llm 동작 수정

backend/src/main/java/com/goldenchart/config/AiVerifyAsyncConfig.java

@@ -0,0 +1,30 @@
+package com.goldenchart.config;
+
+import org.springframework.beans.factory.annotation.Value;
+import org.springframework.context.annotation.Bean;
+import org.springframework.context.annotation.Configuration;
+import org.springframework.scheduling.concurrent.ThreadPoolTaskExecutor;
+
+import java.util.concurrent.Executor;
+
+/**
+ * 전략평가 AI 검증 — LLM·Ta4j 재평가를 HTTP 요청 스레드와 분리.
+ */
+@Configuration
+public class AiVerifyAsyncConfig {
+
+    @Bean(name = "aiVerifyExecutor")
+    public Executor aiVerifyExecutor(
+            @Value("${goldenchart.llm.ai-verify.pool-size:2}") int poolSize,
+            @Value("${goldenchart.llm.ai-verify.queue-capacity:8}") int queueCapacity) {
+        ThreadPoolTaskExecutor ex = new ThreadPoolTaskExecutor();
+        ex.setThreadNamePrefix("ai-verify-");
+        ex.setCorePoolSize(Math.max(1, poolSize));
+        ex.setMaxPoolSize(Math.max(1, poolSize));
+        ex.setQueueCapacity(queueCapacity);
+        ex.setWaitForTasksToCompleteOnShutdown(true);
+        ex.setAwaitTerminationSeconds(120);
+        ex.initialize();
+        return ex;
+    }
+}

backend/src/main/java/com/goldenchart/controller/StrategyEvaluationController.java

@@ -1,8 +1,11 @@
 package com.goldenchart.controller;
 
 import com.goldenchart.dto.LlmTestResponse;
+import com.goldenchart.dto.StrategyEvaluationAiVerifyJobStartResponse;
+import com.goldenchart.dto.StrategyEvaluationAiVerifyJobStatusResponse;
 import com.goldenchart.dto.StrategyEvaluationAiVerifyRequest;
 import com.goldenchart.dto.StrategyEvaluationAiVerifyResponse;
+import com.goldenchart.service.StrategyEvaluationAiVerifyJobService;
 import com.goldenchart.service.StrategyEvaluationLlmService;
 import lombok.RequiredArgsConstructor;
 import org.springframework.http.ResponseEntity;
@@ -13,7 +16,9 @@ import java.util.Map;
 /**
  * 전략 평가 부가 기능.
  *
- * POST /api/strategy/evaluation/ai-verify — LLM 기반 평가·시그널 일치 검증
+ * POST /api/strategy/evaluation/ai-verify/jobs — AI 검증 비동기 시작 (jobId 즉시 반환)
+ * GET  /api/strategy/evaluation/ai-verify/jobs/{jobId} — job 상태·결과 조회
+ * POST /api/strategy/evaluation/ai-verify — (레거시) 동기 검증
  * POST /api/strategy/evaluation/llm-test  — LLM 연결 테스트
  */
 @RestController
@@ -22,6 +27,45 @@ import java.util.Map;
 public class StrategyEvaluationController {
 
