来源：一年打卡100场路演 诚奇量化总结：截至25年12月，规模470亿；两位管理人分别曾在千禧年和世坤工作；23年以后逐步转 向机器学习为主的非线性建模框架 贝小塔(VX:BetaRicher)整理, 仅供合格投资者审阅。 此举出于三方面考虑: 一是上海金融人才储备更丰富,便于招募高质量研究员; 二是员工更倾向在上海落户与发展,尤其在应届生招聘中更具吸引力; 三是上海作为国家金融中心,在政策支持与监管沟通上具备相对优势, 此次变更为非新设主体,统一社会信用代码及中基协登记编号均保 业务与投资运作不受任何影响。 截至25年12月初:基协显示该公司员工数为36人,其中 高度数量为3人。 正在运作产品为313个,延期清算为0,提前清算为104个,正常清算为6个。 公司曾于2022年底达到500亿规模高点,当前体置并未触及策略容量上限,在 现有约2万亿日均成交环境下运作舒适。内部设定若未来规模通近 700 亿将启动 封盘或参数调整机制,但目前多头策略额度充足,无主动控规模计划。 股权占比:股权渗透后分析得出,何文奇 占比 50.5089%,张万成 占比 49.4911%,两人合计绝对控股。为激励年轻化的核心投研队伍 ...

Core Insights - The article emphasizes the importance of data collection and analysis for quantitative investors to uncover hidden market patterns and gain an edge in stock market trading [1][4][5]. Group 1: Data Types and Importance - Level 1 data includes basic market information such as highest price, lowest price, opening price, closing price, trading volume, and trading amount, updated every three seconds [6][7]. - Level 2 data provides more detailed information, including tick data that captures every order during trading sessions, allowing for deeper analysis of market trends and trading signals [6][9]. Group 2: Key Period Factors - The article introduces a set of Level 2 factors based on key trading periods, categorized into four main types: price changes, price levels, trading amounts, and volume-price coordination, totaling 123 factors [12]. - Specific factors such as KeyPeriod_ret_zero and KeyPeriod_ret_low5pct show historical RankIC averages of -5.36% and 5.47% respectively, with win rates of 85.1% and 84.1% [2]. Group 3: Factor Performance - The performance of various factors is highlighted, with low price period factors like KeyPeriod_price_low5pct achieving a 20-day RankIC average of 5.59% and a win rate of 85.3% [2]. - Trading amount factors such as KeyPeriod_amount_top30pct show a 20-day RankIC average of 11.23% with a win rate of 84.8%, indicating strong predictive power [2]. Group 4: Research and Development - The article outlines ongoing research efforts to refine and develop new factors from Level 2 data, with a focus on enhancing the predictive capabilities of trading strategies [10][12]. - Previous reports have successfully identified effective factors, with some achieving historical RankIC averages above 9.2% and win rates around 76% [10].

炒股就看金麒麟分析师研报，权威，专业，及时，全面，助您挖掘潜力主题机会！ 来源：深网腾讯新闻 2025年12月5日，顶着"国产GPU第一股"光环的摩尔线程正式登陆科创板。上市首日，摩尔线程股价一 度涨超500%，股价拉升至688元/股，随后涨幅逐步收窄，回落至600元/股以下。截至上午10：38，摩尔 线程股价报575.01元/股。 摩尔线程本次在科创板共发行7000万股新股，发行价为114.28元/股。若以盘中最高价格688元/股计算， 中一签（500股）可赚约28.69万元。 在网下发行部分，共有267家机构投资者参与申购，总申购数量达704.06亿股，网下最终仅获配3920万 股。 就在普通投资者"一签难求"之际，一场资本狂欢拉开国产芯片的创富大幕。 张建中身价或超300亿，锁定期超3年 凭借早期入股或股权激励，摩尔线程至少有9位公司高层及核心技术人员因持股而在上市时身价过亿 元。根据发行方案，公司设立了员工持股平台，几位掌握核心技术与公司股权的创始人及高管，其个人 财富将实现跨越式增长。 摩尔线程曾在招股书中公布公司董事、审计委员会委员、高级管理人员及核心技术人员直接或间接持有 公司股份的情况。 摩尔 ...

