LSTM v3 멀티피처, KIS OHLCV 배치, 동적 전략 강화

- deep_learning.py: INPUT_SIZE=7 (close/open/high/low/volume/rsi/macd), feature_scaler/target_scaler 분리, ModelRegistry LRU 종목별 격리 (v3 체크포인트) - kis.py: get_daily_ohlcv() OHLCV 전체 반환, KISAsyncClient 비동기 배치 조회 추가, order() 지정가/조건부 주문 지원 - strategy/process.py: ATR/ADX 기반 동적 손절익절, 트레일링 스탑, 포지션 사이징 강화 - config.py: OLLAMA_NUM_THREAD=8 (9800X3D 최적화), LSTM_COOLDOWN/FAST_EPOCHS 환경변수화 - macro.py: 거시경제 지표 계산 개선 - ollama.py: VRAM 여유량 기반 선택적 언로드 - monitor.py: CPU 서킷 브레이커 연속 횟수 조건 추가 - ipc.py: IPC_STALENESS 600초로 확대 - news.py: 비동기 뉴스 수집 개선 - telegram.py, runner.py: 안정성 개선 Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-02-24 23:08:33 +09:00
parent 37f6d87bec
commit 4e77a1acf1
11 changed files with 939 additions and 268 deletions
--- a/modules/analysis/deep_learning.py
+++ b/modules/analysis/deep_learning.py
@@ -1,8 +1,10 @@
 import os
 import time
+import pickle
 import torch
 import torch.nn as nn
 import numpy as np
+from collections import OrderedDict
 from sklearn.preprocessing import MinMaxScaler

 from modules.config import Config
@@ -10,6 +12,10 @@ from modules.config import Config
 # cuDNN 벤치마크 활성화 (고정 입력 크기에 대해 최적 커널 자동 선택)
 torch.backends.cudnn.benchmark = True

+# 체크포인트 버전 (피처 수 변경 시 기존 모델 자동 재학습)
+CHECKPOINT_VERSION = "v3"
+INPUT_SIZE = 7  # close, open, high, low, volume_norm, rsi_14, macd_hist
+

 class Attention(nn.Module):
    def __init__(self, hidden_size):
@@ -23,7 +29,7 @@ class Attention(nn.Module):


 class AdvancedLSTM(nn.Module):
-    def __init__(self, input_size=1, hidden_size=512, num_layers=4, output_size=1, dropout=0.3):
+    def __init__(self, input_size=INPUT_SIZE, hidden_size=512, num_layers=4, output_size=1, dropout=0.3):
        super(AdvancedLSTM, self).__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
@@ -49,8 +55,24 @@ class AdvancedLSTM(nn.Module):
        return out


+def _get_free_vram_gb():
+    """현재 GPU VRAM 여유량(GB) 반환"""
+    try:
+        if torch.cuda.is_available():
+            total = torch.cuda.get_device_properties(0).total_memory / 1024**3
+            reserved = torch.cuda.memory_reserved(0) / 1024**3
+            return total - reserved
+    except Exception:
+        pass
+    return 99.0  # CUDA 없으면 언로드 불필요
+
+
 def _unload_ollama():
-    """LSTM 학습 전 Ollama 모델 언로드하여 GPU 메모리 확보"""
+    """LSTM 학습 전 Ollama 모델 언로드 (VRAM < 2GB 여유일 때만)"""
+    free_vram = _get_free_vram_gb()
+    if free_vram >= 2.0:
+        print(f"[AI] Ollama 언로드 생략 (VRAM 여유 {free_vram:.1f}GB >= 2GB)")
+        return
    try:
        import requests
        url = f"{Config.OLLAMA_API_URL}/api/generate"
@@ -58,14 +80,17 @@ def _unload_ollama():
            "model": Config.OLLAMA_MODEL,
            "keep_alive": 0
        }, timeout=5)
-        print("[AI] Ollama model unloaded (GPU memory freed)")
-        time.sleep(1)  # 메모리 해제 대기
+        print(f"[AI] Ollama 언로드 (VRAM 여유 {free_vram:.1f}GB)")
+        time.sleep(1)
    except Exception:
        pass


 def _preload_ollama():
-    """LSTM 학습 후 Ollama 모델 다시 로드"""
+    """LSTM 학습 후 Ollama 모델 리로드 (언로드했던 경우만)"""
+    free_vram = _get_free_vram_gb()
+    if free_vram >= 2.0:
+        return  # 언로드하지 않았으니 리로드도 불필요
    try:
        import requests
        url = f"{Config.OLLAMA_API_URL}/api/generate"
@@ -86,25 +111,99 @@ def _log_gpu_memory(tag=""):
        print(f"[AI GPU {tag}] Allocated: {allocated:.2f}GB / Reserved: {reserved:.2f}GB")


+def _compute_rsi(close_arr, period=14):
+    """RSI 계산 (numpy 기반)"""
+    if len(close_arr) < period + 1:
+        return np.full(len(close_arr), 50.0)
+    delta = np.diff(close_arr, prepend=close_arr[0])
+    gain = np.where(delta > 0, delta, 0.0)
+    loss = np.where(delta < 0, -delta, 0.0)
+    alpha = 1.0 / period
+    rsi_arr = np.zeros(len(close_arr))
+    avg_gain = gain[0]
+    avg_loss = loss[0]
+    for i in range(1, len(close_arr)):
+        avg_gain = alpha * gain[i] + (1 - alpha) * avg_gain
+        avg_loss = alpha * loss[i] + (1 - alpha) * avg_loss
+        rs = avg_gain / (avg_loss + 1e-9)
+        rsi_arr[i] = 100 - (100 / (1 + rs))
+    return rsi_arr
+
+
+def _compute_macd_hist(close_arr, fast=12, slow=26, signal=9):
+    """MACD Histogram 계산 (numpy 기반)"""
+    if len(close_arr) < slow + signal:
+        return np.zeros(len(close_arr))
+    ema_fast = np.zeros(len(close_arr))
+    ema_slow = np.zeros(len(close_arr))
+    alpha_f = 2 / (fast + 1)
+    alpha_s = 2 / (slow + 1)
+    ema_fast[0] = close_arr[0]
+    ema_slow[0] = close_arr[0]
+    for i in range(1, len(close_arr)):
+        ema_fast[i] = alpha_f * close_arr[i] + (1 - alpha_f) * ema_fast[i - 1]
+        ema_slow[i] = alpha_s * close_arr[i] + (1 - alpha_s) * ema_slow[i - 1]
+    macd = ema_fast - ema_slow
+    sig = np.zeros(len(close_arr))
+    alpha_sig = 2 / (signal + 1)
+    sig[0] = macd[0]
+    for i in range(1, len(close_arr)):
+        sig[i] = alpha_sig * macd[i] + (1 - alpha_sig) * sig[i - 1]
+    return macd - sig
+
+
+def _build_feature_matrix(ohlcv_data):
+    """
+    OHLCV 딕셔너리 → 7차원 numpy 피처 행렬 생성
+    피처: [close, open, high, low, volume_norm, rsi_14, macd_hist]
+    """
+    close = np.array(ohlcv_data.get('close', []), dtype=np.float64)
+    open_ = np.array(ohlcv_data.get('open', close), dtype=np.float64)
+    high = np.array(ohlcv_data.get('high', close), dtype=np.float64)
+    low = np.array(ohlcv_data.get('low', close), dtype=np.float64)
+    volume = np.array(ohlcv_data.get('volume', []), dtype=np.float64)
+
+    n = len(close)
+    if len(open_) != n: open_ = close.copy()
+    if len(high) != n: high = close.copy()
+    if len(low) != n: low = close.copy()
+
+    # 거래량 정규화 (최대값 기준, 0이면 0)
+    if len(volume) == n and volume.max() > 0:
+        volume_norm = volume / (volume.max() + 1e-9)
+    else:
+        volume_norm = np.zeros(n)
+
+    rsi = _compute_rsi(close, period=14)
+    rsi_norm = rsi / 100.0  # 0~1 정규화
+
+    macd_hist = _compute_macd_hist(close)
+
+    # 7차원 피처 스택 (n x 7)
+    features = np.column_stack([close, open_, high, low, volume_norm, rsi_norm, macd_hist])
+    return features  # shape: (n, 7)
+
+
 class PricePredictor:
    """
-    주가 예측 Deep Learning 모델 (GPU 최적화)
-    - 전체 학습 데이터를 GPU에 상주 (CPU↔GPU 전송 최소화)
-    - Ollama 모델 언로드/리로드로 GPU 메모리 확보
-    - Early Stopping + Mixed Precision (FP16)
-    - 종목별 모델 체크포인트
+    [v3.0] 주가 예측 Deep Learning 모델 (GPU 최적화)
+    - 7차원 멀티피처 LSTM (close/open/high/low/vol_norm/rsi/macd_hist)
+    - feature_scaler(6개) + target_scaler(1개) 분리
+    - 데이터 누수 수정: train 데이터로만 fit
+    - 체크포인트에 scaler 상태 저장/로드
+    - VRAM 여유량 기반 Ollama 언로드 (충분하면 생략)
    """
    def __init__(self):
-        self.scaler = MinMaxScaler(feature_range=(0, 1))
+        self.feature_scaler = MinMaxScaler(feature_range=(0, 1))  # 입력 6개 피처
+        self.target_scaler = MinMaxScaler(feature_range=(0, 1))   # 타겟: close 가격

        self.hidden_size = 512
        self.num_layers = 4

-        self.model = AdvancedLSTM(input_size=1, hidden_size=self.hidden_size,
+        self.model = AdvancedLSTM(input_size=INPUT_SIZE, hidden_size=self.hidden_size,
                                  num_layers=self.num_layers, dropout=0.3)
        self.criterion = nn.MSELoss()

-        # CUDA 설정
        self.device = torch.device('cpu')
        self.use_amp = False

@@ -116,19 +215,18 @@ class PricePredictor:
                self.device = torch.device('cuda')
                self.model.to(self.device)

-                # Mixed Precision (Compute Capability >= 7.0: Volta 이상)
                if torch.cuda.get_device_capability(0)[0] >= 7:
                    self.use_amp = True

-                # Warm-up: CUDA 커널 컴파일 유도
-                dummy = torch.zeros(1, 60, 1, device=self.device)
+                # Warm-up
+                dummy = torch.zeros(1, 60, INPUT_SIZE, device=self.device)
                with torch.no_grad():
                    _ = self.model(dummy)
                torch.cuda.synchronize()

                print(f"[AI] GPU Mode: {gpu_name} ({vram_gb:.1f}GB)"
                      f" | FP16={'ON' if self.use_amp else 'OFF'}"
-                      f" | cuDNN Benchmark=ON")
+                      f" | Features={INPUT_SIZE} | cuDNN Benchmark=ON")
                _log_gpu_memory("init")

            except Exception as e:
@@ -139,9 +237,8 @@ class PricePredictor:
            print("[AI] No CUDA GPU detected. Running on CPU.")

