Merge pull request #12 from jackyu66git/dev

Dev

ChanKLC.py

@@ -46,6 +46,7 @@ class ChanKLC():
 		self.continue_div = False
 		self.separate_div = False
 		self.ema24 = klu.ema24
+		self.ema26 = klu.ema26
 		self.ema52 = klu.ema52
 		self.ema104 = klu.ema104
 		self.ema156 = klu.ema156
@@ -387,6 +388,7 @@ class ChanKLC():
 			self.rsi += self.klu_list[index].rsi
 			self.volume_ratio += self.klu_list[index].volume_ratio
 			self.macdhist += self.klu_list[index].macdhist
+			self.ema26 += self.klu_list[index].ema26
 			self.ema24 += self.klu_list[index].ema24
 			self.ema52 += self.klu_list[index].ema52
 			self.ema104 += self.klu_list[index].ema104
@@ -404,6 +406,7 @@ class ChanKLC():
 		self.volume_ratio = self.volume_ratio / n
 		self.volume = self.volume / n
 		self.macdhist = self.macdhist / n
+		self.ema26 = self.ema26 / n
 		self.ema24 = self.ema24 / n
 		self.ema52 = self.ema52 / n
 		self.ema104 = self.ema104 / n

ChanKLU.py

@@ -46,6 +46,7 @@ class ChanKLU:
 		self.near0_return = 0
 		self.ema52 = 0
 		self.ema24 = 0
+		self.ema26 = 0
 		self.ema104 = 0
 		self.ema156 = 0
 		self.ema208 = 0
@@ -175,6 +176,7 @@ class ChanKLU:
 		self.macd = float(item['macd']) if 'macd' in item and item['macd'] else 0
 		self.signal = float(item['macdsignal']) if 'macdsignal' in item and item['macdsignal'] else 0
 		self.macdhist = float(item['macdhist']) if 'macdhist' in item and item['macdhist'] else 0
+		self.ema26 = float(item['ema26']) if 'ema26' in item and item['ema26'] else 0
 		self.ema52 = float(item['ema52']) if 'ema52' in item and item['ema52'] else 0
 		self.ema24 = float(item['ema24']) if 'ema24' in item and item['ema24'] else 0
 		self.ema104 = float(item['ema104']) if 'ema104' in item and item['ema104'] else 0
@@ -230,15 +232,15 @@ class ChanKLU:
 				elif self.close > self.ema52 and self.high > self.ema52 and self.low < self.ema52:
 					self.near0_return = 0
 		if self.close > self.ema52 and self.open < self.ema52:
-			if self.pre.near0_return == 0:
+			self.near0_return = 7
-				self.near0_return = 7
-			elif self.pre.near0_return == 8:
-				self.pre.near0_return = 0
 		elif self.close < self.ema52 and self.open > self.ema52:
-			if self.pre.near0_return == 0:
+			self.near0_return = 8
-				self.near0_return = 8
+		if self.pre.near0_return == 7:
-			elif self.pre.near0_return == 7:
+			if self.low > self.ema52 and self.close > self.open:
-				self.pre.near0_return = 0
+				self.near0_return = 9
+		if self.pre.near0_return == 8:
+			if self.high < self.ema52 and self.close < self.open:
+				self.near0_return = 10
 		# CROSS0 仅以 Signal 穿越零轴判定
 		if self.pre.signal >= 0 and self.signal < 0:
 			self.macd_state = Chan_MACD_STATE.CROSS0_DOWN

ChanZS.py

@@ -70,3 +70,22 @@ class ChanZS():
         self.gg = gg
     def set_dd(self, dd):
         self.dd = dd
+
+
+# 大级别中枢：由多个区间重叠（扩张）的笔/线段中枢合并而成，用于显示更大级别的震荡区间
+class ChanZS_Big():
+    def __init__(self, zs_list):
+        assert len(zs_list) >= 1
+        self.zs_list = list(zs_list)
+        first = self.zs_list[0]
+        last = self.zs_list[-1]
+        self.start_time = first.start_time
+        self.end_time = last.end_time if last.end_time else None
+        self.start_klc = first.start_klc
+        self.end_klc = last.end_klc
+        # 大级别区间取并集：包住所有子中枢
+        self.zd = min(zs.zd for zs in self.zs_list)
+        self.zg = max(zs.zg for zs in self.zs_list)
+        self.dd = min(zs.dd for zs in self.zs_list)
+        self.gg = max(zs.gg for zs in self.zs_list)
+        self.index = 0  # 由外部设置

