策略跟踪月报：300ETF择时策略月报

Quantitative Models and Construction Methods 1. Model Name: 300ETF Intelligent Investment Strategy - Model Construction Idea: This strategy uses the 300ETF index fund as the investment target. It adjusts the investment amount based on the valuation level of the PE ratio of the CSI 300 Index. The lower the valuation, the higher the investment multiple, and vice versa. When the valuation is extremely high, the strategy attempts to reduce or clear positions to avoid potential losses from valuation reversion[18] - Model Construction Process: 1. Divide the historical PE ratio of the CSI 300 Index into several valuation intervals based on its mean and standard deviation 2. Determine the investment multiple for each interval: higher multiples for lower valuations and lower multiples for higher valuations 3. Adjust the investment amount dynamically based on the current valuation level[18] - Model Evaluation: The strategy demonstrates better excess returns and risk control compared to the 300ETF benchmark[20] 2. Model Name: 300ETF Intelligent Investment and Covered Call Strategy - Model Construction Idea: This strategy builds on the intelligent investment strategy by adding a covered call component. When the market is in a long-term oscillation phase, it opens covered call positions to earn option premiums and enhance returns[19] - Model Construction Process: 1. Hold 300ETF spot positions based on the intelligent investment strategy 2. During long-term market oscillations, sell 300ETF call options to collect premiums 3. Adjust the strategy dynamically based on market conditions[19] - Model Evaluation: The strategy achieves higher excess returns and better risk control compared to the 300ETF benchmark[25] 3. Model Name: Support Vector Machine (SVM) Timing Strategy - Model Construction Idea: The SVM model, a supervised machine learning algorithm, is used to predict market trends for the CSI 300 Index. It utilizes 12 indicators as feature vectors to classify and forecast market movements[29][30] - Model Construction Process: 1. Select 12 indicators as feature vectors: turnover rate, ADTM, ATR, CCI, MACD, MTM, ROC, SOBV, STD26, STD5, margin trading volume as a percentage of A-share turnover, and previous week's return rate 2. Train the SVM model using historical data to classify market trends into "buy" or "sell" signals 3. Implement the strategy: - For "buy" signals: execute long positions in both single-direction and dual-direction strategies - For "sell" signals: single-direction strategy exits positions, while dual-direction strategy takes short positions[30] - Model Evaluation: The SVM model demonstrates effectiveness in market timing, achieving significant excess returns and lower drawdowns compared to the CSI 300 Index[31][32] --- Model Backtesting Results 1. 300ETF Intelligent Investment Strategy - Final Net Value: 0.96 - Annualized Return: -1.00% - Annualized Volatility: 16.78% - Maximum Drawdown: 22.39% - Sharpe Ratio: -0.24[24] 2. 300ETF Intelligent Investment and Covered Call Strategy - Final Net Value: 0.96 - Annualized Return: -1.12% - Annualized Volatility: 17.22% - Maximum Drawdown: 19.61% - Sharpe Ratio: -0.25[27] 3. SVM Timing Strategy (2020-2024) - Single-Direction Strategy: - Final Net Value: 1.12 - Annualized Return: 2.6% - Annualized Volatility: 27.36% - Maximum Drawdown: 30.06% - Sharpe Ratio: -0.02[34] - Dual-Direction Strategy: - Final Net Value: 1.50 - Annualized Return: 9.2% - Annualized Volatility: 38.96% - Maximum Drawdown: 27.92% - Sharpe Ratio: 0.16[34] 4. SVM Timing Strategy (2024 YTD) - Single-Direction Strategy: - Final Net Value: 1.02 - Annualized Return: 3.0% - Annualized Volatility: 25.07% - Maximum Drawdown: 5% - Sharpe Ratio: 0.00[37] - Dual-Direction Strategy: - Final Net Value: 1.05 - Annualized Return: 8.6% - Annualized Volatility: 33.18% - Maximum Drawdown: 10% - Sharpe Ratio: 0.17[37]