Search Results: python-for-finance

Python for Finance

Analyze Big Financial Data

Author: Yves Hilpisch

Publisher: "O'Reilly Media, Inc."

ISBN: 1491945389

Category: Computers

Page: 606

View: 7161

The financial industry has adopted Python at a tremendous rate recently, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. This hands-on guide helps both developers and quantitative analysts get started with Python, and guides you through the most important aspects of using Python for quantitative finance. Using practical examples through 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, with topics that include: Fundamentals: Python data structures, NumPy array handling, time series analysis with pandas, visualization with matplotlib, high performance I/O operations with PyTables, date/time information handling, and selected best practices Financial topics: mathematical techniques with NumPy, SciPy and SymPy such as regression and optimization; stochastics for Monte Carlo simulation, Value-at-Risk, and Credit-Value-at-Risk calculations; statistics for normality tests, mean-variance portfolio optimization, principal component analysis (PCA), and Bayesian regression Special topics: performance Python for financial algorithms, such as vectorization and parallelization, integrating Python with Excel, and building financial applications based on Web technologies

Mastering Python for Finance

Author: James Ma Weiming

Publisher: Packt Publishing Ltd

ISBN: 1784397873

Category: Computers

Page: 340

View: 9584

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.

Python for Finance

Author: Yuxing Yan

Publisher: Packt Publishing Ltd

ISBN: 1783284382

Category: Computers

Page: 408

View: 9244

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.

Derivatives Analytics with Python - Data Analytics, Models, Simulation, Calibration and Hedging + WS

Author: Yves Hilpisch

Publisher: John Wiley & Sons

ISBN: 1119037999

Category: Business & Economics

Page: 352

View: 7273

Derivatives Analytics with Python: Data Analysis, Models, Simulation, Calibration and Hedging provides the necessary background information, theoretical foundations and numerical tools to implement a market-based valuation of stock index options. Topics are, amongst others, stylized facts of equity and options markets, risk-neutral valuation, Fourier transform methods, Monte Carlo simulation, model calibration, valuation and dynamic hedging. The financial models introduced in this book exhibit features like stochastic volatility, jump components and stochastic short rates. The approach is a practical one in that all important aspects are illustrated by a set of self-contained Python scripts. Benefits of Reading the Book: Data Analysis: Learn how to use Python for data and financial analysis. Reproduce major stylized facts of equity and options markets by yourself. Models: Learn risk-neutral pricing techniques from ground up, apply Fourier transform techniques to European options and advanced Monte Carlo pricing to American options. Simulation: Monte Carlo simulation is the most powerful and flexible numerical method for derivatives analytics. Simulate models with jumps, stochastic volatility and stochastic short rates. Calibration: Use global and local optimization techniques (incl. penalties) to calibrate advanced option pricing models to market quotes for options with different strikes and maturities. Hedging: Learn how to use advanced option pricing models in combination with advanced numerical methods to dynamically hedge American options. Python: All results, graphics, etc. presented are in general reproducible with the Python scripts accompanying the book. Benefit from more than 5,500 lines of code.

Financial Modelling in Python

Author: Shayne Fletcher,Christopher Gardner

Publisher: John Wiley & Sons

ISBN: 0470747897

Category: Business & Economics

Page: 244

View: 5385

"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.

Personal Finance with Python

Using pandas, Requests, and Recurrent

Author: Max Humber

Publisher: Apress

ISBN: 1484238028

Category: Computers

Page: 117

View: 6615

Deal with data, build up financial formulas in code from scratch, and evaluate and think about money in your day-to-day life. This book is about Python and personal finance and how you can effectively mix the two together. In Personal Finance with Python you will learn Python and finance at the same time by creating a profit calculator, a currency converter, an amortization schedule, a budget, a portfolio rebalancer, and a purchase forecaster. Many of the examples use pandas, the main data manipulation tool in Python. Each chapter is hands-on, self-contained, and motivated by fun and interesting examples. Although this book assumes a minimal familiarity with programming and the Python language, if you don't have any, don't worry. Everything is built up piece-by-piece and the first chapters are conducted at a relaxed pace. You'll need Python 3.6 (or above) and all of the setup details are included. What You'll Learn Work with data in pandas Calculate Net Present Value and Internal Rate Return Query a third-party API with Requests Manage secrets Build efficient loops Parse English sentences with Recurrent Work with the YAML file format Fetch stock quotes and use Prophet to forecast the future Who This Book Is For Anyone interested in Python, personal finance, and/or both! This book is geared towards those who want to manage their money more effectively and to those who just want to learn or improve their Python.

