Quantitative Models and Construction Methods 1. Model Name: Credit Impulse Timing Strategy - **Model Construction Idea**: The model uses credit impulse as a timing indicator for A-shares, where the direction of credit impulse determines the market position (full position when upward, empty position when downward) [6][13][14] - **Model Construction Process**: - Calculate the year-on-year growth rate of long-term corporate loans (TTM) as the credit impulse indicator - Use the direction of the credit impulse to determine market positions: full position when the indicator is upward, empty position when downward - Formula: $ \text{Credit Impulse} = \frac{\text{Long-term Corporate Loans (TTM)} - \text{Long-term Corporate Loans (TTM, previous year)}}{\text{Long-term Corporate Loans (TTM, previous year)}} $ - **Model Evaluation**: The model has shown high effectiveness in avoiding major downtrends in the market [6][13][14] 2. Model Name: Beta Dispersion Timing Strategy - **Model Construction Idea**: The model uses beta dispersion as an indicator to measure local market sentiment overheating, with significant monthly timing effectiveness [6][17] - **Model Construction Process**: - Calculate the monthly beta dispersion of the market - Use the beta dispersion to determine market positions: higher beta dispersion indicates higher risk - Formula: $ \text{Beta Dispersion} = \frac{\sum_{i=1}^{N} (\beta_i - \bar{\beta})^2}{N} $ where $\beta_i$ is the beta of stock i, $\bar{\beta}$ is the average beta, and N is the number of stocks - **Model Evaluation**: The model has shown significant monthly timing effectiveness since 2013 [6][17] 3. Model Name: Trading Volume Timing Strategy - **Model Construction Idea**: The model uses trading volume as an indicator for market timing, with significant daily timing effectiveness [6][17] - **Model Construction Process**: - Calculate the daily trading volume and its 60-day moving average - Use the trading volume to determine market positions: higher trading volume indicates stronger market support - Formula: $ \text{Trading Volume Indicator} = \frac{\text{Daily Trading Volume}}{\text{60-day Moving Average of Trading Volume}} $ - **Model Evaluation**: The model has shown significant daily timing effectiveness since 2013 [6][17] 4. Composite Model: Credit Impulse, Beta Dispersion, Trading Volume - **Model Construction Idea**: The composite model combines credit impulse, beta dispersion, and trading volume indicators for market timing [6][18] - **Model Construction Process**: - Use equal weighting to combine the three indicators - Adjust positions based on the combined signal: average 2-week signal change frequency - Formula: $ \text{Composite Indicator} = \frac{\text{Credit Impulse Indicator} + \text{Beta Dispersion Indicator} + \text{Trading Volume Indicator}}{3} $ - **Model Evaluation**: The composite model has shown a high annual turnover rate and significant annualized returns since 2013 [6][18] Model Backtesting Results 1. Credit Impulse Timing Strategy - **Annualized Return**: 10.83% [6][13][14] - **Avoided Major Downtrends**: 2015 H2, 2018, 2022-2024 H1 [6][13][14] 2. Beta Dispersion Timing Strategy - **Annualized Return**: 13.12% [6][17] - **Monthly Timing Effectiveness**: Significant since 2013 [6][17] 3. Trading Volume Timing Strategy - **Annualized Return**: 14.33% [6][17] - **Daily Timing Effectiveness**: Significant since 2013 [6][17] 4. Composite Model: Credit Impulse, Beta Dispersion, Trading Volume - **Annualized Return**: 19.98% [6][18] - **Annual Turnover Rate**: 24 times [6][18] Quantitative Factors and Construction Methods 1. Factor Name: Manufacturing PMI Timing Strategy - **Factor Construction Idea**: The factor uses manufacturing PMI as a timing indicator for A-shares, with positions adjusted based on PMI levels [6][13] - **Factor Construction Process**: - Calculate the rolling 5-year percentile of manufacturing PMI - Adjust positions based on PMI levels: full position when >60%, empty position when <40%, half position when between 40%-60% - Formula: $ \text{PMI Timing Indicator} = \begin{cases} \text{Full Position} & \text{if PMI Percentile} > 60\% \\ \text{Empty Position} & \text{if PMI Percentile} < 40\% \\ \text{Half Position} & \text{if 