백테스팅, 앙상블, 워밍업 재시작 스크립트 추가

- analysis/backtest.py: 백테스팅 프레임워크 신규 추가
- analysis/ensemble.py: 적응형 앙상블 가중치 신규 추가
- warmup_and_restart.py: 봇 워밍업 및 재시작 스크립트 신규 추가

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

modules/analysis/backtest.py

@@ -0,0 +1,255 @@
+"""
+백테스팅 프레임워크 (Phase 3-1)
+- 과거 OHLCV 데이터로 전략 시뮬레이션
+- 성과지표: Sharpe ratio, MDD, 승률, 평균손익비
+- Phase 2 모델 변경 전후 비교 검증용
+"""
+
+import json
+import numpy as np
+from dataclasses import dataclass, field
+from typing import Dict, List, Optional, Callable
+
+
+@dataclass
+class Trade:
+    ticker: str
+    entry_date: int     # 데이터 인덱스
+    entry_price: float
+    exit_date: int
+    exit_price: float
+    qty: int
+    direction: str = "LONG"  # LONG / SHORT
+
+    @property
+    def pnl(self):
+        if self.direction == "LONG":
+            return (self.exit_price - self.entry_price) * self.qty
+        return (self.entry_price - self.exit_price) * self.qty
+
+    @property
+    def pnl_pct(self):
+        return (self.exit_price - self.entry_price) / self.entry_price * 100
+
+
+@dataclass
+class BacktestResult:
+    total_return_pct: float
+    sharpe_ratio: float
+    max_drawdown_pct: float
+    win_rate: float
+    avg_win_pct: float
+    avg_loss_pct: float
+    profit_factor: float
+    total_trades: int
+    winning_trades: int
+    losing_trades: int
+    trades: List[Trade] = field(default_factory=list)
+
+    def summary(self) -> str:
+        lines = [
+            "=" * 50,
+            "📊 백테스팅 결과",
+            "=" * 50,
+            f"총 수익률:      {self.total_return_pct:+.2f}%",
+            f"Sharpe Ratio:   {self.sharpe_ratio:.3f}",
+            f"Max Drawdown:   {self.max_drawdown_pct:.2f}%",
+            f"승률:           {self.win_rate:.1f}% ({self.winning_trades}/{self.total_trades})",
+            f"평균 수익:      {self.avg_win_pct:+.2f}%",
+            f"평균 손실:      {self.avg_loss_pct:.2f}%",
+            f"손익비(PF):     {self.profit_factor:.2f}",
+            "=" * 50,
+        ]
+        return "\n".join(lines)
+
+
+class Backtester:
+    """
+    OHLCV 기반 전략 백테스터
+
+    사용 예시:
+        bt = Backtester(initial_capital=10_000_000)
+        result = bt.run(
+            ohlcv_data={"close": [...], "high": [...], "low": [...], "volume": [...]},
+            strategy_fn=my_strategy,
+            ticker="005930"
+        )
+        print(result.summary())
+    """
+
+    def __init__(self, initial_capital: float = 10_000_000,
+                 commission_rate: float = 0.00015,  # 0.015% (증권사 기본)
+                 slippage_rate: float = 0.001):     # 0.1% 슬리피지
+        self.initial_capital = initial_capital
+        self.commission_rate = commission_rate
+        self.slippage_rate = slippage_rate
+
+    def run(self, ohlcv_data: Dict, strategy_fn: Callable,
+            ticker: str = "UNKNOWN", warmup: int = 60) -> BacktestResult:
+        """
+        단일 종목 백테스팅
+
+        Args:
+            ohlcv_data: {'close':[], 'high':[], 'low':[], 'open':[], 'volume':[]}
+            strategy_fn: (ohlcv_slice: dict) -> str ("BUY" | "SELL" | "HOLD")
+            ticker: 종목 코드
+            warmup: 초기 웜업 기간 (기술지표 안정화)
+
+        Returns:
+            BacktestResult
+        """
+        closes = np.array(ohlcv_data.get('close', []), dtype=float)
+        highs = np.array(ohlcv_data.get('high', closes), dtype=float)
+        lows = np.array(ohlcv_data.get('low', closes), dtype=float)
+        volumes = np.array(ohlcv_data.get('volume', np.zeros_like(closes)), dtype=float)
+
+        n = len(closes)
+        if n < warmup + 10:
+            return self._empty_result()
+
+        capital = self.initial_capital
+        position = 0       # 보유 수량
+        entry_price = 0.0
+        entry_idx = 0
+        equity_curve = [capital]
+        trades: List[Trade] = []
+
+        for i in range(warmup, n):
+            # 전략 함수에 현재까지의 슬라이스 전달
+            slice_data = {
+                'close': closes[:i+1].tolist(),
+                'high': highs[:i+1].tolist(),
+                'low': lows[:i+1].tolist(),
+                'volume': volumes[:i+1].tolist(),
+            }
+            signal = "HOLD"
+            try:
+                signal = strategy_fn(slice_data)
+            except Exception:
+                pass
+
+            price = closes[i]
+            buy_price = price * (1 + self.slippage_rate)   # 슬리피지 포함 매수가
+            sell_price = price * (1 - self.slippage_rate)  # 슬리피지 포함 매도가
+
+            if signal == "BUY" and position == 0:
+                # 전액 투자 (수수료 포함)
+                qty = int(capital / (buy_price * (1 + self.commission_rate)))
+                if qty > 0:
+                    cost = qty * buy_price * (1 + self.commission_rate)
+                    capital -= cost
+                    position = qty
+                    entry_price = buy_price
+                    entry_idx = i
+
+            elif signal == "SELL" and position > 0:
+                proceeds = position * sell_price * (1 - self.commission_rate)
+                capital += proceeds
+                trades.append(Trade(
+                    ticker=ticker,
+                    entry_date=entry_idx,
+                    entry_price=entry_price,
+                    exit_date=i,
+                    exit_price=sell_price,
+                    qty=position
+                ))
+                position = 0
+                entry_price = 0.0
+
+            # 자산 추적
+            current_equity = capital + (position * closes[i] if position > 0 else 0)
+            equity_curve.append(current_equity)
+
+        # 미청산 포지션 강제 종료
+        if position > 0:
+            last_price = closes[-1] * (1 - self.slippage_rate)
+            proceeds = position * last_price * (1 - self.commission_rate)
+            capital += proceeds
+            trades.append(Trade(
+                ticker=ticker,
+                entry_date=entry_idx,
+                entry_price=entry_price,
+                exit_date=n - 1,
+                exit_price=last_price,
+                qty=position
+            ))
+            equity_curve[-1] = capital
+
+        return self._compute_metrics(equity_curve, trades)
+
+    def run_multi(self, ohlcv_dict: Dict[str, Dict], strategy_fn: Callable,
+                  warmup: int = 60) -> Dict[str, BacktestResult]:
+        """여러 종목 백테스팅"""
+        results = {}
+        for ticker, ohlcv_data in ohlcv_dict.items():
+            results[ticker] = self.run(ohlcv_data, strategy_fn, ticker, warmup)
+        return results
+
+    def _compute_metrics(self, equity_curve: List[float], trades: List[Trade]) -> BacktestResult:
+        equity = np.array(equity_curve, dtype=float)
+        total_return_pct = (equity[-1] / equity[0] - 1) * 100
+
+        # Sharpe Ratio (일별 수익률 기준, 연율화)
+        daily_returns = np.diff(equity) / equity[:-1]
+        if daily_returns.std() > 0:
+            sharpe = (daily_returns.mean() / daily_returns.std()) * np.sqrt(252)
+        else:
+            sharpe = 0.0
+
+        # Max Drawdown
+        peak = np.maximum.accumulate(equity)
+        drawdowns = (equity - peak) / (peak + 1e-9) * 100
+        max_drawdown = abs(drawdowns.min())
+
+        # 승률 / 손익비
+        wins = [t for t in trades if t.pnl_pct > 0]
+        losses = [t for t in trades if t.pnl_pct <= 0]
+
+        win_rate = len(wins) / len(trades) * 100 if trades else 0
+        avg_win = np.mean([t.pnl_pct for t in wins]) if wins else 0
+        avg_loss = np.mean([t.pnl_pct for t in losses]) if losses else 0
+
+        total_win = sum(t.pnl for t in wins)
+        total_loss = abs(sum(t.pnl for t in losses))
+        profit_factor = total_win / (total_loss + 1e-9)
+
+        return BacktestResult(
+            total_return_pct=round(total_return_pct, 2),
+            sharpe_ratio=round(sharpe, 3),
+            max_drawdown_pct=round(max_drawdown, 2),
+            win_rate=round(win_rate, 1),
+            avg_win_pct=round(avg_win, 2),
+            avg_loss_pct=round(avg_loss, 2),
+            profit_factor=round(profit_factor, 3),
+            total_trades=len(trades),
+            winning_trades=len(wins),
+            losing_trades=len(losses),
+            trades=trades
+        )
+
+    def _empty_result(self) -> BacktestResult:
+        return BacktestResult(
+            total_return_pct=0.0, sharpe_ratio=0.0, max_drawdown_pct=0.0,
+            win_rate=0.0, avg_win_pct=0.0, avg_loss_pct=0.0,
+            profit_factor=0.0, total_trades=0, winning_trades=0, losing_trades=0
+        )
+
+
+def compare_strategies(ohlcv_data: Dict, strategies: Dict[str, Callable],
+                       initial_capital: float = 10_000_000) -> Dict[str, BacktestResult]:
+    """
+    여러 전략 동시 비교
+
+    Args:
+        strategies: {"전략명": strategy_fn, ...}
+
+    Returns:
+        {"전략명": BacktestResult, ...}
+    """
+    bt = Backtester(initial_capital=initial_capital)
+    results = {}
+    for name, fn in strategies.items():
+        results[name] = bt.run(ohlcv_data, fn)
+        print(f"\n[{name}]")
+        print(results[name].summary())
+    return results

