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]

专访量化组亚军、资管产品组第一名京盈智投创始人兼投资总监谢黎博 高手云集的第十九届全国期货（期权）实盘交易大赛落下帷幕，京盈智投团队凭借稳健的表现，一举摘 得资管产品组第一名与量化组亚军两项大奖。谈及获奖感受，公司创始人兼投资总监谢黎博——这位兼 具北大物理学士与卡内基梅隆大学统计学博士背景的量化专家，言语中流露出欣慰："这次获奖，对我 与京盈智投团队而言意义非凡。它不仅是短期收益的胜利，更是对我们长期坚持的量化CTA策略的有力 验证。" 在本届大赛中，京盈智投的复合型量化CTA策略成为制胜关键。"由宏观经济周期与产业供需格局等慢 变量驱动的价格运动往往展现出持续的惯性。我们的系统虽不依赖于主观的基本面预判，但通过量化模 型，能够精准识别并系统化捕捉此类趋势机会，从而在单边行情中，实现对多资产类别趋势收益的有效 覆盖。"谢黎博介绍，为克服单一策略的周期性局限，团队构建了多层次策略框架，在趋势跟踪为主体 的基础上，融合了相对价值等低相关性策略，显著提升了系统的适应性与资金曲线的平滑度。 面对今年高度结构化的市场环境，部分板块与品种展现出清晰的趋势性行情。"依托多维度信号识别体 系与科学配置模型，我们实现了对各类 ...

Core Insights - The article emphasizes that retail investors often lose money during bull markets, contrary to the common belief that stock prices will continue to rise [1] - It highlights the misconception that market rebounds present opportunities, noting that no sector has consistently performed well over the first nine months of 2025 [1][6] - The piece argues that institutional investors have been quietly withdrawing from the market, leaving retail investors to engage in self-deception during market rebounds [6] Group 1: Market Behavior - The article points out that the stock market is a zero-sum game, where one investor's profit is another's loss [12] - It mentions the significant drop in the liquor sector following the implementation of a liquor ban, which resulted in a 6% decline over 20 days [4] - The case of Guoju Energy is presented, illustrating how a stock can rise 50% in the first quarter but subsequently lose 60% of that gain [1] Group 2: Institutional vs. Retail Investors - The article discusses how institutional investors have been exiting the market, as indicated by a quantitative indicator called "institutional inventory" [6] - It highlights the case of Notai Bio, which saw a 25% increase after being designated as ST, suggesting that institutional investors had already entered the market beforehand [8] - The author asserts that market movements without institutional participation are unreliable and should be viewed with skepticism [10] Group 3: Data and Analysis - The article stresses the importance of relying on quantitative data rather than intuition or luck in trading decisions [12] - It criticizes technical analysis as largely ineffective, claiming that 90% of it is irrelevant [12] - The author encourages retail investors to adopt a data-driven approach to trading, viewing it as their last line of defense in an information-asymmetric market [11]

"屈身守份"，这个充满传统智慧的昵称背后，是一位在量化交易领域游刃有余的IT工程师。在本届实盘 交易大赛中，他斩获量化组第六名。这已是他连续第二年闯入量化组前20名，展现出令人瞩目的持续盈 利能力。 "以前做IT工程师，炒股那几年并没有赚到钱，直到遇见《海龟交易法则》这本书。""屈身守份"坦言， 2012年与这本书的邂逅改变了他的投资轨迹。编程背景与炒股经验结合，让他顺利切入量化赛道。"就 像打通了任督二脉，终于找到了适合自己的方法。" 在本届大赛中，"屈身守份"的核心策略是在海龟法则基础上优化升级趋势跟踪策略，辅以少量波段策 略。"今年股指期货、黄金、白银等行情均特别适合趋势跟踪，算是赶上了好时机。"他说。 与众不同的是，"屈身守份"采用"量化+主观"的独特模式——策略执行完全程序化，但在品种选择上以 主观判断为主。 "我的系统覆盖近30个品种，采用相同的策略和仓位管理。截至目前，我盈利较大的品 种分别是股指、焦煤、碳酸锂和多晶硅。"他说。 "多晶硅启动那天，我预感到行情会很大，果断以平时2～3倍的仓位入场。平时我很少主观干预，但这 次回报十分丰厚。" "屈身守份"回忆称。 "那段时间很难熬，但这就是趋势交 ...