        self.optimizer = torch.optim.AdamW(self.model.parameters(), lr=0.001, weight_decay=1e-4)
-        # [v2.0] Learning Rate Scheduler (ReduceLROnPlateau: val_loss 정체 시 lr 감소)
        self.lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
-            self.optimizer, mode='min', factor=0.5, patience=7, min_lr=1e-6, verbose=False
+            self.optimizer, mode='min', factor=0.5, patience=7, min_lr=1e-6
        )
        self.scaler_amp = torch.amp.GradScaler('cuda') if self.use_amp else None

@@ -149,7 +246,6 @@ class PricePredictor:
        self.max_epochs = 200
        self.seq_length = 60
        self.patience = 15
-        # [v2.0] Gradient Clipping 값 (exploding gradient 방지)
        self.max_grad_norm = 1.0

        self.training_status = {
@@ -164,7 +260,8 @@ class PricePredictor:
            try:
                gpu_name = torch.cuda.get_device_name(0)
                vram_gb = torch.cuda.get_device_properties(0).total_memory / 1024**3
-                print(f"[AI Check] {gpu_name} ({vram_gb:.1f}GB VRAM) | cuDNN={torch.backends.cudnn.is_available()}")
+                print(f"[AI Check] {gpu_name} ({vram_gb:.1f}GB VRAM) | cuDNN={torch.backends.cudnn.is_available()}"
+                      f" | Features={INPUT_SIZE}")
                return True
            except Exception as e:
                print(f"[AI Check] GPU Error: {e}")
@@ -173,16 +270,25 @@ class PricePredictor:
        return False

    def _get_checkpoint_path(self, ticker):
-        return os.path.join(Config.MODEL_DIR, f"{ticker}_lstm.pt")
+        return os.path.join(Config.MODEL_DIR, f"{ticker}_lstm_{CHECKPOINT_VERSION}.pt")

    def _load_checkpoint(self, ticker):
        path = self._get_checkpoint_path(ticker)
        if os.path.exists(path):
            try:
-                checkpoint = torch.load(path, map_location=self.device, weights_only=True)
+                checkpoint = torch.load(path, map_location=self.device, weights_only=False)
+                # 버전 체크 (v3 이전 체크포인트는 재학습)
+                if checkpoint.get('version', '') != CHECKPOINT_VERSION:
+                    print(f"[AI] Checkpoint version mismatch ({ticker}): 재학습 필요")
+                    return False
                self.model.load_state_dict(checkpoint['model_state_dict'])
                self.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
-                print(f"[AI] Checkpoint loaded: {ticker}")
+                # scaler 복원
+                if 'feature_scaler' in checkpoint:
+                    self.feature_scaler = pickle.loads(checkpoint['feature_scaler'])
+                if 'target_scaler' in checkpoint:
+                    self.target_scaler = pickle.loads(checkpoint['target_scaler'])
+                print(f"[AI] Checkpoint loaded: {ticker} (v3, 7-features)")
                return True
            except Exception as e:
                print(f"[AI] Checkpoint load failed ({ticker}): {e}")
@@ -192,21 +298,127 @@ class PricePredictor:
        path = self._get_checkpoint_path(ticker)
        try:
            torch.save({
+                'version': CHECKPOINT_VERSION,
                'model_state_dict': self.model.state_dict(),
                'optimizer_state_dict': self.optimizer.state_dict(),
                'epoch': epoch,
-                'loss': loss
+                'loss': loss,
+                'feature_scaler': pickle.dumps(self.feature_scaler),
+                'target_scaler': pickle.dumps(self.target_scaler)
            }, path)
        except Exception as e:
            print(f"[AI] Checkpoint save failed ({ticker}): {e}")

-    def train_and_predict(self, prices, forecast_days=1, ticker=None):
+    def _is_checkpoint_fresh(self, ticker, max_age=None):
+        """체크포인트가 최근에 학습된 것인지 확인 (쿨다운 판단)"""
+        if not ticker:
+            return False
+        path = self._get_checkpoint_path(ticker)
+        if not os.path.exists(path):
+            return False
+        age = time.time() - os.path.getmtime(path)
+        threshold = max_age if max_age is not None else Config.LSTM_COOLDOWN
+        return age < threshold
+
+    def _prepare_scaled_features(self, features, split_point):
+        """
+        피처 스케일링 (누수 방지: train split으로만 fit)
+        features: (n, 7) numpy array
+        split_point: train/val 분리 인덱스
+
+        Returns:
+            scaled_features: (n, 7) 스케일된 전체 피처
+            scaled_close: (n, 1) 스케일된 close (타겟용)
+        """
+        # 6개 입력 피처 (close 포함 open/high/low/vol_norm/rsi/macd_hist)
+        # + 타겟은 close만 별도 scaler
+        input_features = features[:, :]   # (n, 7) 전체 7개 피처 입력용
+        target_close = features[:, 0:1]   # (n, 1) close만 타겟용
+
+        # train 데이터로만 fit (데이터 누수 방지)
+        self.feature_scaler.fit(input_features[:split_point])
+        self.target_scaler.fit(target_close[:split_point])
+
+        scaled_features = self.feature_scaler.transform(input_features)
+        scaled_close = self.target_scaler.transform(target_close)
+
+        return scaled_features, scaled_close
+
+    def _predict_only(self, ohlcv_data, ticker=None):
+        """학습 없이 현재 체크포인트로만 빠른 예측 (쿨다운 중 사용)"""
+        prices = ohlcv_data.get('close', []) if isinstance(ohlcv_data, dict) else ohlcv_data
+        if len(prices) < self.seq_length:
+            return None
+        try:
+            features = _build_feature_matrix(
+                ohlcv_data if isinstance(ohlcv_data, dict) else {'close': prices}
+            )
+            if len(features) < self.seq_length:
+                return None
+
+            scaled = self.feature_scaler.transform(features)
+            last_seq = torch.FloatTensor(scaled[-self.seq_length:]).unsqueeze(0).to(self.device)
+
+            self.model.eval()
+            with torch.no_grad():
+                if self.use_amp:
+                    with torch.amp.autocast('cuda'):
+                        pred_scaled = self.model(last_seq)
+                else:
+                    pred_scaled = self.model(last_seq)
+
+                predicted_price = self.target_scaler.inverse_transform(
+                    pred_scaled.cpu().float().numpy())[0][0]
+
+            current_price = prices[-1]
+            trend = "UP" if predicted_price > current_price else "DOWN"
+            change_rate = ((predicted_price - current_price) / current_price) * 100
+
+            cached_loss = self.training_status.get("loss", 0.5)
+            print(f"[AI] {ticker or '?'}: 쿨다운 중 → 캐시 예측 사용 "
+                  f"({predicted_price:.0f} / {change_rate:+.2f}%)")
+            return {
+                "current": current_price,
+                "predicted": float(predicted_price),
+                "change_rate": round(change_rate, 2),
+                "trend": trend,
+                "loss": cached_loss,
+                "val_loss": cached_loss,
+                "confidence": 0.62,
+                "epochs": 0,
+                "device": str(self.device),
+                "lr": self.optimizer.param_groups[0]['lr'],
+                "cached": True
+            }
+        except Exception as e:
+            print(f"[AI] _predict_only 실패 ({ticker}): {e}")
+            return None
+
+    def train_and_predict(self, ohlcv_data, forecast_days=1, ticker=None):
+        """
+        [v3.0] 7차원 멀티피처 LSTM 학습 + 예측
+        ohlcv_data: dict {'close':[], 'open':[], 'high':[], 'low':[], 'volume':[]}
+                    또는 list (하위 호환: close 리스트)
+        """
+        # 하위 호환: list 형태
+        if isinstance(ohlcv_data, list):
+            ohlcv_data = {'close': ohlcv_data}
+
+        prices = ohlcv_data.get('close', [])
        if len(prices) < (self.seq_length + 10):
            return None

+        # ===== 쿨다운 체크 =====
+        if self._is_checkpoint_fresh(ticker):
+            has_ckpt = self._load_checkpoint(ticker)
+            if has_ckpt:
+                result = self._predict_only(ohlcv_data, ticker)
+                if result:
+                    return result
+
        is_gpu = self.device.type == 'cuda'

-        # --- Ollama 모델 언로드 (GPU 메모리 확보) ---
+        # VRAM 여유량 기반 Ollama 언로드
        if is_gpu:
            _unload_ollama()
            torch.cuda.empty_cache()
@@ -214,54 +426,54 @@ class PricePredictor:

        t_start = time.time()

-        # 1. 데이터 전처리 (CPU에서 numpy 작업)
-        data = np.array(prices).reshape(-1, 1)
-        scaled_data = self.scaler.fit_transform(data)
+        # 1. 피처 행렬 구성 (n, 7)
+        features = _build_feature_matrix(ohlcv_data)
+        if len(features) < (self.seq_length + 10):
+            return None

+        n = len(features)
+        split_point = int(n * 0.8)
+
+        # 2. 스케일링 (train 데이터로만 fit → 누수 방지)
+        scaled_features, scaled_close = self._prepare_scaled_features(features, split_point)
+
+        # 3. 시퀀스 생성
        x_seqs, y_seqs = [], []
-        for i in range(len(scaled_data) - self.seq_length):
-            x_seqs.append(scaled_data[i:i + self.seq_length])
-            y_seqs.append(scaled_data[i + self.seq_length])
+        for i in range(n - self.seq_length):
+            x_seqs.append(scaled_features[i:i + self.seq_length])  # (seq, 7)
+            y_seqs.append(scaled_close[i + self.seq_length])        # (1,)

-        # 2. 텐서 생성 → 즉시 GPU로 이동 (이후 CPU↔GPU 전송 없음)
        x_all = torch.FloatTensor(np.array(x_seqs)).to(self.device)
        y_all = torch.FloatTensor(np.array(y_seqs)).to(self.device)

-        # Validation split (80/20)
-        split_idx = int(len(x_all) * 0.8)
-        x_train = x_all[:split_idx]
-        y_train = y_all[:split_idx]
-        x_val = x_all[split_idx:]
-        y_val = y_all[split_idx:]
-
+        # validation split (80/20)
+        seq_split = int(len(x_all) * 0.8)
+        x_train, y_train = x_all[:seq_split], y_all[:seq_split]
+        x_val, y_val = x_all[seq_split:], y_all[seq_split:]
        dataset_size = len(x_train)

-        # 3. 체크포인트 로드
-        has_checkpoint = False
-        if ticker:
-            has_checkpoint = self._load_checkpoint(ticker)
-        max_epochs = 50 if has_checkpoint else self.max_epochs
+        # 4. 체크포인트 로드
+        has_checkpoint = self._load_checkpoint(ticker) if ticker else False
+        max_epochs = Config.LSTM_FAST_EPOCHS if has_checkpoint else self.max_epochs

-        # 4. 학습 (전체 데이터 GPU 상주, DataLoader 미사용)
-        # [v2.0] LR Scheduler 리셋
        self.optimizer.param_groups[0]['lr'] = 0.001 if not has_checkpoint else 0.0005
        self.lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
-            self.optimizer, mode='min', factor=0.5, patience=7, min_lr=1e-6, verbose=False
+            self.optimizer, mode='min', factor=0.5, patience=7, min_lr=1e-6
        )

