【现货】 主流矿粉现货价格：日照港（600017）PB粉-3至732元/吨，巴混粉-3至754元/吨。 【需求】 日均铁水产量241.91万吨，环比-1.69万吨；高炉开工率83.87%，环比+0.18%；高炉炼铁产能利用率 90.69%，环比-0.64个百分点；钢厂盈利率58.87%，环比-0.87个百分点。 【供给】 本周全球发运环比小幅回升。全球发运+242.3万吨至3431万吨。澳洲巴西铁矿发运总量2868.8万吨，环 比增加78.8万吨。澳洲发运量1920.5万吨，环比减少92.7万吨，其中澳洲发往中国的量1499.8万吨，环比 减少281.4万吨。巴西发运量948.3万吨，环比增加171.5万吨。45港口到港量2536.5万吨，环比增加385.2 万吨。 【库存】 截至5月29日，45港库存13866.58万吨，环比-121.25万吨；日均疏港量高位回落，港口库存延续降势。 钢厂进口矿库存环比-171.15至8754.33万吨，钢厂日耗小幅下降，库存环比下滑，减量集中在大型钢 厂。 【期货】 截止昨日收盘，铁矿主力合约-0.93%（-6.5），收于695.5元/吨。 【基差】 最优交割品为巴混。P ...

Core Viewpoint - Multinational companies, particularly Vale, are focusing on sustainable development and adapting their strategies to the Chinese market to create positive social, economic, and environmental impacts [1][4]. Group 1: Sustainable Development Strategy - Vale integrates sustainability into its business practices, aligning its ESG goals with the United Nations' 2030 Sustainable Development Goals (SDGs) [4][5]. - The company has set specific targets to reduce its carbon emissions, including a 33% reduction in absolute emissions by 2030 and achieving net-zero emissions by 2050 for Scope 1 and Scope 2 emissions [6][20]. - Vale is the first mining company to establish quantified reduction targets for Scope 3 emissions, which account for approximately 98% of its total emissions, primarily from steel production [6][10]. Group 2: Technological Innovations - Vale is deploying AI technologies to optimize resource management, carbon reduction, and supply chain efficiency, including projects like "smart mining" to reduce truck fuel consumption and greenhouse gas emissions [2][14]. - The company has invested in AI since 2016, deploying around 40 AI products and 1500 models across 80 projects to enhance operational efficiency and safety [14][15]. Group 3: Future Plans in China - China is Vale's largest market, with 187 million tons of iron ore exported in the previous year, accounting for over 60% of its total iron ore sales [17][19]. - Vale plans to invest 70 billion Brazilian Reais in the "New Carajás" project over the next five years to increase iron ore and copper production [17]. - The company is collaborating with Chinese partners to build a mineral processing plant in Oman, expected to process 18 million tons of iron ore annually by mid-2027 [19].

2024年，淡水河谷铁矿石实际产量为32767万吨，同比增加2%，超出年初设定的产量指导区间（31000万~32000万吨），处于最新上调后的产量指导区间 （32300万~33000万吨）。2025年，淡水河谷设定的产量指导区间为32500~33500万吨，较2024年略有提升。2024年，淡水河谷铁矿石项目的资本支出为 39.43亿美元，同比增加5%，显示其在扩产方面处于持续投入状态。 产能激增 考虑到淡水河谷与力拓采用国际通行的自然年度为财年周期（每年1月1日至12月31日），而必和必拓及FMG采用的是澳大利亚财年（7月1日至次年6月30 日），本文对四大矿山的产量数据及产能规划按财年口径分别进行统计与对比。2024年，淡水河谷与力拓这两大矿山实际产量合计为65565万吨；2025年， 其指导产量区间中值为66050万吨，仅同比增加485万吨。然而，基于两家公司季报披露的数据与项目建设进度进行估算，可以发现，2025年，其计划新增产 能合计约达4005万吨，新增产能远超指导产量的增幅。相比之下，必和必拓与FMG在2024财年（2023年7月至2024年6月）合计产量为45376万吨。2025财 年，两公司 ...

| 钢材产业期现日报 | 投资咨询业务资格:证监许可 [2011] 1292号 | | | | | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | 問敏波 | Z0010559 | 2025年6月3日 | | | | | | | | | | | 钢材价格及价差 | 品种 | 某美 | 现值 | 涨跌 | 单位 | 即值 | | | | | | | 螺纹钢现货(华东) | 3120 | 3120 | O | 153 | 螺纹钢现货(华北) | 3160 | 3160 | O | 193 | | | | 螺纹钢现货(华南) | 3230 | 3250 | -20 | 263 | 螺纹钢05合约 | 2973 | 3003 | 147 | -30 | | | | 螺纹钢10合约 | 2961 | 2978 | -17 | ।ਟੋਰੇ | 螺纹钢01合约 | 153 | 2967 | 2982 | -18 | | | | 元/吨 | 热卷现货(华东) | 3170 | 3200 | 88 | ...

