Market Overview - The A-share market is experiencing a state of stagnation, with the Shanghai Composite Index showing minimal fluctuations, resembling a crowded subway during peak hours [1] - There is a significant divergence in market performance, with some sectors surging while others remain flat, indicating a lack of overall market momentum [1][3] Market Sentiment - The market is characterized by a cautious sentiment, with both retail and institutional investors waiting for clearer signals before making moves [3][5] - The current market environment is described as a "pause" where neither buyers nor sellers are willing to take decisive action [1][4] Investment Strategy - Investors are advised to maintain a balanced approach, holding both stocks and cash, while closely monitoring key signals such as volume near trend lines and external market changes [6] - The focus should be on sectors that are showing signs of recovery, particularly in technology and semiconductor industries, while avoiding overexposure to volatile stocks [4][6] External Influences - The market remains sensitive to external factors, particularly the actions of the Federal Reserve and fluctuations in the currency and commodity markets [4][6] - Recent inflows from foreign capital have been noted, but the overall investment climate remains cautious as investors prepare for potential volatility [3][4]

Group 1: Report Industry Investment Rating - No information provided in the content Group 2: Core Viewpoints of the Report - Optimizing the position strategy can effectively enhance the real - world performance of the quantitative framework, which is a multiplier method for increasing returns, especially in volatile markets [2][6][111] - Binary full - position strategies can capture returns efficiently in obvious trends but come with high volatility, drawdown risks, and high turnover and commission costs; threshold step - by - step addition strategies have low trading frequency but limited ability to capture returns in volatile markets (except for the LG model) [2][111] - Single continuous strategies perform well in volatile markets. Linear and normal strategies show high return stability, while Sigmoid, Atanh, and Atanh - Sigmoid strategies have significant advantages in volatility control, suitable for risk - averse investors. The GRU model shows stable performance in improving odds, while the strategy advantages of LG, SVM and other models are environment - dependent [2][111] - In terms of turnover and commission consumption, single continuous strategies such as Sigmoid and Atanh can reduce turnover and commission consumption in volatile markets, and investors should focus more on returns rather than commission costs [2][111] Group 3: Summary According to the Table of Contents 3.1 Multi - factor Model's Position Strategy Introduction - **Multi - long and short full - position strategy**: It is a binary extreme position management mode, which can be used as a performance benchmark and a reference for other strategies. It performs poorly in bull markets and better in volatile markets, and is more suitable for non - linear models in volatile markets [12][32][33] - **Threshold multi - long and short full - position strategy and step - by - step addition strategy**: The threshold full - position strategy introduces a fuzzy interval filtering mechanism to reduce misjudgment risks and improve the overall risk - return ratio. The step - by - step addition strategy can reduce turnover and trading costs but may sacrifice some returns, except for the LG model in volatile markets [13][14][53] - **Continuous strategies based on different risk preferences and mapping functions**: Continuous strategies can convert binary probability signals into position adjustment signals, which can be divided into risk - seeking, risk - averse, and risk - neutral types according to risk preferences. Different mapping functions such as linear, Sigmoid, normal, Atanh, and Atanh - Sigmoid are used [18] 3.2 Strategy Back - testing - **Back - testing sample interval and key parameters**: The trading target is the Treasury bond futures T contract. The period from January 1, 2024, to December 31, 2024, is regarded as a bull market, and the period from January 1, 2025, to May 9, 2025, is regarded as a volatile market [31] - **Benchmark results of multi - long and short full - position strategy**: It has little effect on increasing returns in bull markets and performs better in volatile markets. Non - linear models such as RF and SVM can better handle the problem of return increase in volatile markets [33][34] - **Threshold full - position strategy and step - by - step position adjustment strategy**: The threshold strategy can optimize the odds of investment strategies in both bull and volatile markets, but the application effect depends on the model type and market environment. The step - by - step position adjustment strategy can significantly reduce turnover and trading costs but usually sacrifices some returns, except for the LG model in volatile markets [37][40][53] - **Analysis of the effect of single continuous strategies**: In volatile markets, continuous position strategies can significantly improve the odds of strategies without increasing the prediction win rate. Different strategies such as Atanh and Sigmoid have different risk - return characteristics, and their turnover is related to the model and market environment [58][73][74] - **Rediscussion of the impact of trading commissions**: The key is to increase returns rather than reduce costs. Although different models and strategies have different commission consumption, the impact of commissions on returns is relatively small, and investors should focus on return enhancement [94][97][110] 3.3 Summary and Strategy Recommendations - Different position management strategies play an important role in return acquisition and risk control. Investors should choose appropriate models and strategies according to their risk preferences and market conditions [111]

Core Viewpoint - The article emphasizes the importance of optimizing position strategies in quantitative frameworks for predicting bond futures, rather than solely focusing on the prediction accuracy of price movements [1][3]. Group 1: Position Strategy Optimization - The study tests various position strategies, including a full position strategy as a benchmark, a threshold-based full position strategy, and a gradual accumulation strategy that incorporates a fuzzy interval filtering mechanism [1][3]. - Continuous trading strategies convert binary probability signals into position adjustment signals, allowing for categorization based on risk preferences, such as risk-seeking, risk-averse, and risk-neutral types [1][3]. Group 2: Model and Market Conditions - The report references a multi-factor model for bond market timing, utilizing recent data to train models for predicting the next trading day, with specific market conditions defined for 2024 and 2025 [2]. - The combination of various position strategies is crucial, particularly in volatile markets, where appropriate strategy selection can significantly enhance overall model performance [3]. Group 3: Performance Insights - Binary full position strategies effectively capture returns during clear trends but come with higher volatility and transaction costs [3]. - Gradual accumulation strategies show lower trading frequency advantages, reducing transaction costs, but may have limited return capture in sideways markets [3]. - Single continuous strategies demonstrate strong performance in volatile markets, with specific strategies like Sigmoid and Atanh showing significant advantages in volatility control, especially for risk-averse investors [3].

Group 1 - The report emphasizes the importance of optimizing position strategies to enhance the performance of quantitative frameworks in the bond market [1][4][12] - It highlights that the choice of position strategy can significantly impact the overall model's performance, especially in volatile market conditions [4][19][50] - The report discusses various position strategies, including full long/short strategies, threshold-based strategies, and gradual accumulation strategies, each with distinct advantages and disadvantages [20][24][25][26] Group 2 - The report presents a detailed analysis of the backtesting results for different strategies, indicating that the full long/short strategy performs well in trending markets but may incur high transaction costs [47][50][51] - It notes that threshold strategies can filter out low-confidence signals, improving the risk-reward ratio in both bull and volatile markets [55][56] - Gradual adjustment strategies are shown to reduce turnover and trading costs, although they may sacrifice some potential returns, particularly in volatile markets [57][58] Group 3 - The report categorizes continuous strategies based on risk preferences, utilizing different mapping functions to adjust positions according to the strength of the signals [32][34][39] - It discusses the effectiveness of various mapping functions, such as linear, Sigmoid, normal, Atanh, and Atanh-Sigmoid strategies, in managing positions based on market signals [33][36][38][39] - The analysis indicates that non-linear models, particularly in volatile markets, can enhance performance and manage risks more effectively than linear models [51][52]