Introduction: scientific computing package realized in python. Including: 1, powerful N-dimensional array object array; 2. A relatively mature (broadcast) function library; 3. Toolkit for integrating C/C++ and Fortran codes; 4. Practical linear algebra, Fourier transform and random number generation function. Numpy and sparse matrix operation package scipy are more convenient to use together.

scipy

Introduction: SciPy is a convenient Python toolkit designed for science and engineering. Including statistics, optimization, linear algebra, Fourier transform, signal and image processing, solving ordinary differential equations and so on.

panda

Introduction: Python data analysis library or pandas is a tool based on NumPy, which was created to solve the data analysis task. Pandas includes a large number of libraries and some standard data models, providing the tools needed for efficient operation of large data sets. Pandas provides a lot of functions and methods, which enable us to process data quickly and conveniently. You will soon find that this is one of the important factors that make Python a powerful and efficient data analysis environment.

quantdsl

Introduction: Quant DSL package is an implementation of quantdsl syntax in Python. Quant DSL is a special language in the field of financial quantitative analysis and a functional programming language for derivative modeling. Quant DSL encapsulates the models used in finance and trading (such as market dynamic model, least square method, Monte Carlo method, time value of money).

statistics

Introduction: python's built-in statistical library provides the function of calculating mathematical statistics of numerical data.

PyQL

Introduction: PyQL is built on Cython, and a shallow python layer is created on QuantLib, which is a package of QuantLib and takes advantage of Cython's better performance.