市场再次触及阻力线

Quantitative Models and Construction Methods Model 1: Hot Trend ETF Strategy - Model Name: Hot Trend ETF Strategy - Model Construction Idea: The strategy is based on selecting ETFs with the highest and lowest price patterns and constructing a risk parity portfolio with the top 10 ETFs showing the highest short-term market attention. - Model Construction Process: - Select ETFs with both highest and lowest prices in an upward pattern. - Construct support and resistance factors based on the relative steepness of the regression coefficients of the highest and lowest prices over the past 20 days. - Choose the top 10 ETFs with the highest turnover rate in the past 5 days relative to the past 20 days. - Construct a risk parity portfolio with these ETFs. - Model Evaluation: The strategy achieved a return of 59.92% since 2025, with an excess return of 36.61% compared to the CSI 300 Index[28][29]. Model 2: Three-Strategy Fusion ETF Rotation - Model Name: Three-Strategy Fusion ETF Rotation - Model Construction Idea: The strategy combines three industry rotation strategies driven by quantitative fundamentals, quality low volatility, and distressed reversal to achieve factor and style complementarity. - Model Construction Process: - Construct industry rotation strategies based on fundamental rotation, quality low volatility, and distressed reversal. - Combine these strategies equally to select industries from different dimensions. - Achieve factor and style complementarity to reduce the risk of a single strategy. - Model Evaluation: The strategy achieved a cumulative return of 12.16% from April 10, 2017, to February 27, 2026, with a Sharpe ratio of 0.74. The strategy's annual performance and latest holdings are also detailed[32][34][37]. Model 3: All-Weather Strategy - Model Name: All-Weather Strategy - Model Construction Idea: The strategy aims to achieve stable returns by avoiding the "prediction" dilemma through diversified risk. It follows three basic principles: asset selection, risk adjustment, and structural hedging. - Model Construction Process: - Use a cyclic hedging design to bypass macro factors and directly address asset volatility for long-term return balance. - Construct high-volatility and low-volatility portfolios based on risk levels. - Model Evaluation: As of 2025, the high-volatility version had an annualized return of 11.8%, an average maximum drawdown of 3.6%, and a Sharpe ratio of 2.3. The low-volatility version had an annualized return of 8.8%, an average maximum drawdown of 2.0%, and a Sharpe ratio of 3.4. Since 2026, the high-volatility and low-volatility versions had returns of 2.7% and 1.1%, respectively[49][59][60]. Model Backtest Results - Hot Trend ETF Strategy: - Return since 2025: 59.92% - Excess return compared to CSI 300 Index: 36.61%[28][29] - Three-Strategy Fusion ETF Rotation: - Cumulative return (April 10, 2017 - February 27, 2026): 12.16% - Sharpe ratio: 0.74 - Annual performance and latest holdings detailed[32][34][37] - All-Weather Strategy: - High-volatility version (as of 2025): Annualized return 11.8%, average maximum drawdown 3.6%, Sharpe ratio 2.3 - Low-volatility version (as of 2025): Annualized return 8.8%, average maximum drawdown 2.0%, Sharpe ratio 3.4 - Returns since 2026: High-volatility 2.7%, Low-volatility 1.1%[49][59][60] Quantitative Factors and Construction Methods Factor 1: Beta Factor - Factor Name: Beta Factor - Factor Construction Idea: Measures the sensitivity of a stock's returns to market returns. - Factor Construction Process: Calculate the beta coefficient of each stock based on its historical returns relative to the market index. - Factor Evaluation: The beta factor recorded a positive return of 3.26% this week, indicating that high-beta stocks regained market favor[62]. Factor 2: Momentum Factor - Factor Name: Momentum Factor - Factor Construction Idea: Measures the tendency of stocks to continue their past performance. - Factor Construction Process: Calculate the momentum of each stock based on its historical returns over a specified period. - Factor Evaluation: The momentum factor recorded a positive return of 2.37% this week, reflecting that high-momentum stocks gained market attention[62]. Factor 3: Liquidity Factor - Factor Name: Liquidity Factor - Factor Construction Idea: Measures the ease with which a stock can be traded. - Factor Construction Process: Calculate the liquidity of each stock based on its trading volume and bid-ask spread. - Factor Evaluation: The liquidity factor recorded a positive return of 2.21% this week, indicating that liquid stocks gained market attention[62]. Factor Backtest Results - Beta Factor: - Weekly return: 3.26%[62] - Momentum Factor: - Weekly return: 2.37%[62] - Liquidity Factor: - Weekly return: 2.21%[62]