It is almost a given to say that the Python language has made things easier in the realm of finance. This technology directly affects the worlds of the stock market, cryptocurrency and forex. We need a closer look and a deeper understanding of the technology before anyone can even consider getting into it.

 

VBA or Visual Basic for Applications and the Google apps program have been the most popular for the longest time. This has changed slightly since the blossoming of Python.

One thing to note is that knowing how to use Python is important. The savvier you are, the better the quality of what you develop and build. As such, a guide to using Python for financial programming is in order.

 

Contents:

Uses of the Python Programming Language

Hundreds of activities can be tracked over time in an M&A Integration PMO setup

Why Finance Professionals Should Use Python

1. It is a high-level programming language
2. Its concise nature
3. It's easy to adapt to
4. It is free

How to Use Python & Finance in Tandem

Creating a Working Prototype

From Prototype to Finished Product

Final Thoughts

 

Uses of the Python Programming Language

It is interesting to discover that most users of Python have said that their experience using the language was quite profound. Some would even go as far as saying that they are obsessed with it. The language has a seemingly perfect match, especially when used for spreadsheets.

 

This perfect match with spreadsheets has made it so that finance workers actively seek it out. The following is the best example of how Python best matches with spreadsheets.

 

Hundreds of activities can be tracked over time in an M&A Integration PMO setup

When it comes to M&A execution and integration, the typical PMO team will try to use hybrid programs that are based on waterfall planning and Gantt charts. These, coupled with a Kanban board can track hundreds of activities over a 100-day plan.

 

MeisterTask can do the same in theory, but for the most part, a custom solution will need to be built. This is where Python can come in. Using Python, a workflow can be automated in the following way:

1. Save status of the whole board over a weekly period as a CSV file
2. All historical CSV files must be read into any data frame
3. All data has to be sorted, filtered, manipulated and grouped into an agreed format
4. The output must be written to an Excel file that contains data from each analysis in its own sheet (this can then be formatted and pasted into think-cell charts)
5. Tables and charts can be created for the purpose of a reporting package in case of a future committee meeting

 

Because the creation of such tech is more than tedious, the automation of Python is desired. Any language that has an API can be connected to a Python application, giving credence to the fact that automation is king.

 

Why Finance Professionals Should Use Python

The popularity of Python in recent times is quite amazing. The main reasons for this are as follows. Let’s check them out!

 

1. It is a high-level programming language

In being a high-level programming language, Python by definition abstracts away many details of a computer’s inner workings. This means that it handles details that lower-level languages can’t, giving you more opportunities to achieve your goals.

 

2. Its concise nature

Python usually gets into the nitty-gritty, focussing more on the goal as opposed to the technical implementation details. This is due to the more concise, short and pretty code. You can also learn how to write clean code by using many different Python guides which you can find online.

 

3. It's easy to adapt to

The Python code looks quite similar to English and this makes it easy to read. If all components are written correctly, it is best for anyone. Moreover, thanks to the biggest community, you can easily find answers on how to make any solution.

 

4. It is free

This is one aspect people really love. This is because Python is developed under an open-source license. You can also find a great community of like-minded people that can help you with questions and issues.

 

How to Use Python & Finance in Tandem

To make a simple version of whatever it is you want to create, there are certain steps to take. Before you begin, you must be familiar with the building blocks of programming. The first thing to do is use a simplified DCF valuation model. The Excel version of it can help gauge the potential outcomes of situations.

 

The two ways of going into development are creating a prototype from scratch and changing its code structure without tampering with functionality.

 

Creating a Working Prototype

This requires you to actually build what you are trying to create from scratch. Python’s easy-to-understand code will allow you to read and write for separate components. These are then used to create the base of what you are trying to build and eventually can be rearranged without changing functionality. The main aspects of this are as follows:

• Setting up the jupyter notebook
• Creating a financial statement
• Perform a DCF valuation
• Exporting the data
• Creating probability distributions in your simulation

From Prototype to Finished Product

The structure of the prototype can be improved upon. This has to be done without changing functionality, however, here are the steps to take into action:

• Sensibly organize the different parts
• Rename variables and functions clearly according to their purpose
• Future features must be allowed and prepared for
• The execution speed and other resource utilization must be improved upon

 

Final Thoughts

If you are working on building financial programming in Python, we are capable of guiding you on your journey. We can help with Python for financial programming and will be of great use to you. We can help you learn how to import and manage financial data in Python, providing reliable solutions using various tools and sources.

You can start programming your financial app with Finage Market Data.

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