Show HN: Agent that mines academic research for novel time series discoveries (github.com)

A new research agent has been developed that autonomously scours academic literature and applies findings to time series datasets, revealing novel insights. This agent operates through an advanced pipeline constructed with Claude Code, where each of the five critical steps—literature search, data construction, strategy implementation, independent verification, and optimization—must pass rigorous checks before proceeding. Notably, the pipeline ensures that all data used in generating trading signals is pre-existing, preventing the inclusion of future information and enhancing the integrity of quantitative experiments. This innovation is significant for the AI/ML community as it combines automated research and data evaluation to minimize repetitive mistakes in quantitative analysis, fostering real-time learning and improvement. The agent operates with a strict protocol that emphasizes transparency and accountability, including multiple layers of verification to avoid statistical pitfalls common in quantitative methods. Market data from diverse sources is processed through TimescaleDB, enhancing efficiency and accuracy in signal generation. This approach promises to refine model robustness and adaptability, potentially driving forward the precision of AI-driven quantitative trading strategies.