40\% \leq PMI Percentile \leq 60\%} \end{cases} $ - **Factor Evaluation**: The factor has shown poor timing performance with an annualized return of only 0.41% since 2009 [6][13] Factor Backtesting Results 1. Manufacturing PMI Timing Strategy - **Annualized Return**: 0.41% [6][13] - **Comparison with Benchmark**: Underperformed the Wind All A Index annualized return of 8.49% [6][13] Style Rotation Models and Construction Methods 1. Model Name: Growth-Value Style Rotation Model - **Model Construction Idea**: The model suggests overweighting growth based on economic cycle analysis, valuation differences, and sentiment indicators [35][36] - **Model Construction Process**: - Analyze economic cycle indicators: profitability slope, interest rate cycle, credit cycle - Calculate valuation differences: PE and PB percentiles - Assess sentiment indicators: turnover and volatility differences - Formula: $ \text{Growth-Value Rotation Indicator} = \frac{\text{Profitability Slope Indicator} + \text{Interest Rate Cycle Indicator} + \text{Credit Cycle Indicator} + \text{PE Difference Indicator} + \text{PB Difference Indicator} + \text{Turnover Difference Indicator} + \text{Volatility Difference Indicator}}{7} $ - **Model Evaluation**: The model suggests overweighting growth based on current indicators [35][36] 2. Model Name: Small-Cap Large-Cap Style Rotation Model - **Model Construction Idea**: The model suggests balanced allocation based on economic cycle analysis, valuation differences, and sentiment indicators [35][41] - **Model Construction Process**: - Analyze economic cycle indicators: profitability slope, interest rate cycle, credit cycle - Calculate valuation differences: PE and PB percentiles - Assess sentiment indicators: turnover and volatility differences - Formula: $ \text{Small-Cap Large-Cap Rotation Indicator} = \frac{\text{Profitability Slope Indicator} + \text{Interest Rate Cycle Indicator} + \text{Credit Cycle Indicator} + \text{PE Difference Indicator} + \text{PB Difference Indicator} + \text{Turnover Difference Indicator} + \text{Volatility Difference Indicator}}{7} $ - **Model Evaluation**: The model suggests balanced allocation based on current indicators [35][41] 3. Composite Model: Four-Dimensional Style Rotation Model - **Model Construction Idea**: The model combines growth-value and small-cap large-cap rotation models for allocation [35][44] - **Model Construction Process**: - Combine the signals from growth-value and small-cap large-cap rotation models - Adjust positions based on combined signals - Formula: $ \text{Four-Dimensional Rotation Indicator} = \frac{\text{Growth-Value Rotation Indicator} + \text{Small-Cap Large-Cap Rotation Indicator}}{2} $ - **Model Evaluation**: The model suggests specific allocation proportions based on current indicators [35][44] Style Rotation Model Backtesting Results 1. Growth-Value Style Rotation Model - **Annualized Return**: 11.65% [35][37] - **Comparison with Benchmark**: Outperformed the benchmark annualized return of 6.91% [35][37] 2. Small-Cap Large-Cap Style Rotation Model - **Annualized Return**: 12.32% [35][42] - **Comparison with Benchmark**: Outperformed the benchmark annualized return of 7.11% [35][42] 3. Composite Model: Four-Dimensional Style Rotation Model - **Annualized Return**: 13.22% [35][44] - **Comparison with Benchmark**: Outperformed the benchmark annualized return of 7.50% [35][44]

Market Performance - The recent five trading days saw the Sci-Tech 50 Index decline by 1.65%, the ChiNext Index by 0.74%, the large-cap value index by 1.27%, the large-cap growth index by 2.58%, the SSE 50 by 1.48%, and the CSI 2000 representing small caps by 0.19% [1] - The pharmaceutical and communication sectors performed well, while coal and non-ferrous metals lagged [1] Risk Premium Analysis - The risk premium, defined as the inverse of the static PE of the CSI All Index (EP) minus the yield of ten-year government bonds, indicates that the implied returns of equity and bond assets are at historically high levels, reaching 4.17% on April 26, 2022, and 4.08% on October 28, 2022 [1] - As of January 19, 2024, the indicator was at 4.11%, marking the fifth occurrence since 2016 of exceeding 4% [1] - The latest figure as of