When AI starts real trading, how can ordinary people use it to make their first trade?

In recent years, artificial intelligence technology has been developing rapidly and gradually integrating deeply with cryptocurrency trading. From quantitative funds to personal trading tools, machine learning, automated strategies, and high-frequency models, AI is changing traditional trading logic. However, the reality is that for most ordinary users, AI trading remains difficult to apply simply and proficiently.

On one hand, there are many AI trading products on the market, but most only stay at the level of result display. For example, indicators like return rates and win rates look good, but there is little explanation of strategy details and how to dynamically adjust positions.

On the other hand, the understanding threshold for AI trading itself is not low. Complex quantitative indicators and abstract algorithmic logic often require users to either have a professional background or to unconditionally trust the model. This has created a long-standing contradiction: while AI continues to lower trading execution costs, it has not truly reduced the cognitive threshold.

Against this backdrop, how to make ordinary users understand, use, and dare to participate in AI strategies has become an unavoidable hurdle for the popularization of AI trading.

What Makes the MEXC AI Copy Trading Challenge Different?

In response to this situation, MEXC has launched a unique "AI Copy Trading Challenge" with the core goal of providing ordinary users with an intuitive window to observe various AI strategies in real market conditions.

This challenge brings together six well-known AI models and two mysterious traders to compete, including the most attention-grabbing large models: DeepSeek, ChatGPT, Gemini, Qwen, Claude, and Grok. These AI models are crafted by MEXC to act as "AI traders," each creating unique trading strategies. For example, some prefer short-term arbitrage, while others focus on cyclical trends. Each model automatically analyzes market conditions, generates strategies, and executes orders through algorithms.

Additionally, two unidentified "mysterious players" have joined. Compared to regular models, their style and rhythm are also unique, adding an element of suspense to this live competition.

As shown in the image above, on the main leaderboard page, users can clearly compare the profit curves, win rates, and style tags of each model, intuitively understanding which strategies are more stable and which are more volatile. For example, the GPT model has a 7-day return of around $700 with a win rate of only 38%, indicating its high odds and average hit rate in arbitrage style; Claude, on the other hand, leans more towards trend positions.

By clicking on any model (such as GPT), users can further see its trading pair distribution (e.g., GPT primarily trades BTC/USDT), daily profit and loss bar charts, position time distribution, and other detailed data, helping them assess whether the trading rhythm and risk preference suit them. The data details are as follows:

1. Overall Profit Overview: Displays the model's return rates, profit-loss ratios, and total win rates over 7 days, 30 days, and 180 days.

2. Trading Style Profile

3. Daily trading performance chart shows daily profit and loss bar charts, making it easy to identify whether the model is consistently profitable or experiences significant fluctuations.

4. Trading pair distribution chart shows the model's preferred trading assets over the past 7 days. For example:

   • BTC/USDT has the highest proportion, indicating its main participation in mainstream coin trends;

   • BNB/USDT has a high proportion but has accumulated losses, suggesting a misjudgment on BNB;

   • ETH performs well, while XRP shows negative values, demonstrating the model's accuracy in judging different cryptocurrencies.

5. Position Rhythm

6. Displays the average holding time, with the maximum holding time reaching over 11 hours, indicating that this model prefers medium to short-term trading rather than high-frequency scalping.

7. The bar chart shows that profitable orders are mostly concentrated in the 3-7 hour holding period, suitable for users who do not pursue extremely short rhythms but wish to capture fluctuations within the day.

8. Historical Trading Records, with each trade data including:

9. Contract name and direction (e.g., long ETH/USDT, short XRP)

10. Leverage ratio (GPT primarily uses 6x leverage, which is moderate risk)

11. Transaction quantity and timestamp

12. Final closing profit and loss amount

This data allows users not only to see whether the model is profitable but also to understand why it is profitable, which coins it lost money on, and whether it consistently follows the same strategy rather than fluctuating randomly. For users who value data backtesting, detailed trade records are equivalent to opening the trader's live records, allowing users to review the model's thought process, rhythm, cryptocurrency preferences, and holding times, and to judge whether it suits their trading habits.

In summary, the MEXC AI copy trading competition has several highlights:

• Data Transparency: The platform publicly discloses position information in real-time, making trading execution transparent and traceable, allowing users to clearly see what each model specifically bought and sold.

• Diverse Strategies, Flexible Choices: Eight distinctly different strategies are built-in for users to copy, providing diversified options to meet different investment preferences.

In other words, this competition is also exploring how to transform AI trading capabilities, which previously belonged only to a few, into tools accessible to a wide range of users.

Breakdown of Activity Mechanism: How to Participate in the AI Copy Trading Challenge?

To enable everyone to participate directly after reading, we have summarized the key points of the MEXC AI copy trading challenge as follows:

From the table above, it can be seen that the rules of this AI copy trading competition ensure fairness and transparency while considering the participation experience of users at different levels.