Core Insights - Supermicro (SMCI.US) has partnered with Intel (INTC.US) and Micron Technology (MU.US) to develop a new system that set a record in the STAC-M3 benchmark test, which is designed for high-speed analysis of time-sensitive market data, particularly in financial services [1][2] Group 1: System Performance - The STAC-M3 benchmark focuses on real-time quantitative trading, utilizing simulated market buy and sell quotes along with settlement transaction data from thousands of assets [1] - The system is built using Supermicro's petascale servers, Intel Xeon 6 processors, Micron 9550 SSDs, DDR5 memory, and KX Software's kdb+ database, and was tested at the STAC Summit in New York City [1] Group 2: Market Impact - Alvaro Toledo, Vice President and General Manager of Micron's Americas Core Data Center Business, emphasized that microsecond differences in trading can lead to millions of dollars in profit, highlighting the critical importance of speed in trading [1] - In market trading on Tuesday, Supermicro's stock rose by 1.53%, Intel's stock increased by 5%, and Micron Technology's stock saw a slight uptick of 0.82% [2]

11月15日，2025全球期货交易者大会暨第十九届全国期货（期权）实盘交易大赛、第十二届全球衍生品 实盘交易大赛颁奖大会将在西安举行。这场一年一度的行业盛会，已成为期货市场各方交流观点、碰撞 思想、展望未来的高端平台。 对即将站上领奖台的优秀选手而言，荣誉的背后是数百个日夜的潜心钻研。从短线投资到长期趋势跟 踪、从主观判断到量化交易，实盘大赛获奖选手的交易策略百花齐放，但他们对期货交易的理解却形成 了共识：市场永远是最好的老师，持续学习并控制风险是在市场中长久生存的"不二法门"。 本届实盘大赛重量组第五名高淼表示，实盘大赛是验证交易能力的大考，也是突破认知局限的重要机 会。"与不同策略高手同台竞技，有助于我跳出原有思维框架，看到多元策略的实战价值。参赛的最大 收获是学会敬畏市场——深刻体会到控制风险远比追求收益更重要。"他说。 在本届实盘大赛重量组第九名、风控专项奖第二名"快乐老玩童"看来，风控是期货交易的生命线。"我 始终坚持三点原则：一是动态仓位管理，根据账户净值与市场波动率严格控制仓位；二是坚决执行止 损，到达预设点位绝不因侥幸心理而动摇；三是及时落袋为安，将浮盈转化为安全垫。这些策略让我在 大赛中平稳 ...

Core Insights - The competition showcased the performance of top AI models in real financial trading, with Chinese models DeepSeek and Qwen3 outperforming their American counterparts significantly [3][4][7] - DeepSeek achieved a remarkable return of 123.04%, growing its account from $10,000 to $22,304, while Qwen3 followed closely with a return of 107.08%, increasing its account to $20,708 [5][6] - In contrast, American models like GPT-5 and Gemini 2.5 Pro suffered substantial losses, with GPT-5 down over 70% and Gemini down over 62% [6][8] Performance Comparison - DeepSeek's strategy involved a diversified investment portfolio, effective risk control, and the use of moderate leverage (10x to 20x), which contributed to its success [4][7] - Qwen3 demonstrated strong market timing and aggressive strategies during market upswings, leading to its high returns [6][7] - American models displayed poor decision-making, including incorrect market direction, lack of stop-loss mechanisms, and emotional trading, resulting in significant losses [8] Implications for AI Development - The results indicate a shift in the perception of AI from being merely an office tool to a powerful asset in real-world trading scenarios [8] - The competition highlights the differences in AI capabilities between China and the U.S., with Chinese models showing superior risk management and decision-making skills [7][8] - The event marks a new phase in global AI development, emphasizing the importance of practical applications and real-time performance in financial markets [7]