August 1, 2025, is 3.48%, with the two-standard-deviation boundary set at 4.76% [1] Valuation Levels - As of August 1, 2025, the CSI All Index's TTM PE is at the 64th percentile, with the SSE 50 and CSI 300 at 66% and 58% respectively, while the ChiNext Index is close to 25% [2] - The CSI 500 and CSI 1000 are at 46% and 37% respectively, indicating that the ChiNext Index's valuation is relatively low compared to historical levels [2] Long-term Market Trends - The technical analysis of the Deep 100 Index shows a pattern of bear markets every three years, followed by bull markets, with previous declines ranging from 40% to 45% [2] - The current adjustment cycle began in the first quarter of 2021, suggesting a potential for upward movement from the bottom [2] Fund Flow and Trading Activity - In the last five trading days, ETF funds experienced an outflow of 13.1 billion yuan, while margin financing increased by approximately 42.6 billion yuan [2] - The average daily trading volume across both markets was 1.7848 trillion yuan [2] AI and Machine Learning Applications - A convolutional neural network (CNN) was utilized to model price and volume data, mapping learned features to industry themes, with a focus on semiconductor materials [2][7] ETF Indexes - Various ETF indexes related to semiconductor materials and innovation were listed, including the SSE Sci-Tech Semiconductor Materials Equipment Theme Index and the CSI Semiconductor Industry Index, all dated August 1, 2025 [8]

Core Viewpoint - The article highlights the investment philosophy and performance of DeYuan Investment, emphasizing its data-driven approach and diverse product offerings aimed at achieving long-term returns with risk-adjusted strategies [2][3]. Company Overview - DeYuan Investment, established in June 2014, is a registered private fund manager in China with a management scale of approximately 900 million [2]. - The company employs a strategy framework that integrates quantitative timing, deep value assessment, and systematic risk control to seek long-term compound growth [2]. Performance Metrics - As of June 30, 2025, DeYuan Investment's products in the 500-1,000 million scale category achieved an average return of ***%, ranking in the top 10 for semi-annual stock strategy returns [3]. - The "DeYuan Yangfan No. 1" product managed by DeYuan Investment recorded a return of ***% in the first half of 2025, placing it fourth in the semi-annual subjective long position returns [3]. Development History - DeYuan Investment was registered in Shenzhen in June 2014 with a paid-in capital of 10 million [7]. - The company received its private fund management registration certificate in July 2015 [7]. Core Team - The core investment committee consists of nine members, most with over ten years of experience, providing a stable and reliable decision-making framework [9]. Core Advantages - The company boasts a stable and professional team with no management changes in the past three years, enhancing product development and investor experience [18]. - DeYuan Investment has developed its own quantitative trading system that is fully automated and designed for low latency [18]. - Strict risk management practices are in place, focusing on preemptive risk identification and real-time monitoring [18]. - The company offers a diverse range of products, including quantitative strategies and alternative investment strategies [19]. Product Lines - The quantitative long position strategy operates fully programmatically, adjusting stock positions dynamically based on mathematical models and algorithms [20]. - The "DeYuan Haichai Quantitative No. 1" product has been established since June 23, 2022, with returns of ***% since inception [21]. - The "DeYuan Mingxuan Quantitative No. 2" product focuses on value investment principles, targeting undervalued stocks with potential for recovery [22]. Alternative Investment Strategy - DeYuan Investment identifies companies in financial distress that are undergoing bankruptcy restructuring but still possess core asset value and growth potential [27]. - The company participates in these restructurings through compliant capital increases, aiming to benefit from value recovery post-restructuring [27].

