Weighted sales forecast from your pipeline

The exact vocabulary the automated check enforces: keys, tokens, entry points, and worked examples. Generated from the verification source.

{
 "constants": {},
 "entryName": "compute_weighted_forecast",
 "expectNote": "expect is the comparison-space value (your return value goes through normalize first when one is published)",
 "hiddenCaseCount": 10,
 "hiddenCaseNames": [
  "single_half_discount",
  "multi_month_rollup",
  "explicit_probability_overrides",
  "unknown_stage_zero",
  "rounding_to_the_cent",
  "full_discount_zeros",
  "live_full_value",
  "empty_pipeline",
  "zero_deal_size",
  "months_sorted_ascending"
 ],
 "inputKeys": [
  "close_month",
  "deal_size",
  "discounted_value",
  "name",
  "opportunities",
  "optimism_discount",
  "stage",
  "stage_ladder",
  "stage_probability"
 ],
 "normalizeSource": "def _forecast_normalize(r):\n    try:\n        per = [{\"name\": str(p[\"name\"]), \"weighted_value\": round(float(p[\"weighted_value\"]), 2),\n                \"discounted_value\": round(float(p[\"discounted_value\"]), 2), \"close_month\": str(p[\"close_month\"])}\n               for p in r[\"per_opportunity\"]]\n        months = [{\"month\": str(m[\"month\"]), \"discounted_value\": round(float(m[\"discounted_value\"]), 2)}\n                  for m in r[\"by_month\"]]\n        dist = {str(k): int(v) for k, v in (r[\"stage_distribution\"] or {}).items()}\n        return {\"per_opportunity\": per, \"discounted_total\": round(float(r[\"discounted_total\"]), 2),\n                \"by_month\": months, \"stage_distribution\": dist}\n    except Exception:\n        return repr(r)\n",
 "returnShapes": [
  [
   "by_month",
   "discounted_total",
   "per_opportunity",
   "stage_distribution"
  ]
 ],
 "signature": "def compute_weighted_forecast(inp):",
 "submission": "python exposing the entry function; inp is one input object; graded on held-out cases",
 "tier": "calculator",
 "visibleCases": [
  {
   "expect": {
    "by_month": [
     {
      "discounted_value": 500,
      "month": "2026-07"
     }
    ],
    "discounted_total": 500,
    "per_opportunity": [
     {
      "close_month": "2026-07",
      "discounted_value": 500,
      "name": "Acme",
      "weighted_value": 500
     }
    ],
    "stage_distribution": {
     "great demo": 1
    }
   },
   "input": {
    "opportunities": [
     {
      "close_month": "2026-07",
      "deal_size": 1000,
      "name": "Acme",
      "stage": "great demo"
     }
    ],
    "optimism_discount": 0,
    "stage_ladder": {
     "demo scheduled": 0.1,
     "good demo": 0.25,
     "great demo": 0.5,
     "live": 1,
     "signed": 0.9,
     "verbal commitment": 0.75
    }
   },
   "name": "single_no_discount"
  }
 ],
 "vocabulary": [
  "by_month",
  "close_month",
  "deal_size",
  "discounted_total",
  "discounted_value",
  "month",
  "name",
  "opportunities",
  "optimism_discount",
  "per_opportunity",
  "stage",
  "stage_distribution",
  "stage_ladder",
  "stage_probability",
  "unknown",
  "weighted_value"
 ]
}