When AI starts trading in real markets, 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 is still difficult to apply simply and proficiently.

On one hand, there are many AI trading products on the market, but most remain at the level of result presentation. For example, indicators like return rates and win rates look good, but there is little explanation of strategy details and how positions should be dynamically adjusted.

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

Against this backdrop, how to make ordinary users understand, utilize, 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 performance.

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, with each model automatically analyzing market conditions, generating strategies, and executing orders through algorithms.

Additionally, two unidentified "mysterious players" have joined. Compared to regular models, their styles and rhythms are 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 return 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 a high odds and average hit rate arbitrage style; Claude, on the other hand, leans more towards trend positions.

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

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

• Trading Style Profile:

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

• The 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, indicating a misjudgment on BNB;

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

• Position Rhythm:

• 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.

• 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.

• Historical Trading Records, with each trade data including:

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

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

• Transaction quantity and timestamp

• Final closing profit and loss amount

This data not only allows users 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, the detailed transaction records are equivalent to opening the trader's live records, allowing users to review the model's thought processes, rhythm, cryptocurrency preferences, and holding times, and judge whether it suits their trading habits.

In summary, MEXC's AI Copy Trading Challenge has several highlights:

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

2. Diverse Strategies, Flexible Choices: Eight distinctly different strategies are built in for users to copy trading, providing diverse options to meet different investment preferences.

In other words, this competition is also exploring how to transform the AI trading capabilities that once belonged only to a few into tools accessible to a broader audience.

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

To enable everyone to participate directly after reading, we have compiled the key points of MEXC's AI Copy Trading Challenge as follows:

From the table above, it can be seen that the rules of this AI Copy Trading Challenge ensure both 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 with the same cryptocurrencies and market conditions, with no one able to exploit by choosing obscure assets. This makes the competition results more valuable, with the outcome 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; they can also boldly bet on positively copying strategies they favor or take the opposite approach to models they do not trust. It is worth mentioning that the introduction of "reverse copy trading" has garnered significant attention from the community, allowing users to take the opposite approach and treat potentially underperforming AIs as counter-indicators for hedging trades.

Finally, regarding the reward mechanism, all users participating in copy trading will receive rewards, allowing them to share the prize pool, which effectively subsidizes part of the experience cost. Even if beginners copy the wrong strategy, they will not leave empty-handed, 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 copy trading, and then to advanced gameplay with reverse betting, users can gradually participate in AI copy trading according to their level of understanding without having to make heavy bets right from the start.

In other words, through this gradual participation model and reward assurance, this AI Copy Trading Challenge creates a friendly experimental ground, encouraging 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 a real market environment, pushing AI trading from theoretical exploration to practical validation.

In NOF1.ai's experiments, an interesting phenomenon is that although the six models use the same prompt framework, they exhibit distinctly different trading styles. Qwen3 Max is aggressive and adventurous, pursuing high risk and high return; DeepSeek is rigorous and steady, resembling 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%, displaying typical high-frequency inefficiency. In this AI competition, we can see that models are not neutral computational tools; the differences in their training data and algorithmic architecture determine their behavioral preferences and decision-making styles in the market.

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

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 fail, leading to significant drawdowns. In complex financial markets, even with vast historical data, AI cannot completely avoid market uncertainty. More complex is the situation where thousands of funds follow the same AI model, making the AI itself a component of market liquidity. Its buying behavior may drive up asset prices, while stop-loss operations could trigger a chain reaction, making simple following strategies often passive. The more followers there are, the greater the market distortion, and the shorter the strategy's lifespan.

In this dilemma, savvy investors are beginning to shift their thinking from following AI to utilizing AI. The simple model of "AI buys what I buy" can no longer meet more complex demands. MEXC's AI Copy Trading Challenge is the first to introduce the "reverse copy trading" mechanism in response to this need. This innovation not only enhances functional diversity but also smartly transforms AI's "strategy failure" into monetization opportunities. When trend-based AIs frequently hit stop-losses in a volatile market, reverse copy traders stand on the opposite side, capturing short-term gains brought by mean reversion. Meanwhile, mature traders can design more diversified strategies, such as positively following stable "quantitative AIs" to capture market Beta returns while inversely following aggressive "speculative AIs" that are failing in the current market to hedge risks. This combination strategy enhances overall integrity and reduces systemic risks brought by reliance on a single model.

Conclusion

With the rapid development of large model technology, AI trading is completing its transformation from "concept hype" to "practical tool." Ordinary traders can see how large models analyze market conditions, control risks, and adjust strategies, finding a style that suits them and gradually building an independent trading system. This may be the core value of the AI Copy Trading Challenge—not merely "mindless" copy trading, but empowering more people to enhance their trading abilities with AI and become more mature investors. Currently, the MEXC AI Copy Trading Challenge is in full swing, continuing 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 capabilities. 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.