Python's application in Finance, data analysis, and artificial intelligence
Python has recently achieved such success, and the future seems likely to continue, for many reasons. This includes its syntax, the scientific ecosystem and data Analysis library available to Python developers, ease of integration with almost all other technologies, and its open source status.
Since 1991 it has appeared in programming scenarios, Python has made a rare position than in other programming languages. Object-oriented, easy to learn, use of syntax, and the resulting low maintenance costs are part of the reason that Python continues to receive praise. Open source is a clear advantage, cross-platform effectiveness, multi-objective, garbage collection (automatic), code simplicity, and neat indentation are other notable features of Python.
The application of Python in finance
Technological innovation has made a great contribution to the efficiency improvement of the financial derivatives market. These powerful improvements are only possible when derivatives exchanges and clearing houses provide sustained and high investment in information technology. --German Stock Exchange Group, 2008
Over the past 10 years, with the advent of automation technology, technology has finally become a part of outstanding financial institutions, banks, insurance and investment companies, stock exchange companies, hedge funds, brokerages and other companies. According to the 2013 Crosman report, banks and financial companies spent 4.2% more on technology in 2014 than in 2013. The technical cost of financial services for one year is expected to reach $500 million in 2020. When the system needs to be maintained and constantly upgraded, it is normal for some famous banks to hire some developers. So where does Python work?
Python's syntax is easy to implement for those financial algorithms and mathematical calculations, each mathematical statement can be transformed into a line of Python code, each line allows more than 100,000 of the calculation.
No other language can be used for math like Python, Python is proficient in computing, and permutation combinations in math and science. The second feature of Python is the representation of numbers, sequences, and algorithms. The SciPy library, for example, is ideal for computing in the technical and scientific fields, and the sicpy library is used by many engineers, scientists and analysts. NumPy, also an extension of Python, can handle mathematical functions, arrays, and matrices well. At the same time, Python supports strict coding patterns, so make it a balanced choice, or method.
Using fewer people to achieve the same results and achieve what other programming languages do not do is the first advantage of Python. The precision and simplicity of the Python syntax, as well as its vast and valuable third-party tools, make it the only reliable choice for dealing with the intricacies of the financial industry.
"Cross-market risk management and trading systems are using Python (sometimes mixed with C + +), and many banks choose to use Python from the front-end of the bank to the asset risk system," said Stephen Grant, technical recruiting manager at Cititec (London, England). Financial companies using Python include ABN Amro, Deutsche Börse Group, Bellco Credit Union, JPMorgan Chase and Altis Investment Management.
Python for analytical studies
In recent years, analysis has gained prominence in the fields of data, network and finance. Using a variety of software combination for , data management, and data analysis, the conclusions are used for business decision-making, business needs analysis and so on. Analysis is used to study the market effect of a product, and the bank's lending decisions are just the tip of the iceberg. It has far-reaching implications in the areas of big data, security, digital and software analysis, and here is a continuation of Python's main role in analytics:
In this world of information overload, only those who can take advantage of analytic data to derive insights will benefit. Python plays an important role in the interpretation and analysis of big data. Many of the tools developed by the analysis company are based on Python to constrain large chunks of data. Analysts will find that Python is not hard to learn, it is a powerful medium for data management and business support.
Using a single language to process data has its benefits. If you've ever used C + + or Java before, Python should be easy for you. Data analysis can be implemented using Python, with enough Python libraries to support data analysis. Pandas is a good data analysis tool because its tools and structures are easily mastered by the user. It is undoubtedly the most suitable choice for big data. Even in the field of data science, Python dwarfs other languages because of its "developer friendliness". The likelihood that a data scientist is familiar with Python is much higher than the likelihood of familiarity with other languages.
In addition to the obvious advantages of Python in data analysis (easy to learn, a large number of online communities, etc.), the widespread use of data science, and most of the web-based analysis we see today, is the main reason Python is widely disseminated in the field of data analysis.
Whether it's financial derivatives or big data analytics, Python plays an important role. In the former, Python is well-integrated with other systems, software tools, and data streams, including, of course, R. Using Python to chart Big data is better, and it's equally reliable in terms of speed and help. Some companies use Python for predictive analysis and statistical analysis. According to an December 29, 2014 Forbes article, the 2014 Python-related big data recruitment demand rose 96.9% from a year earlier.
The application of Python in the field of artificial intelligence
Python, like other good technologies, spreads quickly as a virus in your development team and then finds ways to apply it to a variety of applications and tools. In other words, Python starts out like a hacker, and the code task is like a nail. --mustafa Thamer,firaxis Games
While AI is today's "thing", Python has also achieved significant results in this field, and in the field of business intelligence, Python has proved its usefulness. Back to the AI topic, Python has become part of some AI algorithms, from simple double-player to complex data engineering tasks. Python's AI Library plays an important role in today's software, including NLYK,PYBRAIN,OPENCV, and Aima. For some AI software features, just one block of code is sufficient. From face recognition technology to conversational interfaces to other areas, Python is constantly covering new areas.
When it comes to AI, Python is a modern choice. Why, in addition to the general reason, Python makes prototyping faster and has a more stable architecture. For example, Scikit-learn (a machine learning library).
Debugging in Python is a fast process. It also provides an application design interface (API) for other languages. Python's vast library of libraries is helpful, but you have to be proficient in python to make good use of it.
Python will be used in Bi, which is also a force in network intelligence. Automated judicial investigations, security checks, and web analytics can all be implemented using Python. For BI, there's a whole bunch of tools python can use to make your job easier, and the language has a natural tendency towards algorithms, mathematical equations, and makes it a multipurpose medium.
The application of Python in mathematics
Python vs. Matlab: Python is also the expert language matlab that threatens numerical computing, and many people using MATLAB are considering turning to Python. The use of Matlab is too expensive, it to check the portability of code, you can not run your code on another computer. It uses proprietary algorithms, which means that most of the algorithms you use are not available to view, but only believe they have been implemented correctly.
At the same time, MATLAB is supported by the scientific community and is part of many universities, although some of them may not be able to afford it for cost reasons. Python requires a comprehensive development environment (IDE) and additional packages.
As an open source program, Python is designed for simplicity and ease of use. Having third-party libraries and data types makes it easy to organize data using Python. Because it is not proprietary, with its class and customizable functions, you can easily migrate Python code from anywhere in the program, depending on your needs. The user graphical interface (GUI) toolkit, such as QT, is useful for creating an impressive front end. Finally, Python provides a full range of programming packages.
Python's application in Finance, data analysis, and artificial intelligence