转自：EarlETF 聊行情前，先闲聊几句。 今天推文的标题，告别，指的是 Wind 的 API。 我是一个数据驱动的投资者，每天都会看、算、画大量数据，每天的《数据看盘》与周末的《图表周刊》和日常分析，都是通过写 python 程序，通过 Wind 金融终端的 API 接口调用数据的。 是的，曾经的 Wind 还是挺豪爽，购买金融终端，能给 API 试用，虽然数据调用量很有限，但手搓一套缓存，我这种业余用户用用也足够了。 这次借此机会，正好重新梳理架构，争取更合理一些。幸好现在有 AI 编程辅助，很多琐碎功能也能代劳。 除了代码梳理结构，有的图表应该也会梳理。 但周三突然接到消息，试用政策很快就要暂停了。虽然很不舍，但本来也就是 "白嫖"，商家的试用政策变动，也说不了什么，只能逆来顺受。 所以这两天都在找新的 API 方案。 之前我曾经提到过，我是一个挺喜欢 "Reset" 的人，从快十年前抛弃打字挺快的全拼学双拼，到 2022 年末弃 Windows 拥抱 MacOS，我还是愿意主动或者 被动，拥抱一些重启的。 程序员都知道，很多代码不断叠加，变成了屎山代码，最好的方法不是继续维护，而是从头写过。 这其实 ...

Core Insights - The article discusses the journey of Michael Truell, co-founder and CEO of Cursor, an AI programming platform, highlighting the evolution of the company and its focus on AI-assisted coding [4][39]. Group 1: Company Background and Evolution - Cursor was founded in late 2022, transitioning to AI-assisted programming, and quickly gained users through word-of-mouth [6][24]. - The initial idea for Cursor stemmed from a long-standing interest in AI among the founders, who had previously explored various projects, including a robot dog and CAD systems [13][14]. - The company faced challenges in its early projects, realizing the need to pivot towards code completion tools after several unsuccessful attempts [19][20]. Group 2: Product Development and Features - The first product was developed within three months, utilizing open-source components and focusing on creating a competitive code editor [25][28]. - Early iterations of the product included basic AI functionalities, which evolved through user feedback and internal iterations [27][30]. - The company emphasized the importance of building a product that genuinely improved user experience, leading to significant growth in 2024 [34][35]. Group 3: Market Position and Growth Strategy - Cursor's growth was driven by continuous improvements in product features, allowing for rapid user adoption and engagement [34][36]. - The company recognized the competitive landscape, particularly with established players like GitHub Copilot, but aimed to differentiate itself through innovative solutions [20][21]. - The founders maintained a focus on user needs and market trends, ensuring that the product remained relevant and effective in a rapidly evolving industry [31][32]. Group 4: Future Outlook and Industry Insights - The article discusses the transformative potential of AI in programming, suggesting that AI will increasingly act as a collaborator for developers [39][40]. - The importance of foundational skills in programming and mathematics is emphasized, indicating that these will remain valuable in the future [41]. - The company encourages aspiring entrepreneurs to pursue their interests seriously and collaborate with respected peers to achieve long-term success [41].

Quantitative Models and Construction Methods - **Model Name**: "One-sentence" automated backtesting framework **Model Construction Idea**: The framework leverages AI programming tools and a rules-driven workflow to transform natural language strategy descriptions into structured data queries, stock screening, portfolio construction, and backtesting results[7][10][13] **Model Construction Process**: 1. **Natural Language Input**: Users describe strategies in plain language, e.g., "monthly strategy, select stocks with market cap < 40 billion, ROE and ROA in the top 50%, and choose the 30 stocks with the lowest PE"[24] 2. **Data Mapping**: The system uses a standardized database query interface and a WIND data dictionary to map strategy elements (e.g., market cap, ROE, ROA, PE) to specific database tables and fields[7][13][22] - Example tables: - **AShareEODDerivativeIndicator**: Fields include `S_VAL_MV` (market cap) and `S_VAL_PE` (PE ratio)[24] - **AShareFinancialIndicator**: Fields include `S_FA_ROE` (ROE) and `S_FA_ROA` (ROA)[24] 3. **Portfolio Construction**: The system generates standardized portfolio data with three key elements: `date`, `asset`, and `weight`. It automatically adjusts for user-defined rebalancing frequencies (daily, weekly, monthly, quarterly)[13][15] 4. **Backtesting**: The framework runs backtests using the constructed portfolio and outputs performance metrics, risk analysis, and detailed reports[7][24] **Model Evaluation**: The framework is innovative in bridging natural language and structured data, enabling rapid strategy validation. However, its reliance on WIND data quality and AI model accuracy may introduce risks[7][13][24] Model Backtesting Results - **"One-sentence" automated backtesting framework**: - **Annualized Return**: 2020: 18.96%, 2021: 16.70%, 2022: 8.21%, 2023: 15.66%, 2024: 7.15%, 2025: 37.70%[29] - **Annualized Volatility**: 2020: 20.95%, 2021: 23.41%, 2022: 24.94%, 2023: 13.94%, 2024: 33.71%, 2025: 17.02%[29] - **Sharpe Ratio**: 2020: 0.91, 2021: 0.71, 2022: 0.33, 2023: 1.12, 2024: 0.21, 2025: 2.21[29] - **Maximum Drawdown**: 2020: -5.90%, 2021: -9.59%, 2022: -18.31%, 2023: -6.72%, 2024: -14.18%, 2025: -3.76%[29] - **Win Rate**: 2020: 66.67%, 2021: 66.67%, 2022: 66.67%, 2023: 50.00%, 2024: 50.00%, 2025: 75.00%[29] - **Calmar Ratio**: 2020: 3.21, 2021: 1.74, 2022: 0.45, 2023: 2.33, 2024: 0.50, 2025: 10.03[29] Quantitative Factors and Construction Methods - **Factor Name**: Small-cap value factor **Factor Construction Idea**: Select stocks with small market capitalization and strong financial performance, then rank by valuation metrics[24] **Factor Construction Process**: 1. **Stock Pool Definition**: Limit to stocks listed on Shanghai and Shenzhen exchanges with market cap < 40 billion[24] 2. **Financial Screening**: Filter stocks with ROE and ROA in the top 50% of the defined pool[24] 3. **Valuation Ranking**: Rank remaining stocks by ascending PE ratio and select the top 30[24] **Factor Evaluation**: The factor effectively combines size, profitability, and valuation metrics, aligning with traditional value investing principles[24] Factor Backtesting Results - **Small-cap value factor**: - **Annualized Return**: 2020: 18.96%, 2021: 16.70%, 2022: 8.21%, 2023: 15.66%, 2024: 7.15%, 2025: 37.70%[29] - **Annualized Volatility**: 2020: 20.95%, 2021: 23.41%, 2022: 24.94%, 2023: 13.94%, 2024: 33.71%, 2025: 17.02%[29] - **Sharpe Ratio**: 2020: 0.91, 2021: 0.71, 2022: 0.33, 2023: 1.12, 2024: 0.21, 2025: 2.21[29] - **Maximum Drawdown**: 2020: -5.90%, 2021: -9.59%, 2022: -18.31%, 2023: -6.72%, 2024: -14.18%, 2025: -3.76%[29] - **Win Rate**: 2020: 66.67%, 2021: 66.67%, 2022: 66.67%, 2023: 50.00%, 2024: 50.00%, 2025: 75.00%[29] - **Calmar Ratio**: 2020: 3.21, 2021: 1.74, 2022: 0.45, 2023: 2.33, 2024: 0.50, 2025: 10.03[29]