Are AI bots actually successful?

Automated trading using artificial intelligence (AI trading bots) has become one of the most debated topics in financial markets. Amidst advertisements promising quick riches and effortless wealth accumulation, and warnings regarding scams and financial losses, lies the objective reality.

Are AI trading bots truly successful?

The short answer is yes: they are highly successful and profitable for financial institutions and major funds, yet they often prove unsuccessful or high-risk for retail traders.

Estimates indicate that over 70% to 80% of daily trading volume in global markets—such as stocks and forex—is executed via algorithms and trading bots. However, the vast majority of commercially available bots for individual traders fail to generate sustainable long-term profits.

Why do bots succeed for institutions but fail for individuals?

1. Ultra-high speed and infrastructure

- Institutions: They utilize massive servers located directly within the stock exchanges' data centers (co-location). This grants them execution speeds measured in microseconds, allowing them to capture price differentials before any other trader.

- Individuals: Retail trading bots rely on standard internet connections, where latency reaches hundreds of milliseconds, thereby negating the competitive advantage of speed.

2. The "Overfitting" Problem

Most commercially available bots are built based on historical data testing (backtesting) to produce impressive past results. However, when actual market conditions shift—such as moving from an uptrend to a downtrend or entering a period of sideways volatility—the bot fails to adapt and loses money, as the strategy was optimized solely for the past.

3. The Trap of Off-the-Shelf Bots and Peddlers of False Promises

Most companies selling trading bots promote unrealistic profits (such as a risk-free 10% monthly return). In reality, if these bots actually generated such profits, their creators would not sell them via modest monthly subscriptions; instead, they would use them to invest their own capital.

Optimal Use of Artificial Intelligence in Trading

The modern trend among professional individual traders does not rely on "fully automated, hands-off trading"; instead, it leverages artificial intelligence to act as an assistant and coach (AI Trading Agent):

1. Big Data Analysis: Utilizing AI tools to process news, perform sentiment analysis, and identify price patterns.

2. Partial Execution Automation: Developing tools that calculate position sizes and automatically execute stop-loss and take-profit orders once an entry command is issued.

3. Development and Troubleshooting: Analyzing historical trade logs to identify the trader's most profitable trades and the periods associated with the highest losses.