Python for Data Analysis

Data Wrangling with Pandas, NumPy, and IPython

Author: Wes McKinney

Publisher: "O'Reilly Media, Inc."

ISBN: 1491957611

Category: Computers

Page: 550

View: 2873

Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples

Python for Finance

Author: Yuxing Yan

Publisher: Packt Publishing Ltd

ISBN: 1787125025

Category: Computers

Page: 586

View: 3471

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.

Mastering Pandas for Finance

Author: Michael Heydt

Publisher: N.A

ISBN: 9781783985104

Category: Computers

Page: 298

View: 8877

If you are interested in quantitative finance, financial modeling, and trading, or simply want to learn how Python and pandas can be applied to finance, then this book is ideal for you. Some knowledge of Python and pandas is assumed. Interest in financial concepts is helpful, but no prior knowledge is expected.

An Introduction to Quantitative Finance

Author: Stephen Blyth

Publisher: Oxford University Press

ISBN: 0199666598

Category: Business & Economics

Page: 175

View: 8809

The quantitative nature of complex financial transactions makes them a fascinating subject area for mathematicians of all types. This book gives an insight into financial engineering while building on introductory probability courses by detailing one of the most fascinating applications of the subject.

Java Methods for Financial Engineering

Applications in Finance and Investment

Author: Philip Barker

Publisher: Springer Science & Business Media

ISBN: 1846287413

Category: Computers

Page: 568

View: 2816

This book describes the principles of model building in financial engineering. It explains those models as designs and working implementations for Java-based applications. The book provides software professionals with an accessible source of numerical methods or ready-to-use code for use in business applications. It is the first book to cover the topic of Java implementations for finance/investment applications and is written specifically to be accessible to software practitioners without prior accountancy/finance training. The book develops a series of packaged classes explained and designed to allow the financial engineer complete flexibility.

Advances in Financial Machine Learning

Author: Marcos Lopez de Prado

Publisher: John Wiley & Sons

ISBN: 1119482119

Category: Business & Economics

Page: 400

View: 9630

Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Readers will learn how to structure Big data in a way that is amenable to ML algorithms; how to conduct research with ML algorithms on that data; how to use supercomputing methods; how to backtest your discoveries while avoiding false positives. The book addresses real-life problems faced by practitioners on a daily basis, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their particular setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.

Machine Learning Applications Using Python

Cases Studies from Healthcare, Retail, and Finance

Author: Puneet Mathur

Publisher: Apress

ISBN: 9781484237861

Category: Computers

Page: 285

View: 323

Gain practical skills in machine learning for finance, healthcare, and retail. This book uses a hands-on approach by providing case studies from each of these domains: you’ll see examples that demonstrate how to use machine learning as a tool for business enhancement. As a domain expert, you will not only discover how machine learning is used in finance, healthcare, and retail, but also work through practical case studies where machine learning has been implemented. Machine Learning Applications Using Python is divided into three sections, one for each of the domains (healthcare, finance, and retail). Each section starts with an overview of machine learning and key technological advancements in that domain. You’ll then learn more by using case studies on how organizations are changing the game in their chosen markets. This book has practical case studies with Python code and domain-specific innovative ideas for monetizing machine learning. What You Will Learn Discover applied machine learning processes and principles Implement machine learning in areas of healthcare, finance, and retail Avoid the pitfalls of implementing applied machine learning Build Python machine learning examples in the three subject areas Who This Book Is For Data scientists and machine learning professionals.

