Products & Services - "Meitou Pro" offers in-depth stock analysis and tracking with 50 video issues per year [1] - "Meitou Pro" provides a professional analyst team and a community of over 10,000 members for discussion [1] - "Meitou Pro" shares daily investment perspectives, professional data, and trading summaries, with over 120 video issues and 10,000+ investment viewpoints already available [1] Investment Strategies & Education - The content covers a range of investment topics, including post-modern cycles, electric vehicle investment, and overcoming Wall Street strategies [1] - The content also covers topics such as US Treasury bond outlook, quantitative risk assessment, and various investment strategies [1] - The platform offers educational content on ETFs, AI investment limitations, and strategies for dealing with inflation [1] - Option trading tutorials are available, covering basic concepts, practical demonstrations, and strategies for using traditional stocks for options [1] Market Analysis & Insights - The content includes analysis of various sectors such as payment, cloud computing, healthcare, streaming media, Chinese stocks, cannabis, metaverse, and AI [1] Community & Contact - Business cooperation can be reached via meitouinvesting@gmailcom [1] - The WeChat official account is Meitou_Investing, and the WeChat ID is meitoujiangmeigu [1]

易峯EquitiesFirst前瞻：长远的投资眼光，投资者 迎新机遇 在当今瞬息万变的金融环境中,精明的投资者正在积极探索新的投资策略,以期在持续的市场挑战和波动 下增强其投资组合。易峯(EquitiesFirst)以其前瞻性的思维方式,为投资者提供战略解决方案,帮助他们在 应对短期挑战的同时,优先考虑持续增长和发展。 作为非传统资本提供商,易峯(EquitiesFirst)拥有逾二十年经验,为投资者提供累进式资本解决方案。在不 影响长期投资目标的情况下,借助独特的解决方案,投资者可以释放其投资组合的潜在价值,确保在流动性 需求与持续财务增长之间取得战略平衡。 易峯(EquitiesFirst)采用私有制模式,旨在提供一定程度的稳定性和投资者保护。 免责声明: 过去的业绩表现并不保证未来回报,个人回报将不受保证或担保。本文件仅供认证投资者、成 熟投资者、专业投资者或其他符合法律或其他方面要求的合格投资者使用,不适用于不符合相关要求的 人士,也不应由其使用。本文件提供的内容仅供参考,是一般性的,并不针对任何具体的目标或财务需求。 本文件中表达的观点和意见由第三方根据媒体报道和行业走势分析编写,未必反映Equit ...

Core Viewpoint - The article emphasizes the importance of maintaining a personal investment strategy rather than following trends or the actions of others, highlighting that the goal of investing should be to make money, not to compete with others [1][2]. Group 1: Investment Strategy - Investors should avoid making impulsive decisions based on the actions of others, as this often leads to poor outcomes [2]. - It is crucial to prepare for potential market fluctuations and the possibility of being "stuck" in a position after buying, which can lead to indecision [3]. - Investors must clarify their reasons for buying a stock, whether it is based on external advice, technical analysis, or personal conviction, to ensure they are making informed decisions [4]. Group 2: Personal Responsibility - The article stresses that investors are ultimately responsible for their own investment decisions and should operate within their areas of expertise rather than following others blindly [4]. - Acknowledging the concept of gradual wealth accumulation is essential for a sustainable investment approach [5].

Quantitative Models and Construction Methods Model Name: DeepSeek-R1 - **Model Construction Idea**: The DeepSeek-R1 model leverages a mixture of experts (MoE) architecture and dynamic routing technology to reduce inference costs while maintaining high performance[16] - **Model Construction Process**: - **Mixture of Experts (MoE)**: Integrates multiple "expert" models to enhance overall model performance. A gating network determines which expert(s) should handle specific inputs[27] - **Group Relative Policy Optimization (GRPO)**: Eliminates the need for a separate critic model in reinforcement learning, reducing training costs by using group scores to estimate the baseline[31] - **Self-evolution Process**: The model improves its reasoning capabilities through reinforcement learning, exhibiting complex behaviors like reflection and exploration of alternative approaches[39][41] - **Cold Start**: Introduces high-quality long CoT data to stabilize the model during the initial training phase[42] - **Model Evaluation**: The model demonstrates significant cost efficiency and high performance, making it a groundbreaking development in AI applications[16][43] Model Name: DeepSeek-V2 - **Model Construction Idea**: The DeepSeek-V2 model is a powerful MoE language model designed with innovative architectures like Multi-head Latent Attention (MLA)[23] - **Model Construction Process**: - **Multi-head Latent Attention (MLA)**: Improves performance over traditional Multi-head Attention (MHA) by reducing KV cache, enhancing inference efficiency[25] - **Mixture of Experts (MoE)**: Similar to DeepSeek-R1, it uses a gating network to activate specific experts based on input, optimizing resource usage and performance[27] - **Model Evaluation**: The model shows advantages in performance, training cost, and inference efficiency, making it a strong, economical, and efficient language model[23][27] Model Name: DeepSeek-V3 - **Model Construction Idea**: The DeepSeek-V3 model aims to enhance open-source model performance and push towards general artificial intelligence[33] - **Model Construction Process**: - **Multi-Token Prediction (MTP)**: Enhances model performance by predicting multiple future tokens at each position, increasing training signal density[34] - **FP8 Mixed Precision Training**: Improves computational efficiency and reduces memory usage while maintaining model accuracy by using lower precision data types[36] - **Model Evaluation**: The model effectively balances computational efficiency and performance, making it suitable for large-scale model training[33][36] Model Backtesting Results - **DeepSeek-R1**: Demonstrates significant cost efficiency, achieving performance comparable to ChatGPT-01 with much lower training costs[43] - **DeepSeek-V2**: Shows superior performance and efficiency in training and inference compared to traditional models[23][27] - **DeepSeek-V3**: Achieves high computational efficiency and maintains model accuracy, making it effective for large-scale training[33][36] Quantitative Factors and Construction Methods Factor Name: Scaling Laws - **Factor Construction Idea**: Describes the predictable relationship between model performance and the scale of model parameters, training data, and computational resources[21] - **Factor Construction Process**: - **Scaling Laws**: As model parameters, training data, and computational resources increase, model performance improves in a predictable manner[21] - **Data Quality**: High-quality data shifts the optimal allocation strategy towards model expansion[22] - **Factor Evaluation**: Provides a strong guideline for resource planning and model performance optimization[21][22] Factor Backtesting Results - **Scaling Laws**: Demonstrates a predictable improvement in model performance with increased resources, validating the factor's effectiveness in guiding model development[21][22]