Trading is and will become more algorithmic and there are plenty of reasons that can explain it. The amount of money flowing into quant-based trading strategies has grown tremendously recently. We have seen the emergence of many new hedge funds that deploy computerized trading strategies and employ hundreds if not thousands of engineers to build them. Discretionary hedge funds are also seeing increasing popularity as stagflation threatens investors. However, the entry barriers into algo trading that existed in the past decades are falling and a new category of algo traders emerge thanks to two main technological drivers: the growing dominance of Python communities and libraries and the growing number of open APIs for trading.
The snake that doesn't bite
You probably heard about algorithmic trading and thought it was something only the big banks and hedge funds did. And that was true, to some extent—but it’s changing. Algorithmic trading is booming, and Python plays an increasingly important role in this space.
Python has many advantages over other languages when it comes to developing algorithms: it's easy to learn, powerful enough for advanced users yet approachable by beginners, and there are many libraries available for common tasks such as data analysis or charting (such as pandas). Some SDKs are also developed for Python users for building and deploying trading bots inside docker containers, making operating in the market more accessible than ever.
And best of all, Python has been used extensively in financial applications for decades now:
The first ever trading bot (for the Chicago Mercantile Exchange) was built on top of Python in 1987!
Stock options pricing models were developed using Pyomo—a Python module that provides interfaces with many different C++ libraries including EViews, Gurobi, Mosek and Matlab.
This success makes it easier for programmers unfamiliar with finance to catch up and start building trading bots. They have access to little-known quantitative models that the industry’s professionals use (e.g., Monte Carlo simulations).That audience gathers online, using Discord or Reddit to share their experiences. The massive success of the subreddit r/algotrading is an excellent example of the interest shown in the field.
Subreddit r/algotrading numbers of followers
Open source finance changes the game
Another key element that explains the development of this new category of developers applying their Python skills to algo trading is the growing numbers of open APIs available on the market to start trading.
The APIs allow the online algo traders to deploy their models at low cost while they can automate their operations and risk management.
Crypto exchanges have been a prominent part of this movement by being among the first to widely open their services by giving access to an open API for trading and managing accounts. Other companies operating in the equity or Forex markets have adopted this strategy of providing open APIs to their general public instead of keeping them to some select hedge fund customers.
However, these elements will not be enough for this community to overtake the dominance of the quant hedge funds in the market. For the algo traders to supplant the competition, they will need more accessible and faster ways to find profitable strategies and scale their operations. A layer of tooling and infrastructure like the one Absurdia is building will not only open up new opportunities to this community. It will also enable a new generation to create wealth for themselves like no other generation could.
It's important to remember that the financial industry is still in the early stages of this data revolution. The growing adoption of Python and the increasing number of open APIs for trading create a new category of traders with skills and capital. The rise of algorithmic trading has surprised most of the world during the last decades of computer development, but it's clearly evolving. In fact, we're going to see more and more technology like Absurdia being used as we move forward—which means it will only get more complicated for quant hedge funds to compete with millions of developers online trying to beat them at their own game.