Core Insights - The fund market is experiencing significant year-end activities, with large distributions from ETFs, such as 2.87 billion yuan from Huaxia CSI 300 ETF and 8 billion yuan from Huatai-PineBridge, contrasting with retail investors' struggles to see gains in their portfolios [1][3] Group 1: Market Dynamics - ETFs are becoming the main players in dividend distributions due to their scale effects, low turnover rates, and stable returns, which are characteristics that contribute to their success [3][11] - Retail investors often find themselves trapped in emotional trading and misinterpret market signals, leading to losses despite a rising index [3][6] Group 2: Behavioral Insights - Many investors fall into two major misconceptions: believing their stocks will always rise and viewing market adjustments as buying opportunities, which often leads to poor investment outcomes [6][9] - The market operates like a casino, where institutional players use data analytics to predict outcomes, leaving retail investors at a disadvantage [6][9] Group 3: Quantitative Analysis - Institutional inventory data reveals that market fluctuations are often orchestrated, serving as a form of manipulation to mislead retail investors [9][11] - The ability of ETFs to consistently distribute large dividends is attributed to their management fee advantages, low turnover rates, and systematic operations that minimize human errors [11][13] Group 4: Recommendations for Investors - Investors are encouraged to establish their own quantitative observation lists, focus on fund behavior rather than price fluctuations, and treat trading records as experimental data for analysis [13]

Core Insights - The article discusses a unique competition where AI models are tested in real-time trading of cryptocurrencies, aiming to determine which model can generate the highest returns without human intervention [1][2]. Group 1: AI Trading Competition - The competition involves six AI models, each with a capital of $10,000, trading major cryptocurrencies like BTC, ETH, and others [1]. - The event has generated significant interest, surpassing traditional stock trading discussions among participants [1][2]. - The performance of the models is evaluated based on their ability to analyze market data and sentiment, akin to human traders [2]. Group 2: Performance of AI Models - After six days, the leading model, DeepSeek Chat v3.1, initially achieved a return of nearly 40%, but has since stabilized around 10% due to market fluctuations [3]. - The most well-known model, GPT-5, has suffered a loss of 68.9%, indicating a poor performance compared to its peers [4]. - Qwen3 Max has outperformed DeepSeek Chat v3.1 with a return of 13.41% by employing a more aggressive trading strategy [7]. Group 3: Insights on AI Models - DeepSeek's strong performance may be attributed to its quantitative background, although initial tests showed mixed results for various models [7]. - The competition highlights the unpredictability of the market and the need for models to adapt to changing conditions [9]. - Observing the trading strategies and decisions of the models provides valuable insights beyond just the final returns [11]. Group 4: AI in Stock Trading - The article emphasizes the importance of selecting the right AI model for stock trading, as many retail investors are beginning to rely on AI tools for investment decisions [12]. - The development of financial AI models has evolved significantly, with notable examples like BloombergGPT, which faced challenges due to its high costs and closed systems [14]. - Despite the potential of AI in trading, many users report dissatisfaction with the outputs, indicating a need for better data quality and model customization [15][18]. Group 5: Challenges and Limitations of AI - AI models often struggle with understanding complex market dynamics and may produce similar strategies, limiting their effectiveness against larger, more sophisticated quantitative firms [16]. - The article warns that relying solely on AI without a solid understanding of investment principles can lead to significant losses [19][23]. - AI's limitations in predicting "black swan" events and its reliance on historical data highlight the need for human oversight in investment decisions [24][26].

智通财经APP获悉，在华尔街市场表面平静的背后，一些重量级专业投资者正经历动荡。 本月，因此前拥挤且盈利丰厚的头寸出现反转，量化基金陷入亏损。本周三的交易日更是暴露了过度动 量交易的风险——此前表现亮眼的黄金、科技股与加密货币价格同时暴跌。 野村证券跨资产策略董事总经理查理·麦克埃利戈特(Charlie McElligott)在报告中写道，10月初部分投机 性标的上涨、近期却遭抛售的"过山车"行情，"具备了又一次'量化地震'的特征"。 "当前宏观经济变量与个股对走势反转的敏感度似乎在上升——我们在黄金和原油市场已看到这种情 况，"英仕曼集团(Man Group)外部阿尔法首席投资官亚当·辛格尔顿(Adam Singleton)表示，"目前市场正 试图为宏观形势寻找合理叙事。" 在股票市场，这种突然轮动在动量策略中表现最为明显——该策略做多近期涨幅大的股票，同时做空近 期跌幅大的股票。高盛股票销售专员约安尼斯·布莱科斯(Ioannis Blekos)本周指出，由于对冲基金持续减 持历史高位的动量因子敞口，动量策略的亏损可能持续。 量化对冲基金采用多种交易信号，其中许多属于专有信号。但沃尔夫研究(Wolfe Re ...