公募FOF产品在2025年成为基金业令人瞩目的品种，这实在是在情理之中。 差异化的结果其实来源于更具差异化的投研体系和团队配置。 01 不是一支团队在"战斗" 在目前公募基金的产品线里，只有FOF产品天然具备"多元投资"和"资产配置"功能。尤其是前者，近年对于基金业绩 的正面效应，日益凸显、备受关注。 同时，市场也逐渐意识到，一个产品定位稳定，配置逻辑清晰，并由经验丰富的团队管理的FOF产品，可能是最接近 广大老百姓需要的"一站式"投资标的。 赵强是富达基金多元资产部的负责人，也是养老业务的负责人，某种程度上，他是富达基金FOF领域的"一号位"。 但赵强却强调，在富达基金，FOF的管理不仅是基金经理的主动抉择，更有团队的分工合作，以及富达庞大投研体系 多年累积的理念和体系"支撑"。 赵强提及，在富达基金管理FOF时，不仅是基金经理的主动抉择，有团队的分工合作，也有富达庞大投研体系多年累 积的理念和体系"支撑"。比如，相关金融科技系统内有富达全球丰富的策略实时表现供FOF基金经理参考，有全球通 用的风控体系和重要风险事件的提示，有海外成熟的AI投资工具来协助组合优化。 但随着渠道和投资者投资经验的不断积累，越来 ...

最近公募基金圈子里发生了一件有趣的事，让我这个量化交易老手都忍不住要说道说道。2025年接近尾声，本该是基金经理们忙着 冲规模的时节，今年却玩起了新花样——一边是分红潮汹涌澎湃，一边是绩优基金纷纷限购。这葫芦里到底卖的什么药？ 作为一名量化交易者，我向来对市场异动特别敏感。Wind数据显示，截至12月4日，2025年以来3364只基金累计分红约2155.17亿 元。其中华泰柏瑞沪深300ETF以83.94亿元居首。与此同时，中欧旗下4只产品将单日申购限额降至1万元。这种看似矛盾的操作背 后，隐藏着机构资金的真实意图。 一、分红与限购：机构的两副面孔 记得我刚入行时，一位老前辈说过："市场就像个婊子养的赌场，但量化数据不会骗人。"这话虽然粗俗，但道理不假。现在的公募 基金就像个精明的老鸨，一边用分红吸引客户，一边又用限购把大客户拒之门外。 易方达科翔混合11月以来两次分红，合计1.05亿元。华夏基金说分红是盈利兑现，这话没错。但作为一个量化交易者，我更关心的 是：为什么现在分？为什么是这个金额？这些问题的答案都藏在数据里。 我翻看了近十年的基金分红数据，发现一个有趣的规律：大规模分红往往出现在市场转折点前。这不 ...

Group 1 - The private equity market is experiencing a surge in product registrations, with November seeing a nearly 30% month-on-month increase, marking the second highest registration volume of the year [1] - A total of 1,285 private equity securities products were registered in November, reflecting a strong willingness among private equity firms to issue products as the year-end approaches [1] - Equity strategies remain the dominant focus for private equity firms, with 849 equity strategy products registered in November, accounting for 66.07% of the total [1] Group 2 - Multi-asset strategies and futures and derivatives strategies are also maintaining high levels of interest, with 193 multi-asset strategy products registered, representing 15.02% of the total [1] - Quantitative private equity products have shown particularly strong performance, with 565 products registered in November, making up 43.97% of the total [2] - Within quantitative strategies, equity strategies dominate with 402 products registered, while futures and derivatives strategies account for 80 products, representing 66.12% of that strategy's total [2]