+        # 5. 학습
        self.model.train()
        self.training_status["is_training"] = True
        if ticker:
            self.training_status["current_ticker"] = ticker

        best_val_loss = float('inf')
-        best_model_state = None  # [v2.0] Best Model 저장
+        best_model_state = None
        patience_counter = 0
        final_loss = 0.0
        actual_epochs = 0

        for epoch in range(max_epochs):
-            # --- Training (GPU 내에서 셔플 + 미니배치) ---
            perm = torch.randperm(dataset_size, device=self.device)
            x_shuffled = x_train[perm]
            y_shuffled = y_train[perm]
@@ -281,7 +493,6 @@ class PricePredictor:
                        outputs = self.model(batch_x)
                        loss = self.criterion(outputs, batch_y)
                    self.scaler_amp.scale(loss).backward()
-                    # [v2.0] Gradient Clipping (AMP 호환)
                    self.scaler_amp.unscale_(self.optimizer)
                    torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.max_grad_norm)
                    self.scaler_amp.step(self.optimizer)
@@ -290,7 +501,6 @@ class PricePredictor:
                    outputs = self.model(batch_x)
                    loss = self.criterion(outputs, batch_y)
                    loss.backward()
-                    # [v2.0] Gradient Clipping
                    torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.max_grad_norm)
                    self.optimizer.step()

@@ -299,7 +509,6 @@ class PricePredictor:

            train_loss = epoch_loss / max(1, steps)

-            # --- Validation (GPU에서 직접 수행) ---
            self.model.eval()
            with torch.no_grad():
                if self.use_amp:
@@ -311,23 +520,19 @@ class PricePredictor:
                    val_loss = self.criterion(val_out, y_val).item()
            self.model.train()

-            # [v2.0] LR Scheduler step (val_loss 기반)
            self.lr_scheduler.step(val_loss)
-
            final_loss = train_loss
            actual_epochs = epoch + 1

            if val_loss < best_val_loss:
                best_val_loss = val_loss
                patience_counter = 0
-                # [v2.0] Best model 상태 저장 (overfitting 방지)
                best_model_state = {k: v.clone() for k, v in self.model.state_dict().items()}
            else:
                patience_counter += 1
                if patience_counter >= self.patience:
                    break

-        # [v2.0] Best model 복원 (early stopping 후 최적 상태로 복구)
        if best_model_state:
            self.model.load_state_dict(best_model_state)

@@ -340,17 +545,17 @@ class PricePredictor:
        elapsed = time.time() - t_start
        print(f"[AI] {ticker or '?'}: {actual_epochs} epochs in {elapsed:.1f}s"
              f" | loss={final_loss:.6f} val={best_val_loss:.6f}"
-              f" | device={self.device}")
+              f" | device={self.device} | features={INPUT_SIZE}")

-        # 5. 체크포인트 저장
+        # 6. 체크포인트 저장 (scaler 포함)
        if ticker:
            self._save_checkpoint(ticker, actual_epochs, final_loss)

-        # 6. 예측
+        # 7. 예측
        self.model.eval()
        with torch.no_grad():
            last_seq = torch.FloatTensor(
-                scaled_data[-self.seq_length:]
+                scaled_features[-self.seq_length:]
            ).unsqueeze(0).to(self.device)

            if self.use_amp:
@@ -359,12 +564,11 @@ class PricePredictor:
            else:
                predicted_scaled = self.model(last_seq)

-            predicted_price = self.scaler.inverse_transform(
+            predicted_price = self.target_scaler.inverse_transform(
                predicted_scaled.cpu().float().numpy())[0][0]

-        # 7. GPU 메모리 정리 + Ollama 리로드
+        # 8. GPU 정리 + Ollama 리로드
        if is_gpu:
-            # 학습 중간 텐서 해제
            del x_all, y_all, x_train, y_train, x_val, y_val
            torch.cuda.empty_cache()
            _log_gpu_memory("post-train")
@@ -374,27 +578,22 @@ class PricePredictor:
        trend = "UP" if predicted_price > current_price else "DOWN"
        change_rate = ((predicted_price - current_price) / current_price) * 100

-        # [v2.0] 개선된 신뢰도 계산
-        # 1. 학습 손실 기반 (낮을수록 좋음)
+        # 신뢰도 계산
        loss_confidence = 1.0 / (1.0 + (best_val_loss * 50))

-        # 2. Train/Val 괴리도 (overfitting 감지)
        overfit_ratio = final_loss / (best_val_loss + 1e-9)
        if overfit_ratio < 0.5:
-            # Train loss가 Val loss보다 훨씬 낮음 = overfitting
            overfit_penalty = 0.7
        elif overfit_ratio > 2.0:
-            # Train loss가 Val loss보다 훨씬 높음 = underfitting
            overfit_penalty = 0.8
        else:
            overfit_penalty = 1.0

-        # 3. 에포크 수 기반 (너무 적거나 많으면 불신)
        epoch_factor = 1.0
        if actual_epochs < 10:
-            epoch_factor = 0.6  # 학습 부족
+            epoch_factor = 0.6
        elif actual_epochs >= max_epochs:
-            epoch_factor = 0.8  # 수렴 실패
+            epoch_factor = 0.8

        confidence = min(0.95, loss_confidence * overfit_penalty * epoch_factor)

@@ -411,28 +610,32 @@ class PricePredictor:
            "lr": self.optimizer.param_groups[0]['lr']
        }

-    def batch_predict(self, prices_dict):
+    def batch_predict(self, ohlcv_dict):
+        """여러 종목을 배치로 예측 (체크포인트 있는 종목만)"""
        results = {}
        seqs = []
        metas = []

-        for ticker, prices in prices_dict.items():
+        for ticker, ohlcv_data in ohlcv_dict.items():
+            if isinstance(ohlcv_data, list):
+                ohlcv_data = {'close': ohlcv_data}
+            prices = ohlcv_data.get('close', [])
            if len(prices) < (self.seq_length + 10):
                results[ticker] = None
                continue

-            data = np.array(prices).reshape(-1, 1)
-            scaler = MinMaxScaler(feature_range=(0, 1))
-            scaled_data = scaler.fit_transform(data)
-
-            seq = torch.FloatTensor(scaled_data[-self.seq_length:]).unsqueeze(0)
+            try:
+                features = _build_feature_matrix(ohlcv_data)
+                scaled = self.feature_scaler.transform(features)
+                seq = torch.FloatTensor(scaled[-self.seq_length:]).unsqueeze(0)
                seqs.append(seq)
-            metas.append((ticker, scaler, prices[-1]))
+                metas.append((ticker, prices[-1]))
+            except Exception:
+                results[ticker] = None

        if not seqs:
            return results

-        # 배치로 합쳐서 한번에 GPU 추론
        batch = torch.cat(seqs, dim=0).to(self.device)

        self.model.eval()
@@ -445,8 +648,8 @@ class PricePredictor:

            preds_cpu = preds.cpu().float().numpy()

-        for i, (ticker, scaler, current_price) in enumerate(metas):
-            predicted_price = scaler.inverse_transform(preds_cpu[i:i+1])[0][0]
+        for i, (ticker, current_price) in enumerate(metas):
+            predicted_price = self.target_scaler.inverse_transform(preds_cpu[i:i+1])[0][0]
            trend = "UP" if predicted_price > current_price else "DOWN"
            change_rate = ((predicted_price - current_price) / current_price) * 100

@@ -461,3 +664,52 @@ class PricePredictor:
            torch.cuda.empty_cache()

        return results
+
+
+class ModelRegistry:
+    """
+    [v3.0] 종목별 LSTM 모델 격리 (LRU 퇴출, max_models=5)
+    - 싱글톤 패턴: 워커 프로세스마다 하나의 Registry 유지
+    - 16GB VRAM에서 LSTM 5개(~250MB) + Ollama 7B(~4GB) 동시 적재 가능
+    """
+    _instance = None
+
+    @classmethod
+    def get_instance(cls):
+        if cls._instance is None:
+            cls._instance = cls()
+        return cls._instance
+
+    def __init__(self, max_models=5):
+        self.max_models = max_models
+        self._predictors = OrderedDict()  # ticker -> PricePredictor (LRU 순서)
+        print(f"[ModelRegistry] Initialized (max_models={max_models})")
+
+    def get_predictor(self, ticker):
+        """종목별 PricePredictor 반환 (없으면 생성, LRU 관리)"""
+        if ticker in self._predictors:
+            # LRU: 접근 시 맨 뒤로 이동
+            self._predictors.move_to_end(ticker)
+            return self._predictors[ticker]
+
+        # 용량 초과 시 가장 오래된 것 퇴출
+        if len(self._predictors) >= self.max_models:
+            oldest_ticker, oldest_pred = self._predictors.popitem(last=False)
+            print(f"[ModelRegistry] Evicted {oldest_ticker} (LRU, {len(self._predictors)}/{self.max_models})")
+            del oldest_pred
+            if torch.cuda.is_available():
+                torch.cuda.empty_cache()
+
+        predictor = PricePredictor()
+        self._predictors[ticker] = predictor
+        print(f"[ModelRegistry] Created predictor for {ticker} ({len(self._predictors)}/{self.max_models})")
+        return predictor
+
+    def has_predictor(self, ticker):
+        return ticker in self._predictors
+
+    def clear(self):
+        """모든 모델 해제"""
+        self._predictors.clear()
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
--- a/modules/analysis/macro.py
+++ b/modules/analysis/macro.py
@@ -22,7 +22,8 @@ class MacroAnalyzer:
        """
        indicators = {
            "KOSPI": "0001",
-            "KOSDAQ": "1001"
+            "KOSDAQ": "1001",
+            "KOSPI200": "0028",
        }

        results = {}
@@ -34,19 +35,19 @@ class MacroAnalyzer:
            data = kis_client.get_current_index(code)
            time.sleep(0.6)  # Rate Limit 방지 (초당 2회 제한)

-            if data:
-                price = data['price']
-                change = data['change']
-                results[name] = {"price": price, "change": change}
-                print(f"   - {name}: {price} ({change}%)")
+            if data and data.get('price', 0) != 0:
+                results[name] = data
+                print(f"   - {name}: {data['price']} ({data['change']}%)")

-                # 리스크 평가 로직 (단순화: 2% 이상 폭락 장이면 위험)
+                # 리스크 평가 로직 (2% 이상 폭락 장이면 위험)
+                change = data['change']
                if change <= -2.0:
                    risk_score += 2 # 패닉 상태
                elif change <= -1.0:
                    risk_score += 1 # 주의 상태
            else:
-                results[name] = {"price": 0, "change": 0}
+                results[name] = {"price": 0, "change": 0, "high": 0, "low": 0,
+                                 "prev_close": 0, "volume": 0, "trade_value": 0}
        
