Yan yuxing (27 Ergebnisse)

Paperback. Zustand: Fair. 2nd ed. The item might be beaten up but readable. May contain markings or highlighting, as well as stains, bent corners, or any other major defect, but the text is not obscured in any way.

Zustand: Good. Learn and implement various Quantitative Finance concepts using the popular Python libraries About This Book ? 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 Who This Book Is For This book assumes that the readers have some basic knowledge related to Python. However, he/she has no knowledge of quantitative finance. In addition, he/she has no knowledge about financial data. What You Will Learn ? Become acquainted with Python in the first two chapters ? Run CAPM, Fama-French 3-factor, and Fama-French-Carhart 4-factor models ? Learn how to price a call, put, and several exotic options ? Understand Monte Carlo simulation, how to write a Python program to replicate the Black-Scholes-Merton options model, and how to price a few exotic options ? Understand the concept of volatility and how to test the hypothesis that volatility changes over the years ? Understand the ARCH and GARCH processes and how to write related Python programs In Detail This book uses Python as its computational tool. Since Python is free, any school or organization can download and use it. This book is organized according to various finance subjects. In other words, the first edition focuses more on Python, while the second edition is truly trying to apply Python to finance. The book starts by explaining topics exclusively related to Python. Then we deal with critical parts of Python, explaining concepts such as time value of money stock and bond evaluations, capital asset pricing model, multi-factor models, time series analysis, portfolio theory, options and futures. This book will help us to learn or review the basics of quantitative finance and apply Python to solve various problems, such as estimating IBM's market risk, running a Fama-French 3-factor, 5-factor, or Fama-French-Carhart 4 factor model, estimating the VaR of a 5-stock portfolio, estimating the optimal portfolio, and constructing the efficient frontier for a 20-stock portfolio with real-world stock, and with Monte Carlo Simulation. Later, we will also learn how to replicate the famous Black-Scholes-Merton option model and how to price exotic options such as the average price call option. Style and approach This book takes a step-by-step approach in explaining the libraries and modules in Python, and how they can be used to implement various aspects of quantitative finance. Each concept is explained in depth and supplemented with code examples for better understanding.…

Paperback. Zustand: Very Good. Illustrated. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.

Paperback. Zustand: Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.

Zustand: Good. Pages intact with minimal writing/highlighting. The binding may be loose and creased. Dust jackets/supplements are not included. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

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Digital. Zustand: New. Key FeaturesBook DescriptionA hands-on guide with easy-to-follow examples to help you learn about option theory, quantitative finance, financial modeling, and time series using Python. Python for Finance is perfect for graduate students, practitioners, and application developers who wish to learn how to utilize Python to handle their financial needs. Basic knowledge of Python will be helpful but knowledge of programming is necessary.What you will learnBuild a financial calculator based on PythonLearn how to price various types of options such as European, American, average, lookback, and barrier optionsWrite Python programs to download data from Yahoo! FinanceEstimate returns and convert daily returns into monthly or annual returnsForm an nstock portfolio and estimate its variancecovariance matrixEstimate VaR (Value at Risk) for a stock or portfolioRun CAPM (Capital Asset Pricing Model) and the FamaFrench 3factor modelLearn how to optimize a portfolio and draw an efficient frontierConduct various statistic tests such as Ttests, Ftests, and normality testsWho this book is for.…

Zustand: good. May show signs of wear, highlighting, writing, and previous use. This item may be a former library book with typical markings. No guarantee on products that contain supplements Your satisfaction is 100% guaranteed. Twenty-five year bookseller with shipments to over fifty million happy customers.…

Zustand: New. pp. 408 3:B&W 7.5 x 9.25 in or 235 x 191 mm Perfect Bound on White w/Gloss Lam.

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Digital. Zustand: New. Learn and implement various Quantitative Finance concepts using the popular Python librariesAbout This Book. 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 financeWho This Book Is ForThis book assumes that the readers have some basic knowledge related to Python. However, he/she has no knowledge of quantitative finance. In addition, he/she has no knowledge about financial data.What You Will Learn. Become acquainted with Python in the first two chapters. Run CAPM, Fama-French 3-factor, and Fama-French-Carhart 4-factor models. Learn how to price a call, put, and several exotic options. Understand Monte Carlo simulation, how to write a Python program to replicate the Black-Scholes-Merton options model, and how to price a few exotic options. Understand the concept of volatility and how to test the hypothesis that volatility changes over the years. Understand the ARCH and GARCH processes and how to write related Python programsIn DetailThis book uses Python as its computational tool. Since Python is free, any school or organization can download and use it.This book is organized according to various finance subjects. In other words, the first edition focuses more on Python, while the second edition is truly trying to apply Python to finance.The book starts by explaining topics exclusively related to Python. Then we deal with critical parts of Python, explaining concepts such as time value of money stock and bond evaluations, capital asset pricing model, multi-factor models, time series analysis, portfolio theory, options and futures.This book will help us to learn or review the basics of quantitative finance and apply Python to solve various problems, such as estimating IBM's market risk, running a Fama-French 3-factor, 5-factor, or Fama-French-Carhart 4 factor model, estimating the VaR of a 5-stock portfolio, estimating the optimal portfolio, and constructing the efficient frontier for a 20-stock portfolio with real-world stock, and with Monte Carlo Simulation. Later, we will also learn how to replicate the famous Black-Scholes-Merton option model and how to price exotic options such as the average price call option.Style and approachThis book takes a step-by-step approach in explaining the libraries and modules in Python, and how they can be used to implement various aspects of quantitative finance. Each concept is explained in depth and supplemented with code examples for better understanding.…