Quantitative Factors and Construction Factor Name: KeyPeriod_ret_zero - **Construction Idea**: This factor focuses on the return characteristics during horizontal trading periods within key intraday timeframes, leveraging Level 2 tick data to refine minute-frequency factors[7][25][41] - **Construction Process**: - Identify horizontal trading periods based on minimal price fluctuations - Calculate returns during these periods using tick-level data - Aggregate and smooth the data over different time horizons (e.g., 5-day, 20-day)[25][27] - **Evaluation**: Demonstrates strong predictive power for stock selection, with high IC stability and win rates[7][25] Factor Name: KeyPeriod_ret_low5pct - **Construction Idea**: This factor captures return characteristics during significant downward price movements within key intraday timeframes[7][25][64] - **Construction Process**: - Identify periods where returns fall within the bottom 5% of all intraday returns - Calculate and aggregate these returns over different time horizons - Apply smoothing techniques to enhance signal stability[25][27] - **Evaluation**: Exhibits robust performance in identifying underperforming stocks, with high IC values and win rates[7][25] Factor Name: KeyPeriod_price_low5pct - **Construction Idea**: This factor focuses on price levels during periods of low prices (bottom 5%) within key intraday timeframes[7][25][88] - **Construction Process**: - Identify periods where prices fall within the bottom 5% of all intraday prices - Aggregate and smooth the data over different time horizons - Incorporate buy/sell distinctions for further refinement[25][32] - **Evaluation**: Effective in capturing undervalued stocks, with strong IC performance and high win rates[7][25] Factor Name: KeyPeriod_amount_top30pct - **Construction Idea**: This factor targets periods of high transaction amounts (top 30%) within key intraday timeframes[7][25][110] - **Construction Process**: - Identify periods where transaction amounts are in the top 30% of all intraday amounts - Aggregate and smooth the data over different time horizons - Differentiate between buy and sell transactions for enhanced granularity[25][35] - **Evaluation**: Demonstrates strong predictive power for high-liquidity stocks, with high IC values and win rates[7][25] Factor Name: KeyPeriod_amount_low50pct - **Construction Idea**: This factor captures periods of low transaction amounts (bottom 50%) within key intraday timeframes[7][25][133] - **Construction Process**: - Identify periods where transaction amounts are in the bottom 50% of all intraday amounts - Aggregate and smooth the data over different time horizons - Incorporate buy/sell distinctions for further refinement[25][35] - **Evaluation**: Useful for identifying low-liquidity stocks, though performance is less consistent compared to other factors[7][25] Factor Name: KeyPeriod_sync_low50pct - **Construction Idea**: This factor measures volume-price divergence during periods of low synchronization (bottom 50%) within key intraday timeframes[7][25][155] - **Construction Process**: - Identify periods where volume and price movements are least synchronized - Aggregate and smooth the data over different time horizons - Differentiate between buy and sell transactions for enhanced granularity[25][38] - **Evaluation**: Effective in capturing unique market dynamics, with strong IC performance and high win rates[7][25] --- Backtesting Results KeyPeriod_ret_zero - **IC Mean**: -5.36% (20-day horizon)[27] - **Win Rate**: 85.1% (20-day horizon)[27] - **IR**: 1.34 (2020-2025)[55] KeyPeriod_ret_low5pct - **IC Mean**: 5.47% (20-day horizon)[27] - **Win Rate**: 84.1% (20-day horizon)[27] - **IR**: 1.41 (2020-2025)[77] KeyPeriod_price_low5pct - **IC Mean**: 5.59% (20-day horizon)[32] - **Win Rate**: 85.3% (20-day horizon)[32] - **IR**: 2.22 (2020-2025)[97] KeyPeriod_amount_top30pct - **IC Mean**: 11.23% (20-day horizon)[35] - **Win Rate**: 84.8% (20-day horizon)[35] - **IR**: 1.37 (2020-2025)[123] KeyPeriod_amount_low50pct - **IC Mean**: -10.50% (20-day horizon)[35] - **Win Rate**: 75.0% (20-day horizon)[35] - **IR**: 0.77 (2020-2025)[145] KeyPeriod_sync_low50pct - **IC Mean**: 6.00% (20-day horizon)[38] - **Win Rate**: 81.5% (20-day horizon)[38] - **IR**: 1.44 (2020-2025)[172]