Core Viewpoint - The market sentiment score has increased, indicating a bullish outlook for the market as of July 25, with a score of 1.8, up from 0.65 the previous week [1]. Group 1: Market Sentiment Indicators - The sentiment structure indicator is calculated using a scoring method based on the direction of each sub-indicator and its position within the Bollinger Bands, resulting in a 20-day moving average score [1]. - The trading volatility between industries has shown a positive signal, suggesting increased capital activity and reduced uncertainty in short-term sentiment [4][14]. - The financing ratio has decreased, indicating a decline in the heat of margin trading, which requires further observation [4]. Group 2: Trading Activity and Volume - The overall trading volume in the A-share market has maintained an upward trend, with a peak daily trading volume of 19,286.45 billion RMB on July 25 [9]. - The consistency of price and volume remains high, indicating active participation and capital engagement in the market [6]. Group 3: Industry Performance - Industries such as basic chemicals, non-ferrous metals, and electric equipment have shown strong performance, while sectors like public utilities, media, and banking have lagged behind [16]. - The short-term trend scores for industries like coal, food and beverage, and beauty care have significantly increased, with coal showing a remarkable rise of 109.09% [19][20]. Group 4: Style and Trend Analysis - The small-cap growth style is currently favored, with the relative strength index (RSI) indicating a strong preference for growth stocks over value stocks [21][22]. - The trend scoring model shows that industries like coal, food and beverage, and construction materials have strong short-term trend scores, suggesting potential investment opportunities [19][20].

Group 1 - Market sentiment indicators have risen to 1.8, up from 0.65 last week, indicating a bullish outlook [10][18] - Inter-industry trading volatility has increased, signaling a recovery in capital activity and reduced uncertainty in short-term sentiment [14][23] - The total trading volume of the A-share market has continued to rise, with a peak daily trading volume of 1,928.645 billion RMB on Wednesday [18][27] Group 2 - The coal industry shows a significant upward trend, with a short-term trend score increase of 109.09% [32][34] - The model indicates a preference for small-cap growth styles, with strong signals for growth styles as evidenced by the RSI metrics [36][37] - The top five industries with the strongest short-term trends include environmental protection, basic chemicals, social services, non-ferrous metals, and comprehensive sectors [32][34]

Quantitative Models and Construction 1. Model Name: Timing System Model - **Model Construction Idea**: This model uses the distance between the short-term moving average (20-day) and the long-term moving average (120-day) of the WIND All A Index to determine the market trend. If the short-term moving average is above the long-term moving average and the absolute distance exceeds 3%, the market is considered to be in an upward trend[2][10][16] - **Model Construction Process**: 1. Calculate the 20-day moving average (short-term) and the 120-day moving average (long-term) of the WIND All A Index 2. Compute the percentage difference between the two moving averages: $ \text{Distance} = \frac{\text{20-day MA} - \text{120-day MA}}{\text{120-day MA}} \times 100\% $ - 20-day MA: Short-term moving average - 120-day MA: Long-term moving average 3. If the distance is greater than 3% and the short-term moving average is above the long-term moving average, the market is in an upward trend[2][10][16] - **Model Evaluation**: The model effectively identifies upward market trends and provides a clear signal for timing decisions[2][10][16] 2. Model Name: Industry Allocation Model - **Model Construction Idea**: This model identifies sectors with potential for outperformance based on medium-term trends and specific themes, such as "distressed reversal" and "high elasticity" sectors[3][11][16] - **Model Construction Process**: 1. Analyze sector performance and valuation metrics 2. Identify sectors with medium-term growth potential, such as distressed reversal sectors (e.g., Hong Kong innovative drugs, Hong Kong securities, and Hang Seng consumption) 3. Highlight high-elasticity sectors like technology, military, AI applications, and solid-state batteries based on the TWO BETA model[3][11][16] - **Model Evaluation**: The model provides actionable insights for sector allocation during upward market trends, focusing on high-growth and high-elasticity sectors[3][11][16] 3. Model Name: Position Management Model - **Model Construction Idea**: This model determines the optimal stock allocation ratio based on valuation levels and short-term market trends[3][11] - **Model Construction Process**: 1. Assess the valuation levels of the WIND All A Index using PE and PB metrics 2. Combine valuation levels with short-term market trends to recommend stock allocation ratios 3. Current recommendation: Allocate 80% of absolute return products to stocks based on the WIND All A Index[3][11] - **Model Evaluation**: The model provides a systematic approach to managing stock positions, balancing valuation levels and market trends[3][11] --- Model Backtesting Results 1. Timing System Model - **Distance between Moving Averages**: 5.21% (greater than the 3% threshold, indicating an upward trend)[2][10][16] 2. Industry Allocation Model - **Recommended Sectors**: - Distressed reversal sectors: Hong Kong innovative drugs, Hong Kong securities, Hang Seng consumption - High-elasticity sectors: Technology, military, AI applications, solid-state batteries[3][11][16] 3. Position Management Model - **Stock Allocation Recommendation**: 80% allocation to stocks based on the WIND All A Index[3][11]

Core Viewpoint - The market is expected to experience a correction in the upcoming week due to various technical and quantitative indicators suggesting a weakening market sentiment [1][2]. Market Analysis - The liquidity shock indicator for the CSI 300 index was recorded at 1.71, indicating that current market liquidity is 1.71 times higher than the average level over the past year [2]. - The PUT-CALL ratio for the SSE 50 ETF options increased to 0.80, reflecting a growing caution among investors regarding the short-term performance of the SSE 50 ETF [2]. - The five-day average turnover rates for the SSE Composite Index and Wind All A Index were 1.07% and 1.65%, respectively, indicating a decrease in trading activity [2]. Macroeconomic Factors - The onshore and offshore RMB exchange rates experienced slight declines of -0.08% and -0.1% respectively [2]. - New RMB loans in June amounted to 22,400 billion, exceeding the consensus forecast of 18,447.29 billion and the previous value of 6,200 billion [2]. - The broad money supply (M2) grew by 8.3% year-on-year, surpassing both the consensus forecast of 8.08% and the previous value of 7.9% [2]. Technical Analysis - The Wind All A Index remains above the SAR point, but the index and SAR point are now closely aligned [2]. - The moving average strength index is currently at 253, placing it in the 93.8 percentile since 2021 [2]. - The sentiment model score is 1 out of 5, indicating a decrease in market sentiment, while the trend model signal is positive and the weighted model signal is negative [2]. Performance Overview - For the week of July 14-18, the SSE 50 Index rose by 0.28%, the CSI 300 Index increased by 1.09%, the CSI 500 Index gained 1.2%, and the ChiNext Index surged by 3.17% [3]. - The overall market PE (TTM) stands at 20.4 times, which is at the 65.3 percentile since 2005 [3]. Factor Crowding Observation - The small-cap factor crowding is at a high level with a score of 1.07, while the low valuation factor crowding is at 0.36 [3]. - The industry crowding levels are relatively high in banking, comprehensive, non-ferrous metals, steel, and non-bank financial sectors, with notable increases in steel and pharmaceutical industries [3].

