Sunday, September 6, 2015

An Option Greeks Primer: Building Intuition with Delta Hedging and Monte Carlo Simulation using Excel (repost)




An Option Greeks Primer: Building Intuition with Delta Hedging and Monte Carlo Simulation using Excel (Global Financial Markets) by Jawwad Farid


English | 2015 | ISBN: 1137371668 | 280 pages | PDF | 29,2 MB




Trading requires a combination of intuition, discipline and process. Of the three, intuition is the most difficult to teach. While individual intuition can be built over years of experience, there are tools that make it easier to pick up and transfer intuition faster. Furthermore, a lack of intuition and over-reliance on computational schemes is considered one of the key contributors to the financial crisis.




This book provides a hands-on, practical guide to delta hedging and Greeks, with a focus on intuition. Written by an experienced consultant, teacher and trainer, it is written for the many practitioners who need to understand the myriad relationships between options Greeks but lack the PhD necessary to penetrate much of the current literature. Written in accessible language, the book builds up a foundation of knowledge on basic quantitative finance concepts, before moving on to explain advanced topics and approaches for Delta, Gamma, Vega, Vanna, Volga, Theta and Rho. Using an Excel based Delta Hedging simulation model the book examines the impact of Greeks on option trading P&L and shows how to hedge higher order Greeks and build volatility surfaces.




The book will appeal to many in the investment banking arena, from traders and risk managers, to sales and marketing teams within capital markets and FICCs groups who need a thorough but not overly quantitative understanding of option Greeks.












William Graham Sumner - L’uomo dimenticato




William Graham Sumner – L’uomo dimenticato


Italian | 2012 | ASIN: B009935UAO | EPUB/PDF/AZW3/MOBi | 22 pages | 4.0 MB




Un testo classico di un pioniere della sociologia americana – ardente difensore del laissez-faire, anti-imperialista e ammiratore delle idee di Herbert Spencer – in cui si prendono le difese di quanti non chiedono aiuto alle autorità pubbliche e preferiscono affrontare in autonomia e con grande dignità le difficoltà della vita.







Mastering Python for Data Science




Mastering Python for Data Science by Samir


English | 31 Aug. 2015 | ISBN: 1784390151 | 294 Pages | EPUB/MOBI/PDF (True) | 30.1 MB




If you are a Python developer who wants to master the world of data science then this book is for you. Some knowledge of data science is assumed.




Explore the world of data science through Python and learn how to make sense of data




About This Book




Master data science methods using Python and its libraries


Create data visualizations and mine for patterns


Advanced techniques for the four fundamentals of Data Science with Python – data mining, data analysis, data visualization, and machine learning




What You Will Learn




Manage data and perform linear algebra in Python


Derive inferences from the analysis by performing inferential statistics


Solve data science problems in Python


Create high-end visualizations using Python


Evaluate and apply the linear regression technique to estimate the relationships among variables.


Build recommendation engines with the various collaborative filtering algorithms


Apply the ensemble methods to improve your predictions


Work with big data technologies to handle data at scale




In Detail




Data science is a relatively new knowledge domain which is used by various organizations to make data driven decisions. Data scientists have to wear various hats to work with data and to derive value from it. The Python programming language, beyond having conquered the scientific community in the last decade, is now an indispensable tool for the data science practitioner and a must-know tool for every aspiring data scientist. Using Python will offer you a fast, reliable, cross-platform, and mature environment for data analysis, machine learning, and algorithmic problem solving.




This comprehensive guide helps you move beyond the hype and transcend the theory by providing you with a hands-on, advanced study of data science.




Beginning with the essentials of Python in data science, you will learn to manage data and perform linear algebra in Python. You will move on to deriving inferences from the analysis by performing inferential statistics, and mining data to reveal hidden patterns and trends. You will use the matplot library to create high-end visualizations in Python and uncover the fundamentals of machine learning. Next, you will apply the linear regression technique and also learn to apply the logistic regression technique to your applications, before creating recommendation engines with various collaborative filtering algorithms and improving your predictions by applying the ensemble methods.




Finally, you will perform K-means clustering, along with an analysis of unstructured data with different text mining techniques and leveraging the power of Python in big data analytics.




Style and approach




This book is an easy-to-follow, comprehensive guide on data science using Python. The topics covered in the book can all be used in real world scenarios.