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)")
+        print(f"[AI] Ollama 언로드 (VRAM 여유 {free_vram:.1f}GB)")
-        time.sleep(1)  # 메모리 해제 대기
+        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)
+    [v3.0] 주가 예측 Deep Learning 모델 (GPU 최적화)
-    - 전체 학습 데이터를 GPU에 상주 (CPU↔GPU 전송 최소화)
+    - 7차원 멀티피처 LSTM (close/open/high/low/vol_norm/rsi/macd_hist)
-    - Ollama 모델 언로드/리로드로 GPU 메모리 확보
+    - feature_scaler(6개) + target_scaler(1개) 분리
-    - Early Stopping + Mixed Precision (FP16)
+    - 데이터 누수 수정: train 데이터로만 fit
-    - 종목별 모델 체크포인트
+    - 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
+                # Warm-up
-                dummy = torch.zeros(1, 60, 1, device=self.device)
+                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)
+        # 1. 피처 행렬 구성 (n, 7)
-        data = np.array(prices).reshape(-1, 1)
+        features = _build_feature_matrix(ohlcv_data)
-        scaled_data = self.scaler.fit_transform(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):
+        for i in range(n - self.seq_length):
-            x_seqs.append(scaled_data[i:i + self.seq_length])
+            x_seqs.append(scaled_features[i:i + self.seq_length])  # (seq, 7)
-            y_seqs.append(scaled_data[i + self.seq_length])
+            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)
+        # validation split (80/20)
-        split_idx = int(len(x_all) * 0.8)
+        seq_split = int(len(x_all) * 0.8)
-        x_train = x_all[:split_idx]
+        x_train, y_train = x_all[:seq_split], y_all[:seq_split]
-        y_train = y_all[:split_idx]
+        x_val, y_val = x_all[seq_split:], y_all[seq_split:]
-        x_val = x_all[split_idx:]
-        y_val = y_all[split_idx:]
-
         dataset_size = len(x_train)
 
-        # 3. 체크포인트 로드
+        # 4. 체크포인트 로드
-        has_checkpoint = False
+        has_checkpoint = self._load_checkpoint(ticker) if ticker else False
-        if ticker:
+        max_epochs = Config.LSTM_FAST_EPOCHS if has_checkpoint else self.max_epochs
-            has_checkpoint = self._load_checkpoint(ticker)
-        max_epochs = 50 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
+        # 신뢰도 계산
-        # 1. 학습 손실 기반 (낮을수록 좋음)
         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)
+            try:
-            scaler = MinMaxScaler(feature_range=(0, 1))
+                features = _build_feature_matrix(ohlcv_data)
-            scaled_data = scaler.fit_transform(data)
+                scaled = self.feature_scaler.transform(features)
-
+                seq = torch.FloatTensor(scaled[-self.seq_length:]).unsqueeze(0)
-            seq = torch.FloatTensor(scaled_data[-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):
+        for i, (ticker, current_price) in enumerate(metas):
-            predicted_price = scaler.inverse_transform(preds_cpu[i:i+1])[0][0]
+            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()

modules/analysis/ensemble.py

@@ -0,0 +1,230 @@
+"""
+앙상블 예측 모듈 (Phase 3-2)
+- LSTM + 기술지표 + LLM 감성 → 적응형 가중치
+- 과거 매매 결과 기반 가중치 자동 조정
+- process.py의 하드코딩된 w_tech/w_news/w_ai 대체
+"""
+
+import os
+import json
+import numpy as np
+from dataclasses import dataclass, field
+from typing import Dict, Optional
+
+from modules.config import Config
+
+
+@dataclass
+class SignalWeights:
+    """앙상블 가중치"""
+    tech: float = 0.35
+    sentiment: float = 0.30
+    lstm: float = 0.35
+
+    def normalize(self):
+        total = self.tech + self.sentiment + self.lstm
+        if total > 0:
+            self.tech /= total
+            self.sentiment /= total
+            self.lstm /= total
+        return self
+
+    def to_dict(self):
+        return {"tech": self.tech, "sentiment": self.sentiment, "lstm": self.lstm}
+
+    @classmethod
+    def from_dict(cls, d):
+        return cls(tech=d.get("tech", 0.35),
+                   sentiment=d.get("sentiment", 0.30),
+                   lstm=d.get("lstm", 0.35))
+
+
+class AdaptiveEnsemble:
+    """
+    적응형 앙상블 가중치 관리자
+
+    핵심 로직:
+    1. 종목별 최근 N 매매의 결과를 추적
+    2. 어떤 신호가 정확했는지 소급 평가
+    3. 정확도가 높은 신호의 가중치를 점진적으로 증가
+    4. 시장 상황(ADX, 거시경제) 반영한 컨텍스트별 가중치 분리
+    """
+
+    def __init__(self, history_file=None, max_history=50):
+        self.max_history = max_history
+        self.history_file = history_file or os.path.join(
+            Config.DATA_DIR, "ensemble_history.json"
+        )
+        # {ticker: [{"tech": f, "sentiment": f, "lstm": f, "decision": str, "outcome": float}, ...]}
+        self._trade_history: Dict[str, list] = {}
+        # {context: SignalWeights} - context: "strong_trend" | "sideways" | "danger"
+        self._context_weights: Dict[str, SignalWeights] = {
+            "strong_trend": SignalWeights(tech=0.50, sentiment=0.20, lstm=0.30),
+            "sideways": SignalWeights(tech=0.30, sentiment=0.40, lstm=0.30),
+            "danger": SignalWeights(tech=0.20, sentiment=0.50, lstm=0.30),
+            "default": SignalWeights(tech=0.35, sentiment=0.30, lstm=0.35),
+        }
+        self._load()
+
+    def _load(self):
+        if os.path.exists(self.history_file):
+            try:
+                with open(self.history_file, "r", encoding="utf-8") as f:
+                    data = json.load(f)
+                    self._trade_history = data.get("history", {})
+                    weights_raw = data.get("weights", {})
+                    for ctx, w in weights_raw.items():
+                        self._context_weights[ctx] = SignalWeights.from_dict(w)
+            except Exception as e:
+                print(f"[Ensemble] Load failed: {e}")
+
+    def _save(self):
+        try:
+            data = {
+                "history": {k: v[-self.max_history:] for k, v in self._trade_history.items()},
+                "weights": {ctx: w.to_dict() for ctx, w in self._context_weights.items()}
+            }
+            with open(self.history_file, "w", encoding="utf-8") as f:
+                json.dump(data, f, ensure_ascii=False, indent=2)
+        except Exception as e:
+            print(f"[Ensemble] Save failed: {e}")
+
+    def get_context(self, adx: float, macro_state: str) -> str:
+        """현재 시장 컨텍스트 결정"""
+        if macro_state == "DANGER":
+            return "danger"
+        if adx >= 25:
+            return "strong_trend"
+        if adx < 20:
+            return "sideways"
+        return "default"
+
+    def get_weights(self, ticker: str, adx: float = 20.0,
+                    macro_state: str = "SAFE",
+                    ai_confidence: float = 0.5) -> SignalWeights:
+        """
+        종목 + 시장 컨텍스트에 맞는 가중치 반환
+
+        1. 기본: 컨텍스트별 기준 가중치
+        2. AI 신뢰도 높으면 lstm 가중치 보정
+        3. 종목별 학습 결과 반영
+        """
+        context = self.get_context(adx, macro_state)
+        base = self._context_weights.get(context, self._context_weights["default"])
+
+        # 적응형 조정: 해당 종목의 과거 성과 반영
+        ticker_history = self._trade_history.get(ticker, [])
+        adjusted = SignalWeights(tech=base.tech, sentiment=base.sentiment, lstm=base.lstm)
+
+        if len(ticker_history) >= 5:
+            # 최근 5회 신호별 정확도 평가
+            recent = ticker_history[-10:]
+            tech_acc = self._accuracy([h["tech_score"] for h in recent],
+                                      [h["outcome"] for h in recent])
+            news_acc = self._accuracy([h["sentiment_score"] for h in recent],
+                                      [h["outcome"] for h in recent])
+            lstm_acc = self._accuracy([h["lstm_score"] for h in recent],
+                                      [h["outcome"] for h in recent])
+
+            # 정확도 기반 가중치 미세 조정 (±0.1 범위)
+            alpha = 0.05
+            adjusted.tech = max(0.1, min(0.6, base.tech + alpha * (tech_acc - 0.5)))
+            adjusted.sentiment = max(0.1, min(0.6, base.sentiment + alpha * (news_acc - 0.5)))
+            adjusted.lstm = max(0.1, min(0.6, base.lstm + alpha * (lstm_acc - 0.5)))
+
+        # AI 신뢰도 보정
+        if ai_confidence >= 0.85:
+            adjusted.lstm = min(0.70, adjusted.lstm * 1.3)
+        elif ai_confidence < 0.5:
+            adjusted.lstm = max(0.10, adjusted.lstm * 0.7)
+
+        return adjusted.normalize()
+
+    def record_trade(self, ticker: str, tech_score: float, sentiment_score: float,
+                     lstm_score: float, decision: str, outcome_pct: float):
+        """
+        매매 결과 기록 (가중치 학습 데이터)
+
+        outcome_pct: 실현 수익률 (%). 양수=이익, 음수=손실
+        """
+        if ticker not in self._trade_history:
+            self._trade_history[ticker] = []
+
+        record = {
+            "tech_score": tech_score,
+            "sentiment_score": sentiment_score,
+            "lstm_score": lstm_score,
+            "decision": decision,
+            "outcome": outcome_pct
+        }
+        self._trade_history[ticker].append(record)
+        # 히스토리 크기 제한
+        if len(self._trade_history[ticker]) > self.max_history:
+            self._trade_history[ticker] = self._trade_history[ticker][-self.max_history:]
+
+        # 가중치 점진적 업데이트
+        self._update_weights(ticker)
+        self._save()
+
+    def _update_weights(self, ticker: str):
+        """종목별 성과를 반영해 컨텍스트 가중치 점진적 업데이트"""
+        history = self._trade_history.get(ticker, [])
+        if len(history) < 5:
+            return
+
+        recent = history[-10:]
+        outcomes = [h["outcome"] for h in recent]
+        mean_outcome = np.mean(outcomes)
+
+        if mean_outcome > 0:
+            # 전략이 효과적 → 현재 가중치 유지 (강화)
+            pass
+        elif mean_outcome < -2.0:
+            # 손실이 큰 경우 → 기본값으로 리셋
+            for ctx in self._context_weights:
+                self._context_weights[ctx] = SignalWeights(
+                    tech=0.35, sentiment=0.30, lstm=0.35)
+
+    def compute_ensemble_score(self, tech_score: float, sentiment_score: float,
+                               lstm_score: float, investor_score: float = 0.0,
+                               weights: Optional[SignalWeights] = None) -> float:
+        """
+        앙상블 통합 점수 계산
+
+        Args:
+            weights: 가중치 (None이면 기본값 사용)
+        """
+        if weights is None:
+            weights = SignalWeights()
+
+        total = (weights.tech * tech_score
+                 + weights.sentiment * sentiment_score
+                 + weights.lstm * lstm_score)
+
+        # 수급 가산점 (최대 +0.15)
+        total += min(investor_score, 0.15)
+        return min(1.0, max(0.0, total))
+
+    @staticmethod
+    def _accuracy(scores: list, outcomes: list) -> float:
+        """신호와 결과의 상관도 계산 (0.5 = 무관, 1.0 = 완전 일치)"""
+        if len(scores) < 3:
+            return 0.5
+        # 신호가 높을 때 수익, 낮을 때 손실이면 정확
+        correct = sum(
+            1 for s, o in zip(scores, outcomes)
+            if (s >= 0.5 and o > 0) or (s < 0.5 and o <= 0)
+        )
+        return correct / len(scores)
+
+
+# 전역 싱글톤
+_ensemble_instance: Optional[AdaptiveEnsemble] = None
+
+
+def get_ensemble() -> AdaptiveEnsemble:
+    """워커 프로세스 내 싱글톤 앙상블 관리자"""
+    global _ensemble_instance
+    if _ensemble_instance is None:
+        _ensemble_instance = AdaptiveEnsemble()
+    return _ensemble_instance

