-20% -10% 0% 10% 20% 30% 40% 50% Apr/24 May/24 Jun/24 Jul/24 Aug/24 Sep/24 Oct/24 Nov/24 Dec/24 Jan/25 Feb/25 Mar/25 航空航天与国防 沪深300 推荐公司及评级 相关研究报告 工业资本货物 军工行业周报(2025.04.07-2025.04.13)：中国交付全球最大"人造太阳"重要部 件 走势比较 2025-04-13 行业周报 看好/维持 航空航天与国防Ⅲ 【太平洋证券】国防军工 2025 年度 策略：聚焦新域新质，迎接景气拐点 本周要闻： 中国交付全球最大"人造太阳"重要部件 据新华社报道，4 月 11 日，全球最大"人造太阳"国际热核聚 变实验堆(ITER)计划磁体馈线采购包项目迎来关键节点，其最后一套 校正场线圈内馈线部件在合肥竣工，并交付起运位于法国的 ITER 现 场。这标志着 ITER 磁体馈线系统中所有超大部件的研制顺利完成。 ITER 磁体馈线系统由中国科学院合肥物质科学研究院等离子体物理 研究所研制，被称为 ITER 磁体系统的"生命线"，校正场线圈内馈线 则是这一系统中尺寸 ...

Investment Rating - The report maintains a positive outlook on the engineering machinery sector, expecting overall returns to exceed the CSI 300 index by more than 5% in the next six months [46]. Core Viewpoints - In March, domestic excavator sales exceeded expectations, with 19,517 units sold, representing a year-on-year increase of 28.5%. Export sales also grew by 2.87% to 10,073 units [10][11]. - The domestic market is anticipated to enter a new upward cycle, supported by continuous policy efforts, a gradual recovery in construction activity, and an ongoing equipment replacement cycle [10][11]. - The government plans to allocate 4.4 trillion yuan in special bonds for local governments, an increase of 500 billion yuan from the previous year, aimed at investment in construction and infrastructure [10]. - The average working hours for major engineering machinery products in March 2025 reached 90.1 hours, a year-on-year increase of 6.53% and a month-on-month increase of 94.3% [10]. - The report expects a strong recovery in domestic excavator sales throughout 2025, driven by both domestic and international demand [10][11]. Summary by Sections Industry Viewpoints and Investment Recommendations - The report emphasizes the strong performance of excavator sales in March and maintains a positive outlook on the engineering machinery sector [10][11]. Key Industry News - The bauma 2025 exhibition in Munich showcased the latest technologies and products in the engineering machinery sector, attracting significant global participation [11][12]. - The Ministry of Finance and the Ministry of Housing and Urban-Rural Development announced continued support for urban renewal actions, which will benefit the engineering machinery sector [13]. - Major engineering projects are ramping up, leading to increased demand for construction machinery, as highlighted by a report on SANY Heavy Industry's production capacity [14][15]. Key Company Announcements - Various companies in the sector reported significant revenue growth in their 2024 annual reports, with notable increases in both revenue and net profit [28][31][32].

2025 年 04 月 10 日 行业日报 中性/维持 钢铁 太平洋钢铁日报（20250410）2025 年湖南省重点建设项目涉及 3 个钢铁项目 ◼ 走势比较 (30%) (18%) (6%) 6% 18% 30% 24/4/10 24/6/21 24/9/1 24/11/12 25/1/23 25/4/5 ◼ 推荐公司及评级 报告摘要 市场行情： 行业数据： 期货情况：螺纹钢 2510（+2.01%）；线材 2505（+3.49%）；热卷 2510 （+2.01%）；铁矿石 2509（+3.06%）；焦炭 2505（+1.91%）；焦煤 2505（- 0.38%）。 产品涨跌幅：铁矿石（+2.62%）；线材（+2.54%）；热轧卷板（+1.44%）； 螺纹钢（+1.65%）；焦炭（+1.59%）；焦煤（+0.27%）。 钢材现价（元/吨）：铁矿石（717.51）；线材（3354.59）；热轧卷板 （3247.64）；螺纹钢（3112.99）；焦炭（1567.24）；焦煤（964.37）。 铁矿石普氏指数：65%粉（108）；58%粉（81.45）；62%粉（95.75）。 2025 年 4 月 10 日， ...

