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]