Report Industry Investment Rating No relevant information provided. Core Viewpoints of the Report - T0 strategies, with low risk exposure and high return - drawdown ratios, are attracting more attention. Stock T0 strategies have an annualized return of 5% - 20% and a drawdown of around 1%, making them popular as alternative absolute - return strategies. Futures T0 strategies are more advantageous carriers, offering high liquidity, low costs, and leverage, and having lower slippage compared to commodity futures [1]. - Combining deep - learning - based medium - low - frequency momentum/reversal strategies is a viable approach for futures T0 strategies. The K - Shape algorithm is used to classify intraday trends into three types: upward, downward, and sideways. An MLP + GRU neural network is used to predict these trends, with a validation set win - rate increasing from 33% to 40%. By integrating these predictions with an intraday CTA base strategy, the strategy can achieve a post - fee annualized return of 11.19% and a drawdown of 3.62%, and over 30% annualized return on the IM contract [2][3][4]. Summary According to the Table of Contents 1. Analysis of the Characteristics of Futures T0 Strategies 1.1 From Stock T0 to Futures T0 Strategies - **Stock T0 Strategies**: T0 strategies are less affected by index trends and macro - economic conditions, and more related to turnover and intraday amplitude. They can be divided into manual and programmed T0. Their annualized returns range from 5% to 20%, with small drawdowns, and are suitable for small - scale funds or large - position strategies. There are already mature third - party algorithm providers collaborating with brokerages [9][12][13]. - **Advantages of Futures T0 Strategies**: Futures offer a T + 0 trading mechanism, high liquidity, sufficient amplitude, low trading costs, and leverage. They also have lower slippage compared to commodity futures, providing a better platform for T0 strategies [16][18]. - **Significance for Multi - asset and Multi - strategy Allocation**: Futures T0 strategies can diversify asset allocation, provide free leverage and short - side returns, and improve the performance of traditional asset portfolios. For example, adding a CTA - like strategy to a basic asset pool can increase the annualized return of a risk - parity strategy from 5.50% to 6.67% and reduce the maximum drawdown from 6.71% to 3.74% [19][21]. 1.2 Exploration of Futures T0 Strategy Paradigms - **Differences from Traditional Strategies**: T0 strategies have time - limited opening and closing positions, a narrowing decision - making space, and are highly susceptible to high - frequency information flows, requiring strict trading discipline [23]. - **Specific Implementation Logics**: - **Micro - structure Strategies Based on Order Books**: Analyze high - frequency data such as order book volume and price distribution to predict short - term price trends, with high trading frequencies [25]. - **Momentum/Reversal and Statistical Arbitrage Strategies**: Based on financial time - series statistical laws, with medium - low trading frequencies. Momentum strategies follow trends, while reversal strategies capture corrective rebounds [26]. - **Combination of Machine/Deep Learning with Traditional Paradigms**: Machine and deep learning can automatically learn complex non - linear patterns from large - scale, high - dimensional, and noisy data, and are used in the above two types of strategies [27]. 2. T0 Framework Based on Intraday Trend Pattern Prediction 2.1 Review of Time - Series Clustering Algorithms - **DTW + K - Means**: DTW can measure the similarity between time series, overcoming translation, scaling, and periodic invariance. Combined with K - Means, it can cluster intraday index trends, but is affected by outliers and has high computational complexity [33][39][40]. - **K - Shape**: A time - series clustering algorithm using shape - based distance (SBD) to measure similarity, with translation and scaling invariance. It has better computational efficiency and cluster - center representation, and is used for subsequent analysis [41]. 2.2 Clustering Performance of the K - Shape Algorithm on Stock Index Spot - The K - Shape algorithm is used to cluster the intraday trends of the Shanghai 50, CSI 300, CSI 500, and CSI 1000. Initially, 20 - category clustering is performed, and then reduced to 8 categories. The cluster centers are explicitly initialized, and the final three - category classification (upward, downward, and sideways) is used for subsequent prediction [48][51][53]. 2.3 Prediction of Trend Pattern Labels Based on Deep Learning - For medium - low - frequency T0 strategies, predicting trend types is more meaningful than predicting returns. An MLP neural network with a Softmax output layer is used, integrating cross - sectional and time - series price - volume features. The validation set win - rate can increase from 12.5% to 20.35%, and for the three - category classification, it can increase from 33% to 40%. The model is retrained quarterly to ensure stable performance [57][58][65]. 2.4 T0 Baseline Strategy: Intraday ATR Breakout - The intraday ATR breakout strategy is a trend - following strategy that uses the previous day's ATR to set trading intervals, with opening, stop - profit, and stop - loss thresholds. It is sensitive to trading fees. Under a unilateral fee rate of 0.0025%, the CSI 300, CSI 500, and CSI 1000 can achieve positive long - term returns [72][75][80]. 2.5 Futures T0 Strategy Based on Trend Pattern Prediction - By predicting intraday trends, the application and parameters of the base strategy can be adjusted. For example, on four equal - weighted contracts from January 2023 to June 2025, the annualized return can increase from 6.65% to 11.19%, and the maximum drawdown can be reduced from 7.45% to 3.62% [84][86][87]. 2.6 Summary and Outlook - Futures T0 strategies are more advantageous than stock T0 strategies, and an intraday trend pattern prediction + intraday CTA framework is used to construct the strategy. Future research can focus on improving trend prediction by adding more information and developing reversal CTA strategies [92][93][96].