2025 年 04 月 08 日 大类资产跟踪 投资策略 策略日报（2025.04.08）：央行、汇金发声维稳，内需依旧是主线 相关研究报告 <<策略日报（2025.04.07）：恐慌加 剧，以内需为主>>--2025-04-07 <<估值与盈利周观察——4 月第 1 期：市场分化，红利上成长下>>-- 2025-04-07 <<流动性与仓位周观察——4 月第 1 期：杠杆资金加速流出>>--2025-04- 07 证券分析师：张冬冬 E-MAIL：zhangdd@tpyzq.com 分析师登记编号：S1190522040001 证券分析师：吴步升 E-MAIL：wubs@tpyzq.com 分析师登记编号：S1190524110002 债券市场：利率债普跌，长端跌幅大于短端。后续展望：关税超预期 下，外需难有较大期待时期，稳内需的必要性增强，预期时间点提前，降 准降息和流动性宽松可有更多期待。技术面上十年期国债突破 60 日均线 压力，后续目标指向新高。 股票市场：央行释放维稳信号，沪指反弹 1.58%，内需为主的农业、 消费领涨。今日市场个股涨多跌少，沪深京三市超 3200 股飘红，成交额 1.65 万亿 ...

Investment Rating - The report maintains a "Buy" rating for the company [1][7] Core Views - The company has shown continuous high growth in performance, with a focus on expanding its computing power services and robotics sector [1][6] - In 2024, the company achieved a revenue of 7.41 billion yuan, representing a year-on-year growth of 59.08%, and a net profit of 692 million yuan, up 140.80% year-on-year [4][8] - The company is actively expanding its computing power services and has obtained NVIDIA Cloud Partner certification, enhancing its capabilities in AI model services and cloud computing [6] Financial Performance - The company reported a significant increase in revenue across various business segments, with intelligent IoT business revenue reaching 2.26 billion yuan, up 60.82% year-on-year [5] - The overall gross margin improved by 3.81 percentage points to 17.36% in 2024, driven by high-margin businesses [5][10] - The company forecasts revenues of 10.95 billion yuan, 14.52 billion yuan, and 18.55 billion yuan for 2025, 2026, and 2027 respectively, with net profits projected at 1.09 billion yuan, 1.55 billion yuan, and 2.06 billion yuan [7][8] Business Expansion - The company is strategically focusing on the robotics sector, integrating its subsidiary to enhance its capabilities in developing products like robotic dogs and humanoid robots [6] - Collaborations with major enterprises in cloud computing and AI applications are underway, indicating a robust growth trajectory in these areas [6][8]

Quantitative Model and Construction - **Model Name**: Transportation Index Tracking Model - **Model Construction Idea**: The model assumes that the price movement of the target has strong local continuity, always following a certain trend. Reversal periods are shorter than trend continuation periods. In cases of narrow-range consolidation, the model assumes the continuation of the previous trend. When a major trend exists, given a short observation window, the movement will follow the local trend within the window. Reversals are identified when price changes at the start and end of the observation window exceed the range caused by random fluctuations, eliminating the impact of randomness[3] - **Model Construction Process**: 1. Calculate the difference between the closing price on day T and day T-20, denoted as `del` 2. Calculate the volatility (`Vol`) from day T-20 to day T (excluding day T) 3. If the absolute value of `del` exceeds N times `Vol`, the current price is considered to have exited the original oscillation range and formed a trend. The trend direction (long/short) corresponds to the sign of `del`. If the absolute value of `del` is less than or equal to N times `Vol`, the current movement is considered to continue the previous trend direction (same as day T-1) 4. For tracking, N is set to 1, considering the higher volatility and smaller wave opportunities in the stock market compared to the bond market 5. Combine the returns from both long and short directions to evaluate the model's overall performance[3] - **Model Evaluation**: The model is not suitable for direct application to the Transportation Index due to its inability to achieve significant cumulative returns during the tested period. Additionally, the model's drawdown is relatively large compared to its annualized return[4] Model Backtesting Results - **Annualized Return**: 2.27%[3] - **Annualized Volatility**: 16.87%[3] - **Sharpe Ratio**: 0.13[3] - **Maximum Drawdown**: 13.66%[3] - **Total Return of Index During Period**: -9.74%[3]

Quantitative Model and Construction - **Model Name**: Real Estate Index Trend Tracking Model [2] - **Model Construction Idea**: The model assumes that the price movement of the target has strong local continuity and is always in a certain trend. Reversal trends are shorter in duration compared to trend continuations. In cases of narrow-range consolidation, the model assumes the continuation of the previous trend. When in a large-scale trend, given a short observation window, the movement will follow the local trend within the window. If a reversal occurs, the price change at the start and end of the observation window will exceed the range caused by random fluctuations, thus eliminating the impact of randomness. The model also assumes the ability to perform both long and short operations for more rigorous evaluation of relative returns. [3] - **Model Construction Process**: 1. Calculate the difference `del` between the closing price on day T and the closing price on day T-20: $ del = P_T - P_{T-20} $ [3] 2. Calculate the volatility `Vol` during the period from T-20 to T (excluding T): $ Vol = \sqrt{\frac{1}{n-1} \sum_{i=1}^{n} (P_i - \bar{P})^2} $ [3] 3. If the absolute value of `del` exceeds N times `Vol`, it is considered that the current price has broken out of the original oscillation range and formed a trend. The trend direction (long or short) corresponds to the sign of `del`. Otherwise, the trend direction remains the same as on day T-1. [3] 4. For tracking, the parameter N is set to 1, considering the higher volatility and more frequent small-wave opportunities in the stock market compared to the bond market. [3] 5. The combined results of both long and short returns are used as the final evaluation basis. [3] - **Model Evaluation**: The model effectively identifies trends in the real estate index. It performs well even when the index's total return is negative, achieving high annualized returns without significant long-term drawdowns. [4] Model Backtest Results - **Annualized Return**: 26.03% [3] - **Annualized Volatility**: 30.70% [3] - **Sharpe Ratio**: 0.85 [3] - **Maximum Drawdown**: 18.18% [3] - **Total Return of the Index During the Period**: -27.38% [3]