     private final StrategyEvaluationLlmService llmService;
+    private final StrategyEvaluationAiVerifyJobService aiVerifyJobService;
+
+    @PostMapping("/ai-verify/jobs")
+    public ResponseEntity<?> startAiVerifyJob(
+            @RequestBody StrategyEvaluationAiVerifyRequest body,
+            @RequestHeader Map<String, String> headers,
+            @RequestHeader(value = "X-Device-Id", required = false) String deviceId) {
+        TradingControllerSupport.requireRegisteredUser(headers);
+        if (body == null || body.getContext() == null) {
+            return ResponseEntity.badRequest().body(Map.of("message", "context 가 필요합니다."));
+        }
+        try {
+            Long userId = TradingControllerSupport.parseUserId(headers);
+            StrategyEvaluationAiVerifyJobStartResponse res =
+                    aiVerifyJobService.startJob(body.getContext(), userId, deviceId);
+            return ResponseEntity.accepted().body(res);
+        } catch (IllegalArgumentException e) {
+            return ResponseEntity.badRequest().body(Map.of("message", e.getMessage()));
+        } catch (IllegalStateException e) {
+            return ResponseEntity.status(503).body(Map.of("message", e.getMessage()));
+        }
+    }
+
+    @GetMapping("/ai-verify/jobs/{jobId}")
+    public ResponseEntity<?> getAiVerifyJob(
+            @PathVariable String jobId,
+            @RequestHeader Map<String, String> headers) {
+        TradingControllerSupport.requireRegisteredUser(headers);
+        try {
+            Long userId = TradingControllerSupport.parseUserId(headers);
+            StrategyEvaluationAiVerifyJobStatusResponse res = aiVerifyJobService.getJob(jobId, userId);
+            if (res == null) {
+                return ResponseEntity.notFound().build();
+            }
+            return ResponseEntity.ok(res);
+        } catch (IllegalArgumentException e) {
+            return ResponseEntity.badRequest().body(Map.of("message", e.getMessage()));
+        }
+    }
 
     @PostMapping("/ai-verify")
     public ResponseEntity<?> aiVerify(

backend/src/main/java/com/goldenchart/dto/StrategyEvaluationAiVerifyJobStartResponse.java

@@ -0,0 +1,16 @@
+package com.goldenchart.dto;
+
+import lombok.AllArgsConstructor;
+import lombok.Builder;
+import lombok.Data;
+import lombok.NoArgsConstructor;
+
+@Data
+@Builder
+@NoArgsConstructor
+@AllArgsConstructor
+public class StrategyEvaluationAiVerifyJobStartResponse {
+    private String jobId;
+    /** QUEUED | RUNNING */
+    private String status;
+}

backend/src/main/java/com/goldenchart/dto/StrategyEvaluationAiVerifyJobStatusResponse.java

@@ -0,0 +1,20 @@
+package com.goldenchart.dto;
+
+import lombok.AllArgsConstructor;
+import lombok.Builder;
+import lombok.Data;
+import lombok.NoArgsConstructor;
+
+@Data
+@Builder
+@NoArgsConstructor
+@AllArgsConstructor
+public class StrategyEvaluationAiVerifyJobStatusResponse {
+    private String jobId;
+    /** QUEUED | RUNNING | COMPLETED | FAILED */
+    private String status;
+    private String error;
+    private StrategyEvaluationAiVerifyResponse result;
+    private long createdAtMs;
+    private Long completedAtMs;
+}

backend/src/main/java/com/goldenchart/service/StrategyEvaluationAiVerifyJobRunner.java

@@ -0,0 +1,31 @@
+package com.goldenchart.service;
+
+import com.fasterxml.jackson.databind.JsonNode;
+import lombok.RequiredArgsConstructor;
+import lombok.extern.slf4j.Slf4j;
+import org.springframework.scheduling.annotation.Async;
+import org.springframework.stereotype.Component;
+
+@Component
+@RequiredArgsConstructor
+@Slf4j
+public class StrategyEvaluationAiVerifyJobRunner {
+
+    private final StrategyEvaluationLlmService llmService;
+    private final StrategyEvaluationAiVerifyJobService jobService;
+
+    @Async("aiVerifyExecutor")
+    public void execute(String jobId, JsonNode context, Long userId, String deviceId) {
+        jobService.markRunning(jobId);
+        try {
+            var result = llmService.verify(context, userId, deviceId);
+            jobService.markCompleted(jobId, result);
+        } catch (Exception e) {
+            log.warn("[AiVerifyJob] fail jobId={}: {}", jobId, e.getMessage());
+            String msg = e.getMessage() != null && !e.getMessage().isBlank()
+                    ? e.getMessage()
+                    : "AI 검증 중 오류가 발생했습니다.";
+            jobService.markFailed(jobId, msg);
+        }
+    }
+}

backend/src/main/java/com/goldenchart/service/StrategyEvaluationAiVerifyJobService.java

