Worked on the MQSMaster repository to enhance risk visualization for active trading hours, focusing on delivering clearer risk signals to support trader decision-making. Developed a feature that filters risk graphs by specific trading session hours and weekdays, ensuring that visualizations reflect only meaningful trading periods and exclude non-trading times. Refactored the rolling risk graph logic for improved maintainability and performance, and resolved rendering issues to ensure accurate display. Utilized Python and Jupyter Notebook, leveraging Pandas for data manipulation and Plotly for interactive data visualization. The work emphasized precise backtesting and visualization, resulting in more actionable insights for trading strategies.