Python is the versatile programming language that is taking the world by storm and has quickly become synonymous with technology and innovation. First released in 1991 by Dutch programmer Guido van Rossum, Python was designed to improve code readability and simplicity.
The simplicity of Python made it transferrable and that made it a popular choice not just within one or two select industries but a wide range from web development and data science to the futuristic sci-fi worlds of artificial intelligence and machine learning.
Python also has its uses in the finance industry where it has been used to change how countries interact with financial markets and how they innovate. Python’s versatility is further demonstrated by its use in another financial area – sports wagering. There it has been, and is still, used to calculate horse betting odds!
The main focus of today’s article however is less focused on sports wagering and instead on the wider world of the financial sector. Read on to find out how Python is changing the way we interact with finance.
Revolutionizing the Finance Industry
In Morgan Housel’s critically acclaimed book The Psychology of Money, the New York Times bestselling author successfully argues that psychology has more impact than any other factor on financial success in the stock market.
The main thrust of his argument is that investments have been steadily rising in value over the past 150 years and if you can keep your head in the tough times and remain balanced, your discipline will be rewarded with increasingly compounding returns.
However, he also argues in the book that what worked yesterday may not necessarily work tomorrow, citing the many editions of The Intelligent Investor by Benjamin Graham in which successful formulas are later proved to be unsuccessful.
That’s because the world changes, and as a result, the means of becoming successful change in line with it. Housel would therefore not be shocked to find out that his assertion that psychology is the most important aspect of successful investing is being proved wrong by Python.
Python’s extensive library ecosystem incorporating Pandas, NumPy and SciPy enable financial analysts to test out intricate date manipulations and analyses. In addition to that visualization tools like Matplotlib and Seaborn allow those findings to be represented visually in graphs and charts.
All of which to say is that Python is allowing investing to become more and more formulaic. Through its risk analysis capabilities it is also enabling investors to more accurately spot risk and giving them an opportunity to act in their best interests.
To circle back to Morgan Housel, however, he would perhaps argue that Python showing an investor that a deal has a 95% chance of doubling their interests but a 5% chance of wiping them out completely still wouldn’t allow most investors to see that a 5% probability of financial obliteration is a price far too high to pay.
Python’s Future Potential in Finance
Rather than dedicate an entire section to telling you how all the benefits listed above are beneficially impacting the financial industry, we trust you to make those inferences yourself. Needless to say it is reducing risk and educating investors, which, in the right hands is a good thing.
How though, will Python continue to influence the industry in the coming years? Most presently it looks like playing a role in cryptocurrency. Its integration into Blockchain looks set to make transactions in crypto more secure and efficient.
In addition to that Python will be continued to be used in artificial intelligence and machine learning, which if scientists are to be believed, holds the potential to eradicate problems that the current human mind has not hopes of solving.
That however, is a long way off and a huge potential impact. In the short to medium term, with far less long-term impacts, Python is just likely to make investing more precise and open up the insights to a far greater market.
As Morgan Housel so eloquently points out though, investments are ultimately made by human beings and human beings have feelings and emotions that drive them. Python may very well highlight risk and opportunity, but people will still be susceptible to their egos, to outside pressures and to laziness.
Perhaps the ideal scenario is that Python allows investors to completely outsource their portfolio to rational, pragmatic AI investors.
