LEVEL : Beginner

Jan 1, 2021
Start Python for Finance

How to build your own
financial analysts team

Part 1/6
Let’s make a fresh start with financial markets: how to choose the right tool for beginners? Coming from the financial world, it could be hard to leave Excel. Especially when used daily. Let me explain why I chose Python.

Start Python for finance – Menu 

Long live the king

Are you part of the 1.2 billion club?

I won’t deny. I am one of them. 1.2 billion is the number of users of the office suite around the world. On our planet, around one in seven people have fun (or not) with Microsoft Office products. Tools like Excel are essential. And in Finance too.

Well, the figures are instructive. Let me share with you a study from a well-known recruitment agency. Here is the number I keep in mind. 69 % of American companies with annual revenues under $25 million use Excel as a primary tool.

It’s OK. Excel is easy to access, intuitive and everywhere. Excel is the King. Why not deal with it?

The problem is not Excel but Algorithmic Trading. Or more broadly: the use of new technologies in Financial Markets.

Market objectives are clear: take an advantage and determine the best financial opportunities. Competition, speed and data processing are keys.

 I am a beginner:

  1. With a limited capital.
  2. Without an army of financial analysts ready to run the business 24 hours a day.

So finally, the questions are:

  • How to stand out from this crazy competition? 
  • What tool or solutions exist when you start? 
  • Let’s be optimistic: could I dream of having my own team of financial analyst?

Python is the best solution I found.

Future is your best investment

I will not rewrite the story of Python. Created in the late 1980s, first released in 1991, its popularity is currently exploding. Python is literally eating the world.

Hope you will like it: It is also invading the finance territory. You can find Python in fintech, hedge funds, investment banks and financial services.

Ok Python. But is it really more efficient? Is it only a trend? After all, isn’t it possible to do the same job with Excel?

 Here is a non-exhaustive list of the advantages of Python. But the critical point is not there. (you can skip this section if you wish) Some experts will tell you that:


  • You can use and learn Python easily: Python syntax is simple
  • You can handle much larger volume of data: Python is powerful
  • You can handle any complex financial calculations: Python has an almost-perfect collection of libraries
  • Help will always be available online for you: Python has a great and active community
  • Python is free.

It’s not a secret. There are real benefits to using Python. And Excel can be fine when tasks are kept simple.

More, the point is not to compare Excel and Python point by point. Absolutely not. The goal is how not to miss the boat? And, more specifically, how to invest in the future?

 These 3 points can also be decisive for you.

1. The power of Python comes from its libraries integrating Artificial Intelligence and Machine Learning.

These words may sound familiar to you. Such technologies allow you to create predicting and forecasting models that Excel cannot.

Good news: Python allows a smooth initiation to these advanced technologies.

So, a question comes to mind. Can Python and machine learning predict or even anticipate financial markets?

In this lab, we will try to apply Artificial Intelligence and Machine Learning and observe the results.

2. A great strength of Python comes from its connectivity.

I am amazed at the possibilities offered. Python can fetch data anywhere. I mean everywhere.

API connections are easy. In other words, thanks to API, your program can connect to others. A trading platform is a good example where your code can connect. With just a few lines of code, you can retrieve data in real time and send orders in the markets. Financial markets have never been so close.

More, beyond APIs, Python also has the ability to analyze and retrieve data from any web page. We will talk about this topic in the next articles. Don’t miss it.

Data is the fuel of technologies like Artificial Intelligence and Machine Learning. The possibilities then become endless.

3. Python is dominating FinTech.

The term FinTech combines the terms “finance” and “technology”. it refers to innovative start-up that uses technology to rethink financial and banking services.

Well, for Fintech, the future of finance is drawn in python. If you have any doubts, take a look at the HackerRank study. For sure, learning Python is a golden opportunity to jump on the bandwagon.

Are you ready?

Investing in learning new technologies, Data, and Fintech means investing in the future.

Is Excel dead? I don’t think so. The processing and exploration of visual data make Excel an essential educational and sharing tool.

On the other hand, Python is the promise of a bright future.

Can you imagine building programs, analyzing markets and corporate financial data, determining opportunities and taking into account different risks of an investment.

What if create an army of virtual financial analysts (for free) were possible? An army working – only for you – 24 hours a day.

Finance of tomorrow requires learning the best tools. And to take this one step further, the solution is not only learning but testing concrete case. It also means practice.

 Ready to get started?

We will begin the leveling right now in the next article:

Welcome in the lab.

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