Worked on the ai-hedge-fund repository to enhance the reliability of sentiment signal evaluation for hedging strategies. Focused on improving the precision of sentiment analysis by addressing a floating-point rounding issue in the confidence score calculation. Utilized Python for both data analysis and machine learning tasks, ensuring that sentiment evaluations reflected accurate confidence levels. Delivered a targeted bug fix that corrected the rounding logic, thereby increasing the trustworthiness of sentiment signals used in automated decision-making. No new features were introduced during this period, with efforts concentrated on refining the core sentiment analysis component to support more precise and dependable model outputs.