        # [신규] 시장 스트레스 지수(MSI) 추가
        time.sleep(0.6)
--- a/modules/config.py
+++ b/modules/config.py
@@ -12,7 +12,12 @@ class Config:
    # 2. NAS 및 AI 서버
    NAS_API_URL = os.getenv("NAS_API_URL", "http://192.168.45.54:18500")
    OLLAMA_API_URL = os.getenv("OLLAMA_API_URL", "http://localhost:11434")
-    OLLAMA_MODEL = os.getenv("OLLAMA_MODEL", "llama3.1:8b-instruct-q8_0")
+    # [최적화] qwen2.5:7b-instruct-q4_K_M: JSON 정확도↑, 속도↑, VRAM 4GB
+    # 14B 원하면: qwen2.5:14b-instruct-q4_K_M (VRAM ~9GB, 품질 더 좋음)
+    OLLAMA_MODEL = os.getenv("OLLAMA_MODEL", "qwen2.5:7b-instruct-q4_K_M")
+    OLLAMA_NUM_CTX = int(os.getenv("OLLAMA_NUM_CTX", "4096"))   # 8192→4096 (2배 속도)
+    OLLAMA_NUM_PREDICT = int(os.getenv("OLLAMA_NUM_PREDICT", "200"))  # 응답 토큰 제한
+    OLLAMA_NUM_THREAD = int(os.getenv("OLLAMA_NUM_THREAD", "8"))  # CPU 스레드 (9800X3D 최적화)
    
    # 3. KIS 한국투자증권
    KIS_ENV_TYPE = os.getenv("KIS_ENV_TYPE", "virtual").lower()
@@ -53,7 +58,7 @@ class Config:
    # 7. IPC 설정
    SHM_NAME = "web_ai_bot_ipc"
    SHM_SIZE = 131072  # 128KB
-    IPC_STALENESS = 120  # 120초 (메인 봇 사이클 60초 + 여유)
+    IPC_STALENESS = 600  # 600초 (LSTM 분석 사이클이 길어도 portfolio 명령어 정상 작동)

    # 8. GPU 설정
    VRAM_WARNING_THRESHOLD = 12.0  # GB (14 → 12로 조기 경고)
@@ -65,6 +70,16 @@ class Config:
    # 10. 타임아웃 등
    HTTP_TIMEOUT = 10

+    # 11. LSTM 학습 최적화
+    # 동일 종목을 이 시간(초) 내에 재학습하지 않음 → CPU/GPU 절약
+    LSTM_COOLDOWN = int(os.getenv("LSTM_COOLDOWN", "1200"))  # 20분
+    # 체크포인트가 있을 때 빠른 재학습 에포크 수 (기존 50 → 30)
+    LSTM_FAST_EPOCHS = int(os.getenv("LSTM_FAST_EPOCHS", "30"))
+
+    # 12. CPU 서킷 브레이커
+    CPU_CIRCUIT_BREAKER_THRESHOLD = 92   # CPU% 이상 시 분석 스킵
+    CPU_CIRCUIT_BREAKER_CONSECUTIVE = 2  # 연속 N회 초과 시 발동
+
    @staticmethod
    def validate():
        """필수 설정 검증"""
--- a/modules/services/kis.py
+++ b/modules/services/kis.py
@@ -271,38 +271,48 @@ class KISClient:
        except Exception as e:
            return {"error": str(e)}

-    def order(self, ticker, qty, buy_sell, price=0):
-        """주문 (시장가)
+    def order(self, ticker, qty, buy_sell, price=0, order_type="market"):
+        """주문
        buy_sell: 'BUY' or 'SELL'
+        order_type: 'market'(시장가), 'limit'(지정가), 'conditional'(조건부지정가)
+        price: 지정가일 때 주문 가격 (market이면 무시)
        """
        self._throttle()
        self.ensure_token()

        # 모의투자/실전 TR ID 구분
-        # 매수: VTTC0802U / TTTC0802U
-        # 매도: VTTC0801U / TTTC0801U
        if buy_sell == 'BUY':
            tr_id = "VTTC0802U" if self.is_virtual else "TTTC0802U"
        else:
            tr_id = "VTTC0801U" if self.is_virtual else "TTTC0801U"

+        # 주문 구분 코드
+        # 00: 지정가, 01: 시장가, 03: 최유리지정가, 05: 장전시간외, 06: 장후시간외
+        if order_type == "limit" and price > 0:
+            ord_dvsn = "00"
+            ord_unpr = str(int(price))
+            order_type_str = f"지정가({price:,.0f})"
+        elif order_type == "conditional" and price > 0:
+            ord_dvsn = "03"  # 최유리지정가
+            ord_unpr = str(int(price))
+            order_type_str = f"조건부({price:,.0f})"
+        else:
+            ord_dvsn = "01"  # 시장가
+            ord_unpr = "0"
+            order_type_str = "시장가"
+
        url = f"{self.base_url}/uapi/domestic-stock/v1/trading/order-cash"

-        # 주문 파라미터
        datas = {
            "CANO": self.cano,
            "ACNT_PRDT_CD": self.acnt_prdt_cd,
            "PDNO": ticker,
-            "ORD_DVSN": "01", # 01: 시장가
+            "ORD_DVSN": ord_dvsn,
            "ORD_QTY": str(qty),
-            "ORD_UNPR": "0"   # 시장가는 0
+            "ORD_UNPR": ord_unpr
        }

-        # 헤더 준비
        headers = self._get_headers(tr_id=tr_id)
-        
-        # [중요] POST 요청(주문 등) 시 Hash Key 필수
-        # 단, 모의투자의 경우 일부 상황에서 생략 가능할 수 있으나, 정석대로 포함
        hash_key = self.get_hash_key(datas)
        if hash_key:
            headers["hashkey"] = hash_key
@@ -310,7 +320,7 @@ class KISClient:
            print("⚠️ [KIS] Hash Key 생성 실패 (주문 전송 시도)")

        try:
-            print(f"📤 [KIS] 주문 전송: {buy_sell} {ticker} {qty}ea (시장가)")
+            print(f"📤 [KIS] 주문 전송: {buy_sell} {ticker} {qty}ea ({order_type_str})")
            res = requests.post(url, headers=headers, json=datas)
            res.raise_for_status()
            data = res.json()
@@ -348,34 +358,188 @@ class KISClient:
            print(f"❌ 현재가 조회 실패: {e}")
            return None

-    def get_daily_price(self, ticker, period="D"):
-        """일별 시세 조회 (기술적 분석용)"""
+    def _get_daily_ohlcv_by_range(self, ticker, period="D", count=100):
+        """기간별시세 API (FHKST03010100) - OHLCV 전체 반환
+        output2에서 stck_oprc, stck_hgpr, stck_lwpr, stck_clpr, acml_vol 파싱
+        """
        self._throttle()
        self.ensure_token()
-        url = f"{self.base_url}/uapi/domestic-stock/v1/quotations/inquire-daily-price"
-        headers = self._get_headers(tr_id="FHKST01010400")
+
+        end_date = datetime.now().strftime("%Y%m%d")
+        start_date = (datetime.now() - timedelta(days=int(count * 1.6))).strftime("%Y%m%d")
+
+        url = f"{self.base_url}/uapi/domestic-stock/v1/quotations/inquire-daily-itemchartprice"
+        headers = self._get_headers(tr_id="FHKST03010100")

        params = {
            "FID_COND_MRKT_DIV_CODE": "J",
            "FID_INPUT_ISCD": ticker,
+            "FID_INPUT_DATE_1": start_date,
+            "FID_INPUT_DATE_2": end_date,
            "FID_PERIOD_DIV_CODE": period,
-            "FID_ORG_ADJ_PRC": "1" # 수정주가
+            "FID_ORG_ADJ_PRC": "1"
        }

        try:
-            res = requests.get(url, headers=headers, params=params)
+            res = requests.get(url, headers=headers, params=params,
+                               timeout=Config.HTTP_TIMEOUT)
            res.raise_for_status()
            data = res.json()
-            if data['rt_cd'] != '0':
+
+            if data.get('rt_cd') != '0':
+                return None
+
+            output = data.get('output2', [])
+            if not output:
+                return None
+
+            opens, highs, lows, closes, volumes = [], [], [], [], []
+            for item in output:
+                try:
+                    c = int(item.get('stck_clpr', 0) or 0)
+                    o = int(item.get('stck_oprc', 0) or 0)
+                    h = int(item.get('stck_hgpr', 0) or 0)
+                    l = int(item.get('stck_lwpr', 0) or 0)
+                    v = int(item.get('acml_vol', 0) or 0)
+                    if c > 0:
+                        opens.append(o if o > 0 else c)
+                        highs.append(h if h > 0 else c)
+                        lows.append(l if l > 0 else c)
+                        closes.append(c)
+                        volumes.append(v)
+                except (ValueError, TypeError):
+                    pass
+
+            if not closes:
+                return None
+
+            # API는 최신순 → 과거→현재 순으로 변환
+            opens.reverse(); highs.reverse(); lows.reverse()
+            closes.reverse(); volumes.reverse()
+
+            result = {
+                'open': opens[-count:],
+                'high': highs[-count:],
+                'low': lows[-count:],
+                'close': closes[-count:],
+                'volume': volumes[-count:]
+            }
+            print(f"[KIS] {ticker} OHLCV: {len(result['close'])}개 ({start_date}~{end_date})")
+            return result
+
+        except Exception as e:
+            print(f"⚠️ [KIS] OHLCV 조회 실패 ({ticker}): {e}")
+            return None
+
+    def get_daily_ohlcv(self, ticker, period="D", count=100):
+        """일별 OHLCV 시세 조회 (기술적 분석 + LSTM 7차원 입력용)
+        1차: 기간별시세 API OHLCV 파싱 (100일)
+        2차: 기존 close-only fallback
+        """
+        ohlcv = self._get_daily_ohlcv_by_range(ticker, period, count)
+        if ohlcv and len(ohlcv['close']) >= 30:
+            return ohlcv
+
+        # fallback: close만 반환 (가짜 OHLCV)
+        print(f"[KIS] {ticker} OHLCV 실패 → close-only fallback")
+        prices = self._get_daily_price_by_range(ticker, period, count)
+        if not prices:
+            return None
+        return {
+            'open': prices, 'high': prices, 'low': prices,
+            'close': prices, 'volume': []
+        }
+
+    def _get_daily_price_by_range(self, ticker, period="D", count=100):
+        """기간별시세 API (FHKST03010100) - 날짜 범위로 최대 100일 데이터 반환
+        inquire-daily-price(FHKST01010400)가 30일만 반환하는 한계 극복"""
+        self._throttle()
+        self.ensure_token()
+
+        end_date = datetime.now().strftime("%Y%m%d")
+        # 영업일 count개 확보를 위해 역일 1.6배 요청 (주말/공휴일 여유)
+        start_date = (datetime.now() - timedelta(days=int(count * 1.6))).strftime("%Y%m%d")
+
+        url = f"{self.base_url}/uapi/domestic-stock/v1/quotations/inquire-daily-itemchartprice"
+        headers = self._get_headers(tr_id="FHKST03010100")
+
+        params = {
+            "FID_COND_MRKT_DIV_CODE": "J",
+            "FID_INPUT_ISCD": ticker,
+            "FID_INPUT_DATE_1": start_date,
+            "FID_INPUT_DATE_2": end_date,
+            "FID_PERIOD_DIV_CODE": period,
+            "FID_ORG_ADJ_PRC": "1"
+        }
+
+        try:
+            res = requests.get(url, headers=headers, params=params,
+                               timeout=Config.HTTP_TIMEOUT)
+            res.raise_for_status()
+            data = res.json()
+
+            if data.get('rt_cd') != '0':
                return []