A Course on Statistics for Finance

Author: Stanley L. Sclove

Publisher: CRC Press

ISBN: 1439892547

Category: Business & Economics

Page: 269

View: 7152

Taking a data-driven approach, A Course on Statistics for Finance presents statistical methods for financial investment analysis. The author introduces regression analysis, time series analysis, and multivariate analysis step by step using models and methods from finance. The book begins with a review of basic statistics, including descriptive statistics, kinds of variables, and types of data sets. It then discusses regression analysis in general terms and in terms of financial investment models, such as the capital asset pricing model and the Fama/French model. It also describes mean-variance portfolio analysis and concludes with a focus on time series analysis. Providing the connection between elementary statistics courses and quantitative finance courses, this text helps both existing and future quants improve their data analysis skills and better understand the modeling process.

Algorithmic Trading

Winning Strategies and Their Rationale

Author: Ernie Chan

Publisher: John Wiley & Sons

ISBN: 1118460146

Category: Business & Economics

Page: 207

View: 4270

Markets are now almost always electronic, and as a result of decimalization, algorithmic trading is booming. Algorithmic trading (or "algo trading" as it is referred) is the use of computer programs (such as Excel or MatLab) for entering and executing trading orders with the computer algorithm deciding on certain aspects of the order, such as the timing, price, or even the final quantity of the order. Algorithmic trading may be used in any investment strategy, including market-making, inter-market spreading, arbitrage, or pure speculation (including trend following). The investment decision and implementation may be augmented at any stage with algorithmic support or may operate completely automatically. Algorithmic Trading provides an understanding of the core concepts in quantitative trading, an understanding of the process of using mathematics and statistics to analyze the profitability of a trading model, a “hands on” experience of how backtesting is done, and an understanding of pair trading in stocks, ETFs, futures and currencies. Chan shows investors how to use Excel and MatLab to build their own algo trading tools, as well as FX1, an FX trading platform growing in popularity.He also shows investors how to conduct quantitative research and analysis, and turn quantitative trading strategies into profits using stocks, ETFs, futures, and other financial instruments.

Violent Python

A Cookbook for Hackers, Forensic Analysts, Penetration Testers and Security Engineers

Author: TJ O'Connor

Publisher: Newnes

ISBN: 1597499641

Category: Computers

Page: 288

View: 8214

Violent Python shows you how to move from a theoretical understanding of offensive computing concepts to a practical implementation. Instead of relying on another attacker’s tools, this book will teach you to forge your own weapons using the Python programming language. This book demonstrates how to write Python scripts to automate large-scale network attacks, extract metadata, and investigate forensic artifacts. It also shows how to write code to intercept and analyze network traffic using Python, craft and spoof wireless frames to attack wireless and Bluetooth devices, and how to data-mine popular social media websites and evade modern anti-virus. Demonstrates how to write Python scripts to automate large-scale network attacks, extract metadata, and investigate forensic artifacts Write code to intercept and analyze network traffic using Python. Craft and spoof wireless frames to attack wireless and Bluetooth devices Data-mine popular social media websites and evade modern anti-virus

Python for Experimental Psychologists

Author: Edwin S. Dalmaijer

Publisher: Routledge

ISBN: 1317206436

Category: Psychology

Page: 228

View: 9080

Programming is an important part of experimental psychology and cognitive neuroscience, and Python is an ideal language for novices. It sports a very readable syntax, intuitive variable management, and a very large body of functionality that ranges from simple arithmetic to complex computing. Python for Experimental Psychologists provides researchers without prior programming experience with the knowledge they need to independently script experiments and analyses in Python. The skills it offers include: how to display stimuli on a computer screen; how to get input from peripherals (e.g. keyboard, mouse) and specialised equipment (e.g. eye trackers); how to log data; and how to control timing. In addition, it shows readers the basic principles of data analysis applied to behavioural data, and the more advanced techniques required to analyse trace data (e.g. pupil size) and gaze data. Written informally and accessibly, the book deliberately focuses on the parts of Python that are relevant to experimental psychologists and cognitive neuroscientists. It is also supported by a companion website where you will find colour versions of the figures, along with example stimuli, datasets and scripts, and a portable Windows installation of Python.