Zustand: As New. Unread book in perfect condition.

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Zustand: As New. Unread book in perfect condition.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book serves as a bridge, leveraging the familiarity of Excel and the power of R to make FinTech accessible to all. Financial Technology (FinTech) has revolutionized areas once dominated by traditional finance. However, the need to learn a programming language often creates a barrier for many learners.Excel-based learning builds confidence with tools that are already familiar to advanced students, while minimal R programming is required no prior R skills needed, just two simple lines of code. Hidden functions unlock powerful FinTech capabilities with ease.With this book, students can learn to generate public and private keys effortlessly,create a Hash for any given phrase, use the Merkle Tree to combine 100 transactions into a block's Hash, develop QR codes for websites or public keys, verify (x,y) values on the Elliptic curve for cryptography, and run models for both Unsupervised and Supervised Learning.The book includes definitions, exercises, and solutions for students to develop the skills to navigate and excel in the world of FinTech. …

Hardcover. Zustand: Brand New. 459 pages. 9.26x6.11x9.36 inches. In Stock.

Digital. Zustand: New. Key FeaturesBook DescriptionA hands-on guide with easy-to-follow examples to help you learn about option theory, quantitative finance, financial modeling, and time series using Python. Python for Finance is perfect for graduate students, practitioners, and application developers who wish to learn how to utilize Python to handle their financial needs. Basic knowledge of Python will be helpful but knowledge of programming is necessary.What you will learnBuild a financial calculator based on PythonLearn how to price various types of options such as European, American, average, lookback, and barrier optionsWrite Python programs to download data from Yahoo! FinanceEstimate returns and convert daily returns into monthly or annual returnsForm an nstock portfolio and estimate its variancecovariance matrixEstimate VaR (Value at Risk) for a stock or portfolioRun CAPM (Capital Asset Pricing Model) and the FamaFrench 3factor modelLearn how to optimize a portfolio and draw an efficient frontierConduct various statistic tests such as Ttests, Ftests, and normality testsWho this book is for.…

Zustand: Gut. Zustand: Gut | Seiten: 586 | Sprache: Englisch | Produktart: Bücher | Learn and implement various Quantitative Finance concepts using the popular Python libraries Key Features: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 Book Description: This book uses Python as its computational tool. Since Python is free, any school or organization can download and use it. This book is organized according to various finance subjects. In other words, the first edition focuses more on Python, while the second edition is truly trying to apply Python to finance. The book starts by explaining topics exclusively related to Python. Then we deal with critical parts of Python, explaining concepts such as time value of money stock and bond evaluations, capital asset pricing model, multi-factor models, time series analysis, portfolio theory, options and futures. This book will help us to learn or review the basics of quantitative finance and apply Python to solve various problems, such as estimating IBM's market risk, running a Fama-French 3-factor, 5-factor, or Fama-French-Carhart 4 factor model, estimating the VaR of a 5-stock portfolio, estimating the optimal portfolio, and constructing the efficient frontier for a 20-stock portfolio with real-world stock, and with Monte Carlo Simulation. Later, we will also learn how to replicate the famous Black-Scholes-Merton option model and how to price exotic options such as the average price call option. What You Will Learn:Become acquainted with Python in the first two chapters Run CAPM, Fama-French 3-factor, and Fama-French-Carhart 4-factor models Learn how to price a call, put, and several exotic options Understand Monte Carlo simulation, how to write a Python program to replicate the Black-Scholes-Merton options model, and how to price a few exotic options Understand the concept of volatility and how to test the hypothesis that volatility changes over the years Understand the ARCH and GARCH processes and how to write related Python programs Who this book is for: This book assumes that the readers have some basic knowledge related to Python. However, he/she has no knowledge of quantitative finance. In addition, he/she has no knowledge about financial data.…

