修正逻辑

internal/model/stock.go

@@ -21,6 +21,6 @@ type AnalysisResponse struct {
 	MaxHigh    float64  `json:"max_high"`
 	MaxDate    string   `json:"max_date"`
 	PostMinLow float64  `json:"post_min_low"`
-	DropPct    float64 `json:"drop_pct"`
-	ReboundPct float64 `json:"rebound_pct"`
+	DropPct    *float64 `json:"drop_pct"`    // 改为指针以支持 null
+	ReboundPct *float64 `json:"rebound_pct"` // 改为指针以支持 null
 }

internal/service/analyzer.go

@@ -18,7 +18,6 @@ func roundTo4(val float64) float64 {
 
 func (s *StockService) AnalyzePerformance(startDate, endDate string) ([]model.AnalysisResponse, error) {
 	var results []model.AnalysisResponse
-
 	var limits []struct {
 		TsCode string
 		Name   string
@@ -26,7 +25,7 @@ func (s *StockService) AnalyzePerformance(startDate, endDate string) ([]model.An
 		High   float64
 	}
 
-	// 1. 查找涨停股票
+	// 1. 获取选定日期当天涨停的股票
 	query := `
 		SELECT l.ts_code, l.name, d.open, d.high 
 		FROM stock_limit_data l 
@@ -37,7 +36,6 @@ func (s *StockService) AnalyzePerformance(startDate, endDate string) ([]model.An
 		return nil, err
 	}
 
-	// 2. 分析区间数据
 	for _, stock := range limits {
 		var dailies []model.StockDaily
 		s.DB.Where("ts_code = ? AND trade_date BETWEEN ? AND ?", stock.TsCode, startDate, endDate).
@@ -47,31 +45,57 @@ func (s *StockService) AnalyzePerformance(startDate, endDate string) ([]model.An
 			continue
 		}
 
-		// A. 找最高点
+		// --- 步骤 1：寻找全区间的最高点 (MaxHigh) ---
 		maxPrice := -1.0
 		maxIdx := 0
 		for i, d := range dailies {
-			if d.High >= maxPrice {
+			if d.High > maxPrice {
 				maxPrice = d.High
 				maxIdx = i
 			}
 		}
 
-		// B. 找最高点后的最低点
-		postData := dailies[maxIdx+1:]
-		var minLow, dropPct, reboundPct float64
+		var dropPct, reboundPct *float64
+		var postMinLow float64
+		maxDateStr := dailies[maxIdx].TradeDate.Format("2006-01-02")
 
-		if len(postData) > 0 {
-			minLow = postData[0].Low
-			for _, p := range postData {
-				if p.Low < minLow {
-					minLow = p.Low
+		// --- 步骤 2：寻找最高点之后的最低点 (PostMinLow) ---
+		if maxIdx < len(dailies)-1 {
+			postMaxData := dailies[maxIdx+1:]
+			minLow := postMaxData[0].Low
+			minLowIdx := 0
+
+			for i, d := range postMaxData {
+				if d.Low < minLow {
+					minLow = d.Low
+					minLowIdx = i
 				}
 			}
-			lastClose := postData[len(postData)-1].Close
-			dropPct = roundTo4(((maxPrice - minLow) / maxPrice) * 100)
-			if minLow > 0 {
-				reboundPct = roundTo4(((lastClose - minLow) / minLow) * 100)
+			postMinLow = minLow
+
+			// 只要出现比 MaxHigh 低的价格，就计算回落幅度
+			if minLow < maxPrice {
+				dVal := roundTo4(((maxPrice - minLow) / maxPrice) * 100)
+				dropPct = &dVal
+
+				// --- 步骤 3：寻找最低点之后的反弹 ---
+				if minLowIdx < len(postMaxData)-1 {
+					lastClose := postMaxData[len(postMaxData)-1].Close
+					if lastClose > minLow {
+						rVal := roundTo4(((lastClose - minLow) / minLow) * 100)
+						reboundPct = &rVal
+					}
+				}
+			}
+		}
+
+		// --- 步骤 4：处理特殊形态（如持续上涨无回落） ---
+		if dropPct == nil {
+			lastClose := dailies[len(dailies)-1].Close
+			firstDayHigh := dailies[0].High
+			if lastClose > firstDayHigh {
+				rVal := roundTo4(((lastClose - firstDayHigh) / firstDayHigh) * 100)
+				reboundPct = &rVal
 			}
 		}
 
@@ -81,16 +105,23 @@ func (s *StockService) AnalyzePerformance(startDate, endDate string) ([]model.An
 			DayOpen:    stock.Open,
 			DayHigh:    stock.High,
 			MaxHigh:    maxPrice,
-			MaxDate:    dailies[maxIdx].TradeDate.Format("2006-01-02"),
-			PostMinLow: minLow,
+			MaxDate:    maxDateStr,
+			PostMinLow: postMinLow,
 			DropPct:    dropPct,
 			ReboundPct: reboundPct,
 		})
 	}
 
-	// 3. 按降幅排序
+	// 5. 基础排序（仅按回落幅度降序，不处理 null 特殊逻辑）
 	sort.Slice(results, func(i, j int) bool {
-		return results[i].DropPct > results[j].DropPct
+		valI, valJ := -1.0, -1.0
+		if results[i].DropPct != nil {
+			valI = *results[i].DropPct
+		}
+		if results[j].DropPct != nil {
+			valJ = *results[j].DropPct
+		}
+		return valI > valJ
 	})
 
 	return results, nil