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Python for Finance

Author: Yves Hilpisch
Publisher: O'Reilly Media
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Format Type: PDF, Kindle
Size: 10,99 MB
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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 management systems. 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.


Hands On Python for Finance

Author: Krish Naik
Publisher: Packt Publishing Ltd
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Format Type: PDF
Size: 21,47 MB
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Learn and implement quantitative finance using popular Python libraries like NumPy, pandas, and Keras Key Features Understand Python data structure fundamentals and work with time series data Use popular Python libraries including TensorFlow, Keras, and SciPy to deploy key concepts in quantitative finance Explore various Python programs and learn finance paradigms Book Description 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 book, you will be equipped with the skills you need to perform different financial analysis tasks using Python What you will learn Clean financial data with data preprocessing Visualize financial data using histograms, color plots, and graphs Perform time series analysis with pandas for forecasting Estimate covariance and the correlation between securities and stocks Optimize your portfolio to understand risks when there is a possibility of higher returns Calculate expected returns of a stock to measure the performance of a portfolio manager Create a prediction model using recurrent neural networks (RNN) with Keras and TensorFlow Who this book is for This book is ideal for aspiring data scientists, Python developers and anyone who wants to start performing quantitative finance using Python. You can also make this beginner-level guide your first choice if you’re looking to pursue a career as a financial analyst or a data analyst. Working knowledge of Python programming language is necessary.


Python for Finance

Author: Yuxing Yan
Publisher: Packt Publishing Ltd
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Format Type: PDF
Size: 23,86 MB
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A 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.


Python for Finance

Author: Yves Hilpisch
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Format Type: PDF, Kindle
Size: 13,80 MB
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Mastering Python for Finance

Author: James Ma Weiming
Publisher: Packt Publishing Ltd
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Format Type: PDF
Size: 19,32 MB
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If you are an undergraduate or graduate student, a beginner to algorithmic development and research, or a software developer in the financial industry who is interested in using Python for quantitative methods in finance, this is the book for you. It would be helpful to have a bit of familiarity with basic Python usage, but no prior experience is required.


The Python Bible Volume 5

Author: Florian Dedov
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Format Type: PDF, ePub
Size: 28,65 MB
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ANALYZE YOUR INVESTMENTS WITH PYTHON!Who wants to build long-term wealth needs to invest his capital. But nowadays investing isn't done in the same way as it was a couple of decades ago. Nowadays everything works with computers, algorithms, data science and machine learning. We already know that Python is the lingua franca of these fields. The people who don't educate themselves on this matter will be overrun by the development instead of benefiting from it.In the last volumes we learned a lot about data science and machine learning but we didn't apply these to anything from the real world except for some public datasets for demonstration. This book will focus on applying data science and machine learning onto financial data. We are going to load stock data, visualize it, analyze it and also predict share prices.The Bible of PythonWhy should you spend huge amounts of money and time just to read these 400-500 page books? They are overpriced and very dry to read. Programming is something practical. Of course theory is important but it's possible to keep it simple and precise. This is exactly what you will find in this book! Important theory precisely explained and backed up with lots of practical code. At the same time, you can finish this book in a few days because we are not beating around the bush!After reading this book you will be able to apply the advanced Python knowledge and the machine learning expertise that you've already got to the finance industry. Take time while reading this book and code along. You will learn much more that way. In a nutshell: You will have an amazing basis for your future programming and machine learning career.You'll have the following skills: - Deep Understanding of Machine Learning- Financial Analysis With Python- Analyzing Stock Prices- Visualizing Financial Data and Correlations- Calculating And Plotting Regression Lines - Predicting Share Prices With Machine LearningAlso, more parts of this series will follow and you will have everything structured in the most effective way!Excel at your programming career with The Python Bible


Exam Prep for Mastering Python for Finance

Author: David Mason
Publisher: Rico Publications
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Format Type: PDF
Size: 18,77 MB
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Business is the activity of making one's living or making money by producing or buying and selling products (such as goods and services). This book provides over 2,000 Exam Prep questions and answers to accompany the text Mastering Python for Finance Items include highly probable exam items: Job evaluation, disparate treatment, Employee Free Choice Act, Sweatshop, Offshoring, Alternative Dispute Resolution, social media, Construct validity, Parental leave, Flextime, referent power, Prevailing wage, and more.


Listed Volatility and Variance Derivatives

Author: Yves Hilpisch
Publisher: John Wiley & Sons
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Format Type: PDF, ePub
Size: 23,51 MB
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"Listed Volatility and Variance Derivatives comprehensively covers all aspects related to these now so popular financial products. It is the first to cover European products provided by Eurex and to provide Python codes for implementing all quantitative aspects related to them. Benefits of Reading the Book: - Data Analysis: Learn how to use Python for data and financial analysis. Reproduce major stylized facts of volatility and variance markets by yourself. - Models: Learn the fundamental techniques of modelling volatility (indices) and variance and the model-free replication of variance. - Trading: Learn the micro structure elements of the markets for listed volatility and variance derivatives. - Python: All results, graphics, etc. presented are in general reproducible with the IPython Notebooks and Python codes accompanying the book"--


Financial Modelling in Python

Author: Shayne Fletcher
Publisher: John Wiley & Sons
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Format Type: PDF, Kindle
Size: 28,68 MB
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"Fletcher and Gardner have created a comprehensive resource that will be of interest not only to those working in the field of finance, but also to those using numerical methods in other fields such as engineering, physics, and actuarial mathematics. By showing how to combine the high-level elegance, accessibility, and flexibility of Python, with the low-level computational efficiency of C++, in the context of interesting financial modeling problems, they have provided an implementation template which will be useful to others seeking to jointly optimize the use of computational and human resources. They document all the necessary technical details required in order to make external numerical libraries available from within Python, and they contribute a useful library of their own, which will significantly reduce the start-up costs involved in building financial models. This book is a must read for all those with a need to apply numerical methods in the valuation of financial claims." –David Louton, Professor of Finance, Bryant University This book is directed at both industry practitioners and students interested in designing a pricing and risk management framework for financial derivatives using the Python programming language. It is a practical book complete with working, tested code that guides the reader through the process of building a flexible, extensible pricing framework in Python. The pricing frameworks' loosely coupled fundamental components have been designed to facilitate the quick development of new models. Concrete applications to real-world pricing problems are also provided. Topics are introduced gradually, each building on the last. They include basic mathematical algorithms, common algorithms from numerical analysis, trade, market and event data model representations, lattice and simulation based pricing, and model development. The mathematics presented is kept simple and to the point. The book also provides a host of information on practical technical topics such as C++/Python hybrid development (embedding and extending) and techniques for integrating Python based programs with Microsoft Excel.


Python Guide for Introductory Econometrics for Finance

Author: Chris Brooks
Publisher: Cambridge University Press
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Format Type: PDF, Kindle
Size: 24,36 MB
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This free software guide for Python with freely downloadable datasets brings the econometric techniques to life, showing readers how to implement the approaches presented in Introductory Econometrics for Finance using this highly popular software package. Designed to be used alongside the main textbook, the guide will give readers the confidence and skills to estimate and interpret their own models while the textbook will ensure that they have a thorough understanding of the conceptual underpinnings.