feat: 新增2h/6h时间框架支持，策略重构为增量指标计算

- 数据层: build_aggregates_sql 新增 2h/6h 聚合视图，默认起始时间调整为 2017-05
- 模型层: KlineInterval 类型扩展 2h/6h，DataService 新增对应表名和毫秒映射
- 指标层: 新增 incremental.py 增量指标模块 (EmaInc/AtrInc/RsiInc/BbInc)，O(1) per bar
- 策略重构: long_short.py 和 regime_all.py 从批量 ema/atr 迁移至增量指标，避免每 bar 重复全量计算
- regime 探测器: RegimeDetector3 改为增量 EMA200，detect() 接口简化
- 回测扩展: regime_timeframe_comparison 从 4h/1d 扩展至 2h/4h/6h/1d
- 新增示例: multi_strategy_report, vol_break_compare/periods, intraday_explore, top3_trades 等分析脚本

engine/example/long_short.py

@@ -31,7 +31,7 @@ from engine.common.base import BaseStrategy, Signal, StrategyConfig
 from engine.common.models import Kline
 from engine.common.config import config, DBConfig
 from engine.data import DataService
-from engine.indicators import ema, atr
+from engine.indicators.incremental import EmaInc, AtrInc
 from engine.backtest.models import BacktestConfig, BacktestMetrics, BacktestResult, BacktestTrade
 
 
@@ -364,7 +364,7 @@ class LongShortEmaConfig(StrategyConfig):
 
 
 class LongShortEmaStrategy(BaseStrategy):
-    """EMA金叉做多、死叉做空，始终在场"""
+    """EMA金叉做多、死叉做空，始终在场 — 全部指标增量计算"""
 
     strategy_type = "long_short_ema"
 
@@ -374,6 +374,9 @@ class LongShortEmaStrategy(BaseStrategy):
         self._closes: list[float] = []
         self._highs: list[float] = []
         self._lows: list[float] = []
+        self._ema_fast = EmaInc(c.fast)
+        self._ema_slow = EmaInc(c.slow)
+        self._atr = AtrInc(14)
         self._highest: float = 0.0
         self._lowest: float = float('inf')
         self._position_side: str = ""  # "long" / "short"
@@ -382,15 +385,19 @@ class LongShortEmaStrategy(BaseStrategy):
         self._closes.append(k.close)
         self._highs.append(k.high)
         self._lows.append(k.low)
+
+        # 增量更新（即使在热身期也要更新，保证后续状态正确）
+        self._ema_fast.update(k.close)
+        self._ema_slow.update(k.close)
+        self._atr.update(k.high, k.low, k.close)
+
         n = len(self._closes)
         if n < self.cfg.slow + 5:
             return None
 
-        fast = ema(self._closes, self.cfg.fast)
-        slow = ema(self._closes, self.cfg.slow)
-        atr_vals = atr(self._highs, self._lows, self._closes, 14)
-        cur_f, cur_s, cur_atr = fast[-1], slow[-1], atr_vals[-1]
-        prev_f, prev_s = fast[-2], slow[-2]
+        cur_f, cur_s = self._ema_fast[-1], self._ema_slow[-1]
+        cur_atr = self._atr[-1]
+        prev_f, prev_s = self._ema_fast[-2], self._ema_slow[-2]
         if cur_f == 0 or cur_s == 0 or cur_atr == 0:
             return None