Trading automation and data-driven algorithms aren’t a novel development in trading. While traditional traders have relied solely on manual input, continuous activity, and timing the market, institutional investors built entire trading systems. Algorithmic trading isn’t new, nor is it novel, but it was previously unattainable for retail investors, and to some degree, it’s not as easy to attain due to the high entry barrier.
There is a common misconception that sees algorithmic trading as incapable of producing higher-than-expected returns. In this article, we explore the reasons why algorithmic trading delivers high returns and how that’s achieved.
How does algorithmic trading work
Algorithmic trading relies on pre-defined parameters that are inputted into a computer connected to an exchange, broker, or trading platform to execute trades based on pre-defined rules. These programs have evolved into complex machines capable of analyzing data, learning from past performance, and adapting their trading execution.
As automation isn’t new, algorithms require serious investment, knowledge, and continuous adjustments to reach desired results. The global algorithmic trading market is expected to reach $3.28 billion in 2025, as demand for higher returns grows in line with ease of access to capable technologies.
High returns from algorithmic trading aren’t a matter of luck. In fact, trading algorithms aren’t designed to be perfect, but rather to understand risk management. They are the result of a systematic approach that leverages technology to exploit market inefficiencies. That’s why, across global markets, 70% of all trading is done through trading bots that automate the entire process.
Factors driving high returns in algo trading
Successful implementation of algorithmic trading hinges on automation and a partially hands-off approach. Since automated processes are predictable, quantitative trading companies, which handle hundreds of billions in AUM, rely on algorithmic and high-frequency trading for generating returns.
Let’s explore why high market returns are possible.
Speed and efficiency
In financial markets, a fraction of a second can be the difference between profit and loss. Algorithmic trading systems excel in this high-speed environment. They can monitor thousands of assets simultaneously and execute trades the moment specific conditions are met, far faster than any human could.
This speed allows them to capitalize on small price fluctuations, open and close trades in nanoseconds, and open multiple positions at once. By contrast, retail trading, which involves manual input, takes seconds or even minutes to complete the same tasks.
Advanced data analysis
Algorithmic trading tools can analyze, understand, and implement inputs from large data sets to identify profitable patterns and trends. Through trading indicators and complex mathematical models, these platforms can forecast price movements with a higher degree of probability.
For example, a quantitative trading firm like Yieldfund uses automated systems that trade in the top 10 cryptocurrencies. They rely on three core indicators to run high-frequency trading algorithms, which helped them reach 93% profitability in 2024.
Emotionless trading
As data shows, over 80% of manual trading leads to losses. That’s because humans are driven by emotions that lead to irrational trading decisions due to fear and greed. Algorithms remove the burden of having to analyze each outcome and follow pre-defined rules.
They execute trades based solely on the set strategy. This disciplined, emotionless approach prevents costly mistakes and ensures that the trading plan is followed consistently.
Diversification and risk management
Managing risk is just as important as generating returns. Algorithmic trading allows for advanced diversification strategies that are difficult to implement manually. An algorithm can simultaneously manage a portfolio across dozens or even hundreds of different assets and markets.
This automated asset allocation helps spread risk. If one asset performs poorly, the impact on the overall portfolio is minimized. Quantitative trading companies take this step further by implementing dynamic risk management that considers multiple variables. This ensures portfolios are protected rather than risking unnecessary trades, thus minimizing losses.
Why do algorithms perform better than manual retail trading
Trading algorithms perform better because the outcome relies on what makes the final decision rather than who makes the final decision. A software program, if it follows a proven strategy, relies only on data to open or close trades—even if that takes only a split second. By contrast, retail trading introduces more unknowns into the trading process. This can include emotions, moods, level of market understanding, input, influence, and much more. This means manual trading adds intuition into the mix, which increases complexity.
An algorithm, regardless of its strategy, delegates decision-making to a program. While a human designs the initial strategy and sets the rules, the algorithm handles the execution, making it predictable. If a trader’s experience aligns with automation and data-driven insights, the algorithm can outperform by a large margin. This is because the A-to-Z process is executed instantly.
Are there drawbacks for retail investors
Despite its advantages, algorithmic trading is not without challenges for both institutional and retail investors.
- Infrastructure Costs: Professional-grade algorithmic trading requires significant investment in high-speed computers, reliable data feeds, and server hosting. These infrastructure costs can be prohibitive for individuals.
- Complexity and High Barrier to Entry: Trading algorithms require deep programming knowledge; however, this has decreased in recent years. Regardless, correctly setting up the program parameters and deploying it according to the strategy can be complicated.
- Risk of Significant Losses: All trading involves risk, and algorithmic trading is no exception. A poorly designed algorithm or an unexpected market event can lead to rapid and substantial losses. Even for institutional traders, a faulty strategy or implementation can lead to millions in losses.
- Need for Continuous Monitoring: What regular investors fail to understand is that algorithms are not “set and forget” solutions. They require continuous adjustments as market conditions change, and without active oversight, a once-profitable strategy can quickly become obsolete.
Unlocking new investment potential
While higher-than-usual returns may seem out of reach, trading automation has proven to be a profitable strategy, even for the world’s largest trading companies. Even as investment companies aim for lower profit margins, these come with lower risks. Higher yields of 20%–30% are possible, but that also means the risks of capital loss are higher.
Now, building a trading algorithm is within everyone’s reach. However, that doesn’t mean everyone will profit from it. For those looking to capitalize on consistent returns without prior trading knowledge, the entry barrier has significantly decreased.
Quantitative trading companies like Yieldfund allow investors to access the crypto market through a single investment. In return, users can select from three available plans offering up to 5% monthly returns, which are paid weekly. Ultimately, algorithmic trading represents a powerful evolution in investment strategy that is now accessible to regular investors.
