OKX & AiCoin Review｜Who Earns the Most in Grid Strategies? Unveiling 6 Major AI Trading "Personas"

Recently, the "AI Cryptocurrency Trading Arena" ------ AlphaArena launched by the startup team NOF1 has ignited discussions in the cryptocurrency and fintech circles. This competition provides each AI model with $10,000 in real funds, allowing them to trade autonomously in the cryptocurrency market, making AI's "financial intelligence" a hot topic of discussion.

Amid this trend, a more practical question arises: Can ordinary users leverage AI to enhance already mature fixed trading strategies? To explore the answer, OKX and AiCoin jointly initiated a special experiment: using the same six AI models, GPT-5, Claude Sonnet 4.5, Gemini 2.5 Pro, DeepSeek V3.1, Qwen3 Max, and Grok-4 (for ease of reading, the abbreviations of each AI will be used below), to provide parameters for the OKX BTC contract grid strategy. Through rigorous data backtesting under uniform market conditions, the true level of these AI "traders" was examined.

Excluding transaction fees, in addition to the strategy's inherent returns, if we add the extra profits brought by the "automatic earning" feature of the OKX strategy (which adjusts in real-time with the market, previously reaching around 50% and currently at 3% ), then the highest APY for Claude in the OKX BTC contract grid strategy can reach 50.64% .

Users only need to upgrade the OKX App to version V 6.141.0 or above to automatically enjoy the additional profits brought by the "automatic earning" feature, while the funds remain in the strategy account and can be used as margin, without increasing risk.

Methodology Explanation

Evaluation requirements for this assessment: Each AI is to provide parameters for an AI grid based on the 1-hour chart of BTC/USDT perpetual contracts on OKX, including price range, number of grids, direction (long, short, neutral), and mode (arithmetic, geometric). At the same time, adhering to a uniform capital limit, the investment amount is set at 100,000 USDT, with a leverage of 5 times.

After all parameters are submitted, they will be validated in a unified backtesting environment: The underlying asset is the grid strategy for BTC/USDT perpetual contracts on OKX, with a candlestick period of 15 minutes (or with some margin of error), and the backtesting period is uniformly set from July 25, 2025, to October 25, 2025. Then, the batch backtesting function of the AiCoin platform will be used for simulation verification. This tool will automatically simulate the order placement and transaction process based on the input grid parameters and output detailed trading data and profit statistics. The backtesting results will focus on key indicators such as total profit, return rate, win rate, maximum drawdown, and Sharpe ratio, ensuring that each AI strategy is compared fairly and transparently under completely identical market conditions.

Analysis of Strategy Parameters: The "Personality" Differences of AI

By comparing the key grid parameters of the six AI models, we can identify the core differences in their strategy designs:

From the table above, it can be seen that all AIs chose an arithmetic grid mode rather than a geometric one; and a neutral grid strategy, which involves simultaneous buying and selling arbitrage without needing to predict a one-sided trend. In addition, there are significant differences in the price ranges, grid densities, and other parameters provided by each AI:

1) The extreme high-frequency small amount faction is represented by Grok-4 and Gemini, which tend to accumulate small profits through high-density, high-frequency trading.

They both use the highest 50-grid setup and the smallest single-grid capital. Among them, Gemini's single-grid price interval is the most sensitive to price fluctuations, pursuing extreme high-frequency arbitrage; while Grok-4 combines the widest 20,000U range, aiming for dense order placements over a broader area. Due to the small single-grid capital, these strategies have relatively high capital safety, but require the market to maintain high-frequency fluctuations continuously.

2) The stable moderate faction includes DeepSeek and Claude, which adopt medium-density grids and single-grid capital.

Claude's 10,000U range and parameter configuration are moderate, representing a balanced and stable approach. DeepSeek, on the other hand, chooses the widest 20,000U range, aiming for more considerable single returns under expectations of large fluctuations.

3) The large amount low-frequency faction, represented by GPT-5, adopts an extreme "catch the big, let go of the small" strategy.

It sets the fewest 10 grids and the highest single-grid capital, with the largest single-grid price interval, meaning its trading frequency is the lowest, but the profit from a single arbitrage is the most considerable. This strategy sacrifices small fluctuation profits to focus on capturing large trend swings, thus potentially having a higher win rate, but due to the large single-grid investment, it has the highest risk of liquidation (drawdown) among all strategies if the price breaks through the range.

4) The narrow high-density faction, represented by Qwen3, seeks efficient arbitrage within a limited range.

It adopts the narrowest 4,000U price range among all models, combined with a medium 20-grid setup, resulting in relatively small single-grid price intervals. This is an extremely concentrated strategy, focusing on high-density arbitrage within a specific narrow range, requiring very high prediction accuracy; once the price moves out of the preset range, the strategy will quickly become ineffective.

Comprehensive Performance: Claude Outshines, GPT-5 Wins Steadily

Although AI is not influenced by emotions, the final data shows that the performance of AI "traders" still highly depends on their data training and model design. Through a comprehensive comparison of return rates, risk control, and win rates, significant differentiation appeared among the strategies of each AI model under the same capital and leverage conditions, revealing the trade-offs of different AIs in the actual market (Note: Backtesting does not guarantee future returns; while AI can select favorable market conditions, actual performance still carries uncertainty):

After a comprehensive evaluation of each model, who is the true "smart trader"?

1) Profit Champion and Adventurer: Claude

Total Profit Champion: Claude leads with the highest return rate of 10.23%, indicating that its combination of a stable range and medium grid successfully captured the main fluctuation range of the market, achieving the highest effectiveness of the strategy.

