| name | forecasting |
| description | The output contract for producing a structured probability distribution over the rate decision (cut / hold / hike) — the JSON shape, the calibration 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: the task and as_of cutoff date, the
announcement_date being predicted, the policy_rate path, meeting_outcomes
(decision history + historical base rates), a macro_snapshot, and an
output_schema showing the exact JSON to return.
The output contract
- Assign one probability to each of
cut, hold, hike; the three must
sum to 1.
- Report calibrated probabilities — across many decisions where you say
0.7, that outcome should occur about 70% of the time.
- Anchor on the historical base rates, then adjust for the macro snapshot
and recent decisions. Direct cut→hike reversals between adjacent meetings
essentially never happen, so recent history shapes which tail is plausible.
- Use ONLY information available on or before
as_of.
- Put your reasoning in
reasoning and the decisive inputs in key_signals.
Submit by calling set_model_response with a json_response string that
matches the payload's output_schema exactly. Omit any field not shown.
Domain focus (edit this for your use case)
The 2-year GoC yield trading well below the policy rate means the bond market is
pricing cuts; well above means hikes. CPI relative to the 2% target and
labour-market momentum tell you whether you are in an easing or tightening
cycle. Weigh those against the Bank's gradualism and reluctance to surprise
markets.
Room to grow
- Add your own calibration notes from the backtest leaderboard.
- Encode any decision rules you trust (e.g. how much a soft CPI print moves P(cut)).
