Mastering python for finance: understand, design, and implement state-of-the-art mathematical and statistical applications used in finance with python.

Abstract

Python is widely practiced in various sectors of finance, such as banking, investment management, insurance, and even real estate, for building tools that help in financial modeling, risk management, and trading. Even big financial corporations embrace Python to build their infrastructure for position management, pricing, risk management, and trading systems. Throughout this book, theories from academic financial studies will be introduced, accompanied by their mathematical concepts to help you understand their uses in practical situations. You will see how Python is applied to classical pricing models, linearity, and nonlinearity of finance, numerical procedures, and interest rate models, that form the foundations of complex financial models. You will learn about the root-finding methods and finite difference pricing for developing an implied volatility curve with options. With the advent of advanced computing technologies, methods for the storing and handling of massive amounts of data have to be considered. Hadoop is a popular tool in big data. You will be introduced to the inner workings of Hadoop and its integration with Python to derive analytical insights on financial data. You will also understand how Python supports the use of NoSQL for storing non-structured data. Many brokerage firms are beginning to offer APIs to customers to trade using their own customized trading software. Using Python, you will learn how to connect to a broker API, retrieve market data, generate trading signals, and send orders to the exchange. The implementation of the mean-reverting and trend-following trading strategies will be covered. Risk management, position tracking, and backtesting techniques will be discussed to help you manage the performance of your trading strategies.