Quantitative Models and Construction Methods 1. Model Name: Low Volatility Uptrend Environment Model - **Model Construction Idea**: The model categorizes market environments based on rolling 60-day annualized return and volatility percentiles, defining six distinct market states: low-volatility uptrend, medium-volatility uptrend, high-volatility uptrend, low-volatility downtrend, medium-volatility downtrend, and high-volatility downtrend[5][16] - **Model Construction Process**: 1. Calculate the rolling 60-day annualized return and volatility for the CSI 300 and CSI 800 total return indices since 2010[5][16] 2. Define return > 0 as an uptrend and return ≤ 0 as a downtrend[5][16] 3. Categorize volatility percentiles: - Low volatility: below the 20th percentile - Medium volatility: between the 20th and 80th percentiles - High volatility: above the 80th percentile[5][16] 4. Combine return and volatility categories to form six market states[5][16] - **Model Evaluation**: The low-volatility uptrend environment demonstrates superior performance in terms of future returns, win rates, and payoff ratios, indicating a higher probability of sustained "slow bull" markets[5][16] 2. Model Name: Short-Term Quantitative Timing Model - **Model Construction Idea**: The model integrates macroeconomic, valuation, sentiment, and liquidity signals to generate short-term market timing recommendations[18][19][20] - **Model Construction Process**: 1. **Macroeconomic Signals**: - Manufacturing PMI percentile (44.92%): Neutral signal - Long-term loan growth percentile (0.00%): Cautious signal - M1 growth percentile (94.92%): Optimistic signal[18][22] 2. **Valuation Signals**: - PE percentile (95.70%): Neutral signal - PB percentile (79.32%): Neutral signal[19][22] 3. **Sentiment Signals**: - Beta dispersion percentile (40.68%): Neutral signal - Volume sentiment score percentile (87.76%): Optimistic signal - Volatility percentile (0.58%): Optimistic signal[19][22] 4. **Liquidity Signals**: - Money market rate percentile (33.90%): Optimistic signal - Exchange rate expectation percentile (40.68%): Neutral signal - 5-day average net financing percentile (94.04%): Neutral signal[20][22] 5. Combine signals to derive overall timing recommendations[18][19][20] - **Model Evaluation**: The model has consistently outperformed its benchmark since 2012, with an annualized return of 16.81% and a maximum drawdown of 27.70%, demonstrating robust performance[20][24] 3. Model Name: Growth-Value Style Rotation Model - **Model Construction Idea**: The model evaluates macroeconomic, valuation, and sentiment factors to recommend overweighting growth or value styles[29] - **Model Construction Process**: 1. **Macroeconomic Signals**: - Profit cycle slope (4.17): Favorable for growth - Interest rate cycle level (9.17): Favorable for value - Credit cycle change (-3.33): Favorable for value[31] 2. **Valuation Signals**: - PE spread percentile (16.36%): Favorable for growth - PB spread percentile (36.82%): Favorable for growth[31] 3. **Sentiment Signals**: - Turnover spread percentile (29.45%): Favorable for value - Volatility spread percentile (17.44%): Favorable for balance[31] 4. Combine signals to derive style rotation recommendations[29][31] - **Model Evaluation**: The strategy has delivered an annualized return of 11.71% since 2012, outperforming the benchmark by 4.80% annually[30][33] 4. Model Name: Small-Cap vs. Large-Cap Style Rotation Model - **Model Construction Idea**: The model evaluates macroeconomic, valuation, and sentiment factors to recommend overweighting small-cap or large-cap styles[34] - **Model Construction Process**: 1. **Macroeconomic Signals**: - Profit cycle slope (4.17): Favorable for small-cap - Interest rate cycle level (9.17): Favorable for large-cap - Credit cycle change (-3.33): Favorable for large-cap[36] 2. **Valuation Signals**: - PE spread percentile (78.86%): Favorable for large-cap - PB spread percentile (96.59%): Favorable for large-cap[36] 3. **Sentiment Signals**: - Turnover spread percentile (72.56%): Favorable for small-cap - Volatility spread percentile (62.60%): Favorable for large-cap[36] 4. Combine signals to derive style rotation recommendations[34][36] - **Model Evaluation**: The strategy has delivered an annualized return of 12.38% since 2012, outperforming the benchmark by 5.31% annually[35][38] 