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]