As a trader, your main goal is probably to make as much money as you can, as fast as you can. Trading manually can make it difficult to achieve this goal.
This is because placing trades manually comes with numerous risks, including making mistakes based on emotional and psychological biases, placing trades when prices aren’t favorable, and making incorrect manual order entries (which could be awful if it’s a big error).
In addition, human beings cannot match the speed and processing power of today’s most advanced computer programs, which dominate the financial markets. These computer programs can analyze tons of data and perform actions faster than any human trader can.
If you want to succeed in today’s highly competitive and technology-driven financial markets, you’ll need two things: algorithmic trading and Python, a computer programming language used for algorithmic trading.
But what exactly is algorithmic trading and Python, and how can these two tools help you succeed in today’s highly complex financial markets?
That’s what we’re going to look at today, so keep reading to learn more.
What is Algorithmic Trading?
Also known as algo-trading, automated trading, and black-box trading, algorithmic trading uses a computer program that follows a predefined set of instructions (i.e., an algorithm). The predefined set of instructions could be based on a mathematical model, or KPIs like timing, price, and quantity.
Algorithmic trading is used by the world’s major banks and Wall Street institutions to trade traditional assets (like stocks) and newer markets (like cryptocurrencies).
Traders, investors, and programmers write the code that will execute trades once certain conditions are met. When properly executed, trading algorithms can generate profits at a speed and frequency that cannot be matched by manual traders.
Some of the advantages of algorithmic trading include:
- Enforcing automated, rules-based decision-making (which eliminates risks stemming from human biases).
- Placing trades instantly and accurately (which is more likely to produce optimum and profitable results).
- Simultaneous automated checks on various market conditions.
- Backtesting using historical and real-time data to determine the viability of the trading strategy.
For a more in-depth introduction to algorithmic trading and its pros and cons, check out this great article from Investopedia.
For the best books on algorithmic trading, check out this great list from Trality.
What is Python?
Python is an open-source computer programming language that is used in a wide variety of applications, including algorithmic trading. It has become the preferred choice for algorithmic trading in recent years since all of its packages are free for commercial use.
It’s also widely used in other areas of fintech, such as data analysis, the cryptocurrency markets, risk management, and banking services.
Python is used by investors and institutions every day to perform a wide array of functions, including quantitative research. It’s also used to prototype, test, and execute trading algorithms.
Python allows users to build intricate statistical models using scientific libraries, such as Pandas, NumPy, Scikit-learn, and Zipline. Updates to these libraries are a regular occurrence in the developer community, which means they’re improving every day.
Though there are other programming languages, Python is the most popular in fintech, particularly in quant trading. And since so much algorithmic trading is done using Python, it’s also much easier to collaborate, swap code, and crowdsource for assistance if you’re using this language.
As if those credentials weren’t strong enough, Python is also utilized by some of the world’s biggest companies, including Google, Facebook, Instagram, Stripe, and Dropbox.
Why Use Python for Algorithmic Trading?
Here are the top reasons why traders should consider learning Python:
- Ease of use and accessibility
Python code is renowned for its readability and accessibility, qualities that make it ideal for those who’ve never handled algorithmic trading software before.
And due to its highly functional programming approach, it’s generally much easier to write and evaluate algo trading structures on Python as well as build dynamic Python trading bots.
- Numerous support libraries
Unlike other coding languages, trading with Python requires fewer lines of code due to the aforementioned expansive support libraries. This also means that the most highly used programming tasks are already scripted in, limiting the length of code that needs to be written.
- Adds scalability to trading portfolios
Parallelization and Python’s considerable computational power give your trading portfolio the gift of scalability. Compared to other languages, it’s also easier to affix new modules to Python and make it expansive. And because of existing modules, it’s much easier for traders to share functionality between different programs.
- Debugging is hassle-free
Debugging in Python is both comprehensive and thorough, as live changes to code and data are allowed. This expedites the debugging process since single errors, rather than multiple ones, appear and can be resolved.
Drawbacks of Using Python
Though there are obvious benefits, there are also some drawbacks to using Python for your online trading:
- Variables store unnecessary data
Since each variable is considered to be an object in Python, each will store unnecessary data like value, size, and reference pointer. This could lead to serious performance bottlenecks and memory leaks if the memory management of different variables isn’t done efficiently.
- Mobile computing is less efficient
While Python is great for desktop and server applications, its mobile computing is less efficient. Python is generally seen as a weak language for mobile computing, which is why very few mobile applications are built with it.
How Does Python Compare to Other Programming Languages?
Compared to the other major programming languages (like C++ and R), Python is considered to be easier to master and manipulate. Nevertheless, it helps to note that all of these programming languages have unique features and their own distinct pros and cons. Hence, you’ll need to consider these factors when choosing the right language for your application.
Python versus C++
C++ has a reputation for being a difficult language to learn, making Python the obvious choice for rookie traders who want to learn how to develop dynamic trading algorithms quickly. On the other hand, Python is slower than C++, so if speed is an important part of your trading strategy, then you might want to opt for C++.
Another major factor to consider is trading frequency. Generally, if the trading frequency is less than one second, then C++ would be the better choice. But when selecting a language for backtesting and research environments, the final selection should be based on the available libraries as well as the requirements of the algorithm.
Python versus R
While many traders considered Python and R to be on equal footing just a few years ago, Python has since surpassed its rival in nearly every respect. Python now boasts superior support for modern software development tools and better package libraries.
While many factors go into choosing a programming language for your online trading, Python is generally the best choice for rookies who want to learn a language that is both easy to understand and manipulate. This, in turn, will help you prototype, test, and execute better crypto trading bots and trading algorithms.