fix: keep growth analysis available per terminal

internal/growth/analytics_test.go

@@ -63,6 +63,26 @@ func TestBuildForecastReturnsOrderedNinetyDayIntegerRanges(t *testing.T) {
 	}
 }
 
+func TestBuildForecastUsesLimitedHistoryWithLowConfidence(t *testing.T) {
+	start := date("2026-07-01")
+	history := make([]DailyPoint, 0, 14)
+	for daysAgo := 14; daysAgo > 0; daysAgo-- {
+		day := start.AddDate(0, 0, -daysAgo)
+		value := int64(80 + int(day.Weekday())*5)
+		history = append(history, DailyPoint{Date: day, SessionCount: value, PaidSessionCount: value, PaidAmountRial: value * 100_000})
+	}
+
+	forecast := BuildForecast(history, nil, start, 90)
+	if forecast.Confidence != ConfidenceLow || len(forecast.Points) != 90 {
+		t.Fatalf("limited history should produce a low-confidence forecast: confidence=%s points=%d", forecast.Confidence, len(forecast.Points))
+	}
+	for _, point := range forecast.Points {
+		if point.PaidSessions.P10 > point.PaidSessions.P50 || point.PaidSessions.P50 > point.PaidSessions.P90 {
+			t.Fatalf("limited-history range is unordered: %+v", point.PaidSessions)
+		}
+	}
+}
+
 func TestDetectRisksRequiresTwentyPercentDropAndBaselineSize(t *testing.T) {
 	forecast := Forecast{Confidence: ConfidenceMedium, Points: []ForecastPoint{
 		{Date: date("2026-07-01"), BaselineSessions: 200, PaidSessions: Range{P50: 150}, BaselineAmountRial: 10_000_000, PaidAmountRial: Range{P50: 7_000_000}},

internal/growth/catalog_test.go

@@ -80,7 +80,7 @@ func TestAnalyzeEventEffectUsesCompleteComparableWindows(t *testing.T) {
 	}
 }
 
-func TestForecastSuppressesPoorBacktest(t *testing.T) {
+func TestForecastKeepsPoorBacktestVisibleWithLowConfidence(t *testing.T) {
 	start := date("2026-07-01")
 	history := make([]DailyPoint, 0, 140)
 	for i := 140; i > 0; i-- {
@@ -91,8 +91,8 @@ func TestForecastSuppressesPoorBacktest(t *testing.T) {
 		history = append(history, DailyPoint{Date: start.AddDate(0, 0, -i), SessionCount: value, PaidSessionCount: value, PaidAmountRial: value * 1_000})
 	}
 	forecast := BuildForecast(history, nil, start, 90)
-	if forecast.Backtest.Origins < 4 || forecast.Confidence != ConfidenceInsufficientData || len(forecast.Points) != 0 {
-		t.Fatalf("poor model should be suppressed: %+v", forecast)
+	if forecast.Backtest.Origins < 4 || forecast.Confidence != ConfidenceLow || len(forecast.Points) != 90 {
+		t.Fatalf("poor model should remain visible with low confidence: %+v", forecast)
 	}
 }
 

internal/growth/forecast.go

@@ -17,20 +17,17 @@ func BuildForecast(history []DailyPoint, catalog []Event, start time.Time, horiz
 	}
 	result := Forecast{StartDate: normalizeDate(start), HorizonDays: horizonDays, Confidence: ConfidenceInsufficientData, SuppressionReason: SuppressionInsufficientHistory, Points: []ForecastPoint{}}
 	ordered := normalizedHistory(history, start)
-	if len(ordered) < minimumHistoryDays {
+	if len(ordered) == 0 {
 		return result
 	}
 	result.Backtest = backtest(ordered, catalog)
 	result.Confidence = classifyConfidence(result.Backtest)
-	if result.Confidence == ConfidenceInsufficientData {
-		return result
-	}
 	result.SuppressionReason = ""
-	result.Points = forecastPoints(ordered, catalog, result.StartDate, horizonDays)
+	result.Points = forecastPoints(ordered, catalog, result.StartDate, horizonDays, result.Confidence)
 	return result
 }
 
