Core Viewpoint - The article highlights the rise of illegal securities and futures activities in China, particularly through social media and private groups, which mislead investors and disrupt market order [2][9]. Group 1: Types of Illegal Stock Recommendations - The Shenzhen Securities Regulatory Bureau has identified four main types of illegal stock recommendation schemes that are increasingly deceptive and intertwined with fraud [3]. Scheme 1: High-Priced Product Purchase for Stock Recommendations - Fraudsters create a persona of investment experts through social media, luring victims to purchase high-priced products like wine or tea to gain access to VIP groups where stock tips are shared, often leading to significant losses [4]. Scheme 2: Illegal Stock Recommendations Under the Guise of Education - Unsanctioned entities offer high-priced courses under the pretext of financial education, later promoting stock recommendations within social media groups, resulting in substantial financial losses for participants [5]. Scheme 3: Fraudulent Use of Big Data and AI for Stock Selection - Scammers promote fake stock selection tools claiming to use big data and AI, enticing investors to follow their recommendations, which often leads to severe financial losses [6]. Scheme 4: Impersonation of Licensed Institutions - Fraudsters use forged credentials to impersonate licensed financial institutions, gaining victims' trust and charging fees for stock recommendations, ultimately leading to financial scams [7][8]. Group 2: Regulatory Response - The China Securities Regulatory Commission (CSRC) has been actively conducting special operations to combat illegal stock recommendations and related activities, aiming to protect investors' rights [9]. - The CSRC emphasizes a combined approach of prevention and punishment, enhancing monitoring of illegal stock recommendation information online, and promoting public awareness to curb the spread of such activities [9].

Summary of Key Points Core Viewpoint - The news highlights a stock reduction by Meng Zhenjiang, a senior executive at Haoshanghao, indicating potential changes in the company's stock dynamics and executive sentiment towards the company's performance [1]. Company Information - Haoshanghao's main business includes technology research and development, consulting, and services related to computer hardware and software, big data, IoT, and consumer electronics [1]. - The company reported a total revenue of 7.185 billion yuan from distribution business, accounting for 99.33% of its total revenue, while IoT product design and manufacturing contributed 47.1153 million yuan (0.65%), and custom chip revenue was 713.8 thousand yuan (0.01%) [1]. Stock Activity - On June 3, Meng Zhenjiang sold 21,000 shares at an average price of 20.54 yuan, resulting in a transaction amount of 431,300 yuan, leaving him with 79,900 shares [1][2]. - The total market capitalization of Haoshanghao is reported to be 5.934 billion yuan [3].

Group 1 - The core viewpoint emphasizes the need to improve public service levels to better meet the needs of the population and enhance social stability [1] - The process of improving public service is a long-term task that requires addressing urgent issues faced by the public, particularly for vulnerable groups such as children, the elderly, disabled individuals, migrant workers, and the unemployed [1] - The article highlights the importance of establishing a mechanism for coordinating public interests to ensure fairness and justice in society [1] Group 2 - The article advocates for actively responding to public expectations by aligning public service supply with demand, emphasizing the importance of community participation and feedback [2] - It suggests utilizing advanced technologies like big data, cloud computing, and artificial intelligence to enhance the precision and effectiveness of public services [2] - The need for a diversified supply structure is highlighted, encouraging social forces to participate in public service provision, particularly in response to the aging population [2]

Core Viewpoint - Jiangsu Province's Industrial and Information Technology Department has announced the list of advanced smart factories for 2025, with Lianyungang Hongyang Thermal Power Co., Ltd. recognized for its achievements in smart manufacturing and digital transformation, becoming a model enterprise in the energy sector of the province [1] Company Summary - Lianyungang Hongyang Thermal Power has invested over 9.8 billion in its establishment since 2011, with a total heating capacity of 3,428.5 tons/hour, making it the largest clean energy cogeneration project in the Xuwei New Area [1] - The company has implemented a comprehensive digital transformation strategy, focusing on "data-driven, intelligent empowerment," utilizing advanced technologies such as artificial intelligence, big data, and the Internet of Things to enhance production management and data connectivity [1][2] Industry Summary - The Xuwei New Area is actively promoting the integration of industrial chains, innovation chains, and digital chains, establishing a tiered cultivation system for smart factories to support the intelligent and green transformation of enterprises [2] - In the past two years, three enterprises in the Xuwei New Area have been approved as national industrial internet pilot demonstrations, three as national 5G factories, and ten as provincial-level smart factories [2] - The economic development bureau of Xuwei New Area plans to continue fostering smart factory development through benchmark leadership and tailored strategies to build a trillion-level industrial cluster [2]

