forecasting

name	forecasting
description	The output contract for producing a structured probabilistic forecast — the JSON shape, the calibration and quantile rules, and how to submit it. Load this ONLY when your task payload asks for a forecast; ignore it for open-ended questions. No scripts.

Forecasting skill

Load this when your task payload asks for a structured forecast. For open-ended questions, ignore it and just answer.

What you'll receive

A JSON payload describing the task: a task id, the as_of cutoff date, horizons (steps ahead), the standard_quantiles grid, a target_summary, the recent target_history_csv, and an output_schema showing the exact JSON to return.

The output contract

1. Produce one forecast per horizon in horizons.
2. Use exactly the levels in standard_quantiles — no additions or omissions.
3. point_forecast must equal the 0.50 quantile value.
4. Quantile values must be non-decreasing as the quantile level rises.
5. Use ONLY information available on or before as_of.
6. Put your reasoning in the rationale fields.

Submit by calling set_model_response with a json_response string that matches the payload's output_schema exactly — use "horizon" (an integer), and make "quantiles" a list of {"quantile": <level>, "value": <number>} objects. Omit any field not shown in the schema.

Calibration

Report calibrated intervals, not false precision: across many forecasts where your 80% band is stated, the truth should land inside it about 80% of the time. Anchor the point on the recent level and trend; let recent volatility set how wide the bands are, and widen them as the horizon grows.

Domain focus (edit this for your use case)

For S&P 500 log-returns, keep the point forecast near zero unless you have real signal — returns are close to a random walk. Let recent realised volatility set how WIDE the quantile bands are; it is far more predictable than direction. Note any macro catalyst you lean on in the rationale.

Room to grow

• Tighten the calibration guidance with your own backtest findings.
• Add worked examples of good vs. over-confident forecasts.