feat: add market regime filter and compound reinvestment

- Add market_regime.py: BTC/ETH/SOL/XRP weighted 2h trend score
  Bull(≥+1.5%) / Neutral / Bear(<-1%) regime detection with 10min cache
- strategy.py: dynamic TREND/VOL thresholds based on current regime
  Bull: 3%/1.5x, Neutral: 5%/2.0x, Bear: 8%/3.5x
- price_collector.py: always include leader coins in price history
- trader.py: compound reinvestment (profit added to budget, floor at initial)
- notify.py: regime info in hourly report, P&L icons (✅/❌, 💚/🔴)
- main.py: hourly status at top-of-hour, filter positions held 1h+
- backtest.py: timestop/combo comparison modes

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

core/market_regime.py

@@ -0,0 +1,110 @@
+"""시장 레짐(Bull/Neutral/Bear) 판단.
+
+BTC·ETH·SOL·XRP 가중 평균 2h 추세로 레짐을 결정하고
+매수 조건 파라미터(trend_pct, vol_mult)를 동적으로 반환한다.
+계산된 현재가는 price_history DB에 저장해 재활용한다.
+"""
+
+from __future__ import annotations
+
+import logging
+import time
+
+import pyupbit
+
+from .price_db import get_price_n_hours_ago, insert_prices
+
+logger = logging.getLogger(__name__)
+
+# 대장 코인 가중치
+LEADERS: dict[str, float] = {
+    "KRW-BTC": 0.40,
+    "KRW-ETH": 0.30,
+    "KRW-SOL": 0.15,
+    "KRW-XRP": 0.15,
+}
+TREND_HOURS = 2  # 2h 추세 기준
+
+BULL_THRESHOLD =  1.5   # score ≥ 1.5% → Bull
+BEAR_THRESHOLD = -1.0   # score <  -1.0% → Bear
+
+# 레짐별 매수 조건 파라미터
+REGIME_PARAMS: dict[str, dict] = {
+    "bull":    {"trend_pct": 3.0, "vol_mult": 1.5, "emoji": "🟢"},
+    "neutral": {"trend_pct": 5.0, "vol_mult": 2.0, "emoji": "🟡"},
+    "bear":    {"trend_pct": 8.0, "vol_mult": 3.5, "emoji": "🔴"},
+}
+
+# 10분 캐시 (스캔 루프마다 API 호출 방지)
+_cache: dict = {}
+_cache_ts: float = 0.0
+_CACHE_TTL = 600
+
+
+def get_regime() -> dict:
+    """현재 시장 레짐 반환.
+
+    Returns:
+        {
+            'name':      'bull' | 'neutral' | 'bear',
+            'score':     float,   # 가중 평균 2h 추세(%)
+            'trend_pct': float,   # 매수 추세 임계값
+            'vol_mult':  float,   # 거래량 배수 임계값
+            'emoji':     str,
+        }
+    """
+    global _cache, _cache_ts
+    if _cache and (time.time() - _cache_ts) < _CACHE_TTL:
+        return _cache
+
+    score = 0.0
+    current_prices: dict[str, float] = {}
+
+    for ticker, weight in LEADERS.items():
+        try:
+            current = pyupbit.get_current_price(ticker)
+            if not current:
+                continue
+            current_prices[ticker] = current
+
+            # DB에서 2h 전 가격 조회 → 없으면 API 캔들로 대체
+            past = get_price_n_hours_ago(ticker, TREND_HOURS)
+            if past is None:
+                df = pyupbit.get_ohlcv(ticker, interval="minute60", count=4)
+                if df is not None and len(df) >= 3:
+                    past = float(df["close"].iloc[-3])
+
+            if past:
+                trend = (current - past) / past * 100
+                score += trend * weight
+                logger.debug(f"[레짐] {ticker} {trend:+.2f}% (기여 {trend*weight:+.3f})")
+
+        except Exception as e:
+            logger.warning(f"[레짐] {ticker} 오류: {e}")
+
+    # 현재가 DB 저장 (다음 레짐 계산 및 추세 판단에 재활용)
+    if current_prices:
+        try:
+            insert_prices(current_prices)
+        except Exception as e:
+            logger.warning(f"[레짐] 가격 저장 오류: {e}")
+
+    # 레짐 결정
+    if score >= BULL_THRESHOLD:
+        name = "bull"
+    elif score < BEAR_THRESHOLD:
+        name = "bear"
+    else:
+        name = "neutral"
+
+    params = REGIME_PARAMS[name]
+    result = {"name": name, "score": round(score, 3), **params}
+
+    logger.info(
+        f"[레짐] score={score:+.3f}% → {params['emoji']} {name.upper()} "
+        f"(TREND≥{params['trend_pct']}% / VOL≥{params['vol_mult']}x)"
+    )
+
+    _cache = result
+    _cache_ts = time.time()
+    return result