Core Viewpoint - The article reflects on the author's 16-year career in the finance industry, emphasizing the importance of mentorship, professional growth, and the aspiration to become a top fund manager in China [1][2][7]. Group 1: Career Development - The author began their career in 2006 at Pacific Securities, where they received early encouragement to become an excellent fund manager, which became a guiding principle [2]. - Transitioning to Haitong Securities, the author focused on quantitative analysis, contributing to the team's recognition in the market [3]. - The move to CITIC Securities was influenced by a mentor's recognition of the author's research methodology, reinforcing their career aspirations [4]. Group 2: Market Insights and Contributions - In 2018, during a challenging market environment due to US-China trade tensions, the author utilized a quantitative macro-timing system to provide accurate market predictions [4]. - The author played a significant role in providing market stabilization suggestions during the trade conflict, receiving recognition from the Shanghai Stock Exchange [4]. - In 2020, the author led a pivotal investment decision that significantly boosted the company's quarterly performance, marking a high point in their career [5]. Group 3: Future Aspirations - In 2023, the author set a goal to become the top fund manager in China, supported by the leadership's commitment to talent development [7]. - The author achieved the highest annual timing return in their career, reflecting the effectiveness of their research and investment strategies [8]. - The establishment of Shanghai Ruicheng Fund is aimed at fulfilling the author's long-term vision of creating a competitive investment product that leverages quantitative strategies and economic cycle theories [11].

Quantitative Models and Construction 1. Model Name: Volume Model - **Construction Idea**: This model evaluates market trends based on trading volume dynamics to provide short-term signals [12][65] - **Construction Process**: The model analyzes trading volume data to determine whether the market is in a neutral, bullish, or bearish state. Specific formulas or parameters are not disclosed in the report [12][65] - **Evaluation**: The model currently provides a neutral signal for the short term, indicating no strong directional bias [12][65] 2. Model Name: Low Volatility Model - **Construction Idea**: This model assesses market conditions by analyzing the volatility of stock prices over a short-term horizon [12][65] - **Construction Process**: The model calculates the volatility of stock prices and categorizes the market state as neutral, bullish, or bearish. Detailed formulas are not provided [12][65] - **Evaluation**: The model is currently neutral, suggesting a lack of significant market movement [12][65] 3. Model Name: Institutional Feature Model (LHB) - **Construction Idea**: This model uses institutional trading data from the "Dragon and Tiger List" (龙虎榜) to predict short-term market trends [12][65] - **Construction Process**: The model aggregates institutional trading activity and evaluates its impact on market direction. Specific formulas are not disclosed [12][65] - **Evaluation**: The model is neutral, indicating no clear institutional bias in the market [12][65] 4. Model Name: Feature Volume Model - **Construction Idea**: This model combines trading volume features to assess short-term market trends [12][65] - **Construction Process**: The model analyzes specific volume-related features to determine market sentiment. Detailed formulas are not provided [12][65] - **Evaluation**: The model is bearish, suggesting a negative outlook for the short term [12][65] 5. Model Name: Smart HS300 Model - **Construction Idea**: This model uses intelligent algorithms to predict short-term trends for the CSI 300 Index [12][65] - **Construction Process**: The model applies machine learning or algorithmic techniques to analyze market data. Specific methodologies are not disclosed [12][65] - **Evaluation**: The model is bearish, indicating a negative outlook for the CSI 300 Index [12][65] 6. Model Name: Smart CSI500 Model - **Construction Idea**: This model uses intelligent algorithms to predict short-term trends for the CSI 500 Index [12][65] - **Construction Process**: Similar to the Smart HS300 Model, this model applies algorithmic techniques to analyze market data. Specific methodologies are not disclosed [12][65] - **Evaluation**: The model is bullish, indicating a positive outlook for the CSI 500 Index [12][65] 7. Model Name: Limit-Up/Down Model - **Construction Idea**: This model evaluates mid-term market trends based on the frequency of limit-up and limit-down events [13][66] - **Construction Process**: The model tracks the occurrence of daily limit-up and limit-down events to assess market sentiment. Specific formulas are not disclosed [13][66] - **Evaluation**: The model is neutral, indicating no strong mid-term market bias [13][66] 8. Model Name: Calendar Effect Model - **Construction Idea**: This model analyzes seasonal or calendar-based patterns to predict mid-term market trends [13][66] - **Construction Process**: The model evaluates historical market performance during specific calendar periods. Detailed methodologies are not provided [13][66] - **Evaluation**: The model is neutral, suggesting no significant calendar-based market trends [13][66] 9. Model Name: Long-Term Momentum Model - **Construction Idea**: This model assesses long-term market trends based on momentum indicators [14][67] - **Construction Process**: The model calculates momentum metrics for broad-based indices to determine long-term market direction. Specific formulas are not disclosed [14][67] - **Evaluation**: The model is neutral for all broad-based indices, indicating no strong long-term market trends [14][67] 10. Model Name: A-Share Comprehensive Weapon V3 Model - **Construction Idea**: This composite model integrates multiple short, mid, and long-term signals to provide an overall market outlook [15][68] - **Construction Process**: The model combines signals from various sub-models (e.g., volume, volatility, institutional activity) to generate a comprehensive market view. Specific integration methods are not disclosed [15][68] - **Evaluation**: The model is bearish, indicating an overall negative outlook for the A-share market [15][68] 11. Model Name: A-Share Comprehensive Guozheng 2000 Model - **Construction Idea**: This composite model focuses on the Guozheng 2000 Index, integrating multiple signals to provide an overall market outlook [15][68] - **Construction Process**: Similar to the V3 Model, this model aggregates signals from various sub-models. Specific methodologies are not disclosed [15][68] - **Evaluation**: The model is bearish, indicating a negative outlook for the Guozheng 2000 Index [15][68] 12. Model Name: HK Stock Turnover-to-Volatility Model - **Construction Idea**: This model evaluates mid-term trends in the Hong Kong market by analyzing the ratio of turnover to volatility [16][69] - **Construction Process**: The model calculates the turnover-to-volatility ratio to assess market sentiment. Specific formulas are not disclosed [16][69] - **Evaluation**: The model is bearish, suggesting a negative outlook for the Hong Kong market [16][69] --- Backtesting Results of Models 1. Volume Model - **Signal**: Neutral [12][65] 2. Low Volatility Model - **Signal**: Neutral [12][65] 3. Institutional Feature Model (LHB) - **Signal**: Neutral [12][65] 4. Feature Volume Model - **Signal**: Bearish [12][65] 5. Smart HS300 Model - **Signal**: Bearish [12][65] 6. Smart CSI500 Model - **Signal**: Bullish [12][65] 7. Limit-Up/Down Model - **Signal**: Neutral [13][66] 8. Calendar Effect Model - **Signal**: Neutral [13][66] 9. Long-Term Momentum Model - **Signal**: Neutral [14][67] 10. A-Share Comprehensive Weapon V3 Model - **Signal**: Bearish [15][68] 11. A-Share Comprehensive Guozheng 2000 Model - **Signal**: Bearish [15][68] 12. HK Stock Turnover-to-Volatility Model - **Signal**: Bearish [16][69]