@@ -0,0 +1,112 @@
+package com.goldenchart.service;
+
+import com.fasterxml.jackson.databind.JsonNode;
+import com.goldenchart.dto.StrategyEvaluationAiVerifyJobStartResponse;
+import com.goldenchart.dto.StrategyEvaluationAiVerifyJobStatusResponse;
+import com.goldenchart.dto.StrategyEvaluationAiVerifyResponse;
+import lombok.RequiredArgsConstructor;
+import org.springframework.stereotype.Service;
+
+import java.util.Iterator;
+import java.util.Map;
+import java.util.UUID;
+import java.util.concurrent.ConcurrentHashMap;
+
+@Service
+@RequiredArgsConstructor
+public class StrategyEvaluationAiVerifyJobService {
+
+    private static final long JOB_TTL_MS = 60L * 60 * 1000;
+
+    private final StrategyEvaluationAiVerifyJobRunner jobRunner;
+
+    private final ConcurrentHashMap<String, JobRecord> jobs = new ConcurrentHashMap<>();
+
+    public StrategyEvaluationAiVerifyJobStartResponse startJob(JsonNode context, Long userId, String deviceId) {
+        purgeExpired();
+        String jobId = UUID.randomUUID().toString();
+        JobRecord record = new JobRecord(jobId, userId, System.currentTimeMillis());
+        jobs.put(jobId, record);
+        jobRunner.execute(jobId, context, userId, deviceId);
+        return StrategyEvaluationAiVerifyJobStartResponse.builder()
+                .jobId(jobId)
+                .status(record.status)
+                .build();
+    }
+
+    public StrategyEvaluationAiVerifyJobStatusResponse getJob(String jobId, Long userId) {
+        purgeExpired();
+        JobRecord record = jobs.get(jobId);
+        if (record == null || !record.userId.equals(userId)) {
+            return null;
+        }
+        return toResponse(record);
+    }
+
+    void markRunning(String jobId) {
+        JobRecord record = jobs.get(jobId);
+        if (record != null) {
+            record.status = "RUNNING";
+        }
+    }
+
+    void markCompleted(String jobId, StrategyEvaluationAiVerifyResponse result) {
+        JobRecord record = jobs.get(jobId);
+        if (record != null) {
+            record.status = "COMPLETED";
+            record.result = result;
+            record.error = null;
+            record.completedAtMs = System.currentTimeMillis();
+        }
+    }
+
+    void markFailed(String jobId, String error) {
+        JobRecord record = jobs.get(jobId);
+        if (record != null) {
+            record.status = "FAILED";
+            record.error = error;
+            record.completedAtMs = System.currentTimeMillis();
+        }
+    }
+
+    private static StrategyEvaluationAiVerifyJobStatusResponse toResponse(JobRecord record) {
+        return StrategyEvaluationAiVerifyJobStatusResponse.builder()
+                .jobId(record.jobId)
+                .status(record.status)
+                .error(record.error)
+                .result(record.result)
+                .createdAtMs(record.createdAtMs)
+                .completedAtMs(record.completedAtMs)
+                .build();
+    }
+
+    private void purgeExpired() {
+        long cutoff = System.currentTimeMillis() - JOB_TTL_MS;
+        Iterator<Map.Entry<String, JobRecord>> it = jobs.entrySet().iterator();
+        while (it.hasNext()) {
+            Map.Entry<String, JobRecord> e = it.next();
+            long ts = e.getValue().completedAtMs != null
+                    ? e.getValue().completedAtMs
+                    : e.getValue().createdAtMs;
+            if (ts < cutoff) {
+                it.remove();
+            }
+        }
+    }
+
+    private static final class JobRecord {
+        private final String jobId;
+        private final Long userId;
+        private final long createdAtMs;
+        private String status = "QUEUED";
+        private String error;
+        private StrategyEvaluationAiVerifyResponse result;
+        private Long completedAtMs;
+
+        private JobRecord(String jobId, Long userId, long createdAtMs) {
+            this.jobId = jobId;
+            this.userId = userId;
+            this.createdAtMs = createdAtMs;
+        }
+    }
+}

