Una Libreria di Fixed Income in Python

Financial engineering and algorithmic trading are perhaps two of the most computationally intensive part of all of finance. While algorithmic trading is about finding opportunities in market and so the focus is on the speed and data-mining, the field of financial engineering primarily concerns the pricing of the derivatives products and the hedging with them. Given the complexity of modern structured products almost all pricing and risk management is based on the use of some specialized services/software able to deal with the large amount of the data and with the intricacy of the models. Till a few years ago, none of that sophistiications was available for use in teaching and research, but for the last few years there is a reliable open-source library called Quantlib. Initially written in C++ and then exported to other languages such as Python, Java, R, QuantLib has the purpose to provide a comprehensive software framework for quantitative finance. Appreciated a lot by the practitioners, this library offers tools that are useful both for practical implementation and for advance modelling, with features such as market conventions, yield curve models, solvers and so on. Indeed, generally speaking, any researcher, student, professor, but also any bank or financial institution needs a solid, time-effective, operative implementation of cutting edge pricing models and hedging tools. The aim of our work is to contribute to the Python version of this library in a meaningful way, picking the different attributes and methods offered by QuantLib to construct a Fixed Income Library constituted by different classes for the pricing of the more important interest rate derivatives such as Cap/Floor, Swaption, but also for the Bootstrapping of the Yield Curves and for the Calibration of the Interest Rate Models. Therefore to follow, in the first part we will concentrate on the theoretical framework, showing the structure and the main mathematical results for the princpal Fixed Income products, the main steps for Bootstrapping the Term Structure starting from the quoted instruments in both Single and Multiple Curve Approach and the Calibration of the Hull-White model for the stochastic dynamic of the interest rate. The second part is dedicated to computations and codes, showing the created Python Classes and their use via some key examples. Finally, in the third and last part, we will make some considerations about the results obtained, indicating also some possible further implementations of this library