Visualize weekly forecast backtests against Monte Carlo cone predictions.
npx skills add https://github.com/bbak/mcs-mcp --skill backtest-chart
Please help me install this Agent Skill. Skill: backtest-chart Source: https://github.com/bbak/mcs-mcp/tree/main/docs/skills/Walkforward-Backtest-recharts Command: npx skills add https://github.com/bbak/mcs-mcp --skill backtest-chart
Visualize and validate forecast backtests by comparing weekly actual outcomes to Monte Carlo cone predictions.
Provide forecast_backtest data to render the three-chart visualization and the checkpoint table.
High-intent search queries and answers about installing and using this skill.
To visualize forecast backtest calibration, compare weekly actual outcomes against Monte Carlo cone predictions. This approach uses three dedicated views to reveal calibration drift and gauge forecast reliability over time.
The best way to detect forecast calibration drift during walk-forward analysis is by visualizing the Actual-vs-P50 Gap view. This chart highlights the variance between actual outcomes and the P50 prediction cone to expose reliability issues.
Yes, you can render a Monte Carlo cone prediction chart for walk-forward analysis data. By processing forecast_backtest data, the visualization pipeline generates cones alongside a detailed checkpoint table to handle anomalous data points.
Yes, the backtest visualization supports integration with mcs-charts-base. It reuses styling, layout, and interaction patterns from the base framework to ensure consistent rendering across the forecast cone and rolling accuracy charts.
To handle anomalous checkpoints in forecast backtest visualization, the rendering pipeline applies specific data nuance handling. This ensures anomalous data points do not disrupt the cohesive visualization of the forecast cone and rolling accuracy views.
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