modules/analysis/evaluator.py

@@ -0,0 +1,421 @@
+"""
+성과 평가 엔진 - PerformanceEvaluator
+
+기능:
+  1. compute_metrics()   - 핵심 성과 지표 계산
+  2. get_grade()         - 지표별 S/A/B/C/D/F 등급 산출
+  3. generate_expert_panel() - Ollama LLM 5명 전문가 의견
+  4. generate_weekly_report() - 텔레그램 HTML 주간 보고서
+"""
+import json
+import math
+from datetime import datetime, timedelta
+
+from modules.utils.performance_db import PerformanceDB
+
+
+class PerformanceEvaluator:
+    def __init__(self):
+        self.perf_db = PerformanceDB()
+
+    # ─────────────────────────────────────────
+    # 1. 핵심 지표 계산
+    # ─────────────────────────────────────────
+
+    def compute_metrics(self, snapshots, trades):
+        """성과 지표를 딕셔너리로 반환.
+
+        Args:
+            snapshots (list): daily_snapshots 리스트
+            trades (list): trade_records 리스트
+
+        Returns:
+            dict: 지표 딕셔너리 (또는 {"error": ...})
+        """
+        if not snapshots:
+            return {"error": "스냅샷 데이터 없음 (운영 시작 후 첫 영업일까지 대기)"}
+
+        metrics = {}
+
+        # ── 수익률 ──────────────────────────────
+        initial = snapshots[0].get("total_eval", 0)
+        current = snapshots[-1].get("total_eval", 0)
+        metrics["total_return_pct"] = round(
+            (current - initial) / initial * 100, 2) if initial > 0 else 0.0
+
+        cutoff_7 = (datetime.now() - timedelta(days=7)).strftime("%Y-%m-%d")
+        recent_snaps = [s for s in snapshots if s.get("date", "") >= cutoff_7]
+        if len(recent_snaps) >= 2:
+            w_init = recent_snaps[0].get("total_eval", 0)
+            w_curr = recent_snaps[-1].get("total_eval", 0)
+            metrics["weekly_return_pct"] = round(
+                (w_curr - w_init) / w_init * 100, 2) if w_init > 0 else 0.0
+        else:
+            metrics["weekly_return_pct"] = 0.0
+
+        # ── 리스크 지표 ──────────────────────────
+        daily_returns = [s.get("daily_return_pct", 0.0) / 100.0 for s in snapshots]
+
+        if len(daily_returns) >= 2:
+            mean_daily = sum(daily_returns) / len(daily_returns)
+            variance = sum((r - mean_daily) ** 2 for r in daily_returns) / len(daily_returns)
+            std_daily = math.sqrt(variance) if variance > 0 else 0.0
+            annual_return = mean_daily * 252
+
+            # Sharpe Ratio
+            if std_daily > 0:
+                metrics["sharpe_ratio"] = round(
+                    annual_return / (std_daily * math.sqrt(252)), 3)
+            else:
+                metrics["sharpe_ratio"] = 0.0
+
+            # Sortino Ratio (하방 편차만 사용)
+            downside = [r for r in daily_returns if r < 0]
+            if downside:
+                dv = sum(r ** 2 for r in downside) / len(downside)
+                ds = math.sqrt(dv)
+                metrics["sortino_ratio"] = round(
+                    annual_return / (ds * math.sqrt(252)), 3) if ds > 0 else 0.0
+            else:
+                metrics["sortino_ratio"] = 10.0  # 손실 없음
+
+            # Max Drawdown
+            peak = snapshots[0].get("total_eval", 0)
+            max_dd = 0.0
+            for snap in snapshots:
+                ev = snap.get("total_eval", 0)
+                if ev > peak:
+                    peak = ev
+                if peak > 0:
+                    dd = (peak - ev) / peak * 100
+                    if dd > max_dd:
+                        max_dd = dd
+            metrics["max_drawdown_pct"] = round(max_dd, 2)
+
+            # Calmar Ratio
+            ann_pct = annual_return * 100
+            metrics["calmar_ratio"] = round(
+                ann_pct / max_dd, 3) if max_dd > 0 else 0.0
+        else:
+            metrics["sharpe_ratio"] = 0.0
+            metrics["sortino_ratio"] = 0.0
+            metrics["max_drawdown_pct"] = 0.0
+            metrics["calmar_ratio"] = 0.0
+
+        # ── 매매 지표 ─────────────────────────────
+        closed = [t for t in trades
+                  if t.get("action") == "BUY" and t.get("outcome_return_pct") is not None]
+
+        if closed:
+            wins = [t for t in closed if t.get("outcome_return_pct", 0) > 0]
+            losses = [t for t in closed if t.get("outcome_return_pct", 0) <= 0]
+
+            metrics["win_rate_pct"] = round(
+                len(wins) / len(closed) * 100, 1)
+
+            total_profit = sum(t["outcome_return_pct"] for t in wins)
+            total_loss = abs(sum(t["outcome_return_pct"] for t in losses))
+            metrics["profit_factor"] = round(
+                total_profit / total_loss, 3) if total_loss > 0 else 10.0
+
+            hd_list = [t["holding_days"] for t in closed if t.get("holding_days") is not None]
+            metrics["avg_holding_days"] = round(
+                sum(hd_list) / len(hd_list), 1) if hd_list else 0.0
+        else:
+            metrics["win_rate_pct"] = 0.0
+            metrics["profit_factor"] = 0.0
+            metrics["avg_holding_days"] = 0.0
+
+        metrics["total_trades"] = len(closed)
+
+        # ── 벤치마크 Alpha ────────────────────────
+        kospi_vals = [s.get("benchmark_kospi_close") for s in snapshots]
+        kospi_valid = [k for k in kospi_vals if k is not None]
+        if len(kospi_valid) >= 2:
+            kospi_ret = (kospi_valid[-1] - kospi_valid[0]) / kospi_valid[0] * 100
+            metrics["alpha"] = round(metrics["total_return_pct"] - kospi_ret, 2)
+            metrics["kospi_return_pct"] = round(kospi_ret, 2)
+        else:
+            metrics["alpha"] = 0.0
+            metrics["kospi_return_pct"] = 0.0
+
+        # ── AI 품질 지표 ──────────────────────────
+        if closed:
+            # LSTM 방향 정확도
+            correct = 0
+            direction_n = 0
+            for t in closed:
+                pred = t.get("ai_prediction_change")
+                outcome = t.get("outcome_return_pct")
+                if pred is not None and outcome is not None:
+                    if (pred > 0) == (outcome > 0):
+                        correct += 1
+                    direction_n += 1
+            metrics["lstm_direction_accuracy"] = round(
+                correct / direction_n * 100, 1) if direction_n > 0 else 0.0
+
+            # 신호별 수익 상관도
+            outcomes = [t.get("outcome_return_pct", 0) for t in closed]
+
+            def pearson(xs, ys):
+                n = len(xs)
+                if n < 2:
+                    return 0.0
+                mx = sum(xs) / n
+                my = sum(ys) / n
+                num = sum((x - mx) * (y - my) for x, y in zip(xs, ys))
+                denom_x = sum((x - mx) ** 2 for x in xs)
+                denom_y = sum((y - my) ** 2 for y in ys)
+                denom = math.sqrt(denom_x * denom_y)
+                return num / denom if denom > 0 else 0.0
+
+            corr_tech = pearson([t.get("tech_score", 0) for t in closed], outcomes)
+            corr_sent = pearson([t.get("sentiment_score", 0) for t in closed], outcomes)
+            corr_lstm = pearson([t.get("lstm_score", 0) for t in closed], outcomes)
+
+            metrics["signal_correlation"] = {
+                "tech": round(corr_tech, 3),
+                "sentiment": round(corr_sent, 3),
+                "lstm": round(corr_lstm, 3)
+            }
+            metrics["best_signal_source"] = max(
+                ["tech", "sentiment", "lstm"],
+                key=lambda k: abs(metrics["signal_correlation"][k])
+            )
+        else:
+            metrics["lstm_direction_accuracy"] = 0.0
+            metrics["signal_correlation"] = {"tech": 0.0, "sentiment": 0.0, "lstm": 0.0}
+            metrics["best_signal_source"] = "unknown"
+
+        metrics["snapshot_count"] = len(snapshots)
+        return metrics
+
+    # ─────────────────────────────────────────
+    # 2. 등급 산출
+    # ─────────────────────────────────────────
+
+    def get_grade(self, metric, value):
+        """지표 이름과 값으로 S/A/B/C/D/F 등급 반환."""
+        # MDD는 낮을수록 좋음
+        if metric == "max_drawdown_pct":
+            thresholds = [(5, "S"), (10, "A"), (15, "B"), (20, "C"), (30, "D")]
+            for threshold, grade in thresholds:
+                if value < threshold:
+                    return grade
+            return "F"
+
+        grade_rules = {
+            "sharpe_ratio":           [(2.0, "S"), (1.5, "A"), (1.0, "B"), (0.5, "C"), (0.0, "D")],
+            "sortino_ratio":          [(3.0, "S"), (2.0, "A"), (1.5, "B"), (1.0, "C"), (0.0, "D")],
+            "win_rate_pct":           [(70,  "S"), (60,  "A"), (50,  "B"), (40,  "C"), (30,  "D")],
+            "profit_factor":          [(3.0, "S"), (2.0, "A"), (1.5, "B"), (1.0, "C"), (0.5, "D")],
+            "alpha":                  [(15,  "S"), (10,  "A"), (5,   "B"), (0,   "C"), (-5,  "D")],
+            "total_return_pct":       [(30,  "S"), (20,  "A"), (10,  "B"), (0,   "C"), (-10, "D")],
+            "weekly_return_pct":      [(5,   "S"), (3,   "A"), (1,   "B"), (0,   "C"), (-1,  "D")],
+            "lstm_direction_accuracy":[(70,  "S"), (60,  "A"), (55,  "B"), (50,  "C"), (40,  "D")],
+            "calmar_ratio":           [(3.0, "S"), (2.0, "A"), (1.0, "B"), (0.5, "C"), (0.0, "D")],
+        }
+