市场前景值得期待 近日，国家金融监督管理总局（以下简称"金融监管总局"）发布了今年前4个月健康险原保险保费收入 （以下简称"保费收入"）情况。数据显示，今年前4个月，保险公司合计实现健康险保费收入4557亿 元，同比增长4.06%。其中，财险公司健康险保费收入同比增长8.47%，人身险公司健康险保费收入同 比增长2.39%。 受访专家表示，随着市场康养需求的提升、消费者对保险产品认知的深化以及保险产品预定利率的下 调，具有保障功能的健康险更容易获得消费者的认同，因此增速较快。展望未来，护理险、失能险等健 康险的市场潜力有望逐步释放。 保费收入稳健增长 健康险，是指由保险公司对被保险人因健康原因或者医疗行为的发生给付保险金的保险，主要包括医疗 保险、疾病保险、失能收入损失保险、护理保险以及医疗意外保险等。 今年以来，在保险公司特别是人身险公司保费收入增长整体放缓的背景下，健康险保费收入同比仍保持 较为稳健的增长。 金融监管总局披露的最新数据显示，今年前4个月，保险公司合计实现健康险保费收入4557亿元，同比 增长4.06%。其中，财险公司健康险保费收入为1302亿元，同比增长8.47%，在其经营的各险种中保费 ...

Report Industry Investment Rating - Not provided in the content Core Viewpoints of the Report - The report systematically studies the construction method of futures automated trading strategies, emphasizing the core advantages of automated trading in efficiency, discipline, and data processing, and points out that successful strategy construction requires developers to have comprehensive capabilities such as market cognition, programming skills, and psychological qualities. It also provides a complete risk control framework and a gradual implementation plan from simulation to live trading, and believes that AI - driven and compliance - transparent will be the main future development directions [3]. Summary by Relevant Catalogs 1. Big Data Era's Automated Trading Revolution 1.1 Market Background and Development Status of Automated Trading - In the era of big data and AI, the proportion of automated trading in the global foreign exchange market is rising. The daily average trading volume of the global foreign exchange market is $7.5 trillion, with 65% of transactions conducted electronically. Barclays plans to increase the proportion of automated spot foreign exchange transactions. Automated trading improves efficiency, reduces manual intervention, and has a significant speed advantage over manual trading, with an average execution delay of 300 - 500 milliseconds for manual trading and less than 5 milliseconds for automated systems [6]. 1.2 Core Advantage Analysis of Automated Trading - Automated trading has discipline advantages as it follows preset rules without being affected by emotions, avoiding behavior biases like over - trading after consecutive losses. It can also monitor multiple markets and thousands of varieties 24/7. In terms of data processing, modern quantitative systems can process TB - level market data daily, providing a basis for trading decisions [7]. 2. Core Competency System for Building Automated Trading 2.1 Market Cognition and Market Judgment Ability - Developers need multi - dimensional professional capabilities, including understanding of variety characteristics, participant structures, and price formation mechanisms. For example, trading crude oil futures requires knowledge of OPEC policies, geopolitical factors, and inventory data, as well as technical analysis skills [8]. 2.2 Programming and Quantitative Analysis Skills Requirements - Python is the industry - standard programming language, and statistical modeling involves advanced techniques such as time - series analysis and machine learning. For instance, a simple mean - reversion strategy may need ADF tests and Z - score standardization [9]. 2.3 Psychological Quality and Risk Management Ability - Psychological quality is crucial. During strategy development, developers face a 3 - 6 - month trial - and - error period, and in live trading, they need to maintain emotional stability. Professional traders often establish psychological training mechanisms [10]. 3. Tool Selection and Platform Evaluation 3.1 Comparison of Mainstream Automated Trading Platforms - There are three types of automated trading tools: retail - level platforms (e.g., MT5, TradingView), professional - level platforms (e.g., Infinite Easy, MultiCharts), and institutional - level systems (e.g., QuantConnect, AlgoTrader), each with different features [11]. 3.2 Data Interface and Execution Efficiency Evaluation - The quality of data interfaces affects strategy performance. The CTP interface of SHFE can process over 5000 orders per second, and the penetration - style regulatory interface balances data richness and compliance. Different platforms have different order round - trip times (RTT), and developers should choose tools according to strategy types [12]. 4. Strategy Development Process and Practice Guide 4.1 Methodology and Trap Avoidance of Historical Backtesting - Strategy development starts with historical backtesting. Reliable backtesting needs to address issues like survivorship bias, look - ahead bias, and slippage. Backtesting has limited reference value for high - frequency strategies [13]. 4.2 Construction Principles of Risk Control System - A complete risk control module includes fund management, position control, circuit - breaker mechanisms, and exception handling. It should be tested under extreme market conditions, and the risk control system needs continuous optimization in live trading [14]. 5. Live Trading Deployment and Continuous Optimization 5.1 Key Transition from Simulation to Live Trading - It is recommended to use a three - stage transition method: 3 - month simulation trading, 1 - month trial with 10% of live - trading funds, and then gradually increase the position to the target level [15]. 5.2 Wrong - Order Handling and System Monitoring Mechanism - The wrong - order handling system should have multi - level protection, including syntax checking, rationality verification, and emergency processing. A complete log system should be established to record order life cycles for strategy optimization [16]. 6. Typical Case Analysis and Strategy Evolution 6.1 Implementation Path of Market - Maker Strategy - A complete market - maker system includes order - book analysis, quote generation, and risk - hedging modules. For copper futures, factors such as the price difference between SHFE and LME copper and spot premium/discount need to be considered. The income from market - making is gradually decreasing, and higher requirements are placed on speed and strategy [17]. 6.2 Modern Evolution of Trend - Following Strategy - Traditional double - moving - average strategies are being replaced by LSTM - based waveform prediction models. For example, adding a volatility - adaptive mechanism to the iron ore futures breakout system can increase the return - risk ratio by over 15% [18]. 7. Conclusion and Outlook 7.1 Double - Edged Sword Characteristic of Automated Trading - Automated trading has both advantages in execution efficiency and scale and risks such as technical failures and strategy homogenization. Developers should maintain awe of the market and establish a human - machine collaboration mechanism [19]. 7.2 Future Development Directions and Technological Trends - The future development directions of automated trading are AI - driven, multi - modal integration, and compliance - transparent. Individual developers are advised to start with simple rule - based strategies and continuously learn and adapt [20].