-            # 과거 데이터부터 오도록 정렬 필요할 수 있음 (API는 최신순)
-            # output 리스트: [ {stck_clpr: 종가, ...}, ... ]
-            prices = [int(item['stck_clpr']) for item in data['output']]
-            prices.reverse() # 과거 -> 현재 순으로 정렬
+            # 기간별시세는 output2에 배열로 반환
+            output = data.get('output2', [])
+            if not output:
+                return []
+
+            prices = []
+            for item in output:
+                clpr = item.get('stck_clpr', '')
+                if clpr and clpr != '0':
+                    try:
+                        prices.append(int(clpr))
+                    except ValueError:
+                        pass
+
+            prices.reverse()  # API는 최신순 → 과거→현재 순으로 변환
+            result = prices[-count:]
+            print(f"[KIS] {ticker} 기간별시세: {len(result)}개 "
+                  f"({start_date}~{end_date})")
+            return result
+
+        except Exception as e:
+            print(f"⚠️ [KIS] 기간별시세 조회 실패 ({ticker}): {e}")
+            return []
+
+    def get_daily_price(self, ticker, period="D", count=100):
+        """일별 시세 조회 (기술적 분석 + LSTM용)
+        1차: 기간별시세 API (100일, LSTM 학습 가능)
+        2차: 구형 API fallback (30일)
+        """
+        # 1차: 기간별시세 API (FHKST03010100) - 100일
+        prices = self._get_daily_price_by_range(ticker, period, count)
+        if prices and len(prices) >= 30:
+            return prices
+
+        # 2차: 구형 API fallback (FHKST01010400) - 30일
+        print(f"[KIS] {ticker} 기간별시세 실패 → 구형 API(30일) fallback")
+        self._throttle()
+        self.ensure_token()
+        url = f"{self.base_url}/uapi/domestic-stock/v1/quotations/inquire-daily-price"
+        headers = self._get_headers(tr_id="FHKST01010400")
+        params = {
+            "FID_COND_MRKT_DIV_CODE": "J",
+            "FID_INPUT_ISCD": ticker,
+            "FID_PERIOD_DIV_CODE": period,
+            "FID_ORG_ADJ_PRC": "1"
+        }
+        try:
+            res = requests.get(url, headers=headers, params=params,
+                               timeout=Config.HTTP_TIMEOUT)
+            res.raise_for_status()
+            data = res.json()
+            if data.get('rt_cd') != '0':
+                return []
+            prices = [int(item['stck_clpr']) for item in data['output']
+                      if item.get('stck_clpr')]
+            prices.reverse()
            return prices
        except Exception as e:
-            print(f"❌ 일별 시세 조회 실패: {e}")
+            print(f"❌ 일별 시세 조회 실패 ({ticker}): {e}")
            return []

    def get_volume_rank(self, limit=5):
@@ -437,9 +601,18 @@ class KISClient:
            data = self._request_api("GET", endpoint, "FHKUP03500100", params=params)
            if data['rt_cd'] != '0':
                return None
+            o = data['output']
+            def _f(val): return float(val) if val else 0.0
+            def _i(val): return int(float(val)) if val else 0
            return {
-                "price": float(data['output']['bstp_nmix_prpr']),      # 현재지수
-                "change": float(data['output']['bstp_nmix_prdy_ctrt']) # 등락률(%)
+                "price": _f(o.get('bstp_nmix_prpr')),       # 현재지수
+                "change": _f(o.get('bstp_nmix_prdy_ctrt')), # 등락률(%)
+                "change_val": _f(o.get('bstp_nmix_prdy_vrss')), # 전일 대비 포인트
+                "high": _f(o.get('bstp_nmix_hgpr')),        # 장중 고가
+                "low": _f(o.get('bstp_nmix_lwpr')),         # 장중 저가
+                "prev_close": _f(o.get('prdy_nmix')),       # 전일 종가
+                "volume": _i(o.get('acml_vol')),             # 누적 거래량(천주)
+                "trade_value": _i(o.get('acml_tr_pbmn')),   # 누적 거래대금(백만원)
            }
        except Exception as e:
            print(f"❌ 지수 조회 실패({ticker}): {e}")
@@ -533,9 +706,8 @@ class KISAsyncClient:
            return None

    async def get_daily_price_async(self, ticker):
-        """비동기 일별 시세 조회"""
+        """비동기 일별 시세 조회 (close only, 하위 호환)"""
        import aiohttp
-        import asyncio

        self.sync.ensure_token()
        url = f"{self.sync.base_url}/uapi/domestic-stock/v1/quotations/inquire-daily-price"
@@ -555,6 +727,52 @@ class KISAsyncClient:
                return prices
        return []

+    async def get_daily_ohlcv_async(self, ticker, count=100):
+        """비동기 OHLCV 조회 (기간별시세 API 사용)"""
+        import aiohttp
+        from datetime import datetime, timedelta
+
+        self.sync.ensure_token()
+        end_date = datetime.now().strftime("%Y%m%d")
+        start_date = (datetime.now() - timedelta(days=int(count * 1.6))).strftime("%Y%m%d")
+
+        url = f"{self.sync.base_url}/uapi/domestic-stock/v1/quotations/inquire-daily-itemchartprice"
+        headers = self.sync._get_headers(tr_id="FHKST03010100")
+        params = {
+            "FID_COND_MRKT_DIV_CODE": "J",
+            "FID_INPUT_ISCD": ticker,
+            "FID_INPUT_DATE_1": start_date,
+            "FID_INPUT_DATE_2": end_date,
+            "FID_PERIOD_DIV_CODE": "D",
+            "FID_ORG_ADJ_PRC": "1"
+        }
+
+        async with aiohttp.ClientSession() as session:
+            data = await self._async_get(session, url, headers, params)
+            if data and data.get('rt_cd') == '0':
+                output = data.get('output2', [])
+                opens, highs, lows, closes, volumes = [], [], [], [], []
+                for item in output:
+                    try:
+                        c = int(item.get('stck_clpr', 0) or 0)
+                        if c > 0:
+                            opens.append(int(item.get('stck_oprc', 0) or c))
+                            highs.append(int(item.get('stck_hgpr', 0) or c))
+                            lows.append(int(item.get('stck_lwpr', 0) or c))
+                            closes.append(c)
+                            volumes.append(int(item.get('acml_vol', 0) or 0))
+                    except (ValueError, TypeError):
+                        pass
+                if closes:
+                    opens.reverse(); highs.reverse(); lows.reverse()
+                    closes.reverse(); volumes.reverse()
+                    return {
+                        'open': opens[-count:], 'high': highs[-count:],
+                        'low': lows[-count:], 'close': closes[-count:],
+                        'volume': volumes[-count:]
+                    }
+        return None
+
    async def get_investor_trend_async(self, ticker):
        """비동기 투자자 동향 조회"""
        import aiohttp
@@ -582,7 +800,7 @@ class KISAsyncClient:
        return None

    async def get_daily_prices_batch(self, tickers):
-        """여러 종목의 일별 시세를 병렬로 조회"""
+        """여러 종목의 일별 시세(close only)를 병렬로 조회 (하위 호환)"""
        import aiohttp
        import asyncio

@@ -592,7 +810,6 @@ class KISAsyncClient:
        async with aiohttp.ClientSession() as session:
            tasks = []
            for i, ticker in enumerate(tickers):
-                # rate limit: 0.5초 간격으로 요청 생성
                if i > 0:
                    await asyncio.sleep(self.min_interval)

@@ -617,6 +834,67 @@ class KISAsyncClient:

        return results

+    async def get_daily_ohlcv_batch(self, tickers, count=100):
+        """여러 종목의 OHLCV를 병렬로 조회 (기간별시세 API)"""
+        import aiohttp
+        import asyncio
+        from datetime import datetime, timedelta
+
+        self.sync.ensure_token()
+        results = {}
+
+        end_date = datetime.now().strftime("%Y%m%d")
+        start_date = (datetime.now() - timedelta(days=int(count * 1.6))).strftime("%Y%m%d")
+
+        async with aiohttp.ClientSession() as session:
+            tasks = []
+            for i, ticker in enumerate(tickers):
+                if i > 0:
+                    await asyncio.sleep(self.min_interval)
+
+                url = f"{self.sync.base_url}/uapi/domestic-stock/v1/quotations/inquire-daily-itemchartprice"
+                headers = self.sync._get_headers(tr_id="FHKST03010100")
+                params = {
+                    "FID_COND_MRKT_DIV_CODE": "J",
+                    "FID_INPUT_ISCD": ticker,
+                    "FID_INPUT_DATE_1": start_date,
+                    "FID_INPUT_DATE_2": end_date,
+                    "FID_PERIOD_DIV_CODE": "D",
+                    "FID_ORG_ADJ_PRC": "1"
+                }
+                tasks.append((ticker, self._async_get(session, url, headers, params)))
+
+            for ticker, task in tasks:
+                data = await task
+                if data and data.get('rt_cd') == '0':
+                    output = data.get('output2', [])
+                    opens, highs, lows, closes, volumes = [], [], [], [], []
+                    for item in output:
+                        try:
+                            c = int(item.get('stck_clpr', 0) or 0)
+                            if c > 0:
+                                opens.append(int(item.get('stck_oprc', 0) or c))
+                                highs.append(int(item.get('stck_hgpr', 0) or c))
+                                lows.append(int(item.get('stck_lwpr', 0) or c))
+                                closes.append(c)
+                                volumes.append(int(item.get('acml_vol', 0) or 0))
+                        except (ValueError, TypeError):
+                            pass
+                    if closes:
+                        opens.reverse(); highs.reverse(); lows.reverse()
+                        closes.reverse(); volumes.reverse()
+                        results[ticker] = {
+                            'open': opens[-count:], 'high': highs[-count:],
+                            'low': lows[-count:], 'close': closes[-count:],
+                            'volume': volumes[-count:]
+                        }
+                    else:
+                        results[ticker] = None
+                else:
+                    results[ticker] = None
+
+        return results
+
    async def get_investor_trends_batch(self, tickers):
        """여러 종목의 투자자 동향을 병렬로 조회"""
        import aiohttp
--- a/modules/services/news.py
+++ b/modules/services/news.py
@@ -28,6 +28,7 @@ class AsyncNewsCollector:
        self._cache = None
        self._cache_time = 0
        self._cache_ttl = 300  # 5분
+        self._stock_cache = {}  # {stock_name: (items, timestamp)}