+        thresholds = grade_rules.get(metric, [])
+        for threshold, grade in thresholds:
+            if value >= threshold:
+                return grade
+        return "F"
+
+    # ─────────────────────────────────────────
+    # 3. 전문가 패널 (Ollama LLM)
+    # ─────────────────────────────────────────
+
+    def generate_expert_panel(self, metrics):
+        """5명의 전문가 역할로 Ollama에 평가를 요청.
+
+        Returns:
+            list[dict]: [{role, grade, comment, suggestion}, ...]
+        """
+        from modules.services.ollama import OllamaManager
+        ollama = OllamaManager()
+
+        sig_corr = metrics.get("signal_correlation", {})
+        experts = [
+            {
+                "role": "Risk Manager",
+                "focus": "risk level assessment and bankruptcy risk",
+                "data": (
+                    f"Sharpe={metrics.get('sharpe_ratio', 0):.2f}, "
+                    f"Sortino={metrics.get('sortino_ratio', 0):.2f}, "
+                    f"MDD={metrics.get('max_drawdown_pct', 0):.1f}%, "
+                    f"Calmar={metrics.get('calmar_ratio', 0):.2f}"
+                )
+            },
+            {
+                "role": "Fund Manager",
+                "focus": "alpha generation vs market benchmark",
+                "data": (
+                    f"TotalReturn={metrics.get('total_return_pct', 0):.2f}%, "
+                    f"Alpha={metrics.get('alpha', 0):.2f}%, "
+                    f"KOSPI={metrics.get('kospi_return_pct', 0):.2f}%, "
+                    f"WeeklyReturn={metrics.get('weekly_return_pct', 0):.2f}%"
+                )
+            },
+            {
+                "role": "Quant Analyst",
+                "focus": "AI model validity and signal quality",
+                "data": (
+                    f"LSTM_Accuracy={metrics.get('lstm_direction_accuracy', 0):.1f}%, "
+                    f"TechCorr={sig_corr.get('tech', 0):.3f}, "
+                    f"SentCorr={sig_corr.get('sentiment', 0):.3f}, "
+                    f"LSTMCorr={sig_corr.get('lstm', 0):.3f}, "
+                    f"BestSignal={metrics.get('best_signal_source', 'N/A')}"
+                )
+            },
+            {
+                "role": "Trader",
+                "focus": "trading strategy effectiveness",
+                "data": (
+                    f"WinRate={metrics.get('win_rate_pct', 0):.1f}%, "
+                    f"ProfitFactor={metrics.get('profit_factor', 0):.2f}, "
+                    f"AvgHolding={metrics.get('avg_holding_days', 0):.1f}days, "
+                    f"TotalTrades={metrics.get('total_trades', 0)}"
+                )
+            },
+            {
+                "role": "Portfolio PM",
+                "focus": "overall strategy direction and sustainability",
+                "data": (
+                    f"WeeklyReturn={metrics.get('weekly_return_pct', 0):.2f}%, "
+                    f"Sharpe={metrics.get('sharpe_ratio', 0):.2f}, "
+                    f"WinRate={metrics.get('win_rate_pct', 0):.1f}%, "
+                    f"Alpha={metrics.get('alpha', 0):.2f}%, "
+                    f"MDD={metrics.get('max_drawdown_pct', 0):.1f}%"
+                )
+            }
+        ]
+
+        results = []
+        for exp in experts:
+            prompt = (
+                f"You are a professional {exp['role']} evaluating an AI stock trading bot. "
+                f"Your focus: {exp['focus']}. "
+                f"Performance data: {exp['data']}. "
+                f"Respond ONLY with valid JSON (no markdown, no extra text): "
+                f"{{\"grade\":\"S|A|B|C|D|F\","
+                f"\"comment\":\"1 sentence evaluation in Korean\","
+                f"\"suggestion\":\"1 sentence improvement tip in Korean\"}}"
+            )
+            try:
+                resp = ollama.request_inference(prompt)
+                if not resp:
+                    raise ValueError("Empty response from Ollama")
+                data = json.loads(resp)
+                results.append({
+                    "role": exp["role"],
+                    "grade": data.get("grade", "C"),
+                    "comment": data.get("comment", "(응답 없음)"),
+                    "suggestion": data.get("suggestion", "데이터 축적 필요")
+                })
+            except Exception as e:
+                print(f"[Evaluator] Expert panel [{exp['role']}] error: {e}")
+                results.append({
+                    "role": exp["role"],
+                    "grade": "C",
+                    "comment": "평가 데이터가 부족합니다.",
+                    "suggestion": "더 많은 거래 데이터 축적 후 재평가를 권장합니다."
+                })
+
+        return results
+
+    # ─────────────────────────────────────────
+    # 4. 주간 보고서 생성
+    # ─────────────────────────────────────────
+
+    def generate_weekly_report(self):
+        """주간 성과 보고서 (텔레그램 HTML 형식) 반환."""
+        snapshots = self.perf_db.load_snapshots(days=7)
+        # 매매 완료 건은 30일치 사용 (주간 거래 수가 적을 수 있음)
+        trades = self.perf_db.load_trades(days=30)
+
+        metrics = self.compute_metrics(snapshots, trades)
+
+        if "error" in metrics:
+            return (
+                f"<b>[주간 성과 평가 보고서]</b>\n"
+                f"⚠️ {metrics['error']}\n"
+                f"<i>매일 오전 09:05~09:15에 스냅샷이 저장됩니다.</i>"
+            )
+
+        # 등급 계산
+        g_sharpe = self.get_grade("sharpe_ratio", metrics.get("sharpe_ratio", 0))
+        g_win    = self.get_grade("win_rate_pct", metrics.get("win_rate_pct", 0))
+        g_mdd    = self.get_grade("max_drawdown_pct", metrics.get("max_drawdown_pct", 0))
+        g_alpha  = self.get_grade("alpha", metrics.get("alpha", 0))
+        g_weekly = self.get_grade("weekly_return_pct", metrics.get("weekly_return_pct", 0))
+        g_lstm   = self.get_grade("lstm_direction_accuracy",
+                                  metrics.get("lstm_direction_accuracy", 0))
+
+        # 종합 등급 (Sharpe/Win/MDD/Alpha 평균)
+        grade_map = {"S": 5, "A": 4, "B": 3, "C": 2, "D": 1, "F": 0}
+        grade_rev = {v: k for k, v in grade_map.items()}
+        key_grades = [grade_map[g] for g in [g_sharpe, g_win, g_mdd, g_alpha]]
+        overall_grade = grade_rev[round(sum(key_grades) / len(key_grades))]
+
+        # 전문가 패널 (Ollama 호출)
+        try:
+            experts = self.generate_expert_panel(metrics)
+        except Exception as e:
+            print(f"[Evaluator] Expert panel skipped: {e}")
+            experts = []
+
+        now_str = datetime.now().strftime("%Y/%m/%d %H:%M")
+        corr = metrics.get("signal_correlation", {})
+
+        report = (
+            f"📊 <b>[주간 성과 평가 보고서]</b>  <code>{now_str}</code>\n"
+            f"━━━━━━━━━━━━━━━━━━━━━━\n"
+            f"\n<b>■ 수익률</b>\n"
+            f"  주간: <code>{metrics.get('weekly_return_pct', 0):+.2f}%</code> [{g_weekly}]"
+            f"  누적: <code>{metrics.get('total_return_pct', 0):+.2f}%</code>\n"
+            f"  Alpha: <code>{metrics.get('alpha', 0):+.2f}%</code> [{g_alpha}]"
+            f"  vs KOSPI <code>{metrics.get('kospi_return_pct', 0):+.2f}%</code>\n"
+            f"\n<b>■ 리스크</b>\n"
+            f"  Sharpe: <code>{metrics.get('sharpe_ratio', 0):.2f}</code> [{g_sharpe}]"
+            f"  Sortino: <code>{metrics.get('sortino_ratio', 0):.2f}</code>\n"
+            f"  MDD: <code>{metrics.get('max_drawdown_pct', 0):.1f}%</code> [{g_mdd}]"
+            f"  Calmar: <code>{metrics.get('calmar_ratio', 0):.2f}</code>\n"
+            f"\n<b>■ 매매 통계</b>\n"
+            f"  승률: <code>{metrics.get('win_rate_pct', 0):.1f}%</code> [{g_win}]"
+            f"  PF: <code>{metrics.get('profit_factor', 0):.2f}</code>\n"
+            f"  평균보유: <code>{metrics.get('avg_holding_days', 0):.1f}일</code>"
+            f"  완료매매: <code>{metrics.get('total_trades', 0)}건</code>\n"
+            f"\n<b>■ AI 품질</b>\n"
+            f"  LSTM 방향정확도: <code>{metrics.get('lstm_direction_accuracy', 0):.1f}%</code>"
+            f" [{g_lstm}]\n"
+            f"  신호 상관도 — Tech: <code>{corr.get('tech', 0):.3f}</code>"
+            f"  Sent: <code>{corr.get('sentiment', 0):.3f}</code>"
+            f"  LSTM: <code>{corr.get('lstm', 0):.3f}</code>\n"
+            f"  최고기여 신호: <code>{metrics.get('best_signal_source', 'N/A')}</code>\n"
+        )
+
+        if experts:
+            role_icons = {
+                "Risk Manager":  "🛡",
+                "Fund Manager":  "💼",
+                "Quant Analyst": "🧮",
+                "Trader":        "📈",
+                "Portfolio PM":  "🏦"
+            }
+            report += "\n<b>■ 전문가 패널 의견</b>\n"
+            for exp in experts:
+                icon = role_icons.get(exp["role"], "👤")
+                report += (
+                    f"{icon} <b>{exp['role']}</b> [{exp['grade']}]\n"
+                    f"  {exp['comment']}\n"
+                    f"  💡 {exp['suggestion']}\n"
+                )
+
+        report += (
+            f"\n━━━━━━━━━━━━━━━━━━━━━━\n"
+            f"🏆 <b>종합 등급: [{overall_grade}]</b>\n"
+            f"<i>스냅샷 {metrics.get('snapshot_count', 0)}일 | 완료매매 {metrics.get('total_trades', 0)}건 기준</i>"
+        )
+
+        return report

