本文首发于公众号"私募排排网"。 （点击↑↑ 上图查看详情 ） | 排名 | 产品简称 | 公司简称 | 三级策略 | 基金经理 | 产品规模 近5年收益 (河元) | 近5年超额 | | --- | --- | --- | --- | --- | --- | --- | | 2 | 九坤日享中证1000指数增 91号 明法量化中小盘增强1号B 类份额 | 九坤投资 中证1000指增 明泫投资 中证1000指增 | | 姚齐聪 凝意明,解 环宇 | | 应监管要求 | | 3 | 天演赛能 | 天演资本 | 量化选股 | 谢晓阳 | | | | ব 5 | 世纪前沿指数增强2号 聚宽进取一号 | 世纪前沿 中证500指增 聚宽投资 中证500指增 | | 景散 高斯蒙,满 | | | | | | | | 奇 | | 烦情扫码 | | 6 | 千象盛世量化选股1号 | 千象资产 | 量化选股 | 陈斌 | | | | 7 | 龙旗红旭 | 龙旗科技 中证500指增 | | 朱晓康 | | | | 8 | 致远激进一号B类份额 | 致诚卓远 | 量化选股 | 史帆 | | | | g | 鸣石春天28号 | 鸣石 ...

AI赋能投资 打造量化智享体系 泓德基金自成立以来，始终高度重视投研领域的资源投入，积极探索人工智能、大数据等新兴技术在投研领域的应用，以科技提升投研效率，探索多元 投资策略，为投资者提供特色化产品。 尤其在量化投资领域，泓德基金在2015年公司成立之初即开始组建量化团队，2016年推出首只量化公募产品，2018年设立量化投资部，初期以"基本面 +量价"的多因子策略为主。2020年引入人工智能技术，研发AI选股模型，2022年应用于实盘投资。2023年成立AI Lab，专注前沿人工智能技术的发展和应 用，AI选股模型正式应用于公募产品。2025年成立智能投资部，量化体系进一步完善。 历经10年，泓德基金已形成以"量化投资部+智能投资部"双擎驱动为核心，专户、风控、IT、产品等部门协同的多元组织架构。依托扎实的专业能力和 实战经验，通过Alpha、风险控制、交易成本、组合优化四大模型，为旗下产品提供强有力的投研支持，为客户提供优质的量化投资服务，最终凝聚成一套 能较好适应国内股票市场的综合量化投资体系——泓德量化智享体系。 泓德量化智享体系采用"多因子模型＋AI选股模型"为主的多元量化策略，能够根据不同产品的定 ...

研究报告合集下载链接（下载密码欢迎联系团队成员或对口销售）： https://pan.baidu.com/s/1d2oPPwOo4jMsF-kYQ5XpMg AI量化系列专题报告 | 《系列一：深度学习之股指期货日内交易策略》 | | --- | | 《系列二：深度学习算法掘金 Alpha 因子》 | | 《系列三：深度学习新进展，Alpha 因子的再挖掘》 | | 《系列四：趋势策略的深度学习增强》 | | 《系列五：风险中性的深度学习选股策略》 | | 《系列六：深度学习在指数增强策略上的应用》 | | 《系列七：深度学习框架下的高频数据因子挖掘》 | | 《系列八：基本面因子模型的深度学习增强》 | | 《系列九：基于条件随机场的周频择时策略》 | | 《系列十：机器学习多因子动态调仓策略》 | | 《系列十一：人工智能在资产管理行业的应用和展望》 | | 《系列十二：基于涨跌模式识别的指数和行业择时策略》 | | 《系列十三：再探西蒙斯投资之道：基于隐马尔科夫模型的选股策略研究》 | | 《系列十四：机器学习模型在因子选股上的比较分析》 | | 《系列十五：多周期机器学习选股模型》 | | 《系列十六 ...

责任编辑：郭栩彤 MACD金叉信号形成，这些股涨势不错！ ...