    def get_market_news(self, query="주식 시장"):
        """동기 인터페이스 (하위 호환)"""
@@ -62,11 +63,43 @@ class AsyncNewsCollector:
                    self._cache_time = now
                    return items
        except ImportError:
-            # aiohttp 미설치 시 동기 fallback
            return self.get_market_news(query)
        except Exception as e:
            print(f"[News Async] Collection failed: {e}")
-            # 캐시가 있으면 반환, 없으면 동기 fallback
            if self._cache:
                return self._cache
            return self.get_market_news(query)
+
+    async def get_stock_news_async(self, stock_name, max_items=3):
+        """종목별 뉴스 수집 (5분 캐싱)
+        stock_name: 종목 이름 (e.g. '삼성전자', 'SK하이닉스')
+        """
+        now = time.time()
+        cached = self._stock_cache.get(stock_name)
+        if cached and (now - cached[1]) < self._cache_ttl:
+            return cached[0]
+
+        try:
+            import aiohttp
+            import urllib.parse
+            query = urllib.parse.quote(f"{stock_name} 주가")
+            url = f"https://news.google.com/rss/search?q={query}&hl=ko&gl=KR&ceid=KR:ko"
+            async with aiohttp.ClientSession() as session:
+                async with session.get(url, timeout=aiohttp.ClientTimeout(total=5)) as resp:
+                    content = await resp.read()
+                    root = ET.fromstring(content)
+                    items = []
+                    for item in root.findall(".//item")[:max_items]:
+                        title_el = item.find("title")
+                        if title_el is not None and title_el.text:
+                            items.append({"title": title_el.text, "source": "Google News"})
+
+                    self._stock_cache[stock_name] = (items, now)
+                    return items
+        except Exception as e:
+            print(f"[News] 종목 뉴스 수집 실패 ({stock_name}): {e}")
+            return []
+
+    def clear_stock_cache(self):
+        """종목 뉴스 캐시 전체 초기화"""
+        self._stock_cache.clear()
--- a/modules/services/ollama.py
+++ b/modules/services/ollama.py
@@ -113,20 +113,24 @@ class OllamaManager:
            "model": self.model_name,
            "prompt": prompt,
            "stream": False,
-            "format": "json", # JSON 강제
+            "format": "json",
            "options": {
-                "num_ctx": 8192,    # [5070Ti 최적화] 컨텍스트 크기 2배 증가 (4096 -> 8192)
-                "temperature": 0.2,  # 분석 일관성 유지
-                "num_gpu": 1,        # GPU 사용 명시
-                "num_thread": 8      # CPU 스레드 수 (9800X3D 활용)
+                "num_ctx": Config.OLLAMA_NUM_CTX,      # 4096 (속도 2배)
+                "num_predict": Config.OLLAMA_NUM_PREDICT,  # 응답 토큰 제한
+                "temperature": 0.1,  # 더 결정론적 (JSON 파싱 안정성)
+                "num_gpu": 1,
+                "num_thread": Config.OLLAMA_NUM_THREAD  # Config 설정값 (기본 8)
            },
-            "keep_alive": "10m"      # [5070Ti 최적화] 10분간 유지 (메모리 여유 있음)
+            "keep_alive": "5m"  # 5분 유지 (불필요한 VRAM 점유 줄임)
        }

        try:
-            response = requests.post(self.generate_url, json=payload, timeout=180)  # 타임아웃 증가
+            response = requests.post(self.generate_url, json=payload, timeout=90)  # 180→90초
            response.raise_for_status()
            return response.json().get('response')
+        except requests.exceptions.Timeout:
+            print(f"❌ Inference Timeout (90s): {self.model_name}")
+            return None
        except Exception as e:
            print(f"❌ Inference Error: {e}")
            return None
--- a/modules/services/telegram.py
+++ b/modules/services/telegram.py
@@ -23,7 +23,7 @@ class TelegramMessenger:
            payload = {
                "chat_id": self.chat_id,
                "text": message,
-                "parse_mode": "Markdown"
+                "parse_mode": "HTML"
            }
            try:
                requests.post(url, json=payload, timeout=5)
--- a/modules/services/telegram_bot/runner.py
+++ b/modules/services/telegram_bot/runner.py
@@ -30,6 +30,9 @@ def run_telegram_bot_standalone(ipc_lock=None, command_queue=None, shutdown_even
    # IPC 초기화 (shared memory + command queue)
    ipc = SharedIPC(lock=ipc_lock, command_queue=command_queue)

+    conflict_retries = 0
+    MAX_CONFLICT_RETRIES = 10
+
    while True:
        # shutdown 체크
        if shutdown_event and shutdown_event.is_set():
@@ -51,6 +54,7 @@ def run_telegram_bot_standalone(ipc_lock=None, command_queue=None, shutdown_even

            if bot_server.should_restart:
                print("[Telegram Bot] Restarting instance...")
+                conflict_retries = 0  # 정상 재시작 시 카운터 리셋
                time.sleep(1)
                continue
            else:
@@ -61,7 +65,17 @@ def run_telegram_bot_standalone(ipc_lock=None, command_queue=None, shutdown_even
            print("[Telegram Bot] Stopped by user")
            break
        except Exception as e:
-            if "Conflict" not in str(e):
+            if "Conflict" in str(e):
+                conflict_retries += 1
+                if conflict_retries >= MAX_CONFLICT_RETRIES:
+                    print(f"[Telegram Bot] Conflict max retries ({MAX_CONFLICT_RETRIES}) reached. Exiting.")
+                    break
+                wait_secs = min(5 * conflict_retries, 30)
+                print(f"[Telegram Bot] Conflict detected. Waiting {wait_secs}s before retry "
+                      f"({conflict_retries}/{MAX_CONFLICT_RETRIES})...")
+                time.sleep(wait_secs)
+                continue
+            else:
                print(f"[Telegram Bot] Error: {e}")
                import traceback
                traceback.print_exc()
--- a/modules/strategy/process.py
+++ b/modules/strategy/process.py
@@ -3,20 +3,25 @@ import json
 import numpy as np
 from modules.services.ollama import OllamaManager
 from modules.analysis.technical import TechnicalAnalyzer
-from modules.analysis.deep_learning import PricePredictor
+from modules.analysis.deep_learning import ModelRegistry

-# [최적화] 워커 프로세스별 전역 변수 (LSTM 모델 캐싱)
-_lstm_predictor = None
+# [최적화] 워커 프로세스별 전역 변수 (Ollama 캐싱)
+_ollama_manager = None

-def get_predictor():
-    """워커 프로세스 내에서 PricePredictor 인스턴스를 싱글톤으로 관리"""
-    global _lstm_predictor
-    if _lstm_predictor is None:
-        print(f"[Worker {os.getpid()}] Initializing LSTM Predictor...")
-        _lstm_predictor = PricePredictor()
-        print(f"[Worker {os.getpid()}] LSTM Device: {_lstm_predictor.device}"
-              f" | AMP: {_lstm_predictor.use_amp}")
-    return _lstm_predictor
+
+def get_predictor(ticker=None):
+    """워커 프로세스 내에서 ModelRegistry로 종목별 PricePredictor 관리"""
+    registry = ModelRegistry.get_instance()
+    return registry.get_predictor(ticker or "default")
+
+
+def get_ollama():
+    """워커 프로세스 내에서 OllamaManager 인스턴스를 싱글톤으로 관리
+    - 종목마다 새 인스턴스를 만들면 Ollama에 동시 요청이 폭주해 데드락 발생"""
+    global _ollama_manager
+    if _ollama_manager is None:
+        _ollama_manager = OllamaManager()
+    return _ollama_manager


 def calculate_position_size(total_capital, current_price, volatility, score, ai_confidence,
@@ -40,9 +45,8 @@ def calculate_position_size(total_capital, current_price, volatility, score, ai_
    base_invest = total_capital * 0.10

    # 2. 변동성 조절 계수 (변동성 높을수록 투자금 감소)
-    # 변동성 1% → 1.0배, 2% → 0.75배, 3% → 0.5배, 5%+ → 0.3배
    if volatility <= 1.0:
-        vol_factor = 1.2  # 안정적 종목은 약간 증가
+        vol_factor = 1.2
    elif volatility <= 2.0:
        vol_factor = 1.0
    elif volatility <= 3.0:
@@ -50,10 +54,9 @@ def calculate_position_size(total_capital, current_price, volatility, score, ai_
    elif volatility <= 5.0:
        vol_factor = 0.45
    else:
-        vol_factor = 0.3  # 고변동 종목
+        vol_factor = 0.3

-    # 3. 확신도 조절 계수 (score가 높을수록 투자금 증가)
-    # score 0.6 → 0.5배, 0.7 → 1.0배, 0.8 → 1.5배, 0.9+ → 2.0배
+    # 3. 확신도 조절 계수
    if score >= 0.85:
        conf_factor = 2.0
    elif score >= 0.75:
@@ -73,45 +76,85 @@ def calculate_position_size(total_capital, current_price, volatility, score, ai_
    # 5. 최종 투자금 계산
    invest_amount = base_invest * vol_factor * conf_factor * ai_bonus

-    # 상한 제한
    invest_amount = min(invest_amount, max_per_stock)
-    invest_amount = min(invest_amount, total_capital * 0.15)  # 최대 15%
-    invest_amount = min(invest_amount, total_capital)  # 잔고 초과 방지
+    invest_amount = min(invest_amount, total_capital * 0.15)
+    invest_amount = min(invest_amount, total_capital)

-    # 수량 계산
    qty = int(invest_amount / current_price)
    return max(0, qty)


-def analyze_stock_process(ticker, prices, news_items, investor_trend=None,
+def analyze_stock_process(ticker, ohlcv_data, news_items, investor_trend=None,
                          macro_status=None, holding_info=None):
    """
-    [v2.0] 종목 분석 + 매매 판단 (ProcessPoolExecutor에서 실행)
+    [v3.0] 종목 분석 + 매매 판단 (ProcessPoolExecutor에서 실행)

-    [v2.0 개선사항]
-    1. ATR 기반 동적 손절/익절 + 트레일링 스탑
-    2. 포지션 사이징 (변동성 + 확신도 기반)
-    3. 시장상황별 동적 매수/매도 임계값
-    4. 보유종목에 대한 분석 기반 매도 판단
-    5. ADX/OBV/MTF 통합 기술적 분석
-    6. 강화된 AI 프롬프트 (종목 고유 뉴스 분석)
+    [v3.0 개선사항]
+    1. OHLCV 전체 수신 (실제 고가/저가/거래량 사용)
+    2. 종목별 ModelRegistry (가중치 덮어쓰기 방지)
+    3. 강화된 LLM 프롬프트 (거시경제 상태, 볼린저밴드, 거래량 급증, 보유 수익률)
    """
    try:
-        print(f"⚙️ [Bot Process] Analyzing {ticker} ({len(prices)} candles)...")
+        # OHLCV 데이터 분리 (하위호환: list 형태도 허용)
+        if isinstance(ohlcv_data, dict):
+            prices = ohlcv_data.get('close', [])
+            high_prices = ohlcv_data.get('high') or None
+            low_prices = ohlcv_data.get('low') or None
+            volume_history = ohlcv_data.get('volume') or None
+            open_prices = ohlcv_data.get('open') or None
+        else:
+            # 하위 호환: 기존 close 리스트
+            prices = ohlcv_data if isinstance(ohlcv_data, list) else []
+            high_prices = None
+            low_prices = None
+            volume_history = None
+            open_prices = None
+
+        # volume이 모두 0이거나 비어있으면 None 처리
+        if volume_history and all(v == 0 for v in volume_history):
+            volume_history = None
+
+        print(f"⚙️ [Bot Process] Analyzing {ticker} ({len(prices)} candles, "
+              f"OHLCV={'yes' if high_prices else 'close-only'}, "
+              f"Vol={'yes' if volume_history else 'no'})...")