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

modules/bot.py

@@ -13,6 +13,7 @@ from modules.services.ollama import OllamaManager
 from modules.services.telegram import TelegramMessenger
 from modules.analysis.macro import MacroAnalyzer
 from modules.utils.monitor import SystemMonitor
+from modules.utils.performance_db import PerformanceDB
 from modules.strategy.process import analyze_stock_process, calculate_position_size
 
 try:
@@ -47,10 +48,6 @@ class AutoTradingBot:
         self.kis = KISClient()
         self.news = AsyncNewsCollector()
         self.executor = ProcessPoolExecutor(max_workers=1, initializer=init_worker)
-        try:
-            list(self.executor.map(lambda x: x, range(1)))
-        except Exception:
-            pass
 
         self.messenger = TelegramMessenger()
         self.theme_manager = ThemeManager()
@@ -95,6 +92,12 @@ class AutoTradingBot:
         self.history_file = Config.HISTORY_FILE
         self.load_trade_history()
 
+        # 7-1. 성과 DB 및 평가 플래그
+        self.perf_db = PerformanceDB()
+        self.weekly_eval_sent = False
+        self._snapshot_taken_today = False
+        self._pending_evaluate = False
+
         # 8. AI 하드웨어 점검
         from modules.analysis.deep_learning import PricePredictor
         PricePredictor.verify_hardware()
@@ -130,6 +133,49 @@ class AutoTradingBot:
         except Exception:
             return {}
 
+    def _take_daily_snapshot(self, macro_status, balance):
+        """일별 자산 스냅샷을 perf_db에 저장 (09:05~09:15 호출)."""
+        try:
+            total_eval_snap = int(balance.get("total_eval", 0))
+            deposit_snap = int(balance.get("deposit", 0))
+            holdings_count_snap = len([
+                h for h in balance.get("holdings", [])
+                if int(h.get("qty", 0)) > 0
+            ])
+
+            # KOSPI 현재가 (macro_status 지표에서 추출)
+            kospi_close = None
+            try:
+                indicators = macro_status.get("indicators", {})
+                kospi_price = float(indicators.get("KOSPI", {}).get("price", 0))
+                if kospi_price > 0:
+                    kospi_close = kospi_price
+            except Exception:
+                pass
+
+            self.perf_db.save_daily_snapshot(
+                total_eval_snap, deposit_snap, holdings_count_snap, kospi_close)
+            self._snapshot_taken_today = True
+        except Exception as e:
+            print(f"[Bot] Daily snapshot error: {e}")
+
+    async def _run_weekly_evaluation(self):
+        """주간 성과 평가 실행 후 텔레그램으로 전송."""
+        try:
+            from modules.analysis.evaluator import PerformanceEvaluator
+            evaluator = PerformanceEvaluator()
+            loop = asyncio.get_running_loop()
+            # Ollama 호출이 동기 블로킹이므로 executor에서 실행
+            report = await loop.run_in_executor(None, evaluator.generate_weekly_report)
+            if len(report) > 4000:
+                report = report[:4000] + "\n... (일부 생략)"
+            self.messenger.send_message(report)
+            self.weekly_eval_sent = True
+            print("[Bot] Weekly evaluation report sent.")
+        except Exception as e:
+            print(f"[Bot] Weekly evaluation error: {e}")
+            self.messenger.send_message(f"[Bot] 주간 평가 오류: {e}")
+
     def _load_peak_prices(self):
         """트레일링 스탑용 최고가 데이터 로드"""
         peak_file = os.path.join(Config.DATA_DIR, "peak_prices.json")
@@ -251,12 +297,20 @@ class AutoTradingBot:
                     except Exception as e:
                         self.messenger.send_message(f"Watchlist update failed: {e}")
 
+            elif command == 'evaluate':
+                self._pending_evaluate = True
+
     async def run_cycle(self):
         now = datetime.now()
 
         # 0. 명령 큐 폴링
         self._process_commands()
 
+        # 0-1. 즉시 평가 요청 처리 (IPC 'evaluate' 명령)
+        if self._pending_evaluate:
+            self._pending_evaluate = False
+            await self._run_weekly_evaluation()
+
         # 1. 거시경제 분석
         macro_status = MacroAnalyzer.get_macro_status(self.kis)
         self.last_macro_status = macro_status
@@ -316,6 +370,8 @@ class AutoTradingBot:
             self.daily_trade_history = []
             self.save_trade_history()
             self.report_sent = False
+            self.weekly_eval_sent = False
+            self._snapshot_taken_today = False
             self.discovered_stocks.clear()
             self.watchlist_updated_today = False
             # 전일 최고가 초기화 (보유하지 않는 종목)
@@ -328,9 +384,28 @@ class AutoTradingBot:
         if not (9 <= now.hour < 15 or (now.hour == 15 and now.minute < 30)):
             if now.hour == 15 and now.minute >= 40:
                 self.send_daily_report()
+            # 일별 스냅샷 (16:00~16:30, 당일 최종 포트폴리오 가치 기록)
+            if now.hour == 16 and now.minute <= 30 and not self._snapshot_taken_today:
+                try:
+                    balance_snap = self.kis.get_balance()
+                    self._take_daily_snapshot(macro_status, balance_snap)
+                except Exception as e:
+                    print(f"[Bot] Snapshot error: {e}")
+            # 주간 평가 (금요일 15:35~15:45, 장 마감 직후)
+            if (now.weekday() == 4 and now.hour == 15
+                    and 35 <= now.minute <= 45 and not self.weekly_eval_sent):
+                await self._run_weekly_evaluation()
+            # 장 외 시간에는 서킷 브레이커도 리셋
+            self.monitor.reset_circuit()
             print("[Bot] Market Closed. Waiting...")
             return
 
+        # [서킷 브레이커] CPU 과부하 시 분석 사이클 일시 중단
+        if self.monitor.is_cpu_critical():
+            print("[Bot] ⛔ CPU Circuit Breaker 발동 중. 분석 사이클 스킵.")
+            return
+
+        cycle_start_time = time.time()
         print(f"[Bot] Cycle Start: {now.strftime('%H:%M:%S')}")
 
         # 7. 종목 분석 및 매매
@@ -365,14 +440,30 @@ class AutoTradingBot:
 
         tracking_deposit = int(balance.get("deposit", 0))
 
+        # [v3.0] 비동기 OHLCV + 투자자 동향 배치 조회
+        tickers_list = list(target_dict.keys())
+        ohlcv_batch = {}
+        investor_batch = {}
+
+        if self.kis_async and tickers_list:
+            try:
+                print(f"[Bot] 비동기 OHLCV 배치 조회: {len(tickers_list)}종목")
+                ohlcv_batch = await self.kis_async.get_daily_ohlcv_batch(tickers_list)
+                investor_batch = await self.kis_async.get_investor_trends_batch(tickers_list)
+            except Exception as e:
+                print(f"[Bot] 비동기 배치 조회 실패: {e} -> 동기 fallback")
+                ohlcv_batch = {}
+                investor_batch = {}
+
         try:
             for ticker, name in target_dict.items():
-                prices = self.kis.get_daily_price(ticker)
+                # OHLCV 데이터 획득 (배치 결과 우선, 실패 시 동기 fallback)
-                if not prices:
+                ohlcv_data = ohlcv_batch.get(ticker)
+                if not ohlcv_data or not ohlcv_data.get('close'):
+                    ohlcv_data = self.kis.get_daily_ohlcv(ticker)
+                if not ohlcv_data or not ohlcv_data.get('close'):
                     continue
 
-                investor_trend = self.kis.get_investor_trend(ticker)
-
                 # [v2.0] 보유 정보 전달 (분석 워커에서 동적 손절/익절 사용)
                 holding_info = None
                 if ticker in current_holdings:
@@ -385,16 +476,22 @@ class AutoTradingBot:
                         'peak_price': self.peak_prices.get(ticker, float(h.get('current_price', 0)))
                     }
 
+                # investor_trend fallback
+                investor_trend = investor_batch.get(ticker)
+                if investor_trend is None:
+                    investor_trend = self.kis.get_investor_trend(ticker)
+
                 future = self.executor.submit(
-                    analyze_stock_process, ticker, prices, news_data,
+                    analyze_stock_process, ticker, ohlcv_data, news_data,
                     investor_trend, macro_status, holding_info)
                 analysis_tasks.append(future)
 
