preface

Python is an advanced multipurpose programming language used in a wide variety of non-technical and technical domains.

Python is an object-oriented interpreted high-level programming language with dynamic semantics. Its high-level built-in data structures, combined with dynamic typing and binding, make it attractive for rapid application development and as a scripting or “glue” language for connecting existing components together. Python’s simple, easy-to-learn syntax emphasizes readability, thus reducing program maintenance costs. Python supports modules and packages, encouraging modularity and code reuse. Python interpreters and numerous standard libraries are freely available in source or binary form for all major platforms and can be distributed freely.

The rise of real-time analytics

One discipline in the financial industry is growing strongly in importance: finance and data analytics. This phenomenon is closely related to the rapid growth of speed, frequency and data rate in the industry. In fact, real-time analytics can be seen as the industry’s response to this trend.

Roughly speaking, “finance and data analytics” refers to the application of software and technology combined with (possibly advanced) algorithms and methods of data collection, processing and analysis to gain insight, make decisions or meet regulatory needs. Examples of such analysis include estimating the impact on sales of a change in the pricing structure of a financial product in a bank’s retail arm. Another example is the massive overnight calculation of credit value adjustments (CVA) for complex derivatives portfolios of investment banks.

Finance and Python syntax

Most people taking their first steps to Python in a financial environment will probably have to work on an algorithm problem. This is similar to a scientist who wants to solve a differential equation, take an integral, or visualize some data. In general, there is not much thought given to the formal development process, testing, documentation, or deployment at this stage. However, this stage seems to be a particularly easy time to fall in love with Python, largely because Python’s syntax is generally quite similar to the mathematical syntax used to describe scientific problems or financial algorithms.

We’ll spend 530 pages on big data analysis of Python finance. Hope you enjoy it!

Because the content is too much, so xiaobian only part of the knowledge points out screenshots, rough introduction, each section has more detailed content, interlocking, with the most brief language to explain the most responsible problems to everyone, I hope you can understand.

This article is divided into three parts, with 19 chapters and 530 pages

The first part introduces the use of Python in finance. It covers the reasons why Python is used in finance, the Python infrastructure and tools, and some concrete introductory examples of Python in econometric finance.

The second part introduces the most important Python libraries, techniques, and methods for financial analysis and application development, covering Python data types and structures, and data visualization using Matplotlib. Financial time series data processing, high-performance input/output operations, high-performance Python technology and libraries, a variety of mathematical tools needed in finance, random number generation and random process simulation, Python statistics application, Integration of Python and Excel, Python object-oriented programming and GUI development, Python and W Eb technology integration, and development based on Web applications and Web services;

The third part focuses on the development of the practical application of Monte Carlo simulation options and derivatives pricing, which covers the introduction of valuation framework, simulation of financial models, derivatives valuation, portfolio valuation, volatility options and other knowledge.

Summary The financial industry has adopted Python at an astonishing rate, with some of the largest investment banks and hedge funds using Python to build core trading and risk management systems. This article will help developers and quantitative analysts get started with Python and guide them through its important applications in quantitative finance.

This article demonstrates how to develop a mature framework for Derivatives and risk analysis based on Monte Carlo simulations, using extensive working examples and a large, real-world case study. Most of it uses the interactive IPython Notebooks and covers the following topics.

■ Basics: Python data structures, NumPy array processing, time series analysis with PANDAS, visualization with Matplotlib, high-performance I/O operations with PyTables, date/time information processing, and selected best practices.

■ Financial themes: mathematical techniques such as regression and SymPy are used in NumPy, SciPy and SymPy; Inferential statistics for Monte Carlo simulation, var, and risk credit value calculation; Statistics for normality testing, mean square error portfolio optimization, principal component analysis (PCA) and Bayesian regression.

■ Special topics: high-performance Python for financial algorithms, such as vectorization and parallelization; Integration of Python with Excel; And building financial applications based on Web technologies.

Due to space constraints, small series here will not do too much to share, need this technical document “Python financial big data analysis” friends, can ↓

You should pay more attention to the high evaluation of this article

Python’s easy-to-understand syntax, easy integration with C/C++, and a variety of numerical tools make it a natural choice for financial analysis

Choose the. It is rapidly replacing the language and tools used in mainstream financial institutions and becoming the de facto standard.”

——Kirat Singh

Co-founder, President and CTO of Square Technologies, Washington, DC