Quantitative Models and Construction Methods Index Enhancement Strategies - **Model Name**: Index Enhancement Strategy for CSI 300, CSI 500, CSI 1000, and CSI 2000 - **Model Construction Idea**: The strategy is based on a multi-factor stock selection model, leveraging an equity factor library to identify effective factors within the constituent stocks of the respective indices[77] - **Model Construction Process**: - **Factor Selection**: Hundreds of factors from the equity factor library are screened for effectiveness within the constituent stocks of CSI 300, CSI 500, CSI 1000, and CSI 2000 indices[77] - **Portfolio Optimization**: - For CSI 300: Strict sector and market capitalization neutrality, individual stock weight capped at 8%, and weight deviation capped at 3%[77] - For CSI 500: Strict sector and market capitalization neutrality, individual stock weight capped at 1%, and weight deviation capped at 1%[77] - For CSI 1000 and CSI 2000: Market capitalization deviation capped at 0.5 standard deviations, sector deviation capped at 2.5%, individual stock weight capped at 1% for CSI 1000 and 0.5% for CSI 2000[77] - **Rebalancing**: Weekly tracking of the performance of the index enhancement strategy within the constituent stocks[77] Model Evaluation - **Evaluation**: The strategy effectively utilizes a multi-factor approach to enhance index performance while maintaining sector and market capitalization neutrality. However, the strategy's performance is subject to transaction costs and historical data limitations[77][83] --- Model Backtesting Results CSI 300 Index Enhancement Strategy - **Weekly Return**: 1.53% (Index Return: 1.64%, Excess Return: -0.12%)[78] - **Monthly Return**: -3.31% (Index Return: -2.46%, Excess Return: -0.85%)[78] - **Year-to-Date Return**: 21.83% (Index Return: 15.04%, Excess Return: 6.8%)[78] - **Maximum Drawdown of Excess Return**: -3.15%[78] CSI 500 Index Enhancement Strategy - **Weekly Return**: 2.97% (Index Return: 3.14%, Excess Return: -0.17%)[78] - **Monthly Return**: -4.54% (Index Return: -4.08%, Excess Return: -0.46%)[78] - **Year-to-Date Return**: 23.41% (Index Return: 22.81%, Excess Return: 0.61%)[78] - **Maximum Drawdown of Excess Return**: -4.77%[78] CSI 1000 Index Enhancement Strategy - **Weekly Return**: 3.77% (Index Return: 3.77%, Excess Return: 0%)[83] - **Monthly Return**: -2.59% (Index Return: -2.3%, Excess Return: -0.29%)[83] - **Year-to-Date Return**: 35.59% (Index Return: 23.1%, Excess Return: 12.49%)[83] - **Maximum Drawdown of Excess Return**: -5.59%[83] CSI 2000 Index Enhancement Strategy - **Weekly Return**: 4.38% (Index Return: 4.99%, Excess Return: -0.61%)[83] - **Monthly Return**: -0.03% (Index Return: -0.4%, Excess Return: 0.37%)[83] - **Year-to-Date Return**: 59.74% (Index Return: 31.65%, Excess Return: 28.08%)[83] - **Maximum Drawdown of Excess Return**: -5.23%[83] --- Quantitative Factors and Construction Methods Single Factors - **Factor Name**: Analyst Forecast ROE-FY3 - **Construction Idea**: Measures the expected return on equity (ROE) for the next three fiscal years as forecasted by analysts[33] - **Construction Process**: Derived from analyst consensus estimates for ROE over the next three fiscal years[33] - **Evaluation**: Demonstrates strong predictive power for stock selection, particularly in CSI 300 and CSI 2000 stock pools[33][36] - **Factor Name**: Standardized Unexpected Quarterly ROE with Drift - **Construction Idea**: Captures the deviation of actual quarterly ROE from expectations, adjusted for drift[35] - **Construction Process**: - Calculate the unexpected component of quarterly ROE - Standardize the values and adjust for drift to account for temporal effects[35] - **Evaluation**: Effective in identifying outperforming stocks, particularly in CSI 1000 and CSI 2000 stock pools[35][36] - **Factor Name**: One-Month Price Change - **Construction Idea**: Reflects short-term momentum by measuring the percentage change in stock price over the past month[36] - **Construction Process**: Calculate the percentage change in stock price over the last 30 days[36] - **Evaluation**: Demonstrates strong performance in CSI 2000 and CSI 1000 stock pools, indicating momentum effects[36] Factor Neutralization - **Neutralization Process**: - Apply absolute median method for outlier removal - Perform Z-score standardization - Conduct cross-sectional regression using log market capitalization and industry dummy variables as independent variables, with the factor as the dependent variable - Use the residuals as the neutralized factor values[32] --- Factor Backtesting Results CSI 300 Stock Pool - **Top Factors (Year-to-Date Excess Return)**: - Single-Quarter Revenue Growth Rate: 25.24%[33] - Single-Quarter ROE: 22.28%[33] - Single-Quarter ROA Change: 22.21%[33] CSI 500 Stock Pool - **Top Factors (Year-to-Date Excess Return)**: - Analyst Forecast Net Profit Growth Rate FY3: 14.53%[34] - Analyst Forecast Revenue Growth Rate FY3: 13.69%[34] - Analyst Forecast Revenue FY3 120-Day Change: 12.81%[34] CSI 1000 Stock Pool - **Top Factors (Year-to-Date Excess Return)**: - Standardized Unexpected Quarterly ROE with Drift: 19.18%[35] - Analyst Forecast ROE-FY3 120-Day Change: 18.4%[35] - Standardized Unexpected Quarterly Net Profit with Drift: 18.34%[35] CSI 2000 Stock Pool - **Top Factors (Year-to-Date Excess Return)**: - 90-Day Report Upward Revision Ratio: 25.01%[36] - Standardized Unexpected Quarterly Net Profit with Drift: 24.46%[36] - 5-Minute Volume Skewness: 23.74%[36] CSI All-Share Stock Pool - **Top Factors (Year-to-Date Excess Return)**: - Analyst Forecast ROE-FY3 120-Day Change: 23.52%[37] - Single-Quarter Revenue Growth Rate: 20.47%[37] - Analyst Forecast Revenue Growth Rate FY3: 19.35%[37]