             # 결과 처리
-            loop = asyncio.get_event_loop()
+            loop = asyncio.get_running_loop()
             for future in analysis_tasks:
                 try:
-                    res = await loop.run_in_executor(None, future.result)
+                    # 240초 타임아웃: LSTM 학습 + Ollama 추론 시간 고려
+                    res = await loop.run_in_executor(None, lambda f=future: f.result(240))
                     ticker = res['ticker']
                     ticker_name = target_dict.get(ticker, 'Unknown')
                     print(f"[Bot] [{ticker_name}] Score: {res['score']:.2f} ({res['decision']})"
@@ -458,6 +555,24 @@ class AutoTradingBot:
                                 "reason": reason
                             })
                             self.save_trade_history()
+
+                            # 성과 DB 기록
+                            pred = res.get("prediction") or {}
+                            self.perf_db.save_trade_record(
+                                action="BUY", ticker=ticker, name=ticker_name,
+                                qty=qty, price=current_price,
+                                scores_dict={
+                                    "tech": res.get("tech", 0.0),
+                                    "sentiment": res.get("sentiment", 0.0),
+                                    "lstm_score": res.get("lstm_score", 0.0),
+                                    "score": res.get("score", 0.0),
+                                    "ai_confidence": res.get("ai_confidence", 0.5),
+                                    "prediction_change": pred.get("change_rate", 0.0)
+                                },
+                                reason=reason,
+                                macro_state=macro_status.get("status", "SAFE")
+                            )
+
                             tracking_deposit -= required_amount
 
                             # 최고가 초기 설정
@@ -484,14 +599,18 @@ class AutoTradingBot:
                                        f"   Reason: {reason}")
 
                                 self.messenger.send_message(msg)
+                                sell_price = float(h.get('current_price', 0))
                                 self.daily_trade_history.append({
                                     "action": "SELL", "name": ticker_name,
-                                    "qty": qty, "price": float(h.get('current_price', 0)),
+                                    "qty": qty, "price": sell_price,
                                     "yield": yld, "profit": profit_loss,
                                     "reason": reason
                                 })
                                 self.save_trade_history()
 
+                                # 성과 DB 매도 결과 기록
+                                self.perf_db.close_trade(ticker, sell_price, yld)
+
                                 # 최고가 기록 삭제
                                 if ticker in self.peak_prices:
                                     del self.peak_prices[ticker]
@@ -510,11 +629,21 @@ class AutoTradingBot:
         except Exception as e:
             print(f"[Bot] Cycle Loop Error: {e}")
 
+        # 사이클 소요시간 로깅 (120초 초과 시 경고)
+        cycle_elapsed = time.time() - cycle_start_time
+        if cycle_elapsed > 120:
+            print(f"[Bot] ⚠️ 사이클 소요 {cycle_elapsed:.0f}초 (120초 초과) → LSTM 쿨다운 활성화 권장")
+        else:
+            print(f"[Bot] Cycle Done: {cycle_elapsed:.1f}초")
+
     def loop(self):
-        print(f"[Bot] Module Started (PID: {os.getpid()}) [v2.0]")
+        print(f"[Bot] Module Started (PID: {os.getpid()}) [v3.0]")
         self.messenger.send_message(
-            "🚀 [Bot Started v2.0]\n"
+            "🚀 <b>[Bot Started v3.0]</b>\n"
-            "개선사항: 동적 손절/익절, 트레일링 스탑, 포지션 사이징, 분석 기반 매도")
+            f"✅ LSTM 쿨다운: {Config.LSTM_COOLDOWN//60}분\n"
+            f"✅ AI 모델: {Config.OLLAMA_MODEL}\n"
+            f"✅ CPU 서킷브레이커: {Config.CPU_CIRCUIT_BREAKER_THRESHOLD}% 기준\n"
+            "✅ 동적 손절/익절, 트레일링 스탑, 포지션 사이징")
 
         # 최고가 데이터 로드
         self._load_peak_prices()

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"""

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):
+    def order(self, ticker, qty, buy_sell, price=0, order_type="market"):
-        """주문 (시장가)
+        """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"):
+    def _get_daily_ohlcv_by_range(self, ticker, period="D", count=100):
-        """일별 시세 조회 (기술적 분석용)"""
+        """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)
+            # 기간별시세는 output2에 배열로 반환
-            # output 리스트: [ {stck_clpr: 종가, ...}, ... ]
+            output = data.get('output2', [])
-            prices = [int(item['stck_clpr']) for item in data['output']]
+            if not output:
-            prices.reverse() # 과거 -> 현재 순으로 정렬
+                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
+                "price": _f(o.get('bstp_nmix_prpr')),       # 현재지수
-                "change": float(data['output']['bstp_nmix_prdy_ctrt']) # 등락률(%)
+                "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

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()

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)
+                "num_ctx": Config.OLLAMA_NUM_CTX,      # 4096 (속도 2배)
-                "temperature": 0.2,  # 분석 일관성 유지
+                "num_predict": Config.OLLAMA_NUM_PREDICT,  # 응답 토큰 제한
-                "num_gpu": 1,        # GPU 사용 명시
+                "temperature": 0.1,  # 더 결정론적 (JSON 파싱 안정성)
-                "num_thread": 8      # CPU 스레드 수 (9800X3D 활용)
+                "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

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)

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()

modules/services/telegram_bot/server.py

@@ -7,9 +7,17 @@ import logging
 import subprocess
 from telegram import Update
 from telegram.ext import Application, CommandHandler, ContextTypes
+
+# [디버깅] 파일 로깅 추가
+log_file = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))),
+                        "telegram_bot.log")
+file_handler = logging.FileHandler(log_file, encoding='utf-8')
+file_handler.setFormatter(logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s'))
+
 logging.basicConfig(
     format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
-    level=logging.INFO
+    level=logging.INFO,
+    handlers=[logging.StreamHandler(), file_handler]
 )
 logging.getLogger("httpx").setLevel(logging.WARNING)
 
@@ -42,6 +50,7 @@ class TelegramBotServer:
         return self.bot_instance is not None
 
     async def start_command(self, update: Update, context: ContextTypes.DEFAULT_TYPE):
+        logging.info(f"[Command] /start from user {update.effective_user.id}")
         await update.message.reply_text(
             "<b>AI Trading Bot Command Center</b>\n"
             "명령어 목록:\n"
@@ -51,7 +60,8 @@ class TelegramBotServer:
             "/update_watchlist - Watchlist 즉시 업데이트\n"
             "/macro - 거시경제 지표 및 시장 위험도\n"
             "/system - PC 리소스(CPU/GPU) 상태\n"
-            "/ai - AI 모델 학습 상태 조회\n\n"
+            "/ai - AI 모델 학습 상태 조회\n"
+            "/evaluate - 즉시 성과 평가 보고서 생성\n\n"
             "<b>[관리 명령어]</b>\n"
             "/restart - 메인 봇 재시작 요청\n"
             "/exec <code>명령어</code> - 원격 명령어 실행\n"
@@ -60,6 +70,7 @@ class TelegramBotServer:
         )
 
     async def status_command(self, update: Update, context: ContextTypes.DEFAULT_TYPE):
+        logging.info(f"[Command] /status from user {update.effective_user.id}")
         if not self.refresh_bot_instance():
             await update.message.reply_text("메인 봇이 실행 중이 아닙니다.")
             return
@@ -172,34 +183,67 @@ class TelegramBotServer:
                 await update.message.reply_text("데이터가 아직 수집되지 않았습니다.")
                 return
 
-            status = "SAFE"
+            msi = float(indices.get('MSI', 0))
-            msi = indices.get('MSI', 0)
             if msi >= 50:
-                status = "DANGER"
+                risk_status = "🔴 DANGER"
+                risk_desc = "시장 극도 불안정 - 매수 중단 권고"
             elif msi >= 30:
-                status = "CAUTION"
+                risk_status = "🟡 CAUTION"
+                risk_desc = "시장 불안정 - 보수적 매매 권고"
+            else:
+                risk_status = "🟢 SAFE"
+                risk_desc = "시장 안정 - 정상 매매 가능"
 
-            msg = f"<b>Market Risk: {status}</b>\n\n"
+            from datetime import datetime
+            now_str = datetime.now().strftime("%m/%d %H:%M")
 
-            if 'MSI' in indices:
+            msg = f"<b>거시경제 지표</b>  <code>{now_str}</code>\n"
-                msg += f"<b>Stress Index:</b> <code>{indices['MSI']}</code>\n"
+            msg += f"━━━━━━━━━━━━━━━━━━\n"
+            msg += f"<b>Market Risk:</b> {risk_status}\n"
+            msg += f"<i>{risk_desc}</i>\n\n"
 
-            for k, v in indices.items():
+            # MSI 상세
-                if k != "MSI":
+            msi_bar = "█" * int(msi / 10) + "░" * (10 - int(msi / 10))
+            msg += f"<b>Stress Index (MSI):</b> <code>{msi:.1f}/100</code>\n"
+            msg += f"<code>[{msi_bar}]</code>\n\n"
+
+            # 지수 상세
+            index_order = ["KOSPI", "KOSDAQ", "KOSPI200"]
+            for k in index_order:
+                if k not in indices:
+                    continue
+                v = indices[k]
+                price = float(v.get('price', 0))
                 change = float(v.get('change', 0))
-                    price = v.get('price', 0)
+                change_val = float(v.get('change_val', 0))
+                high = float(v.get('high', 0))
+                low = float(v.get('low', 0))
+                prev_close = float(v.get('prev_close', 0))
+                volume = int(v.get('volume', 0))
+
+                if price == 0:
+                    msg += f"⚫ <b>{k}:</b> <i>데이터 없음 (장 마감 후)</i>\n\n"
+                    continue
 
                 if change > 0:
                     icon = "🔴"
-                        chg_str = f"+{change}"
+                    chg_str = f"+{change:.2f}%  (+{change_val:.2f}pt)"
                 elif change < 0:
                     icon = "🔵"
-                        chg_str = f"{change}"
+                    chg_str = f"{change:.2f}%  ({change_val:.2f}pt)"
                 else:
                     icon = "⚪"
-                        chg_str = f"{change}"
+                    chg_str = f"{change:.2f}%"
 
-                    msg += f"{icon} <b>{k}</b>: {price} ({chg_str}%)\n"
+                msg += f"{icon} <b>{k}:</b> <code>{price:,.2f}</code>  {chg_str}\n"
+                if high and low:
+                    msg += f"   고: <code>{high:,.2f}</code>  저: <code>{low:,.2f}</code>"
+                    if prev_close:
+                        msg += f"  전일종가: <code>{prev_close:,.2f}</code>"
+                    msg += "\n"
+                if volume:
+                    msg += f"   거래량: <code>{volume:,}천주</code>\n"
+                msg += "\n"
 
             await update.message.reply_text(msg, parse_mode="HTML")
 