Core Viewpoint - Unisoc Co., Ltd. is preparing for a secondary listing on the Hong Kong Stock Exchange, driven by the increasing demand for computing power from its DeepSeek large model, positioning itself as a leading provider of digital and AI solutions in the ICT sector [1][2]. Company Overview - Unisoc is a subsidiary of Tsinghua Unigroup, originally listed on the Shenzhen Stock Exchange in November 1999, and is part of a larger group that includes multiple listed companies in both A-shares and H-shares [1]. - The company ranks third in China's digital infrastructure market and second in both the networking and computing/storage infrastructure markets, according to Frost & Sullivan [1]. Business Model and Revenue - Unisoc offers a comprehensive range of digital solutions, integrating cloud computing, big data, AI, IoT, cybersecurity, and edge computing, which supports various industries in their digital transformation [2]. - The company has four major subsidiaries, with H3C contributing the most to its revenue, recognized as a leading manufacturer of AI servers and Ethernet switches [2]. Financial Performance - Unisoc's revenue has shown steady growth, with reported revenues of approximately 737.52 billion RMB, 775.38 billion RMB, and 790.24 billion RMB for the years 2022, 2023, and 2024, respectively [3]. - The digital solutions segment has become the main revenue driver, accounting for 62.7%, 68.4%, and 70.5% of total revenue in the same years [3][4]. Profitability - The company's net profits from continuing operations were approximately 37.42 billion RMB, 36.85 billion RMB, and 19.82 billion RMB for 2022, 2023, and 2024, respectively, with a declining gross margin from 19.8% to 16.0% over the same period [5]. - Despite a drop in profit for 2024 due to increased costs and reduced margins, the company maintains a strong cash position with 73.17 billion RMB in cash and cash equivalents by the end of 2024 [5]. Market Trends - The global digital solutions market has been growing steadily, projected to increase from 1.5 trillion USD in 2020 to 2.6 trillion USD by 2024, with a compound annual growth rate (CAGR) of 14.1% [7]. - The market is expected to reach 4.8 trillion USD by 2029, with a CAGR of 12.7% from 2024 to 2029, driven by advancements in cloud computing, AI, and other technologies [7]. Competitive Landscape - The company faces increasing competition from major players like Huawei and ZTE, particularly in the telecommunications sector, and must navigate challenges from rising self-developed hardware by cloud service providers [11]. - Unisoc's IPO proceeds are intended for R&D, strategic investments, and global market expansion to strengthen its competitive position [11]. Future Outlook - The successful listing on the Hong Kong Stock Exchange is anticipated to elevate the company's market presence, with potential for strong long-term value driven by technological barriers and favorable policies [12].

