我们给六个 AI 同一段市场数据，它们生成了六种完全不同的交易策略

Core Insights - The article discusses an experiment involving six AI models generating trading strategies for XAU/USD (gold against USD) under identical conditions, revealing diverse approaches and decision-making styles among the models [1][4][5]. Experiment Overview - The experiment utilized hourly market data for XAU/USD, chosen for its volatility, clear structure, and continuous data, making it suitable for observing AI reasoning and strategy differences [2][3]. - The AI models involved were ChatGPT, Claude, Gemini, DeepSeek, Qwen, and Grok, each starting with an initial capital of $10,000 [1][6]. Results and Analysis - The AI models produced six distinct trading strategies, ranging from conservative to aggressive, and from mechanical trend-following to emotional testing, highlighting their unique "personalities" in trading [4][5]. - The focus of the analysis is not on profitability but rather on the underlying thought processes and decision-making logic of each strategy [5]. Performance Metrics - The performance of each model was tracked, with Grok showing the least loss at -0.04%, while Qwen had the highest loss at -0.88% [6][7]. - Current equity values and cumulative returns for each model were provided, indicating varying degrees of success in the trading environment [6][7]. Trading Strategies - ChatGPT's strategy emphasized trend-following based on moving averages, with a disciplined approach to risk management and a preference for not leveraging or shorting [9][12][14]. - Claude's strategy focused on mid-term trend tracking, considering macroeconomic factors and geopolitical events to identify buying opportunities [15][20]. - Gemini's approach involved trading only in bullish market conditions, using long-term moving averages to guide entry and exit points [21][24]. - DeepSeek's strategy was centered on long-term upward trends, avoiding leverage and emphasizing patience in waiting for clear signals [25][26]. Conclusion - The experiment illustrates the potential of AI in trading, showcasing how different models can interpret the same data in varied ways, leading to distinct trading strategies and outcomes [1][4][5].