- The report tracks high-frequency stock selection factors, including Price Range Factor, Price-Volume Divergence Factor, Regret Avoidance Factor, and Slope Convexity Factor, with their out-of-sample performance showing overall excellence[2][3][11] - Price Range Factor measures the activity level of stocks traded within different intraday price ranges, reflecting investors' expectations for future stock trends. It demonstrates strong predictive power and stable performance this year[3][11][17] - Price-Volume Divergence Factor evaluates the correlation between stock prices and trading volumes. Lower correlation typically indicates higher potential for future stock price increases. However, its performance has been unstable in recent years, with multi-long net value curves flattening[3][22][26] - Regret Avoidance Factor examines the proportion and degree of stock rebounds after being sold by investors, showcasing good predictive power. Its out-of-sample excess returns are stable, indicating that A-share investors' regret avoidance sentiment significantly impacts stock price expectations[3][27][36] - Slope Convexity Factor analyzes the slope and convexity of order books to assess the impact of investor patience and supply-demand elasticity on expected returns. It is constructed using high-frequency snapshot data from limit order books[3][37][42] - The report combines three high-frequency factors into an equal-weighted "Gold" portfolio for CSI 1000 Index enhancement strategy, achieving an annualized excess return rate of 10.51% and a maximum excess drawdown of 6.04%[3][44][45] - To further enhance strategy performance, the report integrates high-frequency factors with three effective fundamental factors (Consensus Expectations, Growth, and Technical Factors) to construct a high-frequency & fundamental resonance portfolio for CSI 1000 Index enhancement strategy. This strategy achieves an annualized excess return rate of 14.57% and a maximum excess drawdown of 4.52%[4][49][51] Factor Backtesting Results - Price Range Factor: Weekly excess return 0.40%, monthly excess return 0.51%, annual excess return 5.86%[2][13][17] - Price-Volume Divergence Factor: Weekly excess return -0.24%, monthly excess return 1.53%, annual excess return 9.00%[2][13][26] - Regret Avoidance Factor: Weekly excess return 0.27%, monthly excess return -0.49%, annual excess return 2.32%[2][13][36] - Slope Convexity Factor: Weekly excess return -1.74%, monthly excess return -2.46%, annual excess return -5.90%[2][13][42] Strategy Performance Metrics - "Gold" Portfolio: Annualized return 9.49%, annualized excess return 10.51%, Sharpe ratio 0.39, IR 2.47, maximum excess drawdown 6.04%[45][47][48] - High-frequency & Fundamental Resonance Portfolio: Annualized return 13.62%, annualized excess return 14.57%, Sharpe ratio 0.58, IR 3.50, maximum excess drawdown 4.52%[51][53][55]

Group 1: ETF Rotation Strategy Tracking - The ETF rotation strategy, constructed using GBDT+NN machine learning factors, has shown strong performance in out-of-sample testing, with an annualized excess return of 11.90% and a maximum drawdown of 17.31% [2][12][17] - Recent performance indicates a weekly excess return of 0.77% and a monthly excess return of 1.10%, while the year-to-date excess return stands at -0.19% [20][24] - The strategy's information ratio is 0.68, reflecting its effectiveness in generating excess returns relative to risk [24] Group 2: High-Frequency Factor Overview - High-frequency factors have demonstrated overall strong performance, with the price range factor yielding a year-to-date excess return of 4.79% and the price-volume divergence factor achieving 10.08% [3][20] - The regret avoidance factor has underperformed with a year-to-date excess return of -0.56%, while the slope convexity factor has shown a year-to-date excess return of -3.64% [3][20] - The high-frequency "gold" combination strategy has an annualized excess return of 10.69% and a maximum drawdown of 6.04% [5][60] Group 3: High-Frequency Factor Performance Tracking - The price range factor measures the activity level of stocks within different price ranges, showing strong predictive power and stable performance this year [4][28] - The price-volume divergence factor assesses the correlation between stock price and trading volume, with recent performance indicating a mixed stability [4][39] - The regret avoidance factor reflects investor behavior, showing stable out-of-sample excess returns, while the slope convexity factor illustrates the impact of order book elasticity on expected returns [4][51] Group 4: Combined Strategies Performance - The high-frequency and fundamental resonance combination strategy has an annualized excess return of 14.98% and a maximum drawdown of 4.52% [5][64] - Recent performance for this combined strategy includes a weekly excess return of 0.63% and a monthly excess return of 2.00%, with a year-to-date excess return of 4.69% [67]