@@ -256,10 +300,11 @@ class TelegramBotServer:
             await update.message.reply_text("메인 봇이 실행 중이 아닙니다.")
             return
 
+        from modules.config import Config
         gpu = self.bot_instance.ollama_monitor.get_gpu_status()
 
         msg = "<b>AI Model Status</b>\n"
-        msg += "* <b>LLM Engine:</b> Ollama (Llama 3.1)\n"
+        msg += f"* <b>LLM Engine:</b> Ollama ({Config.OLLAMA_MODEL})\n"
         msg += f"* <b>Device:</b> {gpu.get('name', 'GPU')}\n"
 
         if gpu:
@@ -349,6 +394,29 @@ class TelegramBotServer:
         except Exception as e:
             await update.message.reply_text(f"실행 오류: {e}")
 
+    async def evaluate_command(self, update: Update, context: ContextTypes.DEFAULT_TYPE):
+        """/evaluate: 즉시 성과 평가 보고서 생성 (LLM 분석 포함)"""
+        await update.message.reply_text(
+            "📊 성과 평가를 실행합니다...\n"
+            "<i>LLM 전문가 패널 분석 포함 시 30초~1분 소요됩니다.</i>",
+            parse_mode="HTML"
+        )
+        try:
+            from modules.utils.performance_db import PerformanceDB
+            from modules.analysis.evaluator import PerformanceEvaluator
+
+            evaluator = PerformanceEvaluator()
+            loop = asyncio.get_running_loop()
+            report = await loop.run_in_executor(None, evaluator.generate_weekly_report)
+
+            if len(report) > 4000:
+                report = report[:4000] + "\n... (일부 생략)"
+
+            await update.message.reply_text(report, parse_mode="HTML")
+        except Exception as e:
+            logging.error(f"[Command] /evaluate error: {e}")
+            await update.message.reply_text(f"평가 오류: {e}")
+
     def run(self):
         handlers = [
             ("start", self.start_command),
@@ -359,6 +427,7 @@ class TelegramBotServer:
             ("macro", self.macro_command),
             ("system", self.system_command),
             ("ai", self.ai_status_command),
+            ("evaluate", self.evaluate_command),
             ("restart", self.restart_command),
             ("stop", self.stop_command),
             ("exec", self.exec_command)
@@ -377,6 +446,7 @@ class TelegramBotServer:
 
         self.application.add_error_handler(error_handler)
 
+        logging.info("[Telegram] Command Server Started (Shared Memory IPC Mode).")
         print("[Telegram] Command Server Started (Shared Memory IPC Mode).")
 
         try:

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)
+# [최적화] 워커 프로세스별 전역 변수 (Ollama 캐싱)
-_lstm_predictor = None
+_ollama_manager = None
 
-def get_predictor():
+
-    """워커 프로세스 내에서 PricePredictor 인스턴스를 싱글톤으로 관리"""
+def get_predictor(ticker=None):
-    global _lstm_predictor
+    """워커 프로세스 내에서 ModelRegistry로 종목별 PricePredictor 관리"""
-    if _lstm_predictor is None:
+    registry = ModelRegistry.get_instance()
-        print(f"[Worker {os.getpid()}] Initializing LSTM Predictor...")
+    return registry.get_predictor(ticker or "default")
-        _lstm_predictor = PricePredictor()
+
-        print(f"[Worker {os.getpid()}] LSTM Device: {_lstm_predictor.device}"
+
-              f" | AMP: {_lstm_predictor.use_amp}")
+def get_ollama():
-    return _lstm_predictor
+    """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)
+    # 3. 확신도 조절 계수
-    # score 0.6 → 0.5배, 0.7 → 1.0배, 0.8 → 1.5배, 0.9+ → 2.0배
     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 * 0.15)
-    invest_amount = min(invest_amount, total_capital)  # 잔고 초과 방지
+    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]
+    [v3.0 개선사항]
-    1. ATR 기반 동적 손절/익절 + 트레일링 스탑
+    1. OHLCV 전체 수신 (실제 고가/저가/거래량 사용)
-    2. 포지션 사이징 (변동성 + 확신도 기반)
+    2. 종목별 ModelRegistry (가중치 덮어쓰기 방지)
-    3. 시장상황별 동적 매수/매도 임계값
+    3. 강화된 LLM 프롬프트 (거시경제 상태, 볼린저밴드, 거래량 급증, 보유 수익률)
-    4. 보유종목에 대한 분석 기반 매도 판단
-    5. ADX/OBV/MTF 통합 기술적 분석
-    6. 강화된 AI 프롬프트 (종목 고유 뉴스 분석)
     """
     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 =====
+        # ===== 2. ATR 기반 동적 손절/익절 (실제 고가/저가 사용) =====
-        sl_tp = TechnicalAnalyzer.calculate_dynamic_sl_tp(prices)
+        sl_tp = TechnicalAnalyzer.calculate_dynamic_sl_tp(
+            prices, high_prices=high_prices, low_prices=low_prices)
 
-        # ===== 3. LSTM 주가 예측 =====
+        # ===== 3. 볼린저밴드 위치 계산 =====
-        lstm_predictor = get_predictor()
+        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()
+        news_summary = "; ".join(
-        prompt = f"""
+            [n.get('title', '') for n in (news_items or [])[:3] if n.get('title')]
-[System Instruction]
+        ) or "뉴스 없음"
-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.
 
-[Market Data for Stock {ticker}]
+        # 거시경제 상태
-- Current Price: {current_price:,.0f} KRW
+        macro_state = macro_status.get('status', 'SAFE') if macro_status else 'SAFE'
-- 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}
 
-[Decision Framework]
+        # 거래량 급증 여부
-- Strong BUY signals: Foreigners+Institutions buying, Golden Cross, ADX>25 with bullish trend, AI high confidence UP
+        vol_surge = "급증(x{:.1f})".format(vol_ratio) if vol_ratio >= 2.0 else "정상"
-- 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
 
-[News Data]
+        # 보유종목 수익률
-{json.dumps(news_items[:5] if news_items else [], ensure_ascii=False)}
+        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]
+        ollama = get_ollama()
-{{"sentiment_score": 0.0 to 1.0, "reason": "Brief analysis reason"}}
+        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) =====
+        # ===== 7. 통합 점수 (동적 가중치 v2.0) =====
-        # ADX가 높으면 (추세가 강하면) LSTM과 기술적 분석 비중 증가
         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
+                buy_threshold = 999.0
-                sell_threshold = 0.45  # 매도 기준 상향 (빨리 탈출)
+                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)
+                buy_threshold = 0.72
-                sell_threshold = 0.38  # 매도 기준도 상향
+                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,

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:
+                if length > 0 and length <= Config.SHM_SIZE - 4:
-                    return None
                     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)
 

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)
+        # 1. CPU Check
-        cpu_usage = psutil.cpu_percent(interval=0)
+        cpu_usage = psutil.cpu_percent(interval=1)  # 1초 측정 (더 정확)
 
-        if cpu_usage > 90:
+        if cpu_usage > Config.CPU_CIRCUIT_BREAKER_THRESHOLD:
-            # 검증: 상위 프로세스 CPU 합계 확인
+            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)
+            # 콘솔에만 출력
-            if self.messenger:
+            for alert in alerts:
-                self.messenger.send_message(msg)
+                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)