What Hedge Funds Really Do

An Introduction to Portfolio Management

Author: Philip J. Romero,Tucker Balch

Publisher: Business Expert Press

ISBN: 1631570900

Category: Business & Economics

Page: 146

View: 9089

When I managed a hedge fund in the late 1990s, computer-based trading was a mysterious technique only available to the largest hedge funds and institutional trading desks. We’ve come a long way since then. With this book, Drs. Romero and Balch lift the veil from many of these once-opaque concepts in high-tech finance. We can all benefit from learning how the cooperation between wetware and software creates fitter models. This book does a fantastic job describing how the latest advances in financial modeling and data science help today’s portfolio managers solve these greater riddles. —Michael Himmel, Managing Partner, Essex Asset Management I applaud Phil Romero’s willingness to write about the hedge fund world, an industry that is very private, often flamboyant, and easily misunderstood. As with every sector of the investment landscape, the hedge fund industry varies dramatically from quantitative “black box” technology, to fundamental research and old-fashioned stock picking. This book helps investors distinguish between these diverse opposites and understand their place in the new evolving world of finance. —Mick Elfers, Founder and Chief Investment Strategist, Irvington Capital

Statistical Tools for Finance and Insurance

Author: Pavel Cizek,Wolfgang Karl Härdle,Rafał Weron

Publisher: Springer Science & Business Media

ISBN: 9783642180620

Category: Business & Economics

Page: 420

View: 8989

Statistical Tools for Finance and Insurance presents ready-to-use solutions, theoretical developments and method construction for many practical problems in quantitative finance and insurance. Written by practitioners and leading academics in the field, this book offers a unique combination of topics from which every market analyst and risk manager will benefit. Features of the significantly enlarged and revised second edition: Offers insight into new methods and the applicability of the stochastic technology Provides the tools, instruments and (online) algorithms for recent techniques in quantitative finance and modern treatments in insurance calculations Covers topics such as - expected shortfall for heavy tailed and mixture distributions* - pricing of variance swaps* - volatility smile calibration in FX markets - pricing of catastrophe bonds and temperature derivatives* - building loss models and ruin probability approximation - insurance pricing with GLM* - equity linked retirement plans*(new topics in the second edition marked with*) Presents extensive examples

Learning Quantitative Finance with R

Author: Dr. Param Jeet,Prashant Vats

Publisher: Packt Publishing Ltd

ISBN: 1786465256

Category: Computers

Page: 284

View: 6856

Implement machine learning, time-series analysis, algorithmic trading and more About This Book Understand the basics of R and how they can be applied in various Quantitative Finance scenarios Learn various algorithmic trading techniques and ways to optimize them using the tools available in R. Contain different methods to manage risk and explore trading using Machine Learning. Who This Book Is For If you want to learn how to use R to build quantitative finance models with ease, this book is for you. Analysts who want to learn R to solve their quantitative finance problems will also find this book useful. Some understanding of the basic financial concepts will be useful, though prior knowledge of R is not required. What You Will Learn Get to know the basics of R and how to use it in the field of Quantitative Finance Understand data processing and model building using R Explore different types of analytical techniques such as statistical analysis, time-series analysis, predictive modeling, and econometric analysis Build and analyze quantitative finance models using real-world examples How real-life examples should be used to develop strategies Performance metrics to look into before deciding upon any model Deep dive into the vast world of machine-learning based trading Get to grips with algorithmic trading and different ways of optimizing it Learn about controlling risk parameters of financial instruments In Detail The role of a quantitative analyst is very challenging, yet lucrative, so there is a lot of competition for the role in top-tier organizations and investment banks. This book is your go-to resource if you want to equip yourself with the skills required to tackle any real-world problem in quantitative finance using the popular R programming language. You'll start by getting an understanding of the basics of R and its relevance in the field of quantitative finance. Once you've built this foundation, we'll dive into the practicalities of building financial models in R. This will help you have a fair understanding of the topics as well as their implementation, as the authors have presented some use cases along with examples that are easy to understand and correlate. We'll also look at risk management and optimization techniques for algorithmic trading. Finally, the book will explain some advanced concepts, such as trading using machine learning, optimizations, exotic options, and hedging. By the end of this book, you will have a firm grasp of the techniques required to implement basic quantitative finance models in R. Style and approach This book introduces you to the essentials of quantitative finance with the help of easy-to-understand, practical examples and use cases in R. Each chapter presents a specific financial concept in detail, backed with relevant theory and the implementation of a real-life example.

Find eBook