Taschenbuch. Zustand: Neu. Introduction to FinTech using Excel | In an R-assisted Learning Environment | Yuxing Yan | Taschenbuch | Springer Texts in Business and Economics | xxxi | Englisch | 2026 | Springer | EAN 9783031897818 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

Digital. Zustand: New. Learn and implement various Quantitative Finance concepts using the popular Python librariesAbout This Book. 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 financeWho This Book Is ForThis book assumes that the readers have some basic knowledge related to Python. However, he/she has no knowledge of quantitative finance. In addition, he/she has no knowledge about financial data.What You Will Learn. Become acquainted with Python in the first two chapters. Run CAPM, Fama-French 3-factor, and Fama-French-Carhart 4-factor models. Learn how to price a call, put, and several exotic options. Understand Monte Carlo simulation, how to write a Python program to replicate the Black-Scholes-Merton options model, and how to price a few exotic options. Understand the concept of volatility and how to test the hypothesis that volatility changes over the years. Understand the ARCH and GARCH processes and how to write related Python programsIn DetailThis book uses Python as its computational tool. Since Python is free, any school or organization can download and use it.This book is organized according to various finance subjects. In other words, the first edition focuses more on Python, while the second edition is truly trying to apply Python to finance.The book starts by explaining topics exclusively related to Python. Then we deal with critical parts of Python, explaining concepts such as time value of money stock and bond evaluations, capital asset pricing model, multi-factor models, time series analysis, portfolio theory, options and futures.This book will help us to learn or review the basics of quantitative finance and apply Python to solve various problems, such as estimating IBM's market risk, running a Fama-French 3-factor, 5-factor, or Fama-French-Carhart 4 factor model, estimating the VaR of a 5-stock portfolio, estimating the optimal portfolio, and constructing the efficient frontier for a 20-stock portfolio with real-world stock, and with Monte Carlo Simulation. Later, we will also learn how to replicate the famous Black-Scholes-Merton option model and how to price exotic options such as the average price call option.Style and approachThis book takes a step-by-step approach in explaining the libraries and modules in Python, and how they can be used to implement various aspects of quantitative finance. Each concept is explained in depth and supplemented with code examples for better understanding.…

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book serves as a bridge, leveraging the familiarity of Excel and the power of R to make FinTech accessible to all. Financial Technology (FinTech) has revolutionized areas once dominated by traditional finance. However, the need to learn a programming language often creates a barrier for many learners.Excel-based learning builds confidence with tools that are already familiar to advanced students, while minimal R programming is required no prior R skills needed, just two simple lines of code. Hidden functions unlock powerful FinTech capabilities with ease.With this book, students can learn to generate public and private keys effortlessly,create a Hash for any given phrase, use the Merkle Tree to combine 100 transactions into a block's Hash, develop QR codes for websites or public keys, verify (x,y) values on the Elliptic curve for cryptography, and run models for both Unsupervised and Supervised Learning.The book includes definitions, exercises, and solutions for students to develop the skills to navigate and excel in the world of FinTech.…

Hardcover. Zustand: Brand New. 459 pages. 9.26x6.11x9.36 inches. In Stock.

Hardcover. Zustand: new. Hardcover. This book serves as a bridge, leveraging the familiarity of Excel and the power of R to make FinTech accessible to all. Financial Technology (FinTech) has revolutionized areas once dominated by traditional finance. However, the need to learn a programming language often creates a barrier for many learners.Excel-based learning builds confidence with tools that are already familiar to advanced students, while minimal R programming is requiredno prior R skills needed, just two simple lines of code. Hidden functions unlock powerful FinTech capabilities with ease.With this book, students can learn to generate public and private keys effortlessly,create a Hash for any given phrase, use the Merkle Tree to combine 100 transactions into a block's Hash, develop QR codes for websites or public keys, verify (x,y) values on the Elliptic curve for cryptography, and run models for both Unsupervised and Supervised Learning.The book includes definitions, exercises, and solutions for students to develop the skills to navigate and excel in the world of FinTech. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

paperback. Zustand: New. Paperback. Pub Date: 2020-05-01 Pages: 284 language: Chinese Publishing House Anaconda is a powerful open source data science platform. which integrates a lot of good tools. greatly simplifies the work of users Process. and help users solve a series of data scientific puzzles. Anacon .…

paperback. Zustand: New. Paperback. Pub Date: 2017-06-01 Pages: $number Language: Chinese Publisher: People's post and telecommunications publishing house Python. with its simplicity. readability. scalability. and a large and dynamic scientific computing community. The financial sector. which requires data analysis and processing of large amounts of data. has been widely and rapidly applied. and has become an increasing number of professionals .…