深度学习研究报告之四：趋势策略的深度学习增强

Quantitative Models and Construction Methods 1. Model Name: Recurrent Neural Network (RNN) for Trend Strategy Enhancement - Model Construction Idea: Use RNN to predict whether a trend-following strategy will be profitable on a given trading day based on early morning market data. If the model predicts profitability, execute the trend-following strategy; otherwise, refrain from trading[3][22][61] - Model Construction Process: - Input: Early morning market data (e.g., opening price, closing price, high, low, volume, etc.) - Output: Binary classification (1 for profitable trend trading, 0 for non-profitable trend trading) - Model Structure: 13 input features → 200 LSTM units → 1 output node with Sigmoid activation function - Loss Function: Cross-entropy loss - Optimization: Gradient descent with backpropagation through time (BPTT) to minimize the loss function[46][61][66] - Training Data: Historical intraday data from 2010 to 2013, labeled based on profitability of trend strategies using metrics like Hurst index or K-line body ratio (R)[61][63] - Trading Rules: - Predict profitability at 33 minutes after market open - If predicted probability (p) > 120-day moving average of p (MA120(p)), execute trend-following trades; otherwise, no trades - Positions are closed at the end of the day or upon hitting stop-loss levels[63][66] - Model Evaluation: The RNN effectively filters out low-profitability signals, improving overall strategy performance. It demonstrates robustness to transaction costs and parameter stability[73][74][85] 2. Factor Name: Component Stock Consistency Indicator (R) - Factor Construction Idea: Measure the consistency of component stock movements to estimate market trend strength. Higher consistency indicates a stronger trend, suitable for trend-following strategies[14][21] - Factor Construction Process: - Formula: $R = \frac{\lambda_1}{\sum_{i=1}^{300} \lambda_i} \times 100\%$ where $\lambda_1, \lambda_2, ..., \lambda_{300}$ are eigenvalues of the covariance matrix of component stock returns - Interpretation: $R$ represents the variance contribution of the first principal component. Higher $R$ values indicate stronger consistency among component stocks[14] - Factor Evaluation: The indicator effectively identifies market conditions suitable for trend-following strategies, as demonstrated by its ability to differentiate between high-consistency and low-consistency markets[14][21] --- Model Backtesting Results 1. RNN Model - Annualized Return: 18.47% (out-of-sample)[72] - Cumulative Return: 80.72% (out-of-sample)[72] - Maximum Drawdown: -8.63% (out-of-sample)[72] - Win Rate: 39.52% (out-of-sample)[72] - Profit-Loss Ratio: 2.27 (out-of-sample)[72] - Average Return per Trade: 0.17% (out-of-sample)[72] 2. Component Stock Consistency Indicator (R) - High Consistency Example: $R = 86.4$, suitable for trend trading[16] - Low Consistency Example: $R = 45.2$, unsuitable for trend trading[19] --- Factor Backtesting Results 1. Component Stock Consistency Indicator (R) - High $R$ Market: Demonstrates strong trend-following profitability[16] - Low $R$ Market: Demonstrates poor trend-following profitability[19]