backend/src/main/java/com/goldenchart/service/StrategyEvaluationLlmService.java

@@ -26,31 +26,36 @@ public class StrategyEvaluationLlmService {
 
     private static final String SYSTEM_PROMPT = """
             당신은 GoldenChart 전략 평가(Ta4j Rule) 검증 전문가입니다.
-            사용자 JSON에는 선택 봉 시점의 조건 평가 결과, 차트 매매 시그널, 전략 DSL 요약이 포함됩니다.
-            ta4jDiagnostics 섹션은 서버가 동일 경로로 Ta4j Rule 을 재빌드·재평가한 결과(Rule descriptor, trace 로그, frontend vs backend 비교)입니다.
+            사용자 JSON에는 선택 봉 시점의 조건 평가, 차트 시그널, 전략 DSL, llmGuidance가 포함됩니다.
+            ta4jDiagnostics는 서버 Ta4j 재평가 결과(frontend vs backend 비교)입니다.
+
+            【중요 — 오판 방지】
+            - evaluation.buyConditions/sellConditions 각 항목의 dslCondition·dslExpression·evaluationSemantics를
+              판단의 1차 근거로 사용하세요. UI용 thresholdLabel(예: "> 20")은 컨텍스트에 없습니다.
+            - CROSS_UP/CROSS_DOWN은 GT/LT(수준 비교)와 다릅니다. 교차는 직전봉·현재봉 2봉 이벤트입니다.
+              현재값>임계값인데 CROSS_UP 미충족은 정상일 수 있습니다(이미 돌파 후 유지).
+            - matchRate는 leaf 조건 satisfied=true 비율입니다. 0%%는 leaf 전부 미충족이지,
+              Rule↔시그널 불일치를 뜻하지 않습니다.
+            - deterministicHints.signalMatchesOverallRule이 "대체로 일치"이면 overall Rule과 차트 시그널은 일치합니다.
 
             다음을 한국어로 분석하세요:
-            1) 선택 봉에서 매수/매도 전체 Rule(overallEntryMet/overallExitMet)과 개별 조건(satisfied)이 논리적으로 일치하는지
-            2) ta4jDiagnostics.reEvaluation·entryRule/exitRule.satisfiedAtEvalIndex 와 프론트 evaluation 이 일치하는지
-            3) entryRule/exitRule.evaluationTraceLog 로 Rule 평가 경로를 추적해 불일치 원인을 설명
-            4) 차트에 표시된 매매 시그널(selectedBarSignals, barSignalHighlight)이 평가 결과와 맞는지
-            5) 시그널이 없을 때: 실제로 조건 미충족이라 시그널이 없는 것인지, 로직·데이터 불일치 가능성이 있는지
-            6) 의심 지점이 있으면 구체적 조건 ID·지표·수치·Rule 타입을 인용
+            1) dslExpression·evaluationSemantics 기준으로 satisfied·overallEntryMet/overallExitMet 논리 일치
+            2) ta4jDiagnostics.reEvaluation·entryRule/exitRule.satisfiedAtEvalIndex 와 evaluation 일치
+            3) 차트 시그널(selectedBarSignals, barSignalHighlight)과 overall Rule 일치
+            4) 실제 불일치만 "오류 의심" — CROSS 조건의 정상 미충족은 "정상"
 
             출력 형식(마크다운):
             ## 종합 판정
             (정상 / 주의 / 오류 의심 — 한 줄 요약)
 
             ## 조건 평가 검증
-            (매수·매도 각각 bullet)
+            (매수·매도 각각 bullet — dslExpression 인용)
 
             ## 차트 시그널 검증
             (선택 봉 시그널 vs Rule 결과)
 
             ## 결론 및 권장 조치
             (1~3문장)
-
-            로직 문제(종합 판정이 주의·오류 의심)인 경우, 위 분석에서 불일치 원인·조건 ID·지표를 구체적으로 명시하세요.
             """;
 
     private static final String TEST_USER_PROMPT = "ping — 연결 테스트입니다. 한 문장으로 응답해 주세요.";