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/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

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")