小博说 在前几篇文章中，小博向大家介绍了指数增强基金的特点、配置价值和运作原理，那么对于投资者而 言，还有一个与实操息息相关的问题： 面对各式各样的指数增强产品，究竟该如何选择呢？ 其实，挑选指增基金并不困难，掌握几个关键指标和步骤，你也能找到适合自己的那一只。 定方向：选指数 这是所有决策的起点。 可以先明确自己的投资需求，想投资大盘股、中盘股还是小盘股？追求价值风格还是成长风格？不同的 指数有着不同的风险收益特征。 举几个例子。 想投大盘，可以关注A股核心资产的代表指数，比如沪深300、中证A500等等。 如果没有明确的投资方向、想要全市场布局，也可以考虑中证全指指数，近乎覆盖A股所有标的，一键 打包全市场热门板块。 明确自己想跟踪的指数类型，这就圈定了指数增强基金的选择范围，也确定了组合的基石风格。 评优劣：看指标 接着，针对跟踪同一个指数的同类指数增强产品，我们可以借助几个关键指标来做投资参考： 年化超额收益：这个指标直观反映了基金持续战胜指数的能力。当然，对于指数增强类产品来说，最好 观察长期的超额表现(比如3年以上)，而非短期爆发。 跟踪误差：这个指标反映了基金净值走势与指数走势的偏离程度。过高的 ...

以下文章来源于教你挖掘基 ，作者冰姐 投资理财有方法，我们手把手教你挖掘牛基~ 近来，上证指数刷新了十年新高，市场情绪却显得颇有些复杂。有人扼腕叹息"满仓踏空"，有人无奈调侃"我在XX躲牛 市"…… 红与绿的对比，热与冷的体感，成为了当下基民圈子里最真实的共鸣。 今天不想只谈行情的涨跌，更想和大家聊聊： 当指数与账户分化，我们该如何读懂市场的语言？ 又该如何在变化的浪潮中，守住投资的锚点？ 教你挖掘基 . 01 当指数新高≠账户新高 —— 愈演愈烈的分化与底层逻辑的变迁 近来身边不断有好友向笔者抱怨，自己的持仓是大盘上涨岿然不动，大盘调整溃不成军。 这种两头挨揍的感受，本质是市场生态的重构。这一轮大涨中，以往我们所熟悉的龙头搭台、补涨跟进的轮动节奏并未出 现。取而代之的，是强者更强、主线持续聚焦的结构。 数据无声，却道尽真相。9月初，上证综指已经刷新了10年新高，但大多数行业仅触及2020-2021年的阶段性高点，只有银 行、电子、通信、有色金属、家用电器、食品饮料等少数行业超越了2015年水平。这意味着，指数新高的大旗是由少数行 业扛起，而非雨露均沾。 | 申万一级行业 | 今年以来 | 15年5178点 ...

Core Viewpoint - The article discusses the development and capabilities of the GF Quantitative Alpha Factor Database, which supports various investment strategies through a comprehensive factor library built on extensive research and data accumulation by the GF Quantitative team [1]. Group 1: Database Overview - The GF Quantitative Alpha Factor Database is established on MySQL 8.0 and encompasses over a decade of research experience, integrating fundamental factors, Level-1 and Level-2 high-frequency factors, machine learning factors, and alternative data factors [1]. - The database supports strategies such as long-short strategies, index enhancement, ETF rotation, asset allocation, and derivatives [1]. - The GF Quantitative team possesses a data storage capacity of over 100TB and high-performance CPU/GPU computing servers, collaborating with reliable data providers like Wind, Tianruan, and Tonglian for efficient factor development and dynamic updates [1]. Group 2: Factor Types and Performance - The article lists various factors categorized by type, including deep learning factors, trading volume factors, and market order ratios, each with specific definitions and performance metrics [3]. - For instance, the "agr_dailyquote" factor has a historical average of 14.22% and a historical win rate of 91.97% [3]. - The "bigbuy" factor shows a historical average of 7.85% with a win rate of 66.74% [3]. Group 3: Research Reports - A series of research reports are available for download, covering topics such as style factor-driven quantitative stock selection, industry selection, and macroeconomic observations related to Alpha factor trends [4][5]. - The reports include analyses on the application of factors in the CSI 300 index and various strategies for capturing industry alpha drivers [4].

