Algorithmic Trading: How a System Is Really Built

Algorithmic Trading in a Nutshell

Algorithmic trading is the automated execution of trades based on predefined, coded rules. The software reads market data, checks whether the trader’s conditions have been met, and, when necessary, submits orders through the connectivity tools provided by the trading platform and broker.

However, the process does not begin with code. It begins with a hypothesis, continues with the definition of the rules and their validation using historical data, and reaches the automation stage only later.

For an introductory explanation with step-by-step examples, you can also read “What Is Algorithmic Trading and How Does It Work?.” Here, we will focus on the method used to turn an idea into a system that can be objectively evaluated.

Algorithmic Trading vs. Systematic Trading: What Is the Difference?

Systematic trading is a decision-making approach in which the trader defines the entry, exit, and risk management conditions in advance. Algorithmic trading, on the other hand, concerns the implementation and execution of those rules through software.

A systematic method can also be executed manually. It becomes algorithmic when its rules are translated into instructions that a computer can interpret, allowing it to analyze data, identify signals, and submit orders.

Discretionary, Manual Systematic, and Algorithmic Trading

Trading can be divided into three main operating approaches:

• Discretionary trading: the trader makes decisions while observing the market, based on personal analysis and experience. This approach offers flexibility, but it makes it more difficult to separate the decision-making process from emotions.
• Manual systematic trading: the rules are defined and tested in advance, but the trader manually submits the orders when a signal occurs.
• Algorithmic trading: the rules are coded, and the software manages the predefined process, from reading market data to managing the position.

There is no absolute hierarchy among these approaches. The right choice depends on the method, the trader’s skills, goals, and available time. For those who cannot spend hours in front of charts, automation can reduce the operational workload.

However, it does not eliminate the work involved. It shifts that work from real-time decision-making to system design, testing, and monitoring.

How an Algorithmic Trading System Works

A robust system is built through a structured process. Three stages are essential: defining the rules, backtesting, and implementation.

1. Define Objective Rules

A rule must be clear and unambiguous.

“Buy when the price closes above the 20-period moving average and volume is higher than the average volume of the last 10 days” is a condition that can be coded.

“Buy when the market looks strong” cannot be coded unless the concept of strength is translated into measurable criteria.

At a minimum, a complete system must define:

• when to enter and exit the market;
• how much capital to risk;
• how to size each position;
• how to manage stop losses and any profit targets;
• when the system is allowed to trade and when it must remain inactive.

2. Run a Backtest

A backtest reconstructs how the rules would have performed using historical data. Its purpose is to eliminate ideas that lack a credible statistical basis and to analyze returns, drawdowns, trading frequency, and the stability of the results.

A backtest does not prove that the system will work in the future. A reliable test must use appropriate data and account for commissions, slippage, and the actual characteristics of the instrument being traded. It should also cover different market conditions, rather than focusing only on the period in which the idea produces its best results.

3. Validate and Automate the System

Before real money is put at risk, the system should undergo robustness checks and be tested on data that was not used during development.

Only after this stage should the rules be implemented on a trading platform and connected to the execution infrastructure supported by the broker.

The software can then submit and manage orders according to the programmed logic. However, the trader remains responsible for technical monitoring, risk control, and the system’s periodic evaluation.

What You Need to Get Started With Algorithmic Trading

You do not always need to be a software developer. Some platforms use relatively accessible programming languages, guided editors, or visual tools. However, the amount of programming required will depend on the platform and the complexity of the system.

The essential elements include:

• a platform for defining, testing, and automating the rules;
• reliable historical data covering a sufficiently long period;
• a market that is suitable for the available capital and acceptable risk level;
• realistic trading costs, including commissions and slippage;
• a broker and infrastructure that support automated execution;
• monitoring procedures for connection issues, rejected orders, or abnormal system behavior.

The main challenge, however, is not technical but methodological. The rules must be defined before analyzing the results, and the trader must resist the temptation to change them every time the backtest equity curve gets worse.

The Most Common Mistake: Optimizing Instead of Validating

Overfitting occurs when rules and parameters are adjusted too closely to historical data. The system ultimately describes that specific data sample extremely well, including its random behavior, but becomes less effective when it encounters new data.

An almost perfect equity curve is therefore not, by itself, proof of robustness. Results that appear excessively smooth should be examined carefully, especially when they were produced through numerous parameters, filters, and optimization attempts.

Optimization itself is not necessarily the problem. It can help traders understand how a system behaves. It becomes dangerous when it is used to find the combination that maximizes historical performance without testing parameter stability or separating development data from validation data.

👉 Read also: Overfitting in Trading Systems: How to Tell Whether a System Is Overoptimized, With a Practical Example

From Entry to Exit: What Does the System Actually Decide?

Consider a simplified trend-following system designed to trade a stock index future. An entry condition might be triggered when the price breaks above the highest level of the previous 20 trading sessions and volume is above its average. If all conditions are met, the system generates a signal and submits the order.

After entry, a stop loss can be calculated based on recent market volatility, while a trailing stop can follow a favorable price move. Position size should also be determined by predefined rules and remain consistent with the portfolio’s overall risk.

The trader does not make the decision based on a momentary feeling. This reduces emotional interference during execution, but it does not make the system infallible.

The quality of the results depends on the rules, their robustness, actual trading costs, infrastructure reliability, and risk management.

Algorithmic Trading Is Not Something You Can Simply Turn On and Forget

Automation does not mean giving up control. A trading system can encounter structural changes in the market, data issues, platform malfunctions, or real-world trading conditions that differ from those used in the simulation.

A professional approach includes periodic checks and predefined criteria for evaluating differences between actual and expected results.

Monitoring does not mean changing the rules after every losing trade. It means making sure that the system is operating correctly and that its risk remains consistent with the trading plan.

The value of algorithmic trading does not lie in finding a machine that can generate profits without supervision. It lies in building a measurable, repeatable process that is less dependent on emotional decisions made in the heat of the moment.

Frequently Asked Questions

Why Can an Overly Perfect Backtest Be a Warning Sign?

An equity curve with almost no declines may be the result of excessive curve fitting to historical data. To evaluate the system’s robustness, you also need to examine its out-of-sample results, parameter sensitivity, trading costs, and stability across different market conditions.

Is Algorithmic Trading Suitable for Beginners?

It can be, provided it is approached gradually. It is better to begin with rule logic, risk management, and backtesting before moving on to the technical tools required for automation.

Algorithmic trading is not a shortcut, and it does not eliminate the risk of loss.

Do You Need to Know How to Code?

Advanced programming skills are not always required to create a simple first system. Some platforms offer accessible programming languages or visual tools.

However, learning the basics of programming logic can help you understand and control what the system is doing.

What Is the Most Overlooked Risk?

Overfitting is one of the main risks, but it is not the only one. Traders must also consider data quality, costs, liquidity, execution errors, changing market conditions, and excessive position sizing.

Does an Algorithmic System Remove Emotions From Trading?

It reduces the emotional component involved in executing individual trades, but it does not remove emotions from the overall process.

A trader can still intervene impulsively, turn the system off during a drawdown, or change its rules without properly validating those changes.

Do You Want to Build a More Structured Trading Process?

Algorithmic trading does not begin with the search for perfect code. It begins with a method: formulate a hypothesis, translate it into rules, test it using realistic assumptions, and manage risk consistently.

If you want to understand how to apply these principles to your own trading, you can request a free strategy session with our team by clicking the button below. Together, we will review your goals, your current experience level, and the most appropriate next steps to help you approach systematic trading with greater awareness.

See you soon, and good trading!