Quantitative Models and Construction Methods DTW Timing Model - **Model Name**: DTW Timing Model - **Model Construction Idea**: The model is based on the principle of similarity and the DTW algorithm, focusing on price and volume timing[1][5][14] - **Model Construction Process**: - The model examines the similarity between current index trends and historical trends, selecting several historical segments with high similarity as references[25] - It calculates the weighted average future price change and weighted standard deviation of the selected historical segments (weights are the inverse of the distance)[25] - Based on the average future price change and standard deviation, trading signals are generated[25] - The model uses the DTW distance algorithm instead of the Euclidean distance for similarity measurement, as DTW distance can better handle time series mismatches[27] - Improved DTW algorithms such as Sakoe-Chiba and Itakura Parallelogram are introduced to overcome the "over-bending" issue in traditional DTW algorithms[29][30][35] - **Model Evaluation**: The model has shown stable excess returns in general market conditions, although it faced some drawdowns during periods of sudden macroeconomic policy changes[16] Foreign Capital Timing Model - **Model Name**: Foreign Capital Timing Model - **Model Construction Idea**: The model is based on the divergence between foreign and domestic related assets[1][14] - **Model Construction Process**: - The model uses two foreign-listed assets related to A-shares: FTSE China A50 Index Futures (Singapore market) and Southern A50 ETF (Hong Kong market)[34] - It constructs two indicators from FTSE China A50 Index Futures: premium and price divergence, forming the FTSE China A50 Index Futures timing signal[34] - It constructs a price divergence indicator from Southern A50 ETF, forming the Southern A50 ETF timing signal[34] - The timing signals from both assets are combined to form the foreign capital timing signal[34] - **Model Evaluation**: The model has shown good performance with high annualized returns and low maximum drawdowns[20][23] Model Backtest Results DTW Timing Model - **Absolute Return**: 25.79% since November 2022[5][16] - **Excess Return**: 16.83% relative to CSI 300[5][16] - **Maximum Drawdown**: 21.32%[5][16] - **Absolute Return (2024)**: 23.98% on CSI 300[18] - **Excess Return (2024)**: 2.76%[18] - **Maximum Drawdown (2024)**: 21.36%[18] - **Win Rate (2024)**: 53.85%[18] - **Profit-Loss Ratio (2024)**: 2.93[18] Foreign Capital Timing Model - **Absolute Return (2024)**: 29.11% for long strategy[5][23] - **Maximum Drawdown (2024)**: 8.32% for long strategy[5][23] - **Annualized Return (2014-2024)**: 18.96% for long-short strategy, 14.19% for long strategy[20] - **Maximum Drawdown (2014-2024)**: 25.69% for long-short strategy, 17.27% for long strategy[20] - **Daily Win Rate (2014-2024)**: Nearly 55%[20] - **Profit-Loss Ratio (2014-2024)**: Both exceed 2.5[20]

Group 1 - The "Bank AH + Small Micro Plate" portfolio has achieved a historical high, increasing by 54.97% from last year, with a maximum drawdown of 13.89% [1] - The portfolio's performance has outpaced major indices, with only the CSI 2000 showing a higher growth rate, but with a larger maximum drawdown of 19.65% [1] - The portfolio consists of 40% Bank AH Preferred ETF (517900), 30% 1000 ETF Enhanced (159680), and 30% CSI 2000 Enhanced ETF (159552), employing a "high dividend base + enhanced growth assets" strategy [2][4] Group 2 - The Bank AH Preferred ETF (517900) has shown significant growth, increasing by 24% since the beginning of 2025, with a 411% surge in fund shares [4][6] - The low interest rate environment and the decline in 10-year government bonds have created a demand for bank stocks due to their high dividend and strong risk-averse attributes [6] - The dynamic adjustment mechanism of the Bank AH index allows for the identification of undervalued bank stocks, enhancing returns while providing stability [6] Group 3 - The portfolio's structure is designed to provide a safety net with high dividends while pursuing growth through small-cap stocks, which combine index beta and excess alpha [7] - The CSI 2000 Enhanced ETF (159552) has achieved a net value growth rate of 29.18% in the first half of the year, ranking first among similar broad-based ETFs [9] - Since its inception, the CSI 2000 Enhanced ETF has accumulated a net value growth of 68.21%, significantly outperforming the CSI 2000 index [10] Group 4 - Two signals support the continuation of the small-cap stock trend: ongoing liquidity support and the release of policy dividends from mergers and acquisitions regulations [11] - The CSI 2000 Enhanced ETF (159552) demonstrates the effectiveness of quantitative discipline in achieving sustained excess returns [12]