5. Model Name: Four-Dimensional Style Rotation Model - **Model Construction Idea**: Combines growth-value and small-cap-large-cap rotation models to recommend allocations across four styles: small-cap growth, small-cap value, large-cap growth, and large-cap value[39] - **Model Construction Process**: 1. Integrate signals from the growth-value and small-cap-large-cap models 2. Recommend allocations based on combined signals: - Small-cap growth: 12.5% - Small-cap value: 37.5% - Large-cap growth: 12.5% - Large-cap value: 37.5%[39][40] - **Model Evaluation**: The strategy has delivered an annualized return of 13.29% since 2012, outperforming the benchmark by 5.82% annually[39][40] --- Model Backtest Results 1. Low Volatility Uptrend Environment Model - **Annualized Return**: 18.23% (CSI 300), 10.13% (CSI 800) - **Win Rate**: 63.65% (CSI 300), 55.42% (CSI 800) - **Payoff Ratio**: 1.77 (CSI 300), 1.48 (CSI 800)[5][16][17] 2. Short-Term Quantitative Timing Model - **Annualized Return**: 16.81% - **Annualized Volatility**: 14.55% - **Maximum Drawdown**: 27.70% - **Sharpe Ratio**: 1.0033 - **Monthly Win Rate**: 69.74% - **Quarterly Win Rate**: 69.23%[20][24] 3. Growth-Value Style Rotation Model - **Annualized Return**: 11.71% - **Annualized Volatility**: 20.81% - **Maximum Drawdown**: 43.07% - **Sharpe Ratio**: 0.5409 - **Monthly Win Rate**: 58.28% - **Quarterly Win Rate**: 60.78%[30][33] 4. Small-Cap vs. Large-Cap Style Rotation Model - **Annualized Return**: 12.38% - **Annualized Volatility**: 22.69% - **Maximum Drawdown**: 50.65% - **Sharpe Ratio**: 0.5408 - **Monthly Win Rate**: 60.93% - **Quarterly Win Rate**: 58.82%[35][38] 5. Four-Dimensional Style Rotation Model - **Annualized Return**: 13.29% - **Annualized Volatility**: 21.55% - **Maximum Drawdown**: 47.91% - **Sharpe Ratio**: 0.5951 - **Monthly Win Rate**: 59.60% - **Quarterly Win Rate**: 62.75%[39][40]

- The report identifies the market's uptrend by analyzing the distance between the 120-day and 20-day moving averages of the WIND All A index, which has expanded from 3.04% to 4.04%, indicating a continued uptrend[2][10][17] - The core observation variable for the market's uptrend is the "profitability effect," which is currently positive at 3.76%, suggesting that incremental funds are likely to continue entering the market[2][4][11] - The industry allocation model recommends sectors such as Hong Kong innovative drugs, Hong Kong securities, and Hang Seng consumption, with additional opportunities in the photovoltaic sector due to anti-involution benefits[3][4][11] - The TWO BETA model continues to recommend the technology sector, with a focus on military and AI applications[3][4][11] - The valuation indicators show that the WIND All A index's overall PE is at the 70th percentile, indicating a moderate level, while the PB is at the 30th percentile, indicating a relatively low level[3][11] - Based on the short-term trend judgment and the position management model, the report suggests an 80% position for absolute return products with the WIND All A index as the main stock allocation[3][11] Model Backtesting Results - The distance between the 20-day and 120-day moving averages of the WIND All A index is 4.04%, indicating a continued uptrend[2][10][17] - The profitability effect value is 3.76%, which is significantly positive, suggesting that the market is likely to continue its uptrend despite short-term fluctuations[2][4][11]

Group 1 - Market sentiment indicators have improved, with the sentiment score rising from -0.9 to -0.25, indicating a shift towards a more bullish outlook [9][13][18] - The increase in industry trading congestion and the positive shift in the PCR combined with the VIX index reflect a recovery in market sentiment [13][18] - The total trading volume in the A-share market has shown a significant increase, with the highest daily trading volume reaching 1,736.61 billion RMB [18][19] Group 2 - The construction materials sector has shown a significant upward trend, with a short-term trend score increase of 21.05% [32][33] - The model indicates that small-cap growth stocks are currently favored, with a strong signal for small-cap stocks and a rapid increase in the 5-day RSI relative to the 20-day RSI [32][37] - The sectors with the strongest short-term trends include defense, media, communication, and computer industries [32][33]