-func forecastPoints(history []DailyPoint, catalog []Event, start time.Time, horizon int) []ForecastPoint {
+func forecastPoints(history []DailyPoint, catalog []Event, start time.Time, horizon int, confidence Confidence) []ForecastPoint {
 	sessionResiduals, amountResiduals := modelResiduals(history, catalog)
 	points := make([]ForecastPoint, 0, horizon)
 	for index := 0; index < horizon; index++ {
@@ -38,8 +35,8 @@ func forecastPoints(history []DailyPoint, catalog []Event, start time.Time, hori
 		baselineSessions, baselineAmount, predictedSessions, predictedAmount, eventIDs := predictCenter(history, catalog, day)
 		points = append(points, ForecastPoint{
 			Date: day, BaselineSessions: baselineSessions, BaselineAmountRial: baselineAmount,
-			PaidSessions:   rangeFromResiduals(predictedSessions, sessionResiduals),
-			PaidAmountRial: rangeFromResiduals(predictedAmount, amountResiduals), EventIDs: eventIDs,
+			PaidSessions:   forecastRange(predictedSessions, sessionResiduals, confidence),
+			PaidAmountRial: forecastRange(predictedAmount, amountResiduals, confidence), EventIDs: eventIDs,
 		})
 	}
 	return points
@@ -64,6 +61,11 @@ func weekdayMedian(history []DailyPoint, weekday time.Weekday, value func(DailyP
 			values = append(values, value(history[index]))
 		}
 	}
+	if len(values) == 0 {
+		for index := len(history) - 1; index >= 0 && len(values) < 8; index-- {
+			values = append(values, value(history[index]))
+		}
+	}
 	return median(values)
 }
 
@@ -100,6 +102,13 @@ func rangeFromResiduals(center int64, residualValues []int64) Range {
 	}
 }
 
+func forecastRange(center int64, residualValues []int64, confidence Confidence) Range {
+	if confidence == ConfidenceLow && len(residualValues) == 0 {
+		return Range{P10: center / 2, P50: center, P90: center + center/2}
+	}
+	return rangeFromResiduals(center, residualValues)
+}
+
 func quantile(values []int64, percentile int) int64 {
 	if len(values) == 0 {
 		return 0
@@ -186,7 +195,7 @@ func predictCenter(history []DailyPoint, catalog []Event, day time.Time) (int64,
 
 func classifyConfidence(backtest BacktestResult) Confidence {
 	if backtest.Origins < 4 {
-		return ConfidenceInsufficientData
+		return ConfidenceLow
 	}
 	worstMAPE := maxInt64(backtest.SessionMAPEBPS, backtest.AmountMAPEBPS)
 	if worstMAPE <= 2_000 && backtest.IntervalCoverageBPS >= 7_000 {
@@ -195,7 +204,7 @@ func classifyConfidence(backtest BacktestResult) Confidence {
 	if worstMAPE <= 4_000 && backtest.IntervalCoverageBPS >= 5_000 {
 		return ConfidenceMedium
 	}
-	return ConfidenceInsufficientData
+	return ConfidenceLow
 }
 
 type familyUplift struct{ sessions, amount int64 }

internal/growth/types.go

@@ -4,7 +4,7 @@ import "time"
 
 const (
 	CatalogVersion            = "1405.2"
-	AnalyticsVersion          = "1.1.0"
+	AnalyticsVersion          = "1.2.0"
 	DefaultModelConfigVersion = "avalai-responses-v1-tools4-turns2-output1600"
 
 	MinBaselineObservations = 4
@@ -18,6 +18,7 @@ type Confidence string
 const (
 	ConfidenceHigh             Confidence = "HIGH"
 	ConfidenceMedium           Confidence = "MEDIUM"
+	ConfidenceLow              Confidence = "LOW"
 	ConfidenceInsufficientData Confidence = "INSUFFICIENT_DATA"
 )