Python for Finance: Mastering Data-Driven Finance

Uso esse produto em minhas atividades de pesquisa na área em questão.

Was expecting at least some pages to have colour and to be printed on standard quality textbook paper, especially for this price, but it feels like one of those $20 international versions of a textbook

"Python is the programming language and technology platform of choice, not only for this book but for almost every leading financial institution. However, Python deployment can be tricky at best and sometimes even tedious and nerve-wracking. Fortunately, several technologies that can help with the deployment issue have become available in recent years." Python for Finance, page 56.Dr. Hilpisch's book is an end to end explanation and demonstration of the complete process of setting up and using Python for financial data science.He begins with selection of software and installation on either a local computer or on cloud facilities. He has chosen a set of software packages that are fully compatible with each other, easily installed, open source and free, well documented, and well supported.The next few chapters review the structure and use of Python. The examples are well chosen and clearly explained. Real financial data is used when possible. He addresses the criticism that Python is slow by showing that alternative methods -- sometimes as simple as rewriting a single line of code -- can result in significantly improved execution speed.Analysts spend large portions of their time and effort on data preparation. Beginning with real financial data, well chosen examples show how to inspect, clean, transform, and display data series.Analysis of risk and opportunity requires understanding of the distributions involved. Dr. Hilpisch devotes several chapters to Monte Carlo analysis. Illustrative examples include pricing of derivatives.The sections of the book that discuss algorithmic trading use the FXCM platform. FXCM focuses on currency pairs, along with a few global indexes and a few commodities. The raw historical data is ticks -- each a bid/ask pair. The FXCM API provides tools to form OHLC bars of whatever length is desired. The text provides examples using the raw ticks as well as the consolidated bars in trading systems. The API also allows order placement and management. A free demo account allows access to downloading data (1 minute bars and longer) and testing trading.Several trading systems are illustrated. These range from very simple moving average crossover to machine learning.Profitable trading systems have, at their core, trade secrets. As it does not contain secrets, this book will be of little value to readers hoping to read one book and be rich by Wednesday. You will need to supply your own secret techniques for selection of auxiliary variables, data transformations, and target definition. With those in hand, this book will clarify your path and speed your development. It is exceptionally well done and highly recommended.

Un bellissimo libro, consigliatissimo per chi è alle prime armi o chi vuole approfondire particolari aspetti di Python nell'ambito dei mercati finanziari.

Yves Hilpisch‘s book is by far the best book on Python for finance, I have read. The book covers important basics such as numpy, pandas and time series as well as more sophisticated topics such as machine learning and algorithmic trading strategies. This book belongs on the desk of both students and practitioners who want to use Python to solve real-world problems in finance.