Table Of Contents
- 1. Why is Python A Good Fit For Algorithmic Trading?
- 2. What is Algorithmic Trading?
Want to know a little known fact? Algorithmic trading is one of the best methods to trade without letting those pesky human emotions get in the way of realizing profits. And although it’s relatively low maintenance for a process with high turnover, it does require some level of skill in programming languages for the highest level of optimization.
Having said this, there are several programming languages that’ll be suitable for algorithmic trading, some even without the drawbacks. Python, though, is lauded as the best way to draw out the potential of your trading fully.
In this article, we’re going to talk about the reasons why it’s considered so good.
Let’s dive in, then.
Despite its growing popularity and large community around it, algorithmic trading is still typically seen as a difficult pill to chew, therefore I’ll break it down for you so you completely understand it. In simple terms, algorithmic trading makes trading decisions based on pre-set rules programmed into a computer.
Pretty easy, right? But let’s simplify it some more.
An algorithmic trader is simply someone who writes codes that execute trades when certain conditions are met, for example, automatically buying shares when the prices drop below a certain level. This practice is preferred because it saves a ton of time. You don’t have to scan the markets manually. It lets you jump on an opportunity as soon as one arrives, eliminating the hassle of deciding whether or not you’re making a mistake.
Now, it isn’t as cut as dry as you may think. There are a lot of routes that an algorithmic trader can take to maximize their profits with relatively no added effort, and if you feel an aspect isn’t your speed, there are few options to choose from.
For example, an algorithmic trader (or algo-trader) can decide to go into market making or arbitrage, where they buy stocks, products, and securities from one market and then sell in another less saturated market, profiting on the difference in prices. This can be done for personal gain, for a firm member of some sort, whichever works for you.
To be honest, no mandate says algorithmic traders have to use Python to be good at it. Frankly, it’s best to learn multiple programming languages as each has its unique strengths that you might find helpful.
For example, C++ is a mid-level programming language. Yet, it’s still one of the best options for developing components of high-frequency trading (HFT) that are latency-sensitive because of how efficient it is at processing high volumes of data.
In the same way, C#, which is a higher-level, component-oriented language of C++ and Java, shares similar functions and is used for data modeling, simulations, and low latency execution. Java is even known to be the most sought-after programming language on Wall Street.
Since algorithmic trading depends significantly on the trading software being used, a knowledge of various programming languages will only serve to help you keep up with the constantly changing markets, giving you a leg up over other traders.
However, while learning multiple programming languages is encouraged when going into algorithmic trading, things such as cost, performance, modularity, etc., generally don’t make it feasible. Hence, it’s necessary to focus on the programming language that is the best fit.
Yes, it is. Why? Because for people new to algorithmic trading, Python is far more accessible and easier to read—two things that are invaluable in the work line. Though other programming languages, like Java, have their strengths, Python doesn’t require as many lines of code to get the job done.
This simple fact, combined with an ability to execute code ‘one-by-one’ instead of complied, directly translates to less time spent coding rules and debugging as well as less effort spent executing code.
Python is a free, open-source language and has an in-depth library that can be used for practically every task you could think of. In cases of low/medium trading frequency—that is, trades that don’t last more than a few seconds or less than a minute—Python is absolutely revolutionary because of its APIs (i.e., its libraries) that can be linked to allow more excellent and cheaper development of trade ideas.
And that’s just one of its benefits.
It isn’t a stretch to say that the type of programming language used for trading can make or break your business. The weaknesses of specific programming languages can often be solved through extensive knowledge of the limitations and the experience or skills to bypass them. However, in a business that actively focuses on speed to make the most of opportunities, every second spent working around a limitation can prove costly.
With this in mind, there are many reasons why Python is preferred above other programming languages for algorithmic trading. Some benefits are obvious and easy to exploit, while some are only seen with enough use.
- Python’s parallelization and computational power as a programming language provide significant scalability to an algorithmic trading portfolio.
- Because of its functional programming approach, the language is generally easier to code with and provides less difficulty when evaluating algorithmic trading structures. Its codes can also easily be extended to dynamic algorithms for trading.
- Using Python, it is significantly easier to fix new modules to the programming language and make it more expansive in trading.
- Creating trading platforms using Python takes decidedly less time and effort compared to other programming languages like C and C++.
- Because of its ridiculously extensive and comprehensive libraries, using Python for trading requires fewer lines of code to be written. The libraries can also allow traders to skip various steps that might be needed when using other languages, thus bringing down the overall cost of maintaining the trading system.
Though Python is one of the easiest-to-use programming languages for an aspiring algorithmic trader, it still requires a level of know-how to use it efficiently and to maximize its benefits in one’s business. Everything from the various components of the language—for example, Anaconda, Spyder IDE, Jupyter Notebook, etc.—to its numerous libraries come together when integrating Python into algorithmic trading. Because of this, a basic grasp of their concepts is needed. Fortunately, there is no shortage of resources available to be utilized. You can even find detailed step-by-step guides on how to build your own Python crypto trading bot and achieve a cutting-edge advantage in algorithmic trading.