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].

Core Insights - Chinese AI models DeepSeek and Qwen AI outperform their U.S. counterparts in a cryptocurrency trading challenge organized by Nof1 [1][2] Group 1: Contest Overview - The Alpha Arena contest began on October 17, testing the investment capabilities of various AI models with a starting capital of $10,000 [2] - The challenge involves trading cryptocurrencies on the decentralized exchange Hyperliquid, with models given identical prompts and input data [2] Group 2: Performance Results - DeepSeek V3.1 Chat leads the competition, increasing its capital to $21,600, representing a 116% gain [3] - Qwen 3 Max, developed by Alibaba, follows in second place with a capital increase of approximately 70%, reaching nearly $17,000 [3] - Anthropic's Claude 4.5 Sonnet and xAI's Grok 4 are in third and fourth place with returns of 11% and 4%, respectively [4] - Google's Gemini 2.5 Pro and OpenAI's ChatGPT 5 are the worst performers, with losses exceeding 60% [4] Group 3: Factors Influencing Performance - The advantage of Chinese models may stem from being trained on cryptocurrency-native conversations from Asia-facing forums [5] - DeepSeek is reportedly a side project of a quantitative trading firm, which may contribute to its performance [5] Group 4: Contest Dynamics - The Alpha Arena challenge concludes on November 3, indicating potential for significant changes in rankings before the end [6] - Some analysts suggest that the results may follow a random walk, implying that average trading positions could revert to the starting point over time [6] Group 5: Broader Context - The Alpha Arena is part of a series of experiments assessing AI trading capabilities, with previous studies indicating that AI models can outperform traditional managers significantly [7]

Core Insights - The article discusses a financial competition involving six top AI models, highlighting their performance in real market conditions and the differences in their trading strategies [1][2][13]. Group 1: Competition Overview - The competition is organized by Nof1, a lab focused on AI in financial markets, providing each AI model with $10,000 to trade in real-time [1][2]. - The competition started on October 18 and will last until November 3, with the performance measured by risk-adjusted returns [3][5]. Group 2: AI Performance - The top performers are DeepSeek V3.1 Chat and Qwen 3 Max, with returns of +115.66% and +68.17% respectively, while GPT-5 and Gemini 2.5 Pro are at the bottom with losses of -61.75% and -61.33% [15]. - DeepSeek (DS) employs a steady, quantitative approach, while Qwen takes aggressive positions, leading to significant differences in performance [6][11]. Group 3: Trading Strategies - DS uses a full-cover long strategy with high leverage, while Grok starts with a similar approach but is more aggressive [6][10]. - Gemini and GPT-5 struggle with frequent trading and inconsistent strategies, leading to substantial losses [7][16]. Group 4: Market Dynamics - The competition occurs after a recent market downturn, providing a favorable environment for building positions [5]. - The AI models exhibit different personalities in trading, with DS being conservative and Qwen being opportunistic [2][10]. Group 5: Lessons Learned - The competition illustrates that practical trading performance can differ significantly from backtested results, emphasizing the importance of real-time market dynamics [13][14]. - The article suggests that AI can assist in investment decisions but requires a solid understanding of market conditions and risk management from users [27][29].

Core Insights - The experiment involved six advanced AI models trading in the cryptocurrency market with a starting capital of $10,000 each, aiming to determine which model could generate the highest returns [1][2] - The Chinese model Qwen 3 Max emerged as the top performer, achieving a net value of $15,594, representing a nearly 60% increase, while other models like GPT-5 and Gemini 2.5 Pro suffered significant losses of 72.1% and 61.9% respectively [1][10][11] Group 1: AI Performance - Qwen 3 Max and DeepSeek were the standout performers, with Qwen achieving a net value of $15,594 and DeepSeek at $12,926 by October 24 [10][11] - In contrast, GPT-5 and Gemini 2.5 Pro faced severe declines, with net values dropping to $2,783 and $3,792 respectively, indicating a loss of over 60% of their initial capital [11][12] Group 2: Trading Strategies and Personalities - The AI models exhibited distinct trading styles, with DeepSeek characterized as aggressive, focusing on high-frequency trading and short-term gains [12][13][16] - Qwen 3 Max adopted a more conservative approach, executing only 22 trades with a focus on low volatility and risk management, which contributed to its success [17][18] - In contrast, Gemini 2.5 Pro engaged in excessive trading with high leverage, leading to chaotic decision-making and significant losses [22][24] Group 3: Market Dynamics and AI Adaptability - The experiment highlighted that intelligence alone does not guarantee profitability in trading; adaptability and the ability to self-correct in response to market conditions are crucial [25][26] - The financial market serves as a dynamic environment that tests the judgment, adaptability, and risk management capabilities of AI models, contrasting with more static environments like games [26][27]

Core Insights - Alibaba's Qwen 3 Max won the first place in the AI investment competition "Alpha Arena," outperforming other models including GPT-5, which ranked last [1] - The competition, initiated by the US research lab nof1.ai, serves as a public test for observing AI's autonomous trading capabilities [1] Group 1: Competition Overview - "Alpha Arena" features six major AI models including DeepSeek, GPT-5, Gemini 2.5 Pro, Claude Sonnet 4.5, Grok 4, and Alibaba's Qwen 3 Max [1] - Each model started with an initial capital of $10,000 and engaged in perpetual contract trading on the decentralized exchange Hyperliquid [1] - The competition's sole evaluation criterion is the return on investment, with all trades being publicly visible [1] Group 2: Performance Highlights - Alibaba's Qwen 3 Max currently leads the profitability rankings, demonstrating a stable upward trend in its return curve [1] - The investment strategy of Qwen 3 Max shows its ability to continuously self-optimize in high-frequency market feedback through real-time reinforcement learning [1] - DeepSeek, another Chinese open-source model, maintained a significant lead for a considerable duration before the competition [1]

