AI不是“替代” 而是“赋能”：因诺资产的长期主义与智能进化

Core Insights - Inno Asset has been awarded the "Golden Bull Private Fund Management Company (Three-Year Managed Futures Strategy)" for the fifth time, showcasing its robust capabilities in quantitative investment [1] - The founder and CEO Xu Shunan emphasized that AI is an extension of quantitative methodologies rather than a revolutionary force, enhancing the ability to identify and represent trading patterns in complex data environments [1][3] Group 1: AI and Quantitative Investment - AI is viewed as a powerful statistical tool that enhances the capabilities of quantitative investment, which is fundamentally based on mathematics and statistics [3] - Inno Asset has systematically applied AI across various strategies, including Alpha, CTA, and algorithmic trading, leading to improved model recognition, response speed, and iterability [3] - The application of AI is expected to expand further as data becomes richer and foundational engineering is solidified, providing more "methodological dividends" for strategy evolution [3] Group 2: Efficiency and Creativity - AI is seen as an efficiency amplifier, taking over repetitive tasks such as data cleaning and feature construction, allowing teams to focus on more creative aspects like problem definition and risk control [4] - The integration of AI throughout the data-model-engine-trading chain aims to standardize processes and enhance execution paths while ensuring compliance with regulations [4][9] - The company believes that while AI enhances efficiency and precision, it cannot replace the human element in defining problems and constructing logic [7] Group 3: Methodology and Results - The source of good strategies is not solely dependent on the use of AI but rather on clear problem definitions, reliable data, and robust testing [8] - Inno Asset maintains a principle of methodological neutrality and results orientation, using AI to optimize strategy performance when appropriate, but also valuing traditional methods [8] - AI signals and traditional factors are developed in parallel, calibrated, and combined to create low-correlation multi-source Alpha, evaluated against stability, transaction costs, and capacity constraints [8] Group 4: Future Directions - Inno Asset aims to embed AI into multi-strategy and full-chain processes, focusing on building a solid foundation in local markets while seeking low-correlation opportunities across multiple assets and markets [9] - The company emphasizes the importance of maintaining a balance between innovation and compliance within a risk management framework, ensuring that creativity flourishes within defined boundaries [9] - The direction and pace of AI integration will continue to be guided by human judgment, reinforcing the commitment to delivering verifiable long-term performance to clients and the market [9]