Todays, you will find the explosion in the velocity, variety, and volume of the financial data.There are many financial data coming from the different sources like social media, real-time market data, foreign exchange data and other transactions details. In fact, It is believe that there will data will be next oil in the future.There will be no end of the data.There are many things you can do with the financial data. For example predictions, analyzation e.t.c. Therefore large companies are now focusing on data science for finance. In this article, you will know how data science for finance is useful for the data scientist and the traders. You will learn the following topics.
- Role of Data Scientist in Data Science For Finance
- Which Programming language for data science in finance?
- Why Python for finance?
- What can you do with Python In Finance?
Introduction( Short) to Finance Sector and its Industries –
If you are reading this article , It is clear that you have the basic understanding of Finance . Under this sector there are so many Industries work like –
- Banking Industry which can be further break into Commercial banking and Investment banking .
- Insurance Industry covers the Risk . We all are already engaged in some Insurance activity knowingly or unknowingly . For example Health Insurance , accidental Insurance , Life Insurance and your Car Insurance .
- Forex Service deals with currency exchanges.
- Stock trading and Security exchanges which may be cover in above two but I am taking it separately because we will discuss the most in this article .
- Others . To classify all Industries in four or Five classes is impossible .
Open Challenges for Finance Sector : Rise of FinTech Companies –
As you have seen in the above section there are so many Industries are working under Finance Sector . Each have different actors with different roles . Now each role has different challenges . What FinTech is doing they are developing product which can do automation to minimize the human efforts and making service more better in less cost( For example Calypso etc ) . Some of the Fintech Firm are offering services to implement existing financial product into different client domain . See It will be impossible to categories each challenges individually but at broader level they are –Minimizing the Risk and Maximizing the profit with better service .
To achieve this golden statement , There are so many research and development are going on . Developers are trying to increase efficiency with high accuracy . Client portfolio first went digital on cloud . Now a days Developers are making it more personalized using Data Science and Machine Learning .
Rise of AI encouraging for Virtual Assistant and chatbot developemnt . This Fintech companies are working over three Technology pillar these days –
1.Data Science and Machine Learning
2. Big Data
Which Programming language for data science in finance?
There are many technologies or the programming language you can use in the finance. Java, Python, and R are the two mostly use technologies in the field of data science. But, which programming language in data science for finance are mostly use? The answer is Python. Financial institutions love to use the technologies that are able to speed the financial transactions and gain competitive advantages. There are large chunks of the data and which programming language should use become their attention.
Python is the only language that helps them to end their search. Most of the financial institutions are in the field of trading. They use the Python programming language for designing and coding the trading strategies. These strategies are also known as algorithmic trading strategies.
Why Python for finance?
When you ask this questions from data scientist, they have will have different suggestions. Some will say R is the best, some will Python is the best and other will say Java. But when you will aks the reason behind. Most of them are unable to answer the questions. In this section of the article, you will know the clear reasons behind choosing the python for data science in finance.
Reason for using python in Finance and Data Science
Most of the tools and libraries available are open sources. Therefore you don’t have to worry about the fees or license to pay.Apart from there are several other reason for using python in Finance and Data Science .
- It is Multipurpose: Finance has large sets of data. Python is a multipurpose language. It provides you to code and develops from low-level application to high-level application.
- Cross-Platform: Financial Data Sets can be stored in Windows os, Linux or MacOs. Python is cross-platform. Thus you can run code in Python on any Operating system. It is platform independent.
- Large Community: Python has a very large community. Therefore if you have any difficulties in solving the problem. Go and Ask the world to get the answers.
- Python is Easy: It is One of the popular reason that makes the developer to chose Python as a programming language. It is very easy to learn the language. You cant think it an English version of programming language.
- In Finance you can never compromise with Speed and accuracy. Right? Python is a Dynamic language which makes it high performing . Even though because of the big community support you easily get API for every domain .Finance is no more an exception .If you are beginners in the field of Programming language. Even if you are completely new to the programming language. Then I will recommend learning Python programming language. Below is the list of topics you may cover to learn the Python basics.
Python for Data Analysis Tutorial : A Complete Overview
Learn Python Essentials in 5 Minutes
Python Anaconda Packages as One solution for all Data Science Problem
Go through these topics to understand the basics of Python.
Application of Data Science in Finance-
This is an endless topic . No body can finish it but yes at least We can cover the major Areas-
1. Data Science for Algorithmic Trading ( Forex and Stock Trading ) –
When a company starts a business they want growth. Their main focus is only growth and development of the company for many years. Then there comes a stage when they issue a stock to raise the capital. It is call Initial Public Offering. You may know a single stock represents ownership of the company and it is bought in return for money. Once stocks come in the share market, values of the stocks increase or decrease on the basis of trading. Traders buy and sell shares according to their strategies. Strategies can be manually or automatically.Automation in these strategy is called Algorithmic trading . you see most of the institutional trader rely on algorithmic trading strategies. They make trading strategies for putting buy or sell orders without any human need. The development of algorithmic trading strategies requires the two phases.
Developing a Trading Strategy
Just like you make a machine learning model, to train the machines. In the same way, you build a strategy that meets certain specific conditions. This allows you put the buy orders and sell orders. Then you use Python programming language to develop a coding version of the final strategies. It means it will be able to put buy or sell orders automatically. In fact, There are many brokers allows you to use their Python APIs for development of stocks and shares trading strategies.In this Phase , Rather than hard code logic or if else statement . If you use Data Science , You can make this logic part probabilistic .
