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]

关键因素 图利 绝对差值和边际变化 重要指标 II 价值成长轮动策略 II 深圳证券交易所 ( SHENZHEN STOCK EXCHANGE 深交所ETF投资问答(42) 如何的身上了 II t FE ALKE 0 n - 编者按 - 近年来我国指数型基金迅速发展,交易型开 放式指数基金(ETF) 备受关注。为帮助广 大投资者系统全面认识ETF,了解相关投资 方法,特摘编由深圳证券交易所基金管理部 编著的《深交所ETF投资问答》（中国财政 经济出版社2024年版）形成图文解读。本 篇是第42期,一起来看看如何通过ETF构建 风格配置策略。 风格轮动是依据ETF特征进行交易的 行为，常见的风格轮动有大小盘轮动、 成长价值轮动等。风格轮动的分析框 架需要对比指数间的相对强弱，因此 预测难度更大。 II 影响风格轮动强弱的因素 II 价值和成长两类股票具有明显基本面 的差异。 价值类股票往往具备更好 的安全边际 成长类股票则可能具备更 好的盈利前景 观察风格间的相对业绩增速趋势,有 助于进行风格配置。除此之外，市场 中也有投资者通过估值指数来衡量价 值与成长之间的风格轮动。 u 大小鱼论动策略 ! 大小盘轮动通常 ...