Core Insights - The first AI large model trading competition initiated by the American AI research lab nof1 concluded, with six leading models participating in autonomous trading using market data without human intervention [1][5][7] - Two Chinese models, Alibaba's Qwen3 Max and DeepSeek Chat V3.1, achieved positive returns, with Qwen3 Max leading at a return rate of 22.3% and a profit of $2,232 [1][2][3] Performance Summary - Qwen3 Max achieved a return of 22.3%, with an account value of $12,232 and a win rate of 30.2% [3] - DeepSeek Chat V3.1 had a return of 4.89%, with an account value of $10,489 and a win rate of 24.4% [3] - Other models, including Claude Sonnet 4.5, Grok 4, Gemini 2.5 Pro, and GPT 5, experienced significant losses, with GPT 5 losing 62.66% [2][3] Trading Dynamics - The competition involved trading cryptocurrency derivatives, including Bitcoin, Ethereum, and Dogecoin, with each model starting with $10,000 [5] - Models were required to process quantitative data and execute trades without access to news or market information [5] - Qwen3 Max maintained the largest position size throughout the competition, while Grok 4 had the longest holding period [6] Model Behavior - Grok 4, GPT-5, and Gemini 2.5 Pro exhibited a higher frequency of short-selling compared to others, while Claude Sonnet 4.5 rarely engaged in short-selling [6] - Qwen3 Max had the narrowest stop-loss and take-profit distances, indicating a more conservative exit strategy [6] - The competition highlighted the need for dynamic testing of models in real market conditions, as opposed to static benchmark tests [7]

Core Insights - The inaugural Nof1 AI Model Trading Competition concluded, designed to measure AI investment capabilities, likened to a "Turing test" for the crypto space [1] - Six AI models participated, representing the latest technology from both Chinese and American developers, with Qwen3 Max emerging as the top performer [1][12] Competition Overview - The competition ran from October 17 to November 3, 2025, with each model starting with $10,000 in initial capital [1] - Trading was conducted on Hyperliquid, focusing on six popular cryptocurrencies: BTC, ETH, SOL, BNB, DOGE, and XRP [3] - The trading strategies were limited to buying, selling, holding, or closing positions, with a focus on mid-frequency trading [3] Performance Results - Qwen3 Max ranked first with a return of 22.3%, total profit of $2,232, and a win rate of 30.2% over 43 trades [2][5] - DeepSeek Chat V3.1 secured second place with a return of 4.89%, total profit of $489.08, and a win rate of 24.4% over 41 trades [2][5] - Other models, including Claude Sonnet 4.5, Grok 4, Gemini 2.5 Pro, and GPT-5, experienced significant losses, with GPT-5 showing the worst performance at -62.66% [4][11] Model Characteristics - Qwen3 Max exhibited an aggressive trading style with a high return and significant trading frequency, reflected in its Sharpe ratio of 0.273 [9] - DeepSeek Chat V3.1 demonstrated a more conservative approach with a higher Sharpe ratio of 0.359, indicating better risk management [9] - Claude Sonnet 4.5 and Grok 4 showed cautious strategies but suffered from low win rates and high losses [10] - Gemini 2.5 Pro and GPT-5 were characterized by high trading activity but poor performance, indicating ineffective strategies [11] Industry Implications - The competition has garnered significant attention, with industry leaders like Binance's founder commenting on the potential impact of AI trading strategies on market dynamics [7] - The results suggest that AI models from China, particularly Qwen3 Max and DeepSeek, are currently outperforming their American counterparts in terms of risk control and trend identification [12]

Core Insights - The first Nof1 AI model trading competition concluded with unexpected results, showcasing the investment capabilities of AI models in cryptocurrency trading [1][5][9] Group 1: Competition Overview - The competition was designed as a benchmark test for AI investment capabilities, referred to as the "Turing Test of the cryptocurrency world," initiated by Nof1.ai from October 17 to November 3, 2025 [1] - Six AI models participated, including DeepSeek Chat V3.1, Grok 4, Gemini 2.5 Pro, GPT-5, Qwen3 Max, and Claude Sonnet 4.5, representing the latest technology from both Chinese and American suppliers [1][3] - Each model started with $10,000 in initial capital and traded autonomously on Hyperliquid, focusing on six popular cryptocurrencies: BTC, ETH, SOL, BNB, DOGE, and XRP [3][4] Group 2: Trading Performance - Qwen3 Max ranked first with a return of 22.3%, total profit of $2,232, and a win rate of 30.2% over 43 trades [5][7] - DeepSeek Chat V3.1 secured second place with a return of 4.89%, total profit of $489.08, and a win rate of 24.4% over 41 trades [5][7] - The remaining models, including Claude Sonnet 4.5, Grok 4, Gemini 2.5 Pro, and GPT-5, experienced significant losses, with returns of -30.81%, -45.3%, -56.71%, and -62.66% respectively [6][15] Group 3: Model Characteristics - Qwen3 Max exhibited an aggressive trading strategy with a high return and significant trading frequency, while maintaining a Sharpe ratio of 0.273 [13] - DeepSeek Chat V3.1 demonstrated a more conservative approach with a higher Sharpe ratio of 0.359, indicating better risk management [13] - In contrast, models like Gemini 2.5 Pro and GPT-5 showed poor performance due to excessive trading and lack of effective market judgment, reflected in their negative Sharpe ratios of -0.566 and -0.525 respectively [15][16] Group 4: Market Implications - The competition has garnered significant attention, with industry leaders commenting on the potential impact of AI trading strategies on market dynamics [9][11] - There is speculation that widespread use of similar AI models could influence market behavior, potentially driving prices up through collective demand [10][11]