First, all AI models and mysterious traders start from the same starting line, competing under the same cryptocurrencies and market conditions, with no one able to exploit by choosing obscure assets. This makes the competition results more valuable, with outcomes entirely dependent on the quality of the strategies.

Second, users have ample autonomy to choose; they can first act as observers to see which "player's" style and performance meet their expectations; or they can boldly place bets, positively copying strategies they trust or taking contrary actions against models they do not trust. Notably, the introduction of "contrarian copying" has garnered significant attention from the community, allowing users to view potentially underperforming AIs as contrary indicators to hedge their trades.

Finally, regarding the reward mechanism, all users participating in copying trades will receive rewards, allowing them to share in the prize pool, which effectively subsidizes part of the experience cost. New users will not leave empty-handed even if they follow the wrong strategy, reducing the psychological burden of trying AI strategies. Experienced users who correctly identify the champion strategy can also receive additional rewards. From merely observing and learning to trying small amounts of positive copying, and then to advanced gameplay through contrarian betting, users can gradually participate in AI copy trading according to their understanding level without having to make high-stakes bets right away.

In other words, through this progressive participation model and reward guarantees, this AI copy trading competition creates a friendly testing ground, allowing ordinary users to dare to try new experiences in AI trading without prioritizing profit.

The Evolution of AI Trading: From Passive Following to Active Arbitrage

With the rapid development of large language model technology, the exploration of AI applications in cryptocurrency trading is accelerating. In October 2025, NOF1.ai launched the Alpha Arena competition platform, allowing top large models to compete in real market environments, pushing AI trading from theoretical exploration to practical verification.

In NOF1.ai's experiments, an interesting phenomenon is that although the six models use the same prompt framework, they exhibit completely different trading styles. Qwen3 Max is aggressive and adventurous, pursuing high-risk, high-reward; DeepSeek is rigorous and steady, like a professional quantitative team; Gemini 2.5 Pro is extremely active in trading, with a total of 238 operations, but a win rate of only 25.6%, showing typical high-frequency inefficiency. In this AI competition, we can see that models are not neutral computational tools; their differences in training data and algorithmic architecture determine their behavioral preferences and decision-making styles in the market.

Subsequently, Bitget and BingX also followed suit, introducing AI copy trading competitions. Compared to NOF1.ai's purely observational model, users in copy trading competitions can not only view each AI's positions, entry and exit timing, and profit and loss details but also directly copy strategies. Bitget even supports real-time dialogue with AI traders, asking "why this operation" regarding strategy logic. This interactive experience further enhances users' understanding of the AI decision-making process and allows them to leverage AI for trading more profoundly.

However, as experiments deepen, the limitations of AI in specific market environments gradually become apparent. For example, in extremely volatile or illiquid markets, AI operations may also fail, resulting in significant drawdowns. In complex financial markets, even with vast historical data, AI cannot completely avoid market uncertainties. More complex is that when thousands of funds follow the same AI model, the AI itself becomes part of market liquidity. Its buying behavior may drive up asset prices, while stop-loss operations may trigger chain reactions, making purely following strategies often passive; the more followers there are, the greater the market distortion, and the shorter the strategy's lifespan.

In this predicament, savvy investors are beginning to shift their thinking from following AI to utilizing AI. The simple model of "AI buys what I buy" is no longer sufficient to meet more complex needs. MEXC's AI copy trading competition is the first to introduce the "contrarian copying" mechanism in response to this demand. This innovation not only enhances functional diversity but also cleverly transforms AI's "strategy failure" into monetization opportunities. When trend-based AIs frequently hit stop-losses in a volatile market, contrarian copiers are positioned against them, capturing short-term gains from mean reversion. Meanwhile, seasoned traders can design more diversified strategies, such as positively following stable "quantitative AIs" to capture market beta returns while contrarian copying aggressive but currently ineffective "speculative AIs" to hedge risks. This combination strategy enhances overall integrity and reduces systemic risks associated with reliance on a single model.

Conclusion

With the rapid development of large model technology, AI trading is transitioning from "concept hype" to "practical tools." Ordinary traders can see how large models analyze market conditions, control risks, and adjust strategies, helping them find a style that suits them and gradually build an independent trading system. This may be the core value of the AI copy trading competition—not merely "mindless" copying, but empowering more people to enhance their trading capabilities through AI, becoming more mature investors. The MEXC AI copy trading competition is currently in full swing and will continue until January 13. This is not only an opportunity to share a prize pool of 20,000 USDT but also a valuable chance to refine trading strategies and enhance trading abilities. As AI becomes a fundamental tool for trading, the competition among traders lies in how to collaborate with AI, understand its logic, filter its strategies, and combine its advantages to make AI work for them.