Core Insights - The article discusses an AI trading competition in the cryptocurrency market, highlighting the performance of various AI models and their strategies in a volatile environment [1][5][20]. Group 1: Competition Overview - The AI trading competition, organized by Alpha Arena, runs from October 17 to November 3, featuring real-time trading of cryptocurrencies without human intervention [1][5]. - A benchmark participant employs a simple buy-and-hold strategy for Bitcoin (BTC) to compare the performance of AI models [2]. - The competition includes a betting aspect where spectators can wager on which AI will win, adding a layer of engagement [3]. Group 2: Participating AI Models - Six leading AI models are involved: GPT-5, Gemini 2.5 Pro, Grok-4, Claude Sonnet 4.5, DeepSeek V3.1, and Qwen3 Max, each starting with $10,000 in real funds [5]. - All trades are executed on the Hyperliquid platform, ensuring transparency and security [5]. Group 3: Performance Analysis - As of October 23, Chinese models Qwen3 Max and DeepSeek V3.1 lead the competition, achieving significant profits, while Western models like GPT-5 and Gemini 2.5 Pro face substantial losses [8][10]. - Qwen3 Max adopted an aggressive strategy, leveraging high positions during market surges, resulting in a 13%-47% increase in account value [10]. - DeepSeek V3.1 maintained a steady approach, achieving 8%-21% net gains by adhering to strict risk management and diversified trading [11][12]. Group 4: Challenges Faced by Western Models - GPT-5 suffered from emotional trading and poor stop-loss management, leading to losses of 30%-40% within days, and up to 65%-75% by the end of the week [14]. - Gemini 2.5 Pro's overtrading and excessive leverage resulted in a loss exceeding 55% in the first week, highlighting the risks of high-frequency trading [14]. Group 5: Insights on Trading Strategies - Grok-4 initially gained 35% but later returned to a net loss of approximately 15% due to failure to lock in profits [15]. - Claude Sonnet 4.5, while cautious and conservative, ended with a negative return of about 17%, demonstrating the trade-off between risk and reward [19]. Group 6: Broader Implications - The competition serves as a deep experiment into the capabilities of AI in real market conditions, emphasizing that intelligence in trading is not solely about algorithmic prowess but also about adaptability in unpredictable environments [20].
华尔街之狼，与AI共舞
3 6 Ke·2025-10-28 08:05