本报告中的信息均来源于被广发期货有限公司认为可你的已公开资料，但广发明发对这些信息的准确性及完整性不作任何保证。本报告反映研究人员的不同观点、见解及 分析方法，并不代表广发期货或其附属机构的立场。在任何情况下，报告内容仅供�考，报告中的信息或所靠达的意见并不构成所述品种买实的出价或向价，投资者偶此 投资，风险自担。本报告旨在发送给广发期觉特定客户及其他专业人士，版权归厂发期货所有，未经广发期资书面授权，任何人不得对本报告进行任何形式的发布、复制 。如引用、刊发,需注明出处为"广发期货"。 知识图强,求实奉献,客户至上,合作共赢 | 钢材产业期现日报 | 投资咨询业务资格:证监许可 [2011] 1292号 | | | | | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | 問敏波 | Z0010559 | 2025年6月3日 | | | | | | | | | | | 钢材价格及价差 | 品种 | 某美 | 现值 | 涨跌 | 单位 | 即值 | | | | | | | 螺纹钢现货( ...

投资咨询系列报告 山金期货黑色板块日报 更新时间：2025年06月03日08时25分 一、螺纹、热卷 报告导读： 目前政策面利多基本兑现，中美贸易紧张局势缓解也体现在价格中 。房地产方面，核心城市有所企稳，低线城市房地产市场仍在筑底过程中 ，新开 工面积仍大幅回落，竣工面积、施工面积同比也依然维持较大的跌幅 。上周我的钢铁公布的数据显示，产量有所回落，厂库下降，社库继续回落， 总库存下降，表观需求环比微幅上升，数据对期价有所提振。从需求的季节性规律看，表观需求高峰期已过，随着雨季和高温天气的到来，需求将 进一步走弱。限产传闻对市场提振作用有限。钢企普遍认为，行业确实需要减产，但近期钢企亏损状况有所改善，企业主动减产的动力偏弱。整体 来看，目前市场逐渐由强现实向弱现实转变 ，弱预期可能还没有发生实质性的改变 。从技术上看，价格跌破了近期的震荡区间，形成向下的有效突 破，后市有望延续下行趋势，短线的反弹不影响趋势的延续。 操作建议： 空单持有 表1：螺纹、热卷相关数据 | 数据类别 | 指标 | 单位 | 最新 | | 较上日 | | 较上周 | | --- | --- | --- | --- | --- | - ...

商 品 研 究 研 究 所 晨 投资咨询业务资格： 证监许可[2011]1771号 分[析Ta师ble_Report] 会 观 点 精 萃 从业资格证号：F0256916 投资咨询证号：Z0000671 电话：021-68756925 邮箱：jialj@qh168.com.cn 从业资格证号：F03092124 投资咨询证号：Z0018827 电话：021-68758786 邮箱：mingdy@qh168.com.cn 从业资格证号：F3033924 投资咨询证号：Z0013026 电话：021-68751490 邮箱：Liuhf@qh168.com.cn 从业资格证号：F03091165 投资咨询证号：Z0019876 联系电话：021-58731316 邮箱：liub@qh168.com.cn 从业资格证号：F03089928 投资咨询证号：Z0019740 电话：021-68757092 邮箱：wangyil@qh168.com.cn 从业资格证号：F3077183 投资咨询证号：Z0016121 电话：021-68757092 邮箱：fengb@qh168.com.cn 2025年6月3日 研究所晨会 ...

印度与中国在全球铁矿石市场的角色转换，印度铁矿石需求飙升，铁矿石市场迎来新变局？ 相关链接 期货热点追踪 ...

0% 5% 10% 15% 20% 1/1 2/1 3/1 4/1 5/1 6/1 7/1 8/1 9/1 10/1 11/1 12/1 2021 2022 2023 2024 2025 (200) (150) (100) (50) 0 50 100 1/1 2/1 3/1 4/1 5/1 6/1 7/1 8/1 9/1 10/1 11/1 12/1 - 2022 2023 2024 2025 / 50 80 110 140 170 200 230 1/1 2/1 3/1 4/1 5/1 6/1 7/1 8/1 9/110/111/112/1 -PB 2022 2023 2024 2025 / 0 20 40 60 80 100 120 140 1/1 2/1 3/1 4/1 5/1 6/1 7/1 8/1 9/1 10/1 11/1 12/1 -PB 2022 2023 2024 2025 / 20 40 60 80 100 120 140 1/1 2/1 3/1 4/1 5/1 6/1 7/1 8/1 9/1 10/1 11/1 12/1 PB - 2022 2023 2024 2025 / 50 100 ...

