It’s crazy how quickly technology advances in such a short span of time, right?
Most people reading this article today will be older than Google (scary though, I know) but in the last two decades, technology has begun to do so much more.
AI and machine learning is a new area of technology that is rapidly developing.
Machines can now predict the actions we’re going to make such as predictive text and digital assistants such as Siri and
And so it’s not hard to see why so many people are looking to get involved in learning this fresh and exciting new kind of technology.
One of the best ways to go about this is through the use of TensorFlow. It can allow you to navigate through this world so much easier than ever before.
But you’ll need to select a high-level programming language to get started.
TensorFlow is compatible with several different options such as C++ and Python and in this article, we will take a look at which is best for use in TensorFlow.
What Is TensorFlow?
So, before we begin, let’s take a look at what TensorFlow actually is.
TensorFlow is an open-source tool developed by Google that allows you to build machine-learning apps.
Within TensorFlow, you’ll have a few different tools that you can use to accomplish the creation of your application.
You’ll have learning models, deep learning models, along with all the relevant algorithms needed.
The tool is often used for a variety of different tasks such as voice, speech, and image recognition which can all be helpful for finance.
It’s free to use and quite easy to go about with a little know-how too, making it the perfect option for many people.
What Is Python & C++
So, now that we know a little bit more about TensorFlow let’s learn a little bit more about the options you can choose in terms of programming language.
Python is an interpreted, object-oriented, high-level programming language that comes with dynamic semantics.
It’s very popular for Rapid Application Development due to its dynamic binding and typing.
Along with this it also works well for scripting and glue language to connect already existing components together.
Generally, Python’s syntax is nice and easy to learn and allows for reduced costs in program maintenance which is what makes it so popular.
C++ is a cross-platform language that is also often used to create high-performance applications.
Much like Python it is an object-oriented programming language that gives a clear and concise structure to programs which enables code to be reused and can lower development costs.
It too is fairly simple and easy to understand and learn.
So Which Language Is Better?
Both options are great for coding any application so how do you decide which is the best to use for your finance project?
Well, according to the information I could find, it seems like Python is the better choice. First of all, it is the recommended language for TensorFlow.
And while both are compatible if you have a preference, it would seem that Python is significantly more stable compared to C++.
It seems that there may just be a few extra resources for Python that will help you along the way.
If you opt for the C++ language you’ll generally have just the building models to go off whereas Python has a little bit more.
From inception to deployment, Python will be there to help.
And since there are so many Python libraries available, you’ll have no problems building an end-to-end ML solution.
You’ll also tend to find that online resources and tutorials, for the most part, tend to be aimed toward the Python community so if you get a little stuck along the way, you’ll have more to fall back on.
What Is Python Used For In Finance?
Okay, so now we know about TensorFlow, programming languages, and which one works best.
But we need to link this all back to finance now, right? What does Python get used for in terms of finance? Let’s take a look.
- Analytics Tools – It works wonders for quantitative data in finance. Using Python you can process and analyze astronomically large sets of data. This can come in handy when you need to review your financial data. Pandas is a library option that you can select to simplify the process of data visualization and enables analysis of pretty complex data and statistics. Since Python-based solutions come with very powerful ML algorithms, predicting analytics has never been easier.
- Banking Software – Python can be really handy for banking software too. It is often implemented by financial institutions for providing bank statements and a selection of other applications. Venmo is the perfect example of this – this initially small mobile banking app has converted into a popular and growing social networking site! And since Python is simple and versatile in its nature, it is also often used for designing ATM software.
- Cryptocurrency – Cryptocurrency is something that has increased in popularity over the years and any business that sells it needs tools that can analyze the data and make predictions on the current market. Anaconda is a science-based Python application that is often used for obtaining and analyzing cryptocurrency prices, as well as visualizing financial data.
- Building A Stock Trading Strategy – As you can probably imagine, there is no end to the financial data that the stock markets generate. That’s where Python becomes super useful. It is predominantly used to help make stock trading methods as easy and practical as possible. By using this coding you can gain analytical insights into specific parts of the market.
When it comes to TensorFlow, you can choose to use the programming language that you work best with. Luckily, it is compatible with the most popular options.
However, this can definitely leave you feeling a little bit confused as to which will be the best option for you.
From this article, you hopefully have a clearer understanding of which one works best.
From researching on the internet, it became abundantly clear that most users will use the language that is recommended by TensorFlow itself; Python.
Sure, you can stick to C++ if that’s what works best for you, but you’ll find that there tends to be a lot more resources available that cater to Python scripting than C++.
This means that if you’re new to the scene and are more than likely going to need a little help along the way that Python is probably your best choice.
It also seems to run more smoothly through TensorFlow in general.
And as you can see, in terms of finance, there is no end to what Python can help you achieve.
So, whether you’re looking for data analysis in cryptocurrency or are looking to develop banking software it’s safe to assume that Python is the optimum choice.
With that being said, though, C++ is still an amazing option. Just because it doesn’t work as well doesn’t mean that it has to be written off entirely.
If you have more experience with C++ then there will definitely be no issue in using that either.