Shulou(Shulou.com)06/03 Report--

This article mainly introduces "what are the advantages and disadvantages of different languages in the field of data analysis". In daily operation, I believe that many people have doubts about the advantages and disadvantages of languages in different areas of data analysis. I hope it will be helpful to answer the doubts about "what are the advantages and disadvantages of languages in different areas of data analysis?" Next, please follow the editor to study!

01 Matlab

Up to now, in the field of domestic quantitative research, the utilization rate of Matlab should be the highest. This data comes from Wind, whose quantization interface has the highest utilization rate of Matlab, followed by Python. But Python is the fastest growing.

As a commercial software, Matlab is very powerful and reliable. Many of the earliest people who did scientific calculation and data analysis used Matlab. When quantitative investment first appeared in China, the community ecology of Python and R is not as perfect as it is now, so many people in the industry of quantitative investment are more accustomed to using Matlab.

Without considering the issue of licensing fees, Matlab is indeed a very useful tool for data analysis and even quantitative investment analysis. after all, there are strong companies supporting the development of Matlab, and performance and toolkits are guaranteed.

However, compared with Python, Matlab has many defects in addition to the problem of cost, and it is irreparable. Especially when it comes to system-level development, such as trading system, crawler system and so on. In these areas, Matlab is not only lack of corresponding libraries, but also very slow, so it is difficult to be widely used in industry.

02 R

R is an open source data analysis software. In fact, R was born to assist in statistics and data analysis. Because R is very popular in research institutions and universities, these institutions have in turn developed a large number of corresponding open source projects, which makes a variety of statistical functions and functions of R dazzling.

Many commonly used statistical functions of R have been tested by a lot of practice, and they are very perfect and mature, such as time series analysis, classical statistical model, Bayesian statistics, machine learning and so on. R also has some quantization-related libraries, such as quantmod.

Of course, R also has its shortcomings, for example, for a large number of data processing, R is still unable to catch. Because R is mostly done by people in the statistical field, the lower-level data management is not R's strong point.

Generally speaking, R's statistics and data analysis related functions are very powerful, more suitable for research, not suitable for the development of large-scale systems.

03 C++

The biggest advantage of C++ is its strong performance and high speed. Almost all scientific computing functions that require high performance are based on C++ or Fortran. For example, the bottom layer of Python is actually implemented in C language.

Because of its speed, C++ also has a monopoly in the field of high-frequency trading. However, it is very inconvenient to use C++ in daily data analysis and research. Because the C++ language is at the bottom, the requirements for programmers are very high, the same function, the development is much more difficult, debugging is also more troublesome.

So unless it is in a place with high performance requirements, it is generally not recommended to use C++ for development.

04 Python

Python grammar is very easy to learn and understand, and easy to use quickly. When many people start to learn programming, they often choose to start with Python.

Like Matlab and R, Python is also a scripting language, which can be run directly after it is written, saving the trouble of compiling links, and saving a lot of coding and debugging time for programs that need to be developed and verified quickly.

Python is also an object-oriented language, but its object-oriented is not as conceptual as C++, but more practical. It can use the easiest way for programmers to enjoy the benefits of object-oriented. This is one of the reasons why Python attracts so many supporters like Java and C#.

Although Python is a scripting language, it is not very slow, especially after some libraries have been optimized (writing interfaces directly based on C), which is not much slower than pure C. In this respect, it is far better than R and Matlab.

Python is a feature-rich language with a powerful basic class library and a large number of third-party extension ecology.

Python has open source projects in almost every field, so we don't have to reinvent the wheel. Using Scrapy, we can write a web crawler system to crawl network-related data; using various database interfaces, we can standardize the storage and reading of data; using PyAlgoTrader, we can build a policy backtest system and an automatic trading system.

Python also has many excellent quantification, data analysis, machine learning (ML) tools, such as NumPy, SciPy, Pandas, Scikit-Learn and Maplotlib.

Although Python is very good at machine learning and general data analysis, it still has shortcomings. For example, it does not perform very well in some traditional fields, including many traditional statistical models, time series analysis and so on. Python is not as good as Matlab and R.

In short, we can use Python to build a complete quantitative investment production line. Of course, it is undeniable that for some links, some languages also have their advantages over Python, such as the statistical library of R, the scientific calculation of Matlab, the reliability of SAS, the construction of high-speed trading system by C++, and so on. However, these advantages are only the difference between 95 and 90, with the exception of a few extreme business scenarios, the vast majority of the job Python is actually competent.

In the area of quantitative investment, most of the requirements can be done in Python, which can save the team a lot of time. After all, it takes a lot of energy to switch between different languages.

05 other languages

In addition to the languages described above, there are actually many other languages that are also used in the field of quantitative investment. For example, Java, C#, Scala and so on, these languages also have their corresponding advantages and characteristics. However, compared with the languages introduced above, the domestic users of these languages are still relatively minority. For beginners, it is recommended to choose the Python language.

At this point, the study of "what are the advantages and disadvantages of languages in different areas of data analysis" is over. I hope to be able to solve your doubts. The collocation of theory and practice can better help you learn, go and try it! If you want to continue to learn more related knowledge, please continue to follow the website, the editor will continue to work hard to bring you more practical articles!

Welcome to subscribe "Shulou Technology Information " to get latest news, interesting things and hot topics in the IT industry, and controls the hottest and latest Internet news, technology news and IT industry trends.