Quantitative Models and Construction Methods 1. Model Name: Commodity Term Structure Simulation Portfolio - **Model Construction Idea**: This is a long-short strategy that dynamically holds long positions in commodities with high roll yields and short positions in commodities with low roll yields. The strategy aims to capture the term structure premium in commodity markets while reducing dependency on single market trends[33][35][34]. - **Model Construction Process**: 1. **Roll Yield Factor**: The roll yield is calculated to measure the contango or backwardation state of a commodity. 2. **Dynamic Positioning**: Commodities with high roll yields are dynamically allocated long positions, while those with low roll yields are allocated short positions. 3. **Portfolio Balancing**: The portfolio is rebalanced periodically to maintain the desired exposure to the roll yield factor[35][38]. - **Model Evaluation**: The strategy demonstrates flexibility in adapting to market risks and provides stable returns even in weak market trends[34]. 2. Model Name: Commodity Time-Series Momentum Simulation Portfolio - **Model Construction Idea**: This strategy captures medium- to long-term trends in commodity prices using multiple technical indicators. It dynamically allocates long positions to upward-trending assets and short positions to downward-trending assets[33][35]. - **Model Construction Process**: 1. **Trend Indicators**: Technical indicators such as moving averages and momentum are used to identify price trends. 2. **Dynamic Positioning**: Commodities with upward trends are allocated long positions, while those with downward trends are allocated short positions. 3. **Portfolio Rebalancing**: Positions are adjusted periodically based on updated trend signals[35][45]. - **Model Evaluation**: The strategy effectively tracks price trends but may underperform in volatile or trendless markets[45]. 3. Model Name: Commodity Cross-Sectional Inventory Simulation Portfolio - **Model Construction Idea**: This strategy uses inventory data to capture fundamental changes in commodity markets. Commodities with declining inventories are allocated long positions, while those with increasing inventories are allocated short positions[33][35]. - **Model Construction Process**: 1. **Inventory Factor**: Changes in inventory levels are calculated to assess supply-demand dynamics. 2. **Dynamic Positioning**: Commodities with declining inventories are dynamically allocated long positions, while those with increasing inventories are allocated short positions. 3. **Portfolio Rebalancing**: Positions are adjusted periodically based on updated inventory data[35][49]. - **Model Evaluation**: The strategy is effective in capturing fundamental supply-demand imbalances but may be sensitive to data accuracy and reporting delays[49]. --- Model Backtesting Results 1. Commodity Term Structure Simulation Portfolio - **Annualized Return**: 3.03% (YTD 2025)[33][38] - **Annualized Volatility**: Not explicitly mentioned - **Maximum Drawdown**: Not explicitly mentioned - **Sharpe Ratio**: Not explicitly mentioned - **Calmar Ratio**: Not explicitly mentioned 2. Commodity Time-Series Momentum Simulation Portfolio - **Annualized Return**: -1.33% (YTD 2025)[45] - **Annualized Volatility**: Not explicitly mentioned - **Maximum Drawdown**: Not explicitly mentioned - **Sharpe Ratio**: Not explicitly mentioned - **Calmar Ratio**: Not explicitly mentioned 3. Commodity Cross-Sectional Inventory Simulation Portfolio - **Annualized Return**: 2.88% (YTD 2025)[49] - **Annualized Volatility**: Not explicitly mentioned - **Maximum Drawdown**: Not explicitly mentioned - **Sharpe Ratio**: Not explicitly mentioned - **Calmar Ratio**: Not explicitly mentioned --- Quantitative Factors and Construction Methods 1. Factor Name: Roll Yield Factor - **Factor Construction Idea**: Measures the contango or backwardation state of a commodity to capture the term structure premium[35]. - **Factor Construction Process**: 1. Calculate the roll yield as the difference between the spot price and the futures price. 2. Normalize the roll yield across commodities to ensure comparability. 3. Rank commodities based on their roll yields and allocate positions accordingly[35]. 2. Factor Name: Trend Factor - **Factor Construction Idea**: Captures medium- to long-term price trends using technical indicators[35]. - **Factor Construction Process**: 1. Use moving averages, momentum, and other technical indicators to identify trends. 2. Normalize trend signals across commodities to ensure comparability. 3. Rank commodities based on their trend strength and allocate positions accordingly[35]. 3. Factor Name: Inventory Factor - **Factor Construction Idea**: Measures changes in inventory levels to capture supply-demand imbalances[35]. - **Factor Construction Process**: 1. Calculate the percentage change in inventory levels over a specified period. 2. Normalize inventory changes across commodities to ensure comparability. 3. Rank commodities based on their inventory changes and allocate positions accordingly[35]. --- Factor Backtesting Results 1. Roll Yield Factor - **Annualized Return**: Not explicitly mentioned - **Annualized Volatility**: Not explicitly mentioned - **Maximum Drawdown**: Not explicitly mentioned - **Sharpe Ratio**: Not explicitly mentioned - **Calmar Ratio**: Not explicitly mentioned 2. Trend Factor - **Annualized Return**: Not explicitly mentioned - **Annualized Volatility**: Not explicitly mentioned - **Maximum Drawdown**: Not explicitly mentioned - **Sharpe Ratio**: Not explicitly mentioned - **Calmar Ratio**: Not explicitly mentioned 3. Inventory Factor - **Annualized Return**: Not explicitly mentioned - **Annualized Volatility**: Not explicitly mentioned - **Maximum Drawdown**: Not explicitly mentioned - **Sharpe Ratio**: Not explicitly mentioned - **Calmar Ratio**: Not explicitly mentioned