Investment Rating - The report does not explicitly provide an investment rating for the smart retail industry Core Insights - The smart retail industry is characterized by the integration of AI, big data, and IoT technologies to enhance consumer experience and operational efficiency, shifting from a product-centric to a consumer-centric model [12][13][27] - The future development trends of smart retail include internal system integration, instant delivery services, personalized recommendations, and catering to the aging population [6][18] Summary by Sections Research Purpose & Summary - The report aims to systematically outline the practical paths, development history, policy environment, application scenarios, and future trends of smart retail [2] Smart Retail Practice Path - The core principle of smart retail is to utilize emerging technologies to gain insights into consumer habits, predict consumption trends, and guide production, focusing on demand forecasting and store analysis [4][15] - Key applications include smart stores and instant retail, which aim to optimize supply chains and enhance consumer experiences through data integration [4][6] Smart Retail Industry Overview - Smart retail is defined as the reconstruction of the retail industry through AI, big data, and IoT, focusing on consumer experience rather than just products [12][13] - The evolution of China's retail industry has progressed through traditional retail, e-commerce, new retail, and now smart retail, with significant technological integration [18][19] Policy Environment - Since 2020, the government has introduced various policies to encourage the smart transformation of retail facilities and the application of technological achievements in consumer sectors, aiming to establish a modern retail system by 2029 [21] Application of Smart Retail - Smart stores serve as the core carriers of smart retail, utilizing technologies like facial recognition and AI to enhance customer interaction and operational efficiency [25][27] - Instant retail, as a significant branch of smart retail, leverages technology and local supply to provide rapid delivery services, with a strong growth trend in lower-tier markets [28][32] Industry Application by Sector - The report highlights various sectors such as supermarkets and restaurants, emphasizing the need for data-driven consumer insights and the integration of online and offline channels to enhance customer engagement and operational efficiency [34][35]

记者4日从陕西省人社厅获悉，为进一步激活各类企业技能人才培养主体作用，该省人社厅、省发展改 革委等8部门近日联合印发《陕西省推动技能强企工作13条措施》。 据悉，陕西省打造产教训评技能生态链，大力推行"岗位需求+技能培训+技能评价+就业推荐"的项目化 培训模式。人社部门会同有关行业企业，采取自下而上的方式摸清用工需求和培训需求，发布产业链急 需紧缺技能人才需求目录。 该省鼓励各市(区)结合自身产业特点，制定发布本地区特色产业技能人才需求清单；高等学校、职业学 校、公共实训基地等通过"揭榜挂帅"等形式提供相应教学和培训供给，评价机构根据岗位需求提供评价 服务，人力资源服务机构根据企业和个人需求搭建就业供需对接平台，推动形成产业、教学、培训、评 价、就业衔接融通的技能生态链。 陕西省强化技能强企资金保障，建立健全多元经费投入机制，通过企业职工教育经费、就业补助资金、 失业保险基金、产业引导基金、职工个人付费等多种渠道强化资金投入，加强使用管理，提高资金使用 效益。 陕西省人社厅表示，该省将健全工作机制，凝聚工作合力，在资金、项目、金融等方面加强政策协同， 抓好行业企业技能人才需求预测、工作规划和培养工作，积极宣传 ...

Core Viewpoint - The expansion of service consumption is essential for driving economic growth and improving people's livelihoods, emphasizing the need for a balanced approach that integrates current and long-term goals, as well as supply and demand considerations [1][10]. Group 1: Importance of Service Consumption - Service consumption connects the economy and people's livelihoods, covering various sectors such as dining, accommodation, and healthcare, and is crucial for enhancing living standards and optimizing consumption structure [2][3]. - As China's GDP per capita exceeds $13,000, service consumption is rapidly growing, with a significant increase in residents' willingness to spend on services that meet their spiritual needs [2][7]. - In Q1 2024, service consumption accounted for 43.4% of total household consumption, reflecting its growing importance and potential [2][7]. Group 2: Economic and Employment Impact - Service consumption promotes economic quality improvement and supports multiple industries, creating a virtuous cycle of demand and supply [3][4]. - The service sector is the largest employment reservoir, with 48.1% of jobs in 2023, significantly higher than primary and secondary industries, thus broadening employment opportunities [4][9]. Group 3: Policy and Market Expansion - The National Development and Reform Commission has implemented policies to stimulate service consumption, focusing on innovation and digital transformation to meet consumer needs [6][10]. - By 2024, the service consumption market is projected to grow significantly, with per capita service spending increasing from 5,246 yuan in 2013 to 13,000 yuan in 2024, marking a 148% growth [7][8]. Group 4: Future Trends and Challenges - The demand for high-quality, diverse services in education, healthcare, and cultural tourism is rapidly increasing, with significant potential in sectors like elderly care and home services [8][9]. - Despite the positive trends, challenges such as insufficient quality service supply and a less favorable consumption environment remain, necessitating further efforts to enhance service quality and market openness [10][12].