        # ===== 1. 기술적 지표 계산 =====
        current_price = prices[-1] if prices else 0
        tech_score, rsi, volatility, vol_ratio, ma_info = TechnicalAnalyzer.get_technical_score(
-            current_price, prices, volume_history=None)
+            current_price, prices, volume_history=volume_history)

-        # ===== 2. ATR 기반 동적 손절/익절 =====
-        sl_tp = TechnicalAnalyzer.calculate_dynamic_sl_tp(prices)
+        # ===== 2. ATR 기반 동적 손절/익절 (실제 고가/저가 사용) =====
+        sl_tp = TechnicalAnalyzer.calculate_dynamic_sl_tp(
+            prices, high_prices=high_prices, low_prices=low_prices)

-        # ===== 3. LSTM 주가 예측 =====
-        lstm_predictor = get_predictor()
+        # ===== 3. 볼린저밴드 위치 계산 =====
+        bb_upper, bb_mid, bb_lower = TechnicalAnalyzer.calculate_bollinger_bands(prices)
+        if bb_upper > bb_lower:
+            bb_pos = (current_price - bb_lower) / (bb_upper - bb_lower)  # 0=하단, 1=상단
+            if bb_pos <= 0.2:
+                bb_zone = "하단(과매도)"
+            elif bb_pos >= 0.8:
+                bb_zone = "상단(과매수)"
+            else:
+                bb_zone = f"중간({bb_pos:.0%})"
+        else:
+            bb_pos = 0.5
+            bb_zone = "중간"
+
+        # ===== 4. LSTM 주가 예측 (ModelRegistry 사용) =====
+        lstm_predictor = get_predictor(ticker)
        if lstm_predictor:
            lstm_predictor.training_status['current_ticker'] = ticker
-        pred_result = lstm_predictor.train_and_predict(prices, ticker=ticker)
+
+        # LSTM에 전달할 OHLCV 딕셔너리 구성
+        lstm_ohlcv = {
+            'close': prices,
+            'open': open_prices or prices,
+            'high': high_prices or prices,
+            'low': low_prices or prices,
+            'volume': volume_history or []
+        }
+        pred_result = lstm_predictor.train_and_predict(lstm_ohlcv, ticker=ticker)

        lstm_score = 0.5
        ai_confidence = 0.5
@@ -130,7 +173,7 @@ def analyze_stock_process(ticker, prices, news_items, investor_trend=None,

            lstm_score = max(0.0, min(1.0, lstm_score))

-        # ===== 4. 수급 분석 (외인/기관) =====
+        # ===== 5. 수급 분석 (외인/기관) =====
        investor_score = 0.0
        frgn_net_buy = 0
        orgn_net_buy = 0
@@ -146,26 +189,23 @@ def analyze_stock_process(ticker, prices, news_items, investor_trend=None,
                if day['institutional'] > 0:
                    consecutive_orgn_buy += 1

-            # 외인 수급 점수 (강화)
            if frgn_net_buy > 0:
                investor_score += 0.03
            if consecutive_frgn_buy >= 3:
                investor_score += 0.04
            if consecutive_frgn_buy >= 5:
-                investor_score += 0.03  # 5일 연속 매수 = 추가 보너스
+                investor_score += 0.03

-            # 기관 수급 점수 (신규)
            if orgn_net_buy > 0:
                investor_score += 0.02
            if consecutive_orgn_buy >= 3:
                investor_score += 0.03

-            # 외인+기관 동시 순매수 = 강력 신호
            if frgn_net_buy > 0 and orgn_net_buy > 0:
                investor_score += 0.03
                print(f"   💰 [Investor] Both Foreign & Institutional Buying!")

-        # ===== 5. AI 뉴스 분석 (강화된 프롬프트) =====
+        # ===== 6. AI 뉴스 분석 (강화된 프롬프트) =====
        if pred_result:
            pred_price = pred_result.get('predicted', 0)
            pred_change = pred_result.get('change_rate', 0)
@@ -173,65 +213,59 @@ def analyze_stock_process(ticker, prices, news_items, investor_trend=None,
            pred_price = current_price
            pred_change = 0.0

-        ollama = OllamaManager()
-        prompt = f"""
-[System Instruction]
-1. Role: You are a legendary quant trader with 30 years of experience in Korean stock market.
-2. You MUST analyze the data objectively and respond with a JSON object.
+        news_summary = "; ".join(
+            [n.get('title', '') for n in (news_items or [])[:3] if n.get('title')]
+        ) or "뉴스 없음"

-[Market Data for Stock {ticker}]
- Current Price: {current_price:,.0f} KRW
- Technical Score: {tech_score:.3f} (RSI: {rsi:.1f})
- Moving Average: {ma_info['trend']} (Price is {ma_info['position']})
- ADX Trend Strength: {ma_info.get('adx', 20):.1f} ({ma_info.get('adx_trend', 'N/A')})
- Multi-Timeframe: {ma_info.get('mtf_alignment', 'N/A')}
- AI Prediction: {pred_price:.0f} KRW ({pred_change:+.2f}%)
- AI Confidence: {ai_confidence:.2f} (Training Loss: {ai_loss:.4f})
- Volatility: {volatility:.2f}%
- Volume Ratio: {vol_ratio:.1f}x
- ATR Stop Loss: {sl_tp['stop_loss_pct']:.1f}% / Take Profit: {sl_tp['take_profit_pct']:.1f}%
- Investor Trend (5 Days): Foreigner Net Buy {frgn_net_buy}, Institutional Net Buy {orgn_net_buy}
+        # 거시경제 상태
+        macro_state = macro_status.get('status', 'SAFE') if macro_status else 'SAFE'

-[Decision Framework]
- Strong BUY signals: Foreigners+Institutions buying, Golden Cross, ADX>25 with bullish trend, AI high confidence UP
- Moderate BUY: RSI<40 with bullish reversal, Price near Bollinger Lower Band
- SELL signals: RSI>70, Dead Cross, ADX>25 with bearish trend, Foreigners selling
- AVOID/HOLD: ADX<20 (sideways), Mixed signals, Low confidence
+        # 거래량 급증 여부
+        vol_surge = "급증(x{:.1f})".format(vol_ratio) if vol_ratio >= 2.0 else "정상"

-[News Data]
-{json.dumps(news_items[:5] if news_items else [], ensure_ascii=False)}
+        # 보유종목 수익률
+        holding_yield_str = ""
+        if holding_info and holding_info.get('qty', 0) > 0:
+            yld = holding_info.get('yield', 0.0)
+            holding_yield_str = f" | 보유수익률={yld:+.1f}%"

-[Response Format - JSON Only]
-{{"sentiment_score": 0.0 to 1.0, "reason": "Brief analysis reason"}}
-"""
+        ollama = get_ollama()
+        prompt = (
+            f"Korean stock analyst. JSON only: {{\"sentiment_score\":0.0-1.0,\"reason\":\"1 sentence\"}}\n"
+            f"Stock {ticker} ₩{current_price:,.0f}{holding_yield_str}\n"
+            f"Market={macro_state} | "
+            f"Tech={tech_score:.2f} RSI={rsi:.1f} MA={ma_info['trend']} ADX={ma_info.get('adx',20):.0f} "
+            f"MTF={ma_info.get('mtf_alignment','N/A')}\n"
+            f"BB={bb_zone} | AI={pred_change:+.2f}% conf={ai_confidence:.0%} | "
+            f"Vol={volatility:.1f}% VolRatio={vol_surge}\n"
+            f"Flow: Frgn={frgn_net_buy:+,}({consecutive_frgn_buy}d) "
+            f"Inst={orgn_net_buy:+,}({consecutive_orgn_buy}d)\n"
+            f"News: {news_summary}"
+        )
        ai_resp = ollama.request_inference(prompt)
        sentiment_score = 0.5
        ai_reason = ""
        try:
            data = json.loads(ai_resp)
            sentiment_score = float(data.get("sentiment_score", 0.5))
-            sentiment_score = max(0.0, min(1.0, sentiment_score))  # 범위 강제
+            sentiment_score = max(0.0, min(1.0, sentiment_score))
            ai_reason = data.get("reason", "")
        except Exception:
            print(f"   ⚠️ AI response parse failed, using neutral (0.5)")

-        # ===== 6. 통합 점수 (동적 가중치 v2.0) =====
-        # ADX가 높으면 (추세가 강하면) LSTM과 기술적 분석 비중 증가
+        # ===== 7. 통합 점수 (동적 가중치 v2.0) =====
        adx_val = ma_info.get('adx', 20)

        if ai_confidence >= 0.85 and adx_val >= 25:
-            # 강한 추세 + 높은 AI 신뢰도: AI 최우선
            w_tech, w_news, w_ai = 0.15, 0.15, 0.70
            print(f"   🤖 [Ultra High Confidence + Strong Trend] AI Weight 70%")
        elif ai_confidence >= 0.85:
            w_tech, w_news, w_ai = 0.20, 0.20, 0.60
            print(f"   🤖 [High Confidence] AI Weight 60%")
        elif adx_val >= 30:
-            # 매우 강한 추세: 기술적 분석 우선
            w_tech, w_news, w_ai = 0.50, 0.20, 0.30
            print(f"   📊 [Very Strong Trend ADX={adx_val:.0f}] Tech Weight 50%")
        elif adx_val < 20:
-            # 비추세/횡보: 뉴스와 수급 중시
            w_tech, w_news, w_ai = 0.30, 0.40, 0.30
            print(f"   📰 [Sideways ADX={adx_val:.0f}] News Weight 40%")
        else:
@@ -239,63 +273,53 @@ def analyze_stock_process(ticker, prices, news_items, investor_trend=None,

        total_score = (w_tech * tech_score) + (w_news * sentiment_score) + (w_ai * lstm_score)

-        # 수급 가산점 (최대 +0.15)
        total_score += min(investor_score, 0.15)
        total_score = min(total_score, 1.0)

-        # ===== 7. 시장 상황별 동적 임계값 =====
+        # ===== 8. 시장 상황별 동적 임계값 =====
        buy_threshold = 0.60
        sell_threshold = 0.30

        if macro_status:
-            macro_state = macro_status.get('status', 'SAFE')
            if macro_state == 'DANGER':
-                buy_threshold = 999.0  # 매수 완전 차단
-                sell_threshold = 0.45  # 매도 기준 상향 (빨리 탈출)
+                buy_threshold = 999.0
+                sell_threshold = 0.45
                print(f"   🚨 [DANGER Market] Buy BLOCKED, Sell threshold raised to 0.45")
            elif macro_state == 'CAUTION':
-                buy_threshold = 0.72  # 매수 기준 대폭 상향 (보수적)
-                sell_threshold = 0.38  # 매도 기준도 상향
+                buy_threshold = 0.72
+                sell_threshold = 0.38
                print(f"   ⚠️ [CAUTION Market] Buy threshold raised to 0.72")

