Trading is frequently done in an automated fashion these days. Many of the most successful traders on the market leverage solutions that help them automate the process either partially or completely. While this puts classic manual traders at a disadvantage, it’s important to remember that the tools needed to create an automated trading robot are available to everyone.
Creating your own trading robot from scratch is well within the reach of the average trader – but it does require covering a rather steep initial learning curve.
What Is a Trading Robot?
A trading robot is a program – or a set of programs – that performs automated trading operations on the market with minimal (or in some cases, none) user interaction.
They are designed to be fast and efficient, analyzing trends and looking for opportunities to take advantage of current market conditions.
Why Are Trading Robots Used?
Trading robots offer two main advantages over manual trading: speed and availability. A trading robot can work 24/7 with little to no human interaction, constantly monitoring the market and adjusting its strategy accordingly.
When an opportunity arises, a trading robot can take advantage of it immediately. Even an experienced human trader who happens to be watching the market at the right moment will still be limited by their reaction speed and the interface of the software they use. Meanwhile, a trading robot can act upon new information immediately, utilizing short-term developments to their full potential.
Research has shown that utilizing trading robots can increase trading performance and reduce errors, even when they’re used manually by supervising operators instead of automating the entire process.
Who Can Create a Trading Robot?
Trading robots are created by programmers experienced in one or more languages. A certain level of understanding of the financial market is also essential. The development of a good trading robot requires a good mix of both aspects. Someone with weak programming skills would be prone to making errors which can result in costly mistakes when the robot is deployed, while a developer with inadequate experience in trading might not fully understand the specifications they are working with.
What Languages Are Trading Robots Written in?
Four languages are currently popular for the creation of trading robots: Python, C++, MQL5 language and Java. Each of them has its advantages and disadvantages:
Python
- Easy to learn;
- Has a wide variety of pre-made libraries for various functions, allowing for a “building blocks” approach to development;
- Well-suited for working with large data sets;
- Large-scale applications tend to be slower than those written in C++ and are more difficult to maintain.
C++
- Most difficult to learn, requiring a deeper understanding of computer architecture and working with memory;
- The best choice for performance-critical applications;
- Old and very well established on the market.
MQL5 language
- Specifically designed for automated trading solutions;
- Similar to C++ in structure and syntax;
- Dedicated IDE (MetaEditor);
- Getting Started with Creating a Trading Robot.
Java
- Somewhat easier to learn than C++, but still more difficult than Python;
- Good variety of libraries;
- Doesn’t require an in-depth understanding of hardware and memory constraints;
- Performance can be an issue unless special precautions are taken.
Before deciding to create your own trading robot, check whether your requirements are already fulfilled by existing solutions on the market. There are various marketplaces where pre-made trading robots can be purchased. You can also contact a freelance developer through a platform like Upwork, Fiverr or MQL5.community Freelance and order a custom solution if what’s available on the open market doesn’t fit your requirements.
Prices vary across the board – some solutions cost less than $100, while others may cost several thousand dollars. Each pre-made robot is designed to utilize specific strategies and operates in a unique manner, even if it may share similarities with competing products.
Some of the more expensive solutions are well worth their price compared to implementing them from scratch. Consider the following example: a trading robot that implements specific trading strategies might take around 150 hours of development time just to implement the strategies themselves, not counting the basic setup of the program and other development work. With an average salary of $35/hour for software engineers in the US, this comes out to over $5,000 – again, just for one part of the robot and not the whole solution. A robot with similar features may be available for $1,000 – $5,000, depending on the exact functionality needed, and that price often includes ongoing support and maintenance.
First Steps: Creating a Basic Trading Robot
If you want to get started as quickly as possible and you don’t have any language preferences, you will probably want to work with Python. With the right libraries, a python application can take less time to implement compared to other languages as you can skip developing some of the more complex parts while also benefiting from a simplified syntax.
Building your first trading robot goes through several main steps:
Obtaining and Visualizing Financial Data
If you’re using Python, you can use a library like robin-stocks to get direct access to data provided by Robinhood. If you prefer to work with another platform (or combine data from multiple ones), you should look for an appropriate package for it. Many trading platforms provide API access for automated data retrieval and in some cases even trading operations.
Play around with this step to get a sense of how the retrieved data is structured. Explore different modes of visualization. Make sure to save local caches of the data you’re accessing to avoid repetitive API queries while you’re still working out the initial functionality of the robot.
Buy/sell Functionality
Depending on the library you’re using, you may already have some trading functionality directly available through it. Otherwise, you’ll have to develop your own – an intensive task that requires deeper programming knowledge that goes beyond the scope of this article.
Thorough testing is crucial. It’s advised to go through an in-depth course on testing and evaluating trading robots before you deploy your solution to the real market. Work with local data and never use your robot for actual trading operations until it’s gone through extensive testing.
It’s a good idea to prepare several data sets with specific properties. This can allow you to evaluate how the robot works under different conditions. Adjust different parameters to see how it will respond to certain changes in the market. Prioritize fixing bugs early on. The more you allow them to stack up, the more complex it will become to resolve them later.
If your robot targets several trading strategies, see if you can buy a cheap pre-made one designed for one or more of them. This can allow you to compare the performance of your own robot against tested solutions on the market.
Going Beyond the Basics
Once you’ve covered the basics, you should look into opportunities for building on your work and expanding it in new directions.
Machine learning can be a powerful tool for analyzing market data and even the performance of your own robot. It’s a complex field, but over the last decade, various user-friendly solutions have emerged that can be utilized with little technical knowledge. ML-based tools can help you identify patterns that humans would never spot on their own. They can also be configured to run perpetually, constantly gathering and analyzing new data.
Performance is going to become a bottleneck eventually. While you can run a trading robot on your home computer and an internet connection, it’s advisable to look into moving it to a dedicated data center eventually. This will give you access to more processing power, memory, and higher speed for trading. High-end traders on Wall Street spend thousands of dollars just for the opportunity to have their servers closer to the main exchanges. Shaving off fractions of a second from each transaction might seem negligible, but it can make a difference in the long run.
Closing Thoughts
Writing your own trading robot requires a balanced mix of programming skills and an understanding of the financial market. You can fill gaps on both sides with the use of freely available resources, including programming and trading tutorials. It’s also advisable to investigate the current market to see what’s already available and identify strategies that are not being utilized to their full potential.
Sometimes the choice of building your own robot – which can take more than a hundred development hours, as we described above – makes more sense than spending several thousand dollars on a pre-made solution. Especially in cases where you’ve come up with a unique strategy that you believe has a strong potential. However, testing your solution on mock market data is important before deploying it live and allowing it to trade with real money.