Core Insights - The AI investment competition concluded with Qwen 3 Max emerging as the winner, followed by DeepSeek, both from China, while GPT-5 ranked last with significant losses [1][4]. Group 1: Competition Overview - The competition involved six top AI models, including Qwen 3 Max, DeepSeek V3.1, and GPT-5, trading cryptocurrency perpetual contracts on the Hyperliquid platform [3][4]. - Each AI model was given $10,000 to trade various cryptocurrencies, with performance measured by risk-adjusted returns [4][5]. Group 2: Performance Metrics - Qwen 3 Max achieved an account value of $12,232 with a return of +22.32%, completing 43 trades with a win rate of 30.2% and a Sharpe ratio of 0.273 [5][6]. - DeepSeek V3.1 followed with an account value of $10,489 and a return of +4.89%, having the highest Sharpe ratio of 0.359, indicating strong risk management [6][7]. - Other models, including Claude Sonnet 4.5, Grok 4, Gemini 2.5 Pro, and GPT-5, experienced significant losses, with GPT-5 suffering a -62.66% return [5][7]. Group 3: Trading Strategies - Qwen 3 Max employed a straightforward strategy, focusing solely on BTC with high leverage, while DeepSeek utilized a more analytical approach with independent logic for each asset [8][10]. - GPT-5 maintained all positions despite substantial losses, reflecting a less adaptive strategy compared to others [10][11]. - Gemini 2.5 Pro adopted a strict short-selling approach, disregarding short-term market fluctuations [10][12]. Group 4: Future Implications - The competition highlighted the effectiveness of simple, focused strategies over complex ones, suggesting that less can be more in trading [14][15]. - The organizers aim to use financial markets as a training ground for AI, believing that the dynamic nature of markets presents a greater challenge than fixed-rule environments [12][14].

AI Model Performance in Trading Competition - Major AI models like Claude Sonnet 4.5, Gemini 2.5 Pro, and GPT 5 experienced significant losses within a week since October 18 [1] - Gemini 2.5 Pro and GPT 5 declined by over 50% [1] - DeepSeek Chat V3.1, Qwen3 Max, and Grok 4 remained profitable but with sharp drawdowns [1]

Core Insights - Alpha Arena conducted an experiment to evaluate the performance of six AI models in live crypto markets, with DeepSeek Chat V3.1 achieving a portfolio growth of over 35% in just three days, outperforming Bitcoin and other AI traders [1][7]. Experiment Structure - The experiment involved six leading AI models, each given $10,000 and access to real crypto perpetual markets, trading autonomously based on an identical prompt [1][2]. - The AI models were assessed on their ability to handle risk, timing, and decision-making in live markets [4]. Trading Framework - Each AI was provided with a strict trading framework that required them to trade six major cryptocurrencies (BTC, ETH, SOL, XRP, DOGE, BNB) with specific conditions for each position, including take-profit and stop-loss targets [5][9]. - The models received market data at each tick and had to decide whether to open, close, or hold positions, focusing on consistency, execution, and discipline [6]. Performance Results - After three days, the performance of the AI models was as follows: - DeepSeek Chat V3.1: $13,502.62 (+35%) - Grok 4: $13,053.28 (+30%) - Claude Sonnet 4.5: $12,737.05 (+28%) - BTC Buy & Hold: $10,393.47 (+4%) - Qwen3 Max: $9,975.10 (-0.25%) - GPT-5: $7,264.75 (-27%) - Gemini 2.5 Pro: $6,650.36 (-33%) [7]. Reasons for DeepSeek's Success - DeepSeek's strategy involved holding a diversified portfolio of all six major crypto assets with moderate leverage (10x–20x), which helped spread risk and capitalize on the altcoin rally during the experiment period [8].

Core Insights - The AI trading competition launched by nof1 on the Alpha Arena platform aims to identify the most profitable AI model in crypto trading [1][11] - DeepSeek Chat V3.1 currently leads the competition with a portfolio value increase of 40.38% in just two days [2][5] AI Trading Competition Overview - Six AI models were allocated $10,000 each to trade autonomously in the crypto markets [2] - The competition has generated significant interest, with expectations of increased trading volumes as AI strategies gain popularity [3][10] Performance Metrics - DeepSeek Chat V3.1 achieved a portfolio value of $14,038 after 18 transactions, consisting of 12 buys and 6 sells [5][6] - Grok 4 initially led with a 500% gain on the first day but faced losses, placing it behind DeepSeek [7] Market Implications - Major industry figures, including Changpeng Zhao, have noted that widespread use of the same AI trading models could impact market prices [8][9] - Zhao anticipates higher trading volumes as more users adopt AI for trading [10]