What is an automated trading system?
An automated trading system (ATS) is a subset of algorithmic trading that uses a computer program to place and manage trades on a trader’s behalf. When using an ATS, traders define a pre-set list of rules (often based on price, timing, volume, or technical indicators) that the program then follows without the need for human intervention.
In its simplest form, an automated trading strategy command might look like ‘when X stock reaches Y price, do Z’. The system always sticks to its programmed strategy, so it helps remove emotional decision-making from the trading process.
There are several types of automated trading systems out there. Some are basic tools that refine order entry or alert traders to certain conditions, while others are advanced ‘trading robots’. These advanced bots can scan multiple financial markets, open and close trades, set stop-loss and take-profit levels, and adjust position sizes, all within fractions of a second.
ATS are often used by institutional traders like hedge funds and investment banks to manage large volumes of trades, but they’re also available to retail traders through certain online platforms. According to some estimates, automated systems now account for 70% to 80% of all trades.
How automated trading systems work
Executing trades using predefined rules
Every automated trading system is based on a set of instructions that tell it exactly when to buy or sell. These rules can be based on price levels, volume, timing, or specific technical indicators like moving averages or Relative Strength Index (RSI).
The automated system continuously watches the market, and as soon as conditions match its programmed criteria, will place an order. These systems work quickly – often within milliseconds – allowing them to potentially capitalize on opportunities that may only exist for a few seconds. They can also trade across multiple instruments at the same time.
Performing market analysis automatically
Besides executing trades, automated trading bots can also scan price charts, identify patterns, and process large volumes of market data in an instant. This speed allows them to evaluate multiple markets much faster than any human trader could.
Managing risk with built-in controls
Traders can also program risk management into their automated trading system, defining stop-loss and take-profit levels, position sizing rules, and maximum exposure limits in advance. Once these parameters are in place, the system follows them consistently to exit trades exactly when conditions are met. There’s no hesitation or second-guessing as there might be with a human trader – which brings us to our final point.
Operating without emotion
One of the biggest advantages of automated systems is that they eliminate emotional bias. It’s common for human traders to experience psychological traps like fear, greed, overconfidence, or hesitation, all of which can undermine a strategy. Trading bots feel no such thing; they follow instructions exactly as they’re written and execute every trade with discipline.
Types of automated trading systems
Fully automated trading systems
Fully automated systems manage every step of the trading process, including analyzing the market, opening and close trades, setting stop-loss and take-profit levels, and adjusting positions according to the rules programmed into them. Although the trading process is fully automated, do not be misguided by the name, all automated trading systems rely on technology and need to be constantly monitored for risk management purposes and for any systemic/technological failures.
Semi-automated trading systems
Any add-on, plug-in, or program that extends the functionality of a standard trading platform can be thought of as a semi-automated system. These will generate trade signals or suggest orders, but won’t execute trades without the trader’s approval.
Trading bots based on AI and machine learning
AI-driven systems take automated trading a step further. While traditional trading bots only follow fixed rules programmed into them, AI bots use machine learning and predictive analytics to adapt to changing market conditions and refine their strategies over time.
Advantages and disadvantages of using automated trading
Advantages of automated trading
Some of the advantages of automated trading include:
- Speed & efficiency: Automated systems can analyze markets and execute trades in milliseconds, much faster than any human could. This speed allows you to capitalize on fleeting opportunities, especially in fast-moving markets.
- Consistency & discipline: Automated systems strictly follow the rules pre-programmed into them, removing emotional decision-making from the trading process. This ensures your trading strategies are executed exactly as intended, without any hesitation or deviation.
- Backtesting capabilities: Before you take an automated strategy live, you can test it against historical market data to see how it performs. This allows you to refine and improve your strategy before risking any actual capital. Although backtesting can be used to test the functionality of an automated trading system past performance is not indicative of future results.
- Trade multiple markets & strategies: Automated systems can monitor and trade across different instruments and strategies at the same time, making it easy to diversify and spread risk without burning out.
Disadvantages of automated trading
Automated trading isn’t without its disadvantages. Some drawbacks include:
- Technical failure & bugs: Automated systems are built to follow instructions no matter what. So if there’s an error in the programming, the system will execute it anyway, which can lead to potential losses. This is why backtesting is so important before taking a trading bot live.
- Connectivity & latency issues: To execute orders quickly and efficiently, automated systems need stable, high-speed access to the internet with the lowest possible latency. If there are any disruptions, it could lead to delayed execution, missed opportunities, and potential losses. This is especially important to note if you live in a remote area prone to connectivity issues.
