主动量化周报：小盘题材风格占优，周期板块抛压较小

Quantitative Models and Construction Methods 1. Model Name: Oversold Rebound Signal - Model Construction Idea: This model identifies potential short-term rebound opportunities in industries that have experienced significant price declines, aiming to capture short-term excess returns[13][14] - Model Construction Process: The model uses historical data from 2017 to 2024 to identify oversold conditions. The signal is triggered when specific technical thresholds are met, indicating a potential rebound. The model evaluates the average holding period, win rate, and profit-loss ratio to assess the signal's effectiveness[13][14] - Model Evaluation: The model demonstrates strong short-term predictive power, with high win rates and favorable profit-loss ratios, making it effective for short-term industry rotation strategies[13][14] 2. Model Name: Platform Breakout Signal - Model Construction Idea: This model identifies industries where prices break above a consolidation platform, signaling potential upward momentum[13][14] - Model Construction Process: The model analyzes historical data from 2017 to 2024 to detect breakout patterns. It evaluates the signal's performance based on average returns, win rates, and profit-loss ratios over different holding periods[13][14] - Model Evaluation: The model is moderately effective, with consistent but slightly lower win rates and profit-loss ratios compared to the oversold rebound signal[13][14] 3. Model Name: Low Pressure Industry Rotation - Model Construction Idea: This model identifies industries with minimal short-term selling pressure, aiming to capture excess returns through industry rotation[16][19] - Model Construction Process: The model calculates theoretical selling pressure for each industry and ranks them. A portfolio is constructed by selecting the top 5 industries with the lowest selling pressure. Backtesting from 2017 to 2024 shows the strategy's performance relative to the Wind All A Index[16][19] - Model Evaluation: The model demonstrates strong performance, achieving significant excess returns over the benchmark index, making it suitable for medium-term industry rotation strategies[16][19] --- Model Backtesting Results 1. Oversold Rebound Signal - 5-Day Average Return: 4.56% - 5-Day Win Rate: 71% - 5-Day Profit-Loss Ratio: 1.87 - 10-Day Average Return: 7.22% - 10-Day Win Rate: 62% - 10-Day Profit-Loss Ratio: 1.66 - Average Holding Period: 5.2 days - Average Holding Return: 6.30% - Overall Win Rate: 76% - Overall Profit-Loss Ratio: 2.3[13][14] 2. Platform Breakout Signal - 5-Day Average Return: 3.30% - 5-Day Win Rate: 62% - 5-Day Profit-Loss Ratio: 1.45 - 10-Day Average Return: 5.10% - 10-Day Win Rate: 61% - 10-Day Profit-Loss Ratio: 1.70 - Average Holding Period: 9 days - Average Holding Return: 6.77% - Overall Win Rate: 60% - Overall Profit-Loss Ratio: 2.15[13][14] 3. Low Pressure Industry Rotation - Excess Return Relative to Wind All A Index: ~15% during the backtesting period[19] --- Quantitative Factors and Construction Methods 1. Factor Name: Circulating Market Cap Yield - Factor Construction Idea: This factor measures the average profitability of investors in an industry, reflecting the overall market sentiment and investment attractiveness[10] - Factor Construction Process: The factor is calculated as the average return of circulating market cap within an industry. Historical percentile rankings are used to compare the current profitability level against historical data[10] - Factor Evaluation: The factor effectively identifies industries with strong investor sentiment, making it useful for short-term market timing[10] 2. Factor Name: Profit-Taking Ratio - Factor Construction Idea: This factor evaluates the proportion of profitable positions in an industry, indicating potential selling pressure[13][16] - Factor Construction Process: The factor is calculated as the ratio of profitable positions to total positions within an industry. Industries with lower profit-taking ratios are considered to have lower selling pressure[13][16] - Factor Evaluation: The factor is effective in identifying industries with lower short-term selling pressure, supporting industry rotation strategies[13][16] --- Factor Backtesting Results 1. Circulating Market Cap Yield - Banking Sector Profitability: 72nd percentile, highest among all industries[10] 2. Profit-Taking Ratio - Industries with Lowest Selling Pressure: Building Materials, Steel, Construction, Non-Ferrous Metals[16][19]