中证报中证网讯（记者 王辉）近日，由中国证券报主办，华鑫证券、西岸集团联合承办，深圳数据经 济研究院提供独家学术支持的"2025量化行业高质量发展大会暨金融科技·量化机构金牛奖颁奖典礼"在 上海举行。上海蝶威私募基金管理有限公司（简称蝶威量化）凭借其优秀的长期业绩表现与稳健的投研 体系，荣获"三年期金牛量化机构（指数增强策略）"奖项。 在风控方面，公司坚持风险"提前规划、实时调节"。公司在投研体系内置了动态风险预算与风险平价框 架，能根据市场波动与策略状态实时调整风险分配。这些风控举措和强化学习策略、组合优化器、交易 执行模块整合在同一个闭环系统中，实现了风控与投研交易的深度协同。 对于此次获奖，蝶威量化投研团队表示，这既是对过往阶段的肯定，更意味着一份责任。未来，蝶威量 化将继续聚焦于端到端强化学习、多源数据挖掘与多阶组合优化的主航道，持续加大技术投入，致力于 为专业机构与高净值投资者提供经得起时间检验、体验更稳健的量化投资解决方案，在不确定性中寻找 可持续的确定性。 在量化投研领域，区别于传统量化模型的分段式流程，蝶威量化的核心优势在于构建了一套端到端强化 学习驱动的投研框架。该框架将信号生成、仓位决策、 ...

Group 1 - The private equity market in China is experiencing a surge in product registrations, with November seeing a 29.28% increase compared to the previous month, totaling 1,285 registered private equity securities products, marking the second-highest monthly registration this year [1] - Equity strategies remain the dominant focus for private equity firms, with 849 equity strategy products registered in November, accounting for 66.07% of total registrations, indicating strong investor interest despite recent adjustments in the A-share market [1] - Multi-asset strategies and futures and derivatives strategies are also maintaining high levels of activity, with 193 multi-asset strategy products registered, representing 15.02% of the total [1] Group 2 - Quantitative private equity products are particularly noteworthy, with 565 products registered in November, making up 43.97% of total registrations; equity strategies dominate this category as well, with 402 products registered [2] - A total of 719 private equity firms registered products in November, with 49 firms registering five or more products, highlighting a strong enthusiasm for product registration, especially among leading quantitative firms [2] - Century Frontier leads in product registrations with 20 products, followed by Starstone Investment with 15, and Mingchao Investment, Shanghai Xiaoyong Private Equity, and Tiansuan Quantitative each with 12 products [2] Group 3 - The A-share market has seen fluctuations around the 3,900-point mark, but long-term trends remain positive according to DWSQ, which believes that current adjustments do not alter the medium to long-term bullish outlook for A-shares [3] - Support for market risk appetite is expected from policy and liquidity environments, with expectations of the Federal Reserve entering a rate-cutting phase and overall liquidity in the A-share market remaining ample [3] - Corporate earnings are showing signs of stabilization, with the technology and advanced manufacturing sectors expected to contribute positively to market opportunities due to external demand and technological upgrades [3]