Core Insights - Nof1 conducted a three-day trading competition among six top AI models, each given $10,000 to trade in the decentralized exchange Hyperliquid's perpetual contracts market [4][5][6] - The participating models included Claude 4.5 Sonnet, DeepSeek V3.1 Chat, Gemini 2.5 Pro, GPT 5, Grok 4, and Qwen 3 Max [4] Performance Summary - DeepSeek's success was attributed to a clear and strictly executed trading strategy, effectively diversifying investments across six major cryptocurrencies like Ethereum (ETH) and Bitcoin (BTC) to mitigate risks from price volatility [9][10] - DeepSeek employed moderate leverage and set clear stop-loss points for each trade, allowing for quick exits from losing positions while letting profitable trades run [10] - Grok 4 followed closely with a 30% return, while other models underperformed due to various execution errors, such as order failures and missed trading signals [10] - Some models misinterpreted strategies, with overly cautious approaches missing market opportunities, while others took aggressive positions in rising markets, leading to rapid capital drawdowns [10] - Alpha Arena highlighted that the differences in model performance stemmed from their execution of instructions, risk management, and trading capabilities [10]

Core Insights - A new cryptocurrency trading experiment called Alpha Arena has been launched, where leading AI models are evaluated for their investing abilities, with DeepSeek currently outperforming its competitors [1][2] - The experiment involves six large language models (LLMs) investing in cryptocurrency perpetual contracts on the decentralized exchange Hyperliquid, each starting with US$10,000 [1][2] Performance Summary - As of Tuesday, DeepSeek's V3.1 has achieved a profit of 10.11%, while OpenAI's GPT-5 has recorded losses of 39.73%, making it the worst performer [2] - Other participating models include Alibaba Cloud's Qwen 3 Max, Anthropic's Claude 4.5 Sonnet, Google DeepMind's Gemini 2.5 Pro, and xAI's Grok 4, with Grok also being a top performer [2][6] Experiment Objectives and Methodology - The primary goal of Alpha Arena is to create benchmarks that reflect real-world market dynamics, which are inherently unpredictable and adversarial [3] - The models aim to maximize risk-adjusted returns, executing trades autonomously based on shared prompts and input data, with results tracked on a public leaderboard [4] Market Engagement - DeepSeek is currently leading in prediction markets, with a 41% likelihood of topping the benchmark, and betting volume has reached US$29,707 [7] - The public can monitor trades through each model's Hyperliquid wallet address, and the reasoning behind trades is also displayed, showcasing the models' decision-making processes [4]

Core Insights - Jay Chou's recent troubles involve a Bitcoin account managed by his magician friend, Cai Weize, who claimed the account was locked a year ago, resulting in a loss of funds [1][2] - The article discusses the emergence of AI models competing in the cryptocurrency market, highlighting a competition called Alpha Arena where six top AI models are trading cryptocurrencies [3][4] Group 1: AI Competition Overview - The competition involves six AI models, each given $10,000 to trade perpetual contracts on the Hyperliquid platform, with trading pairs including BTC, ETH, BNB, SOL, XRP, and DOGE [4][6] - The performance of these AI models is measured by risk-adjusted returns, focusing not only on profits but also on the risks taken [6][7] Group 2: Performance of AI Models - As of the latest update, DeepSeek Chat V3.1 leads with an account value of $14,310 and a return of +43.1%, showcasing a strategy of high leverage and concentrated positions [11][12] - Grok 4 follows with an account value of $13,921 and a return of +39.21%, employing a high-leverage long-only strategy [12][21] - Claude Sonnet 4.5 has an account value of $12,528 and a return of +25.28%, focusing on a conservative trading approach [12][23] - In contrast, GPT-5 and Gemini 2.5 Pro are underperforming, with returns of -24.78% and -27.74% respectively, indicating poor trading strategies and high transaction costs [12][30] Group 3: AI's Role in Investment - The article emphasizes that AI's greatest value in investment may lie in transparency, allowing investors to see trading records and decision-making processes, unlike human-managed accounts [40][41] - The ambition behind the AI competition is to use financial markets as a training ground for AI, aiming for continuous learning and adaptation to market dynamics [34][35]

Core Insights - A competition was held by nof1.ai where six leading language models were given $10,000 each to trade in a real market environment, aiming to maximize risk-adjusted returns [1][6] - The models involved include Claude 4.5 Sonnet, DeepSeek V3.1 Chat, Gemini 2.5 Pro, GPT 5, Grok 4, and Qwen 3 Max, all trading perpetual contracts on Hyperliquid [1] Performance Summary - After approximately 60 hours of trading, DeepSeek emerged as the best performer with a total portfolio value of nearly $14,000, achieving a return of about 40% [3] - Grok 4 followed closely with a portfolio value around $13,300, both models profiting primarily from long positions in Bitcoin and Ethereum [5] - Claude and Qwen focused on trading Ripple and Ethereum, ranking third and fourth respectively, while both outperformed Bitcoin's spot market [6] - GPT 5 and Gemini showed significant losses, with portfolio values of $7,300 and $6,900, indicating losses of approximately $2,700 and $3,100 [6] Market Context - The competition aims to provide a benchmark that closely resembles real-world conditions, with financial markets being ideal due to their dynamic, adversarial, open, and highly unpredictable nature [6] - Analysts suggest that there is significant potential for killer applications in the DeFi + AI space, indicating a strong interest in the involvement of LLMs in on-chain gaming [7]