modules/utils/performance_db.py

@@ -0,0 +1,211 @@
+"""
+성과 데이터 영구 저장 - PerformanceDB
+
+데이터 파일:
+  data/performance/daily_snapshots.json  - 일별 자산 스냅샷
+  data/performance/trade_records.json    - 강화 매매 기록 (영구 보관)
+"""
+import os
+import json
+from datetime import datetime, timedelta
+
+from modules.config import Config
+
+PERF_DIR = os.path.join(Config.DATA_DIR, "performance")
+SNAPSHOTS_FILE = os.path.join(PERF_DIR, "daily_snapshots.json")
+TRADES_FILE = os.path.join(PERF_DIR, "trade_records.json")
+
+
+class PerformanceDB:
+    def __init__(self):
+        os.makedirs(PERF_DIR, exist_ok=True)
+        self._snapshots = self._load_json(SNAPSHOTS_FILE, [])
+        self._trades = self._load_json(TRADES_FILE, [])
+
+    def _load_json(self, path, default):
+        if os.path.exists(path):
+            try:
+                with open(path, "r", encoding="utf-8") as f:
+                    return json.load(f)
+            except Exception as e:
+                print(f"[PerformanceDB] Load failed {path}: {e}")
+                return default
+        return default
+
+    def _save_json(self, path, data):
+        try:
+            with open(path, "w", encoding="utf-8") as f:
+                json.dump(data, f, ensure_ascii=False, indent=2)
+        except Exception as e:
+            print(f"[PerformanceDB] Save failed {path}: {e}")
+
+    # ─────────────────────────────────────────
+    # 일별 스냅샷
+    # ─────────────────────────────────────────
+
+    def save_daily_snapshot(self, total_eval, deposit, holdings_count, benchmark_close=None):
+        """일별 자산 스냅샷 저장 (하루 1회 호출 권장).
+
+        Args:
+            total_eval (int): 총 평가액 (원)
+            deposit (int): 예수금 (원)
+            holdings_count (int): 보유 종목 수
+            benchmark_close (float|None): KOSPI 현재가 (벤치마크 비교용)
+        """
+        today = datetime.now().strftime("%Y-%m-%d")
+
+        # 오늘 이미 저장된 스냅샷이 있으면 업데이트
+        for snap in self._snapshots:
+            if snap.get("date") == today:
+                snap["total_eval"] = total_eval
+                snap["deposit"] = deposit
+                snap["holdings_count"] = holdings_count
+                if benchmark_close is not None:
+                    snap["benchmark_kospi_close"] = benchmark_close
+                self._save_json(SNAPSHOTS_FILE, self._snapshots)
+                return
+
+        # 일별/누적 수익률 계산
+        daily_return_pct = 0.0
+        cumulative_return_pct = 0.0
+
+        if self._snapshots:
+            prev_eval = self._snapshots[-1].get("total_eval", 0)
+            if prev_eval > 0:
+                daily_return_pct = (total_eval - prev_eval) / prev_eval * 100
+
+        initial_capital = self.get_initial_capital()
+        if initial_capital and initial_capital > 0:
+            cumulative_return_pct = (total_eval - initial_capital) / initial_capital * 100
+
+        snap = {
+            "date": today,
+            "total_eval": total_eval,
+            "deposit": deposit,
+            "holdings_count": holdings_count,
+            "benchmark_kospi_close": benchmark_close,
+            "daily_return_pct": round(daily_return_pct, 4),
+            "cumulative_return_pct": round(cumulative_return_pct, 4)
+        }
+        self._snapshots.append(snap)
+        self._save_json(SNAPSHOTS_FILE, self._snapshots)
+        print(f"[PerformanceDB] Snapshot saved: {today} "
+              f"total={total_eval:,}원 daily={daily_return_pct:+.2f}%")
+
+    # ─────────────────────────────────────────
+    # 매매 기록
+    # ─────────────────────────────────────────
+
+    def save_trade_record(self, action, ticker, name, qty, price,
+                          scores_dict=None, reason="", macro_state="SAFE"):
+        """매수/매도 기록 저장.
+
+        Args:
+            action (str): "BUY" | "SELL"
+            ticker (str): 종목 코드
+            name (str): 종목명
+            qty (int): 수량
+            price (float): 체결가
+            scores_dict (dict|None): 분석 점수 딕셔너리
+                {tech, sentiment, lstm_score, score, ai_confidence, prediction_change}
+            reason (str): 매매 사유
+            macro_state (str): 매크로 상태 ("SAFE"/"CAUTION"/"DANGER")
+        """
+        sd = scores_dict or {}
+        now_iso = datetime.now().isoformat()
+
+        trade = {
+            "id": f"{ticker}_{now_iso}",
+            "action": action,
+            "ticker": ticker,
+            "name": name,
+            "qty": qty,
+            "price": price,
+            "timestamp": now_iso,
+            "reason": reason,
+            "macro_state": macro_state,
+            # 점수 (BUY 시에만 의미 있음)
+            "tech_score": float(sd.get("tech", 0.0)),
+            "sentiment_score": float(sd.get("sentiment", 0.0)),
+            "lstm_score": float(sd.get("lstm_score", 0.0)),
+            "total_score": float(sd.get("score", 0.0)),
+            "ai_confidence": float(sd.get("ai_confidence", 0.5)),
+            "ai_prediction_change": float(sd.get("prediction_change", 0.0)),
+            # 매도 후 채워지는 결과 필드
+            "outcome_return_pct": None,
+            "holding_days": None,
+            "closed_at": None
+        }
+        self._trades.append(trade)
+        self._save_json(TRADES_FILE, self._trades)
+
+    def close_trade(self, ticker, sell_price, sell_yield_pct=None):
+        """가장 최근 미체결 BUY를 찾아 매도 결과를 기록.
+
+        Args:
+            ticker (str): 종목 코드
+            sell_price (float): 매도 체결가
+            sell_yield_pct (float|None): KIS에서 받은 수익률 (보조용)
+        """
+        for trade in reversed(self._trades):
+            if (trade.get("ticker") == ticker
+                    and trade.get("action") == "BUY"
+                    and trade.get("outcome_return_pct") is None):
+
+                buy_price = trade.get("price", 0)
+                if buy_price and buy_price > 0:
+                    outcome_return_pct = (sell_price - buy_price) / buy_price * 100
+                elif sell_yield_pct is not None:
+                    outcome_return_pct = sell_yield_pct
+                else:
+                    outcome_return_pct = 0.0
+
+                # 보유일 계산
+                holding_days = 0
+                buy_ts = trade.get("timestamp", "")
+                if buy_ts:
+                    try:
+                        buy_dt = datetime.fromisoformat(buy_ts)
+                        holding_days = (datetime.now() - buy_dt).days
+                    except Exception:
+                        pass
+
+                trade["outcome_return_pct"] = round(outcome_return_pct, 4)
+                trade["holding_days"] = holding_days
+                trade["closed_at"] = datetime.now().isoformat()
+                self._save_json(TRADES_FILE, self._trades)
+                print(f"[PerformanceDB] Trade closed: {ticker} "
+                      f"return={outcome_return_pct:.2f}%  holding={holding_days}d")
+                return
+
+        print(f"[PerformanceDB] No open BUY found for {ticker}")
+
+    # ─────────────────────────────────────────
+    # 조회
+    # ─────────────────────────────────────────
+
+    def load_snapshots(self, days=90):
+        """최근 N일 스냅샷 반환."""
+        cutoff = (datetime.now() - timedelta(days=days)).strftime("%Y-%m-%d")
+        return [s for s in self._snapshots if s.get("date", "") >= cutoff]
+
+    def load_trades(self, days=90):
+        """최근 N일 매매 기록 반환."""
+        cutoff = (datetime.now() - timedelta(days=days)).strftime("%Y-%m-%d")
+        return [t for t in self._trades if t.get("timestamp", "")[:10] >= cutoff]
+
+    def get_initial_capital(self):
+        """첫 스냅샷 기준 초기 자본 반환."""
+        if self._snapshots:
+            return self._snapshots[0].get("total_eval", 0)
+        return 0
+
+    def get_summary(self):
+        """간단한 현황 딕셔너리 반환 (디버깅용)."""
+        return {
+            "total_snapshots": len(self._snapshots),
+            "total_trades": len(self._trades),
+            "closed_trades": sum(1 for t in self._trades
+                                 if t.get("outcome_return_pct") is not None),
+            "initial_capital": self.get_initial_capital()
+        }

warmup_and_restart.py

@@ -0,0 +1,139 @@
+"""
+LSTM 사전학습 + 봇 자동 시작 통합 스크립트
+- 구 봇 종료 후 이 스크립트가 백그라운드로 실행됨
+- 12개 종목 LSTM 사전학습 → 완료 시 main_server.py 자동 시작
+"""
+import sys
+import os
+import time
+import subprocess
+import json
+
+# 프로젝트 루트를 path에 추가
+ROOT = os.path.dirname(os.path.abspath(__file__))
+sys.path.insert(0, ROOT)
+os.chdir(ROOT)
+
+LOG_FILE = os.path.join(ROOT, "warmup.log")
+
+def log(msg):
+    ts = time.strftime("%H:%M:%S")
+    line = f"[{ts}] {msg}"
+    print(line, flush=True)
+    with open(LOG_FILE, "a", encoding="utf-8") as f:
+        f.write(line + "\n")
+
+
+def run_warmup():
+    log("=" * 50)
+    log("LSTM 사전학습 시작 (Warmup)")
+    log("=" * 50)
+
+    try:
+        from modules.config import Config
+        from modules.services.kis import KISClient
+        from modules.analysis.deep_learning import PricePredictor
+        from modules.services.telegram import TelegramMessenger
+
+        messenger = TelegramMessenger()
+        messenger.send_message(
+            "🔄 <b>[Bot Restarting]</b>\n"
+            "LSTM 사전학습 중... 완료 후 자동 시작됩니다.\n"
+            f"대상: Watchlist 전체 종목"
+        )
+
+        kis = KISClient()
+        with open(Config.WATCHLIST_FILE, "r", encoding="utf-8") as f:
+            watchlist = json.load(f)
+
+        log(f"대상 종목: {len(watchlist)}개")
+
+        predictor = PricePredictor()
+        total = len(watchlist)
+        success = 0
+        failed = 0
+        total_time = 0.0
+
+        for i, (ticker, name) in enumerate(watchlist.items(), 1):
+            log(f"[{i}/{total}] {name} ({ticker}) 학습 중...")
+            try:
+                # 100일 데이터로 LSTM 학습 (기간별시세 API)
+                prices = kis.get_daily_price(ticker, count=100)
+                if not prices or len(prices) < 70:
+                    log(f"  ⚠️ 데이터 부족 ({len(prices) if prices else 0}개)")
+                    failed += 1
+                    continue
+
+                t0 = time.time()
+                result = predictor.train_and_predict(prices, ticker=ticker)
+                elapsed = time.time() - t0
+                total_time += elapsed
+
+                if result:
+                    log(f"  ✅ {result['epochs']}에포크 | loss={result['loss']:.6f} | "
+                        f"{result['change_rate']:+.2f}% | {elapsed:.1f}초")
+                    success += 1
+                else:
+                    log(f"  ⚠️ 결과 없음 ({elapsed:.1f}초)")
+                    failed += 1
+
+                # KIS API 과호출 방지
+                time.sleep(0.5)
+
+            except Exception as e:
+                log(f"  ❌ 오류: {e}")
+                failed += 1
+
+        log("=" * 50)
+        log(f"Warmup 완료: 성공 {success}개 / 실패 {failed}개 / 총 {total_time:.0f}초")
+        log("=" * 50)
+
+        messenger.send_message(
+            f"✅ <b>[Warmup 완료]</b>\n"
+            f"성공: {success}종목 / 실패: {failed}종목\n"
+            f"소요: {total_time/60:.1f}분\n"
+            f"→ 봇 시작 중..."
+        )
+        return True
+
+    except Exception as e:
+        log(f"❌ Warmup 실패: {e}")
+        import traceback
+        log(traceback.format_exc())
+        return False
+
+
+def start_bot():
+    log("봇 시작: main_server.py")
+    try:
+        # 새 콘솔 창에서 봇 실행 (Windows)
+        if sys.platform == "win32":
+            subprocess.Popen(
+                [sys.executable, "main_server.py"],
+                creationflags=subprocess.CREATE_NEW_CONSOLE,
+                cwd=ROOT
+            )
+        else:
+            subprocess.Popen(
+                [sys.executable, "main_server.py"],
+                cwd=ROOT
+            )
+        log("✅ 봇 프로세스 시작 완료")
+        return True
+    except Exception as e:
+        log(f"❌ 봇 시작 실패: {e}")
+        return False
+
+
+if __name__ == "__main__":
+    # warmup.log 초기화
+    with open(LOG_FILE, "w", encoding="utf-8") as f:
+        f.write(f"Warmup 시작: {time.strftime('%Y-%m-%d %H:%M:%S')}\n")
+
+    warmup_ok = run_warmup()
+
+    if warmup_ok:
+        time.sleep(2)
+        start_bot()
+    else:
+        log("⚠️ Warmup 실패. 봇을 수동으로 시작해주세요: python main_server.py")