Why Python is the language of choice in FinTech
The language’s financial algorithmic performance is extremely strong, making it the ideal choice for FinTech companies. Any company seeking high-level and robust mathematical programming leans toward Python.
Who uses Python in FinTech?
Python is used widely by banks, FinTech startups, hedge funds, and other firms in the financial sector.
JPMorgan’s Athena trading platform consists of 35 million lines of Python code. Bank of America has active job postings looking for Python developer roles.
Revolut, the UK’s most valuable FinTech company, has a team of 100 Python developers and is on the hunt for 30 more.
Uses of Python in FinTech
Python is excellently suited for predictive analysis and machine learning. It boasts a plethora of libraries that solve many of the problems encountered in the financial sector.
Popular libraries include:
- Pandas — a data analysis library
- NumPy — the library for scientific computing
- scikit learn — machine learning and predictive data analysis
- Keras — a deep learning API
- Theano — compiler optimization for evaluating mathematical expressions
- pyalgotrade — an algorithmic trading library
- And many others
These and other libraries facilitate trading, proprietary software development, large file manipulation, big data processing, AI features, machine learning, and much more.
Other FinTech companies that reportedly use Python include:
- Zopa — peer-to-peer lending
- Affirm — credit card and loan alternatives
- Stripe — payment processing
- Robinhood — buying and selling stocks
Career opportunities in FinTech with Python
As banks work to upgrade legacy code to Python and new FinTech startups choose Python by default, the career opportunities in this powerful and versatile language increase commensurately.
CitiGroup now wants their investment bank analysts to have Python skills, and a quick internet search reveals that the bank is also actively hiring Python developers.
Knowledge of Python is becoming important even when the role itself is not a programming one.
Excel problems solved with Python
Excel is another core skill required to obtain work in the FinTech sector. Excel was created specifically for “number crunchers” and so provides an excellent springboard from which to design complex FinTech functionality.
It is entirely possible to work with Excel files in native Python code by using the popular Pandas library.
The usefulness of Excel in the financial industry cannot be underestimated. It is entirely possible that both skills combined — Python and Excel — will be required for future jobs.
Python coding challenges
Python is an extremely intuitive language to learn. Indentations define code blocks, which is unique in programming language styles. This syntax also makes the language easier to read, and gives it less clutter.
Python coding challenges come primarily in the form of learning the huge amounts of libraries that exist for the language. But this can be learned easily with regular study, real Python projects, tutorials and courses.