You can use some Machine Learning Model , You can use regression and predict the parameter for your order ( strategy).
Back Testing of Developed Strategies
Algorithmic Trading strategies can be say completed without backtesting. Backtesting allows to check your strategies whether it is working or not correctly. It uses the historical data of stocks to put orders. It will put buy or sell orders to check the profitability of the strategies. In addition, It also allows checking the effectiveness of the strategies.You can optimize the strategies if there for further improvement. Now, You may think why backtesting is done. Then the answer is very simple. It is believed that if the strategies are doing good in the past then it will be likely to work in the same way in the future. In the same way, it will be not profitable in the future if it is not in the past.
Here If you are using Data science for finance , You can visualize the Back Testing Results and publish in some Reporting Format . Reports are good for visibility . Report can be generated using any of the ETL tool or BI tools .
2. Data Science for Finance Application ( Sentiment Analysis )-
You must be aware how a news can effect the Market . This is Fundamental style of Trading . The Main thing is here .A trader can analyse the effect of news on the market but How machine cant Right ?Now the question is how will you build a system which can response the market on the basis of news . You can achieve it if you can classify the news into the their sentiment .You can classify the news into three or more sentiment classes . For example based on the sentiment a news could be positive , negative or neutral .
Lets take an situation if there is news about a company which state a future deal with any reputed company . Its a positive news for the company which definitely raise the price of company share . Now the biggest challenge begin , How will you predict , “How will you decide how much will be threshold of price . ” . Here Again you can use some machine learning model which can correlate the news with past price changes ” .
This area of sentiment extraction comes under NLP . NLP stands for Natural Language Processing where we train the machine learning model on large language corpus . Corpus is nothing but training data set which is already classified .
3.Data Mining and Analysis of Events and causes in Python –
You must have heard about Black Friday or remember the any day when you heard that Market is crashed . If you want to see and understand the cause of market crash event . You can use the data and data speaks but when it is properly prepossessed and visualize . You reshape the data . There you can look the change in the factors just before the market crash . Was there any clue which can save lot of money for or your client . All you can do with basic data science concepts .
You will be surprise that in the Broader line there are two factors on which every Financial Industries elements works at different posistions
4.Data Science for Finance Application in Insurance –
In insurance Industry ,the main agenda for investment to cover the risk by spreading on large audience .Lets take an example , Most of the medical Insurance company collects the data from the customer about his medical history first . On the basis their medical history record , companies decide the premium for him . Two different individual have to pay different premium for the same cover .
Most of the Insurance companies are automating the process to decide the premium value using Machine Learning . They collect their own past data . Target value will be premium and input training data will be medical history . Existing decision could be feature in Machine Learning .
4. Recommendation System For Financial Product –
In most of the online portal which sell financial product like mutual fund and Insurance policy etc , They are using recommendation system for user which suggest the best policy for him/her . It maintains the user activity , his behavior , His old searches . Data Scientist use as feature in their machine learning model and predict the best product for users .
This recommendation system is increasing the revenue for such policy selling firm in very larger extant .
5. Data Science for Finance Application in Fraud Detection –
With the explosion of technology and Internet the resounding is changed . Way of banking is completely different form last five years .This digital boost in Internet causing explosion of Cyber crime and frauds . Banks are hiring data scientist to work as an army . Actually If Banks manually put a team on surveillance team on transaction . They have to invest a lot whether If some how AI can replace this larger team . There could be too much possibilities for maximizing the profit .
6. Auto Workflow Managements-
Again a great application of data science in finance . There are millions of transaction perform on everyday . If there are transaction , it can break as well . In that situation they go to inbox of operations team . They manually look into it . To handle these breaks is one of the main task in banks because its financial . If there is any delay , There would be customer dissatisfaction Right ?
So whats the solution , Machine learning can solve this problem . Data Scientist are now training the models which learns from historical data . Here the system it self classify the problem and then takes the decision and action to handle them based on the confidence score .
7. Data Science for Credit Card Management –
Most of us are credit card user .Right ? Before we go ahead I would like to make you clear about cibil score . This score is generated by your existing transaction into your existing bank account . It shows the trust score for you . It also decide your credibility . I mean maximum withdrawal limit on your credit card .
Again here Banks and Fintech companies are building machine learning model which auto predict and calculate your cibil score . On the basis of this calculated cibil score machine itself decide your credit limit .
Role of Data Scientist in Data Science For Finance
Data Scientist or Analysist may have other roles in the various field of data science. But in the data science of finance, most of the companies expect the following things from them.
- They have to capture the sources of data and analyses them to build the best predictive models.
- Market backtesting or Live simulations using the Predictive models.
- Set the Predictive mode is set according to the finding of the simulations.
- Most of the financial data are in unstructured form. Data Scientist has to design technologies to convert unstructured data into structure data.
- There are the variety of the data and its increasing exponentially. Data Scientist has to find the way to store these data in the raw form for the future analysis.
Finance is a very large field.You will see the 90% of the word data most of the data generated from the financial sector. Transactional details, stocks and shares data, foreign exchange data, financial news feed, e.t.c are the major contribution. Financial Companies are hiring the data scientist a large scale to design the best predictive models for increasing their revenues. In addition, Institutional traders use the stock and share data for developing profitable algorithmic trading strategies using python programming language. I hope the article on Data Science in finance must be useful if you are choosing finance. You can also comment your suggestion or any queries to improve the article.
Data Science Learner Team
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