Crypto traders who are considering creating their own trading bots or customizing existing trading bots regularly ask which coding language is best for algorithmic trading. The answer is always, “it depends.” This answer may frustrate new bot traders, but there is no other answer to the question. “It depends” is the best answer because the best coding language depends on your trading strategy, size of data volume, memory requirements, tolerance for errors, hardware, required transaction speeds, performance requirements, and budget. Moreover, your priorities will determine which language will best suit you. For example, it is often necessary to choose between speed and performance.
Table Of Contents
- 1. Types of Crypto Traders
- 2. Coding Compromises
- 3. Preferred Bot Coding Languages
Understandably, knowledge of all the factors that affect the choice of coding language is a bit much for most new bot users or those who want to program their own bots. Furthermore, it can be difficult to understand how each choice affects the final selection of the coding language for crypto traders’ bots. While an understanding of the details of bot operation may seem time-consuming and bothersome, it is necessary if you want to create a bot that earns you money while you sleep and live your life. Your bot not only implements your trading strategy, but, to perform optimally, must be able to correctly assess the market and execute the coded buy and sell commands.
The primary decisions that must be made before you select a coding language are the coding strategy that you want deployed by your bot(s), trading priorities, trading compromises, and whether you want to customize or code the bot yourself. So, let’s look at the three most important decisions you’ll be making when selecting your bot’s language.
There are three types of traders who use bots: quantitative, execution, and high-frequency traders (HFT). These traders use different trading strategies and often have preferences for different bot languages because they require bots that cater to their trading priorities, whether they be fast transaction speeds, reduced errors, or the ability to manage large data volumes.
Quantitative traders use trading strategies that are automatically executed by computers that interact with other computers. In essence, they are programs built on computer models that execute commands under specific market conditions. The programs identify the market conditions that trigger specific commands by analyzing crypto markets via large amounts of data analysis and then send a signal to the bot to execute the coded commands that apply to those market conditions.
Execution traders rebalance portfolios. They are akin to traditional portfolio managers. Portfolios are rebalanced by doing the following:
● outsourcing trading to third-party execution algorithms provided by banks,
● developing their algorithms, and
● manual trading (which still outperforms bot trading in certain markets).
These types of traders are likely to need coding skills that enable them to develop and modify bot algorithms for the buy-side market.
High-Frequency traders (HFTs) are similar to quantitative traders in that they both work with large data sets. The difference between them is that HFTs work with shorter time intervals. HFTs also require low latency periods because their trades are extremely time sensitive—seconds matter.
Your trading strategy will determine which coding languages provide you with the best options and will optimize your trading strategy. You may need to choose between fast execution times and high-performance or low latency and the ability to manage large data sets. For example, HFTs require low latency and fast execution, whereas hedge funds are quantitative traders whose trades require the analysis of large volumes of data, so latency is not as serious an issue for them. On the other hand, execution traders will want to balance fast execution with high performance.
There are a growing number of languages being used for bots. All of them have their pros and cons. It is important that you decide which pros and cons are acceptable to you and will not inhibit or cripple your trading.
There are 3 bot languages which are quite popular. Their popularity means that you will easily be able to find a wealth of resources for them. The resources will likely include courses that teach them, lively communities that discuss them, and many libraries and tools that can be used by the coder. These languages can be used for pre-programmed, customizable, and bespoke bots of your own. The languages explored in this article are Python, C++, and Java.
Python is a high-level language used for automated trading and preferred by HFTs. It has free open-source libraries and tools that facilitate bot coding and fast transactions. It can be used to quickly evaluate mathematical models. In fact, it is excellent for complex scientific compilations. Moreover, it’s an excellent language for backtesting. Backtesting is the use of mathematical models with historical market data to test their performance using real market data. Although previous market activity is not a predictor of future market activity, a model’s performance with regard to historical market data is considered a good indicator of its future performance. This is also an area where Python excels because it can run numerous models using large volumes of data much faster than other coding languages.
Python is primarily used by investment banks, HFTs, quantitative traders, and day traders. It is also popular with stock market traders who represent banks and financial institutions.
● Highly scalable.
● Has technological indicators.
● Curates performance metrics.
● Provides real-time technological analyses of markets.
● Has key data mining libraries.
● Cross-platform compatible.
● Excellent for collecting, analyzing, manipulating, storing, plotting, and finding trends in huge data sets.
● Ideal for creating real-time trading apps that are compatible with interfacing with Forex broker systems and other trading platforms.
● Easy to do research and prototyping because it has high performing libraries.
● Facilitates accurate and fast trading.
● Very popular with lots of online resources.
● Easy to read and understand.
● Users can quickly write usable and reusable code.
● Easy for people new to computer code writing to understand and use.
● Coding for big projects can become messy.
● Can be hard to identify errors in the code.
● Best for quick, small to medium bot coding and projects.
● Slower than C++ and C#.
● An excellent language for many tasks, but does not excel at any specific task.
● Not ideal for reliable, high-performance coding.
C++ is a mid-level programming language that is based on the C language. It can be used for low-level to high-level artificial intelligence coding. Its structure makes it great for machine learning, bot coding, and developing complex coding. It’s ideal for creating high-performance code or coding that interacts with low-level robotic hardware.
● Great for high-performance tasks
● Users get access to lots of libraries
● Lowest-level programming language (just above assemble language)
● Many robust libraries and tools
● Fast, versatile, widely available
● Lots of online resources, lively communities, and published material
● Efficient at processing high volumes of data
● Used by banks and legacy systems
● Preferred by HFTs because of its low latency
● Easier to learn if you already know C
● Requires lots of debugging
● Writing the code consumes a lot of time
● Third-party libraries are difficult to use
● Takes a long time to learn and become proficient in the language
● More time needed for development and has a high level of software defects
● Not easy to learn because of steep learning curve
● Coder has full control over the coding resources, which can lead to more run-time errors and a greater likelihood for massive failure
● Preferred by older programmers who charge higher fees
This language is not recommended for trading bots, especially for HFTs and quantitative trading. It is best for data analysis and simulation tasks. For other uses, there are programming languages that are more suitable.
This language is easier for people with previous experience of writing code. Also, given that it is a low-level language that is more popular with older programmers, there is a lot of demand for it and the cost of having its code written is more expensive than alternative bot languages.
Java is an established language that is very popular. It places more stress on computer hardware, can run faster than C++, and has the garbage collection problem. If you avoid the garbage collection problem it can be as fast and efficient as using C++ and possibly surpass it. Its code can be written faster than C++ because it has built-in resources and a simpler design.
Java performs better than C++ because it uses the Java virtual machine (JVM). The JVM allocates resources for the code and prevents the misuse and abuse of some classes. It also has practical security features, lots of libraries and tools. In addition, it has a lot of online and offline resources too.
● Popular with Wall Street traders
● Great for data modeling and simulations
● Low latency execution (compared to C++)
● Can be harder for some people to learn than competing languages
● Lots of libraries and tools
● Faster execution than C++
● Better performance than C++
● Practical security features
● Online and offline resources
● Can be harder to learn than alternative languages
● Not as fast as C++ if you cannot avoid the garbage collection problem
● Heavy load on hardware
The bot language that you select should allow you to optimize your trading strategy, be efficient, cost-effective, and meet your bot trading requirements. While these factors will vary from trader to trader and project to project, it is best to consider your trading constraints and priorities before settling on a code for your bot. Finally, the time that you invest in selecting a coding language that will optimize your bot’s performance will more than pay off in profits that you’ll earn from successful bot trades.