative analysis explained
BIRMINGHAM - MUMBAI
Python for Finance
Copyright © 2017 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, and its dealers and distributors will be held liable for any damages
caused or alleged to be 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.
First published: April 2014
Second edition: June 2017
Production reference: 1270617
Published by Packt Publishing Ltd.
35 Livery Street
Birmingham B3 2PB, UK.
Dr. Param Jeet
Nabih Ibrahim Bawazir, M.Sc.
Content Development Editor
Shweta H Birwatkar
About the Author
Yuxing Yan graduated from McGill University with a PhD in nance. Over the
years, he has been teaching various nance courses at eight universities: McGill
University and Wilfrid Laurier University (in Canada), Nanyang Technological
University (in Singapore), Loyola University of Maryland, UMUC, Hofstra
University, University at Buffalo, and Canisius College (in the US).
His research and teaching areas include: market microstructure, open-source nance
and nancial data analytics. He has 22 publications including papers published in
the Journal of Accounting and Finance, Journal of Banking and Finance, Journal
of Empirical Finance, Real Estate Review, Pacic Basin Finance Journal, Applied
Financial Economics, and Annals of Operations Research.
He is good at several computer languages, such as SAS, R, Python, Matlab, and C.
His four books are related to applying two pieces of open-source software to nance:
Python for Finance (2014), Python for Finance (2nd ed., expected 2017), Python for
Finance (Chinese version, expected 2017), and Financial Modeling Using R (2016).
In addition, he is an expert on data, especially on nancial databases. From 2003 to
2010, he worked at Wharton School as a consultant, helping researchers with their
programs and data issues. In 2007, he published a book titled Financial Databases
(with S.W. Zhu). This book is written in Chinese.
Currently, he is writing a new book called Financial Modeling Using Excel — in an
R-Assisted Learning Environment. The phrase "R-Assisted" distinguishes it from
other similar books related to Excel and nancial modeling. New features include
using a huge amount of public data related to economics, nance, and accounting;
an efcient way to retrieve data: 3 seconds for each time series; a free nancial
calculator, showing 50 nancial formulas instantly, 300 websites, 100 YouTube
videos, 80 references, paperless for homework, midterms, and nal exams; easy to
extend for instructors; and especially, no need to learn R.
For the second edition, it has reorganized the structure of the book by adding more chapters related to finance. This is recognition and response to the feedbacks from numerous readers. For the second edition, the first two chapters are exclusively devoted to Python. After that, all remaining chapters are associated with finance.
Creating a good book involves many talented publishing professionals and external reviewers in addition to the author(s). I would like to acknowledge the excellent efforts and input from the staff of my publisher, Packt Publishing, especially Llewellyn F. Rozario, Swati Kumari, Arwa Manasawala, Ruchita Bhansali, Apeksha Chitnis, and Pramila Balan as well as the external reviewers, Martin Olveyra, Mourad MOURAFIQ, and Loucas Parayiannis, for their valuable advice, suggestions, and criticism.
eBook Description: Hands-On Python for Finance: Learn and implement quantitative finance using popular Python libraries like NumPy, pandas, and Keras Python is one of the most popular languages used for quantitative finance. With this book, you’ll explore the key characteristics of Python for finance, solve problems in finance, and understand risk management. The book starts with major concepts and techniques related to quantitative finance, and an introduction to some key Python libraries. Next, you’ll implement time series analysis using pandas and DataFrames. The following chapters will help you gain an understanding of how to measure the diversifiable and non-diversifiable security risk of a portfolio and optimize your portfolio by implementing Markowitz Portfolio Optimization. Sections on regression analysis methodology will help you to value assets and understand the relationship between commodity prices and business stocks. In addition to this, you’ll be able to forecast stock prices using Monte Carlo simulation. The book will also highlight forecast models that will show you how to determine the price of a call option by analyzing price variation. You’ll also use deep learning for financial data analysis and forecasting. In the concluding chapters, you will create neural networks with TensorFlow and Keras for forecasting and prediction. By the end of this Hands-On Python for Finance book, you will be equipped with the skills you need to perform different financial analysis tasks using Python.
最终版，正规书，不是网站转换过来的。 The financial industry has recently adopted Python at a tremendous rate, with some of the largest investment banks and hedge funds using it to build core trading and risk managementsystems. Updated for Python 3, the second edition of this hands-on book helps you get started with the language, guiding developers and quantitative analysts through Python libraries and tools for building financial applications and interactive financial analytics. Using practical examples throughout the book, author Yves Hilpisch also shows you how to develop a full-fledged framework for Monte Carlo simulation-based derivatives and risk analytics, based on a large, realistic case study. Much of the book uses interactive IPython Notebooks.
Understand the fundamentals of Python data structures and work with time-series data Implement key concepts in quantitative finance using popular Python libraries such as NumPy, SciPy, and matplotlib A step-by-step tutorial packed with many Python programs that will help you learn how to apply Python to finance
These days, Python is undoubtedly one of the major strategic technology platforms in the financial industry. When I started writing the first edition of this book in 2013, I still had many conversations and presentations in which I argued relentlessly for Python’s competitive advantages in finance over other languages and platforms. Toward the end of 2018, this is not a question anymore: financial institutions around the world now simply try to make the best use of Python and its powerful ecosystem of data analysis, visualization, and machine learning packages.
这是一个与推荐系统相关的上海市级大学生创新创业项目,旨在探索如何利用反事实推理对个人转让票务的曝光度进行保护。推荐系统在当今互联网应用中扮演着越来越重要的角色,如何在提供个性化推荐的同时,兼顾用户隐私和公平性,是一个值得深入研究的课题。 本项目的研究方向集中在推荐系统(RS)和推荐系统知识框架两个方面。通过构建合适的知识框架,可以更好地理解和表示推荐系统中的各种实体、关系和约束,为设计高效、可解释的推荐算法奠定基础。同时,项目将重点探索如何利用反事实推理来评估和改进推荐结果,提高系统的公平性和鲁棒性。 在研究过程中,项目组将遵循严谨的学术规范和工程实践。为了便于协作和追踪项目进展,每次新建文件夹时,都会同步上传或更新一个readme文档,对文件夹的内容和用途进行清晰的说明。这样不仅可以让组内成员快速了解每个部分的功能,也能为后续的维护和扩展提供便利。 在代码实现方面,项目组将遵循良好的编程规范。除了必要的注释外,代码中尽量避免使用中文,以免出现编译错误或兼容性问题。同时,代码应当模块化、可重用,并通过适当的封装和接口设计,提高代码的可读性和可维护性。
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