策略参考 投资咨询业务资格：证监许可【2011】1778 号 宝城期货豆类油脂早报（2025 年 10 月 16 日） 品种：豆粕（M） 日内观点：震荡偏弱 中期观点：震荡 参考观点：震荡偏弱 核心逻辑：中美贸易关系前景再起波澜，关税加码与谈判不确定性交织，叠加国内市场近月高库存、 远月缺口预期的矛盾，内豆类期货 2601 合约期价支撑有所减弱，豆类市场情绪反复拉扯，豆粕期价 短期震荡偏弱运行。 品种观点参考 备注： 1.有夜盘的品种以夜盘收盘价为起始价格，无夜盘的品种以昨日收盘价为起始价格，当日日盘收盘 价为终点价格，计算涨跌幅度。 2.跌幅大于 1%为偏弱，跌幅 0~1%为震荡偏弱，涨幅 0~1%为震荡偏强，涨幅大于 1%为偏强。 3.震荡偏强/偏弱只针对日内观点，短期和中期不做区分。 ◼ 主要品种价格行情驱动逻辑—商品期货农产品板块 专业研究·创造价值 1 / 3 请务必阅读文末免责条款 时间周期说明：短期为一周以内、中期为两周至一月(以前一日夜盘收盘价为基准) 品种 短期 中期 日内 观点参考 核心逻辑概要 <点击目录链接，直达品种 策略解析> 豆粕 2601 震荡 震荡 震荡 偏弱 震荡偏弱 中美关 ...

策略参考 投资咨询业务资格：证监许可【2011】1778 号 宝城期货豆类油脂早报（2025 年 10 月 14 日） 品种观点参考 2.跌幅大于 1%为偏弱，跌幅 0~1%为震荡偏弱，涨幅 0~1%为震荡偏强，涨幅大于 1%为偏强。 3.震荡偏强/偏弱只针对日内观点，短期和中期不做区分。 ◼ 主要品种价格行情驱动逻辑—商品期货农产品板块 品种：豆粕（M） 日内观点：震荡偏强 中期观点：震荡 参考观点：震荡偏强 核心逻辑：受到中美贸易摩擦升级的影响，豆类期价内强外弱。国内大豆市场虽然在阿根廷大豆出口 免税期增加了部分阿根廷大豆采购，但 12-1 月船期的尚有采购缺口未完成，远期大豆供应收紧预期 升温，继续从原料端支撑期货 2601 合约期价，短期维持震荡偏强运行。 备注： 1.有夜盘的品种以夜盘收盘价为起始价格，无夜盘的品种以昨日收盘价为起始价格，当日日盘收盘 价为终点价格，计算涨跌幅度。 专业研究·创造价值 1 / 3 请务必阅读文末免责条款 时间周期说明：短期为一周以内、中期为两周至一月(以前一日夜盘收盘价为基准) 品种 短期 中期 日内 观点参考 核心逻辑概要 <点击目录链接，直达品种 策略解析> 豆粕 ...

Quantitative Models and Construction Methods Model 1: Commodity Term Structure Model - **Construction Idea**: This model captures the state of commodity contango and backwardation using the roll yield factor, dynamically going long on commodities with high roll yields and short on those with low roll yields[23][24] - **Construction Process**: - Identify the roll yield for each commodity - Rank commodities based on their roll yields - Go long on commodities with the highest roll yields and short on those with the lowest roll yields - **Evaluation**: The model has shown good performance recently, particularly in the industrial metals and agricultural products sectors[23][24] Model 2: Commodity Time Series Momentum Model - **Construction Idea**: This model captures medium to long-term trends in domestic commodities using multiple technical indicators, dynamically going long on assets with upward trends and short on those with downward trends[23][24] - **Construction Process**: - Use technical indicators to identify trends in commodity prices - Rank commodities based on their trend strength - Go long on commodities with the strongest upward trends and short on those with the strongest downward trends - **Evaluation**: The model has underperformed recently, with significant losses in the black and energy chemical sectors[33][35] Model 3: Commodity Cross-Sectional Inventory Model - **Construction Idea**: This model captures changes in the domestic commodity fundamentals using the inventory factor, dynamically going long on assets with decreasing inventories and short on those with increasing inventories[23][24] - **Construction Process**: - Identify inventory levels for each commodity - Rank commodities based on their inventory changes - Go long on commodities with the largest inventory decreases and short on those with the largest inventory increases - **Evaluation**: The model has shown mixed performance, with significant losses in the agricultural products sector[39][41] Model Backtesting Results Commodity Term Structure Model - **Recent Two-Week Return**: 1.69%[26] - **Year-to-Date Return**: 3.09%[28] - **Top Contributors**: Glass (1.27%), PVC (0.32%), Rubber (0.31%)[30] - **Top Detractors**: Sugar (-0.16%), PTA (-0.24%), Methanol (-0.25%)[30] Commodity Time Series Momentum Model - **Recent Two-Week Return**: -1.22%[26] - **Year-to-Date Return**: -3.17%[33] - **Top Contributors**: Soybean Oil (0.26%), LPG (0.16%), Soybean Meal (0.07%)[37] - **Top Detractors**: Rebar (-0.28%), Soda Ash (-0.30%), Cotton (-0.33%)[37] Commodity Cross-Sectional Inventory Model - **Recent Two-Week Return**: -0.56%[26] - **Year-to-Date Return**: 3.42%[39] - **Top Contributors**: Corn (0.54%), Polypropylene (0.27%), Nickel (0.22%)[43] - **Top Detractors**: PVC (-0.26%), Cotton (-0.39%), Soybean Oil (-0.46%)[43]