....

src/main/kotlin/network/RagService.kt

@@ -199,23 +199,26 @@ object RagService {
 
                     val financialScore = FinancialAnalyzer.calculateScore(financialStmt)
                     val scores = technicalAnalyzer.calculateScores(financialScore)
+                    if (scores.avg() > 50) {
+                        result(tradingDecision, false)
 
-                    result(tradingDecision, false)
+                        tradingDecision.techSummary = technicalAnalyzer.generateComprehensiveReport()
+                        result(tradingDecision, false)
 
-                    tradingDecision.techSummary = technicalAnalyzer.generateComprehensiveReport()
+                        val question = "${corpInfo?.cName} $stockName[$stockCode]의 향후 실적 전망과 관련된 핵심 뉴스"
-                    result(tradingDecision, false)
+                        val questionEmbedding = embeddingModel.embed(question).content()
-
+                        val searchResult = embeddingStore.search(
-                    val question = "${corpInfo?.cName} $stockName[$stockCode]의 향후 실적 전망과 관련된 핵심 뉴스"
+                            EmbeddingSearchRequest.builder()
-                    val questionEmbedding = embeddingModel.embed(question).content()
+                                .queryEmbedding(questionEmbedding)
-                    val searchResult = embeddingStore.search(
+                                .maxResults(3)
-                        EmbeddingSearchRequest.builder()
+                                .build()
-                            .queryEmbedding(questionEmbedding)
+                        )
-                            .maxResults(3)
+                        tradingDecision.newsContext = searchResult.matches().joinToString("\n") { it.embedded().text() }
-                            .build()
+                        result(tradingDecision, false)
-                    )
+                        result(decideTrading(stockCode, scores, financialStmt, tradingDecision), true)
-                    tradingDecision.newsContext = searchResult.matches().joinToString("\n") { it.embedded().text() }
+                    } else {
-                    result(tradingDecision, false)
+                        result(tradingDecision, false)
-                    result(decideTrading(stockCode, scores,financialStmt,tradingDecision), true)
+                    }
                 } else {
                     result(tradingDecision, false)
                 }

src/main/kotlin/service/AutoTradingManager.kt

@@ -96,7 +96,10 @@ object AutoTradingManager {
             TradingLogStore.addLog(completeTradingDecision)
 
             println("🚀 [자동매수 실행] ${completeTradingDecision.stockName}")
-            if (completeTradingDecision != null && !completeTradingDecision.stockCode.isNullOrEmpty()) {
+            if (completeTradingDecision.confidence < 10) {
+                addToReanalysis(RankingStock(mksc_shrn_iscd = completeTradingDecision.stockCode,hts_kor_isnm = completeTradingDecision.stockName))
+                TradingLogStore.addLog(completeTradingDecision,"HOLD","분석 신뢰도 오류 인지로 재분석 대기열에 추가")
+            }else if (completeTradingDecision != null && !completeTradingDecision.stockCode.isNullOrEmpty()) {
                 var basePrice = completeTradingDecision.currentPrice
                 var stockCode = completeTradingDecision.stockCode
                 println("basePrice $basePrice")
@@ -156,8 +159,11 @@ object AutoTradingManager {
                         TradingLogStore.addLog(completeTradingDecision,"HOLD","✋ [관망] 토탈 스코어(${String.format("%.1f[${minScore}]", totalScore)}) 또는 신뢰도 (${String.format("%.1f[${MIN_CONFIDENCE}]", completeTradingDecision.confidence)}) 미달")
                     }
                 }
+                if (completeTradingDecision?.decision?.contains("매수") == true) {
+                    completeTradingDecision.decision = "BUY"
+                }
                 when (completeTradingDecision?.decision) {
-                    "BUY" -> {
+                    "BUY","매수" -> {
                         append = buyWeight
                         TradingLogStore.addLog(completeTradingDecision,"BUY","[$stockCode] 매수 추천 resultCheck: ${completeTradingDecision?.reason}")
                         resultCheck(completeTradingDecision)
@@ -451,7 +457,7 @@ object AutoTradingManager {
                         BrowserManager.closeIfIdle(0)
                         LlamaServerManager.stopAll() // AI 서버 완전 종료
                         TradingLogStore.clear()
-                         // Main.kt에 설정 화면으로 가라고 신호 전송
+                        // Main.kt에 설정 화면으로 가라고 신호 전송
                         stopDiscovery() // 발굴 루프 완전 폭파 (내일 8시 30분에 다시 켜짐)
                         return@launch
                     } else if (now.isAfter(H08M30) && now.isBefore(H08M50) && !isSystemReadyToday) {
@@ -668,10 +674,10 @@ object AutoTradingManager {
                         this.monthly = monthly.await()
                     }
                 }
-
+                if (analyzer.isValid()) {
-
+                    RagService.processStock(currentPrice, analyzer, stock.name, stock.code) { decision, isSuccess ->
-                RagService.processStock(currentPrice,analyzer, stock.name, stock.code) { decision, isSuccess ->
+                        callback(decision?.apply { this.currentPrice = currentPrice }, isSuccess)
-                    callback(decision?.apply { this.currentPrice = currentPrice }, isSuccess)
+                    }
                 }
                 println("✅ [분석 종료] ${stock.name} (${LocalTime.now()})")
             }
@@ -862,6 +868,8 @@ data class InvestmentScores(
         longTerm $longTerm
         """.trimIndent()
     }
+
+    fun avg() = listOf(ultraShort, shortTerm, midTerm, longTerm).average()
 }
 
 @Serializable
@@ -871,6 +879,8 @@ class TechnicalAnalyzer {
     var daily: List<CandleData> = emptyList()
     var min30: List<CandleData> = emptyList()
 
+    fun isValid() = listOf(min30,monthly, weekly,daily).filter { it.size > 0 }.size == 4
+
     fun isOverheatedStock(): Boolean {
         if (min30.size < 20 || daily.size < 20) return false