AI大模型投资比赛落幕，阿里通义千问 Qwen 以 22.32% 收益率夺冠

Core Insights - The Alpha Arena project conducted by Nof1 tested six leading AI language models (LLMs) in a real trading environment, with the goal of assessing their capabilities in quantitative trading [1][3][12] - The top performer, Alibaba's Tongyi Qianwen Qwen3-Max, achieved a return of 22.32%, securing the investment championship [1] Experiment Design - Each model started with $10,000 (approximately 71,218 RMB) to trade cryptocurrency perpetual contracts on the Hyperliquid platform, focusing on assets like BTC, ETH, SOL, BNB, DOGE, and XRP [11] - The models were restricted to making decisions based solely on numerical market data, without access to news or current events [11] - The primary objective for each model was to maximize profit and loss (PnL), with the Sharpe Ratio provided as a risk-adjusted performance metric [11] Initial Results - The models exhibited significant differences in trading styles, risk preferences, holding durations, and trading frequencies, despite operating under the same structure [9] - Some models engaged in short selling more frequently, while others rarely did so; similarly, some held positions longer with lower trading frequency, while others traded more frequently [9] - The research team noted that the order of data presentation could affect model performance, indicating sensitivity to data format [9] Significance and Observations - The project aims to shift AI research from static benchmark testing to real-world, dynamic, and risk-driven assessments [5][12] - Although the experiment did not determine the strongest model, it highlighted challenges faced by advanced LLMs in actual trading scenarios, including execution of actions, risk management, market state understanding, and sensitivity to prompt formatting [12]