- Limited fundamental analysis: Most automated systems are designed to focus on numerical (i.e. technical) data and can’t properly interpret things like market sentiment, news events, geopolitical developments, or economic reports. This can pose a risk when trading around major announcements.
- Oversight: Even though they’re highly automated, these systems still need regular monitoring and updates to make sure they adapt to changing market conditions and are functioning as intended.
- Over-optimization: Sometimes, systems are overly optimized to historical data. This can make them look successful in backtests but they may not be able to hold up in live markets. What worked in the past won’t always be successful in the present.
Components of an automated trading architecture
Every automated trading system requires at least three components:
- A way to receive market data
- A way to process that data into trading decisions
- A way to execute those decisions on the exchange.
Let’s take a look at each of these components in more detail.
Market data adapter (data feed)
The market data adapter is the system’s connection to the outside world. It receives live market information, like current market prices or trading volumes, directly from an exchange or data vendor.
Since every exchange has its own data format, the adapter converts this raw feed into a standardized format the automated system can understand. This ensures the data is ready for analysis in the next step.
Complex event processing (CEP) engine
The CEP engine is the ‘brain’ of the automated trading system. It analyzes the incoming data in real time and looks for patterns, market trends, or anomalies that suggest trading opportunities based on your strategy’s rules.
For example, you might program the CEP engine to detect when a currency pair’s price drops by a certain percentage, triggering a buy signal. The speed at which your engine can process market data is important here, because any delays can lead to missed opportunities or poorly timed entries.
Execution and order routing system
Once the CEP engine generates a signal, the order routing system takes over. This component constructs the trade order (market, limit, stop, etc.), determines the best route to the exchange, and sends it for execution.
The order routing system also manages risk by verifying order size, making sure it meets the trading strategy and regulatory requirements, and minimizing potential errors.
System latency and infrastructure
As we’ve mentioned before, automated trading is all about speed and low latency (i.e. minimal delay between signal and execution) is critical to getting the best possible price. To reduce delays, many institutions or high-frequency traders choose to host their systems on servers located close to exchange data centers. That’s how important speed is! Everything needs to work as fast as possible without sacrificing accuracy.
How to build an automated trading system
So, how do you turn your trading strategy into a fully automatic program that can execute trades on its own? Let’s take a look at how to build an automated trading system.
1. Create a trading strategy or plan
The first step is to create a solid trading plan. Decide what market you want to trade, your risk tolerance, and the rules for entering and exiting trades. Your strategy could be based on your own market observations or from information learned through trading books, blogs, forums, or research papers. The most important thing at this step is to have clear enough rules that can be turned into code.
Step 2: Develop the system architecture
Once you know your rules, you can start building the system that will follow them. This means choosing the tools you’ll use, including programming languages (e.g. Python or C++) and algorithmic trading platforms.
You can then start programming your system with the information it needs to follow, including entry and exit rules, risk controls (e.g. stop-loss and take-profit levels), and any other parameters.
Step 3: Backtest and optimize
Before you take your system live, you’ll want to backtest it to see how it would have performed in the past. Backtesting uses historical data to test your strategy and help identify strengths and weaknesses. You can then fine-tune the settings based on its performance, being careful not to over-optimize too much based on past data. It is important to realize that back testing has it limitation especially since past performance is not indicative of future results.
Step 4: Deploy the system in live markets
Once you’re confident in your system, it’s time to go live! You may want to start trading in a demo account first to test your strategy in real-time market conditions using virtual funds. This lets you see how your strategy holds up in the real-world without risking any actual capital.
When you’re ready to go live and use actual capital, it’s best to start small and keep a close eye on your system’s performance to make sure it’s running smoothly. Keep reviewing results on a regular basis and make adjustments where needed.
Who can benefit from automated trading systems
It might sound like automated trading is reserved for institutions or experienced traders, but that’s not necessarily the case. Even beginner traders can benefit from automated trading systems, and you don’t need to know programming languages, either.
Platforms offer pre-built strategies and marketplaces where you can buy or download trading bots, although it’s still recommended to test these on a demo account before you take them live and do proper research to ensure they are suitable to meet your investment strategies and risk profile. It’s also wise to understand how these bots work to reduce the risk of losses.
If you have some programming skills, you can build your own custom systems from scratch using the language of your choice. This gives you complete control over the strategy logic, execution, and risk management. You can also modify existing bots to better suit your needs.