1. 美国白宫表示，TikTok 的新美国实体将从字节跳动租赁算法的副本，甲骨文将对其进行重新训练；用户 无需重新下载应用程序。 2. GSA将Meta的Llama加入其美国联邦机构批准的AI工具名单，此前已批准微软、谷歌、Anthropic和Open AI的工具。 3.极氪回应现款001售罄 6.轴向磁通电机和无框力矩电机已进入特斯拉Optimus测试环节？卧龙电驱：该消息不实 有投资者在互动平台向卧龙电驱提问，媒体报道及Ai搜索发现，公司生产的轴向磁通电机和无框力矩电机 已进入特斯拉Optimus测试环节，并且公司已锁定20万台电机订单。请问该消息是否属实？卧龙电驱回复 称，该消息不实。 7.贵州茅台否认将下调今年业绩目标，上半年已按计划完成目标进度 日前，有消息显示现款极氪001已售罄，极氪将提前至9月23日开启焕新极氪001的预售，上市时间则按照原 计划十月中旬，届时将同步开启交付。多个门店销售均反馈消息属实。 点评：极氪001售罄即下架，新款升级引期待。 4.腾讯控股：回购86.2万股，花费5.5亿港元 腾讯控股公告，于2025年9月22日以集中竞价交易方式回购86.2万股，每股购回价格介于635至 ...

Core Viewpoint - The article emphasizes the performance of private equity funds in China's capital market, highlighting the challenges of maintaining top rankings over different time frames amidst market volatility [1]. Group 1: Subjective Long/Short Strategies - A total of 23 private equity products have ranked in the top 20% for short-term (January to August), medium-term (three years), and long-term (five years) performance [1]. - As of August 2025, there are 1,974 subjective long/short private equity products reported for this year, 1,353 for the last three years, and 760 for the last five years [1]. Group 2: Quantitative Long/Short Strategies - 21 products from quantitative long/short strategies have also ranked in the top 50% across all three performance periods [5]. - Among these, 11 products belong to large-scale quantitative private equity firms, indicating a strong presence in the market [5]. Group 3: Futures and Derivatives Strategies - 19 private equity products have achieved top 30% performance across short, medium, and long-term periods in the futures and derivatives category [8]. - As of August 2025, there are 678 products reported for this year, 403 for the last three years, and 162 for the last five years in this strategy [8]. Group 4: Multi-Asset Strategies - 18 multi-asset strategy products have ranked in the top 30% for all three performance periods [12]. - The article notes that large-scale private equity firms like Blackwing Asset and Duration Investment have products listed among the top performers [12]. Group 5: Market Outlook - The market is expected to experience fluctuations, with technology, pharmaceuticals, and new consumption identified as key investment areas for the next decade [4]. - The article mentions that the A-share market is likely to remain in a bullish phase, with significant opportunities in sectors like technology and healthcare [4].

Core Viewpoint - The article emphasizes the importance of dynamic strategy configuration in quantitative investing, highlighting the limitations of traditional models and proposing a new framework based on large language models (LLM) for better adaptability to changing market conditions [2][3][5]. Group 1: Evolution of Quantitative Investing - Over the past decade, quantitative investing in the A-share market has evolved significantly, driven by the search for "Alpha factors" that can predict stock returns [5]. - The rapid increase in the number of Alpha factors does not directly translate to improved returns due to the quick decay of Alpha and the homogenization of factors among different institutions [5][12]. Group 2: Challenges in Factor Combination - Different factor combination models exhibit significant performance differences across market phases, making it difficult to find a single model that performs optimally in all conditions [12]. - Traditional models, such as mean-variance optimization, are sensitive to input parameters, leading to instability in performance [14][15]. - Machine learning models, while powerful, often suffer from a "black box" issue, making it hard for fund managers to trust their decisions during critical moments [16][18]. Group 3: Proposed LLM-Based Framework - The proposed "Judgment-Inference Framework" consists of three layers: training, analysis, and decision-making [2][3][19]. - **Training Layer**: Runs a diverse set of selected Alpha models to create a robust strategy library [22]. - **Analysis Layer**: Conducts automated performance analysis of models and generates structured performance reports based on market conditions [24][27]. - **Decision Layer**: Utilizes LLM to integrate information from the analysis layer and make informed weight allocation decisions [28][31]. Group 4: Empirical Results - Backtesting results on the CSI 300 index show that the LLM-based dynamic strategy configuration can achieve an annualized excess return of 7.21%, outperforming equal-weighted and single model benchmarks [3][41]. - The LLM dynamic combination exhibited a maximum drawdown of -9.47%, lower than all benchmark models, indicating effective risk management [44]. Group 5: Future Enhancements - The framework can be further optimized by expanding the base model library to include more diverse strategies and enhancing market state dimensions with macroeconomic and sentiment indicators [46].