Risk and Return: It also boasts a Sharpe ratio of up to 370.58%, second only to GPT-5, demonstrating excellent risk-adjusted returns. However, its maximum drawdown of 5.32% indicates that its high returns are based on bearing larger floating losses, showing strong adaptability to market conditions and a certain level of risk.

2) Risk Control Master and Efficiency Model: GPT-5

Outstanding Risk Control: GPT-5 perfectly embodies the essence of the strategy "do not try to earn every penny in the market." Its low-density grid strategy filters out a lot of market noise, resulting in the lowest maximum drawdown of 3.89%.

Efficient Profitability: It wins with the highest win rate of 89.16% and the highest Sharpe ratio of 379.02%, proving the robustness and efficiency of its large amount low-frequency strategy. GPT-5 is the best example of risk-adjusted returns, reflecting the advantages of reducing trading frequency and focusing on capturing larger fluctuation opportunities.

3) Strategy Differentiation and the Dilemma of High-Frequency Trading

Focused Arbitrage Trader: Qwen3 ranks third with a return rate of 8.06%, performing well. However, its 4,000U extremely narrow range strategy is highly dependent on high-density fluctuations within that range. Its maximum drawdown of 5.32% is tied with Claude, confirming its high-risk concentration—once the market breaks through the narrow range, the strategy will face rapid failure risk.

High-Frequency Inefficiency: Grok-4 and Gemini, although employing similar 50-grid high-density strategies, have relatively lower profit performance (Grok-4 has the lowest return rate at 5.91%). Their lowest win rate (around 72%) and lower Sharpe ratio (Grok-4's lowest at 284.14%) indicate that overly frequent small trades may have eroded profits due to transaction costs such as fees and slippage, failing to reflect the advantages of high-frequency trading.

Stable but Not Outstanding: Gemini-2.5-Pro has the second-lowest maximum drawdown (3.99%), performing steadily, but its profit performance is average, positioning it as a moderate practitioner; DeepSeek-Chat's win rate and drawdown performance are stable (76.11% win rate, 4.68% drawdown), falling between high-frequency and low-frequency strategies.

Core Conclusion: The market has validated that low-frequency large profits (GPT-5) and precise range capturing (Claude) strategies outperform extreme high-frequency small profits (Grok-4/Gemini). GPT-5 wins with excellent risk control and the highest Sharpe ratio, while Claude takes the lead with absolute profit advantages, representing two successful endpoints of risk control and aggressive returns.

Insights for Strategy Users and Risk Warnings

This AI grid trading competition is not only a technical showcase but also a vivid "teaching" of trading strategies. There is no universal strategy, only strategies that fit the market conditions. The differentiation results of this assessment highlight that the success of a strategy depends on its compatibility with the current market conditions: GPT-5's success case clearly tells users that an excellent strategy must not only be profitable but also control drawdowns. Users must prioritize the importance of high win rates and high Sharpe ratios over merely high return rates when setting grids, and establish reasonable stop-loss levels based on their risk tolerance.

Moreover, the combination of grid numbers and price ranges defines the "personality" of the strategy, and users should choose based on their judgment of the market phase.

• Low-Frequency Large Profits vs. High-Frequency Small Profits: GPT-5's low-density strategy proves to be more efficient in filtering market noise and capturing large swings under specific market conditions. In contrast, Grok-4/Gemini's high-density strategy, despite frequent trading, failed to achieve the highest returns due to transaction costs, suggesting that high-frequency small profit strategies have stricter requirements on market conditions.
• Precise Arbitrage: The high returns of Claude and the narrow strategy of Qwen3 both highlight the importance of accurately judging market ranges.

Users can rationally adjust parameters based on the results of this assessment and the functions of the OKX platform.

• New or Conservative Users: Can refer to GPT-5's low-density, high single-grid capital strategy, pursuing stability, reducing trading frequency, and psychological pressure.
• Experienced or Profit-Seeking Users: Can refer to Claude's plan, using medium-density grids to amplify profits after accurately judging market conditions, but should be prepared to endure larger fluctuations.
• Utilizing AI Tools to Assist in Decision-Making and Parameter Adjustment: The parameter combinations provided by AI models are based on backtesting optimizations of historical market conditions. Users can refer to the parameter design ideas provided by the AI strategy functions of OKX's strategy trading, but ultimately need to adjust dynamically based on their judgments of cryptocurrency trends and volatility, for example: in the face of a one-sided market, one can narrow the range or reduce grids, while capturing significant trends may require increasing the range.
• Do not invest all funds into a single strategy; assets and targets should be reasonably diversified: Utilize OKX's "take profit/stop loss" feature or regularly close positions to lock in profits, and set stop-loss orders outside the grid range to mitigate losses in case of severe trend reversals.

Finally, a preview: In addition to backtesting data, we are continuously collecting data on the real performance of the six AI models in the OKX BTC contract grid strategy. For more updates, please keep an eye on the official information from OKX and AiCoin, stay tuned!**

Disclaimer:

This article is for reference only. It represents the author's views and does not reflect the position of OKX. This article does not intend to provide (i) investment advice or recommendations; (ii) offers or solicitations to buy, sell, or hold digital assets; (iii) financial, accounting, legal, or tax advice. We do not guarantee the accuracy, completeness, or usefulness of such information. Holding digital assets (including stablecoins and NFTs) involves high risks and may fluctuate significantly. You should carefully consider whether trading or holding digital assets is suitable for you based on your financial situation. Please consult your legal/tax/investment professionals regarding your specific circumstances. You are responsible for understanding and complying with applicable local laws and regulations.