- The report introduces an "Industry Crowdedness Monitoring Model" to track the crowdedness levels of Shenwan primary industry indices on a daily basis. The model identifies industries with high and low crowdedness levels, such as agriculture, banking, and environmental protection being highly crowded, while automotive and media are less crowded. The model also monitors significant daily changes in crowdedness levels, highlighting industries like automotive and home appliances with notable variations[6][11] - A "Premium Rate Z-score Model" is constructed to screen ETF products for potential arbitrage opportunities. The model uses rolling calculations to identify ETFs with significant deviations in premium rates, which may indicate arbitrage potential or risks of price corrections[6][14]

2025 年 04 月 08 日 行业策略 看好/维持 农林牧渔 农林牧渔 沪深300 ◼ 子行业评级 | 种植业 | 看好 | | --- | --- | | 畜牧业 | 看好 | | 林业 | 中性 | | 渔业 | 中性 | | 农 产 品 加 工 | 看好 | | Ⅱ | | 相关研究报告 <<农业周报（第 14 期）：贸易加征关 税利于农产品进口减少和价格上 涨>>--2025-04-06 <<益生股份年报点评：祖代白鸡行业 龙头，种鸡种猪业务盈利稳健>>-- 2025-04-01 <<优然牧业（09858）2024 年年报点 评：科技赋能+精益管理双轮驱动，经 营业绩逆势改善>>--2025-04-01 农林牧渔 行业点评：加征关税影响下，重视农业板块的防御和反制属性 ◼ 走势比较 (30%) (18%) (6%) 6% 18% 30% 24/4/8 24/6/19 24/8/30 24/11/10 25/1/21 25/4/3 证券分析师：程晓东 电话：010-88321761 E-MAIL：chengxd@tpyzq.com 分析师登记编号：S1190511050002 事件：美国总统特朗普 ...

Quantitative Model and Construction - **Model Name**: Utility Index Trend Tracking Model [3] - **Model Construction Idea**: The model assumes that the price movement of the target has strong local continuity, always following a certain trend. Reversal periods are significantly shorter than trend continuation periods. In cases of narrow-range consolidation, the model assumes the continuation of the previous trend. When observing a large-scale trend, a short observation window is used to capture the local trend. Reversals are identified when price changes at the start and end of the observation window exceed the range caused by random fluctuations, eliminating the impact of random noise. [4] - **Model Construction Process**: 1. Calculate the difference between the closing price on day T and day T-20, denoted as `del`. 2. Calculate the volatility (`Vol`) from day T-20 to day T (excluding day T). 3. If the absolute value of `del` exceeds `N` times `Vol`, the current price is considered to have exited the original oscillation range and formed a trend. The trend direction (long/short) corresponds to the sign of `del`. 4. If the absolute value of `del` is less than or equal to `N` times `Vol`, the current trend direction is assumed to continue, matching the direction on day T-1. 5. For stock markets with higher volatility compared to bond markets, `N` is set to 1 for tracking. 6. Combine the returns from both long and short directions to evaluate the final strategy performance. Formula: $ del = P_{T} - P_{T-20} $ $ Vol = \sqrt{\frac{1}{20} \sum_{i=1}^{20} (P_{T-i} - \bar{P})^2} $ where $ P_{T} $ is the closing price on day T, and $ \bar{P} $ is the average price over the observation window. [4] - **Model Evaluation**: The model is not suitable for direct application to the Utility Index due to its inability to achieve significant cumulative returns and its poor adaptability during periods of continuous market fluctuations, leading to sustained drawdowns. [5] Model Backtesting Results - **Annualized Return**: -16.67% [4] - **Annualized Volatility**: 15.99% [4] - **Sharpe Ratio**: -1.04 [4] - **Maximum Drawdown**: 32.10% [4] - **Total Return of Index During Period**: -0.35% [4]