-        # ===== 8. 매매 결정 =====
+        # ===== 9. 매매 결정 =====
        decision = "HOLD"
        decision_reason = ""

-        # --- 보유 종목 분석 기반 매도 (신규) ---
        if holding_info:
            holding_yield = holding_info.get('yield', 0.0)
            holding_qty = holding_info.get('qty', 0)
            peak_price = holding_info.get('peak_price', current_price)

            if holding_qty > 0:
-                # A. 동적 손절 (ATR 기반)
                if holding_yield <= sl_tp['stop_loss_pct']:
                    decision = "SELL"
                    decision_reason = f"Dynamic Stop Loss ({holding_yield:.1f}% <= {sl_tp['stop_loss_pct']:.1f}%)"

-                # B. 동적 익절 (ATR 기반)
                elif holding_yield >= sl_tp['take_profit_pct']:
                    decision = "SELL"
                    decision_reason = f"Dynamic Take Profit ({holding_yield:.1f}% >= {sl_tp['take_profit_pct']:.1f}%)"

-                # C. 트레일링 스탑 (최고가 대비 하락)
                elif peak_price > 0:
                    drop_from_peak = ((current_price - peak_price) / peak_price) * 100
                    if drop_from_peak <= -sl_tp['trailing_stop_pct'] and holding_yield > 2.0:
-                        # 수익 상태에서만 트레일링 스탑 작동 (2% 이상 수익 확보)
                        decision = "SELL"
                        decision_reason = (f"Trailing Stop ({drop_from_peak:.1f}% from peak, "
                                           f"threshold: -{sl_tp['trailing_stop_pct']:.1f}%)")

-                # D. 분석 기반 매도 (점수가 매도 임계값 이하)
                if decision == "HOLD" and total_score <= sell_threshold:
                    decision = "SELL"
                    decision_reason = f"Analysis Signal (Score: {total_score:.2f} <= {sell_threshold:.2f})"

-                # E. 추세 반전 매도 (ADX 강한 하락추세)
                if decision == "HOLD" and adx_val >= 30:
-                    plus_di = ma_info.get('adx', 0)  # 참고용
                    mtf_align = ma_info.get('mtf_alignment', '')
                    if mtf_align == 'STRONG_BEAR' and holding_yield < 0:
                        decision = "SELL"
@@ -303,11 +327,9 @@ def analyze_stock_process(ticker, prices, news_items, investor_trend=None,

        # --- 매수 판단 ---
        if decision == "HOLD":
-            # 강한 단일 신호 매수 (기준 강화)
            strong_signal = False
            strong_reason = ""

-            # [강화] 복합 조건 매수 (단일 지표가 아닌 복합 조건)
            if tech_score >= 0.75 and lstm_score >= 0.6 and sentiment_score >= 0.6:
                strong_signal = True
                strong_reason = "Triple Confirmation (Tech+AI+News)"
@@ -322,7 +344,6 @@ def analyze_stock_process(ticker, prices, news_items, investor_trend=None,
                strong_reason = f"Strong Multi-Timeframe Bullish + Tech {tech_score:.2f}"

            if strong_signal and total_score >= buy_threshold - 0.05:
-                # 강한 신호는 임계값 약간 완화 허용
                decision = "BUY"
                decision_reason = strong_reason
                print(f"   🎯 [{strong_reason}] → BUY!")
@@ -330,10 +351,9 @@ def analyze_stock_process(ticker, prices, news_items, investor_trend=None,
                decision = "BUY"
                decision_reason = f"Score {total_score:.2f} >= threshold {buy_threshold:.2f}"

-        # ===== 9. 포지션 사이징 =====
+        # ===== 10. 포지션 사이징 =====
        suggested_qty = 0
        if decision == "BUY":
-            # 기본 자산 1000만원으로 가정 (실제 run_cycle에서 덮어씀)
            suggested_qty = calculate_position_size(
                total_capital=10000000,
                current_price=current_price,
--- a/modules/utils/ipc.py
+++ b/modules/utils/ipc.py
@@ -59,6 +59,7 @@ class SharedIPC:

    def read_status(self):
        """텔레그램 봇이 상태를 shared memory에서 읽기"""
+        raw = None
        try:
            shm = self._ensure_shm()

@@ -66,13 +67,15 @@ class SharedIPC:
                self.lock.acquire()
            try:
                length = struct.unpack_from('I', shm.buf, 0)[0]
-                if length == 0 or length > Config.SHM_SIZE - 4:
-                    return None
+                if length > 0 and length <= Config.SHM_SIZE - 4:
                    raw = bytes(shm.buf[4:4 + length])
            finally:
                if self.lock:
                    self.lock.release()

+            if not raw:
+                return None
+
            ipc_data = json.loads(raw.decode('utf-8'))
            age = time.time() - ipc_data.get('timestamp', 0)

--- a/modules/utils/monitor.py
+++ b/modules/utils/monitor.py
@@ -1,6 +1,8 @@
 import psutil
 from datetime import datetime

+from modules.config import Config
+

 class SystemMonitor:
    def __init__(self, messenger, ollama_manager):
@@ -8,52 +10,101 @@ class SystemMonitor:
        self.ollama_monitor = ollama_manager
        self.last_health_check = datetime.now()

+        # CPU 서킷 브레이커 상태
+        self._cpu_overload_count = 0       # 연속 과부하 횟수
+        self._circuit_open = False         # 서킷 브레이커 발동 여부
+        self._circuit_open_since = None
+
+    def is_cpu_critical(self):
+        """서킷 브레이커가 발동 상태인지 반환 (True이면 분석 사이클 스킵)"""
+        return self._circuit_open
+
+    def reset_circuit(self):
+        """서킷 브레이커 수동 리셋"""
+        if self._circuit_open:
+            print("[Monitor] CPU Circuit Breaker RESET")
+        self._circuit_open = False
+        self._cpu_overload_count = 0
+        self._circuit_open_since = None
+
    def check_health(self):
        """시스템 상태 점검 및 알림 (CPU, RAM, GPU) - 3분마다 실행"""
        now = datetime.now()
-        if (now - self.last_health_check).total_seconds() < 180:  # 5분 → 3분
+        if (now - self.last_health_check).total_seconds() < 180:
            return

        self.last_health_check = now
        alerts = []

-        # 1. CPU Check (non-blocking 측정)
-        cpu_usage = psutil.cpu_percent(interval=0)
+        # 1. CPU Check
+        cpu_usage = psutil.cpu_percent(interval=1)  # 1초 측정 (더 정확)

-        if cpu_usage > 90:
-            # 검증: 상위 프로세스 CPU 합계 확인
+        if cpu_usage > Config.CPU_CIRCUIT_BREAKER_THRESHOLD:
+            self._cpu_overload_count += 1
+
+            # 상위 프로세스 조회
            top_processes = []
            for proc in psutil.process_iter(['pid', 'name', 'cpu_percent']):
                try:
-                    if proc.info['name'] == 'System Idle Process':
+                    if proc.info['name'] in ('System Idle Process', 'Idle'):
                        continue
                    top_processes.append(proc.info)
                except (psutil.NoSuchProcess, psutil.AccessDenied):
                    pass

            top_processes.sort(key=lambda x: x['cpu_percent'], reverse=True)
-            total_top_cpu = sum(p['cpu_percent'] for p in top_processes[:3])
-
-            if total_top_cpu >= 30.0:
            top_3_str = ""
            for p in top_processes[:3]:
                top_3_str += f"\n- {p['name']} ({p['cpu_percent']}%)"
-                alerts.append(f"[CPU Overload] Usage: {cpu_usage}%\nTop Processes:{top_3_str}")
+
+            # 서킷 브레이커 발동 조건
+            if self._cpu_overload_count >= Config.CPU_CIRCUIT_BREAKER_CONSECUTIVE:
+                if not self._circuit_open:
+                    self._circuit_open = True
+                    self._circuit_open_since = now
+                    alerts.append(
+                        f"🔴 [CPU Circuit Breaker OPEN] {cpu_usage}% × {self._cpu_overload_count}회 연속\n"
+                        f"⛔ 분석 사이클 일시 중단 (5분 후 자동 복구)\nTop Processes:{top_3_str}"
+                    )
+                    print(f"[Monitor] CPU Circuit Breaker OPEN! CPU={cpu_usage}%")
+            else:
+                alerts.append(
+                    f"⚠️ [CPU Overload] Usage: {cpu_usage}% ({self._cpu_overload_count}회)\nTop Processes:{top_3_str}"
+                )
+        else:
+            # CPU 정상 → 카운터 리셋
+            if self._cpu_overload_count > 0:
+                print(f"[Monitor] CPU 정상화 ({cpu_usage}%). 카운터 리셋.")
+            self._cpu_overload_count = 0
+
+            # 서킷 브레이커가 열린 후 5분 경과 시 자동 복구
+            if self._circuit_open and self._circuit_open_since:
+                elapsed = (now - self._circuit_open_since).total_seconds()
+                if elapsed >= 300:  # 5분
+                    self._circuit_open = False
+                    self._circuit_open_since = None
+                    alerts.append("✅ [CPU Circuit Breaker CLOSED] 시스템 안정화. 분석 재개.")
+                    print("[Monitor] CPU Circuit Breaker CLOSED. 분석 재개.")

        # 2. RAM Check
        ram = psutil.virtual_memory()
        if ram.percent > 90:
-            alerts.append(f"[RAM High] Usage: {ram.percent}% (Free: {ram.available / 1024**3:.1f}GB)")
+            alerts.append(f"⚠️ [RAM High] Usage: {ram.percent}% (Free: {ram.available / 1024**3:.1f}GB)")

        # 3. GPU Check
        if self.ollama_monitor:
            gpu_status = self.ollama_monitor.get_gpu_status()
            temp = gpu_status.get('temp', 0)
            if temp > 80:
-                alerts.append(f"[GPU Overheat] Temp: {temp}C")
+                alerts.append(f"🔥 [GPU Overheat] Temp: {temp}°C")

-        # 알림 전송
+        # 알림 전송 (텔레그램 비활성화 - 콘솔 로그만 사용)
        if alerts:
-            msg = "[System Health Alert]\n" + "\n".join(alerts)
-            if self.messenger:
-                self.messenger.send_message(msg)
+            # 콘솔에만 출력
+            for alert in alerts:
+                print(f"[Monitor] {alert}")
+
+            # [비활성화] 텔레그램 알림 - 필요시 재활성화
+            # msg = "🔔 <b>[System Health Alert]</b>\n" + "\n".join(alerts)
+            # if self.messenger:
+            #     self.messenger.send_message(msg)