Quantitative Trading With R

Quantitative Trading with R PDF
Author: Harry Georgakopoulos
Publisher: Springer
ISBN: 1137437472
Size: 37.70 MB
Format: PDF, ePub
Category : Business & Economics
Languages : en
Pages : 272
View: 5251

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Quantitative Finance with R offers a winning strategy for devising expertly-crafted and workable trading models using the R open source programming language, providing readers with a step-by-step approach to understanding complex quantitative finance problems and building functional computer code.

Learning Quantitative Finance With R

Learning Quantitative Finance with R PDF
Author: Dr. Param Jeet
Publisher: Packt Publishing Ltd
ISBN: 1786465256
Size: 40.74 MB
Format: PDF, Docs
Category : Computers
Languages : en
Pages : 284
View: 5245

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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.

Prac Quantitative Finance W R

PRAC QUANTITATIVE FINANCE W R PDF
Author: Jack Xu
Publisher: Unicad
ISBN: 9780979372575
Size: 24.99 MB
Format: PDF, Mobi
Category : Business & Economics
Languages : en
Pages : 420
View: 2650

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The book provides a complete explanation of R programming in quantitative finance. It demonstrates how to prototype quant models and backtest trading strategies. It pays special attention to creating business applications and reusable R libraries that can be directly used to solve real-world problems in quantitative finance.

Algorithmic Trading With Python

Algorithmic Trading with Python PDF
Author: Chris Conlan
Publisher: Independently Published
ISBN:
Size: 10.97 MB
Format: PDF, ePub, Mobi
Category :
Languages : en
Pages : 126
View: 1020

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Algorithmic Trading with Python discusses modern quant trading methods in Python with a heavy focus on pandas, numpy, and scikit-learn. After establishing an understanding of technical indicators and performance metrics, readers will walk through the process of developing a trading simulator, strategy optimizer, and financial machine learning pipeline. This book maintains a high standard of reprocibility. All code and data is self-contained in a GitHub repo. The data includes hyper-realistic simulated price data and alternative data based on real securities. Algorithmic Trading with Python (2020) is the spiritual successor to Automated Trading with R (2016). This book covers more content in less time than its predecessor due to advances in open-source technologies for quantitative analysis.

Automated Trading With R

Automated Trading with R PDF
Author: Chris Conlan
Publisher: Apress
ISBN: 1484221788
Size: 58.36 MB
Format: PDF, ePub, Mobi
Category : Computers
Languages : en
Pages : 205
View: 4031

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Learn to trade algorithmically with your existing brokerage, from data management, to strategy optimization, to order execution, using free and publicly available data. Connect to your brokerage’s API, and the source code is plug-and-play. Automated Trading with R explains automated trading, starting with its mathematics and moving to its computation and execution. You will gain a unique insight into the mechanics and computational considerations taken in building a back-tester, strategy optimizer, and fully functional trading platform. The platform built in this book can serve as a complete replacement for commercially available platforms used by retail traders and small funds. Software components are strictly decoupled and easily scalable, providing opportunity to substitute any data source, trading algorithm, or brokerage. This book will: Provide a flexible alternative to common strategy automation frameworks, like Tradestation, Metatrader, and CQG, to small funds and retail traders Offer an understanding of the internal mechanisms of an automated trading system Standardize discussion and notation of real-world strategy optimization problems What You Will Learn Understand machine-learning criteria for statistical validity in the context of time-series Optimize strategies, generate real-time trading decisions, and minimize computation time while programming an automated strategy in R and using its package library Best simulate strategy performance in its specific use case to derive accurate performance estimates Understand critical real-world variables pertaining to portfolio management and performance assessment, including latency, drawdowns, varying trade size, portfolio growth, and penalization of unused capital Who This Book Is For Traders/practitioners at the retail or small fund level with at least an undergraduate background in finance or computer science; graduate level finance or data science students

Mastering R For Quantitative Finance

Mastering R for Quantitative Finance PDF
Author: Edina Berlinger
Publisher: Packt Publishing Ltd
ISBN: 1783552085
Size: 14.42 MB
Format: PDF, Mobi
Category : Computers
Languages : en
Pages : 362
View: 1435

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This book is intended for those who want to learn how to use R's capabilities to build models in quantitative finance at a more advanced level. If you wish to perfectly take up the rhythm of the chapters, you need to be at an intermediate level in quantitative finance and you also need to have a reasonable knowledge of R.

Mastering Scientific Computing With R

Mastering Scientific Computing with R PDF
Author: Paul Gerrard
Publisher: Packt Publishing Ltd
ISBN: 1783555262
Size: 35.22 MB
Format: PDF, ePub
Category : Computers
Languages : en
Pages : 432
View: 2332

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If you want to learn how to quantitatively answer scientific questions for practical purposes using the powerful R language and the open source R tool ecosystem, this book is ideal for you. It is ideally suited for scientists who understand scientific concepts, know a little R, and want to be able to start applying R to be able to answer empirical scientific questions. Some R exposure is helpful, but not compulsory.

                                    PDF
Author: روبرت ج. شيللر
Publisher: العبيكان للنشر
ISBN: 9960406490
Size: 50.70 MB
Format: PDF, Kindle
Category : Business & Economics
Languages : ar
Pages : 325
View: 1983

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يقدم روبرت ج. شيللر في هذا الكتاب الهام والعاجل وهو خبير موقر في قابلية التطاير التي يتمتع بها السوق شرحا غير تقليدي لقمم سوق أسهم الولايات المتحدة الحديثة ، ويبين أن تعبير الوفرة اللاعقلانية وصف جيد للحالة السائدة وراء السوق كما يحذر بأن الأداء الأسوء قد يكون في المستقبل القريب وهو يحاول أن يخبرنا كيف يمكننا أن نستجيب لذلك الوضع كمجتمع وبشكل إفرادي

Option Pricing And Estimation Of Financial Models With R

Option Pricing and Estimation of Financial Models with R PDF
Author: Stefano M. Iacus
Publisher: John Wiley & Sons
ISBN: 9781119990208
Size: 30.19 MB
Format: PDF
Category : Business & Economics
Languages : en
Pages : 472
View: 3958

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Presents inference and simulation of stochastic process in the field of model calibration for financial times series modelled by continuous time processes and numerical option pricing. Introduces the bases of probability theory and goes on to explain how to model financial times series with continuous models, how to calibrate them from discrete data and further covers option pricing with one or more underlying assets based on these models. Analysis and implementation of models goes beyond the standard Black and Scholes framework and includes Markov switching models, Lévy models and other models with jumps (e.g. the telegraph process); Topics other than option pricing include: volatility and covariation estimation, change point analysis, asymptotic expansion and classification of financial time series from a statistical viewpoint. The book features problems with solutions and examples. All the examples and R code are available as an additional R package, therefore all the examples can be reproduced.