观点简述： 上周市场全周继续微涨趋势，沪深300指数周涨幅0.38%，上证综指周涨幅0.56%，中证500指数 周涨幅1.20%。市场维持了少见的窄幅震荡状态，资金重点期盼重要会议的方向指引。 每周思考总第626期 《 久盘滞涨，建议再降仓 》 本系列周度择时观点回溯表现（2023.1.1 至今），其中2024年全年累计收益53.69%。2025年至4 月27日累计收益7.79%。 1 本周建议 | 预测标的 | 仓位建议 | | --- | --- | | 主板 | 低仓位 | | 中小市值板块 | 低仓位 | | 风格判断 | 均衡 | 基本面上，美国经济数据的割裂度不断提升。 国内方面，上周央行MLF超额续作，但市场重点 关注的财政与货币刺激政策时间表依旧未能在4月政治局会议中有明确指示，这也意味着4月关税冲击 对国内经济基本面的影响或将难免；海外方面，美国继续披露各维度经济数据，各条线数据分化有所 扩大，如成屋销售继续低迷但新房销售重回高位、就业PMI维持弱势但失业人数保持平稳、最为矛盾 的是消费同比保持高增但消费者信心与投资者信心都已跌至过去4年新低，美国经济基本面的锚定指 标出现如此巨幅分化走势是 ...

每周思考总第620期 《 新高如期兑现，首次左侧预警 》 本系列周度择时观点回溯表现（2023.1.1 至今），其中2024年全年累计收益53.69%。2025年至3 月16日累计收益7.79%。 中小市值板块择时观点： 短期交易中的政策窗口期尾声阶段，同步于主板开启谨慎博弈策略，自1月 翻多后首次建议重回 中等仓位 ，风格建议转向均衡； 短期动量（趋势）模型建议关注行业：无。 | 预测标的 | 仓位建议 | | --- | --- | | 主板 | 中仓位 | | 中小市值板块 | 中仓位 | | 风格判断 | 均衡 | 观点简述： 上周市场如期上行创出本轮新高，沪深300指数涨幅1.59%，上证综指周涨幅1.39%，中证500指 数周涨幅1.43%。上周我们标题观点提示"A股仍在政策窗口期"，实际A股上周五大涨重要催化因素也 是周四央行关于降准降息的最新强调表态。 基本面上，中国经济仍只是温和企稳。 国内方面，上周发布的2月货币供应数据整体喜忧参半， 社融同比回升而M1同比回落，表明国内经济仍在底部夯实状态而非强劲反转走强， 重申留意三月美 国全球关税加征后对中国外需的更显著冲击，在此阶段央行重提"年内择 ...