TF_DF.py

@@ -6,7 +6,7 @@ from ChanKLC import ChanKLC
 from ChanBI import ChanBI
 from ChanSBI import ChanSBI
 from ChanSEG import ChanSEG
-from ChanZS import ChanZS
+from ChanZS import ChanZS, ChanZS_Big
 from ChanBSP import ChanBSP
 import talib.abstract as ta
 import pandas as pd
@@ -46,6 +46,7 @@ class TF_DF():
 		self.bi_list = self.cal_bi_list(self.klc_list)
 		self.seg_list = self.get_seg_list(self.bi_list)
 		self.zs_list = self.get_zs_list(self.bi_list, self.seg_list)
+		self.big_zs_list = self.get_big_zs_list(self.zs_list)
 		self.chanmacd = ChanMACD(self.klu_list)
 		self.klu_list = self.chanmacd.cal_macd_state()
 	
@@ -571,7 +572,27 @@ class TF_DF():
 		last_klu = None
 		macd = ChanMACD(klu_list)
 		klu_list = macd.cal_macd_state()
+		ema_up_list = []
+		ema_down_list = []
+		ema_up_count = 0
+		ema_down_count = 0
+		last_klu = None
 		for klu in klu_list:
+			ema = klu.ema52
+			last_ema = last_klu.ema52 if last_klu else 0
+			if klu.close >= ema:
+				ema_up_count += 1
+			elif klu.close < ema:
+				ema_down_count += 1
+			if last_klu and last_klu.close >= last_ema and klu.close < ema:
+				ema_up_list.append(ema_up_count)
+				#print(last_klu.time, ema_up_count, "UP END")
+				ema_up_count = 0
+			elif last_klu and last_klu.close < last_ema and klu.close >= ema:
+				ema_down_list.append(ema_down_count)
+				#print(last_klu.time, ema_down_count, "DOWN END")
+				ema_down_count = 0
+			last_klu = klu
 			if len(klc_list) > 0:
 				last_klc = klc_list[-1]
 				if klu.exception:
@@ -609,6 +630,7 @@ class TF_DF():
 				klc_list.append(klc)
 			last_klu = klu
 		klc_list = self.cal_trend(klc_list)
+		#print(ema52_up_list, ema52_down_list)
 		return klc_list
 
 	def get_seg_list(self, bi_list):
@@ -965,7 +987,7 @@ class TF_DF():
 								if last_top.high > klc.high:
 									bi_list[-1].add_klc(klc)
 									klc.set_bi(bi_list[-1])
-									klc.set_klc_fx_type(Chan_KLC_FX.TOP3)
+									#klc.set_klc_fx_type(Chan_KLC_FX.TOP3)
 									#print(klc.end_time, klc.fx, "二类卖点Sell 1")
 								else:
 									# A new top found
@@ -979,7 +1001,7 @@ class TF_DF():
 									klc.set_bi(bi_list[-1])
 							# 不满足结合律的分型
 							else:
-								#klc.set_klc_fx_type(Chan_KLC_FX.TOP0)
+								klc.set_klc_fx_type(Chan_KLC_FX.TOP0)
 								if last_bottom.index + bi_klc_min > klc.index:
 									if last_top.high > klc.high:
 										#print(klc.start_time, klc.fx, "二类卖点Sell 1")
@@ -1082,7 +1104,7 @@ class TF_DF():
 								if last_bottom.low < klc.low:
 									bi_list[-1].add_klc(klc)
 									klc.set_bi(bi_list[-1])
-									klc.set_klc_fx_type(Chan_KLC_FX.BOTTOM3)
+									#klc.set_klc_fx_type(Chan_KLC_FX.BOTTOM3)
 									#print(last_bottom.start_time, last_bottom.end_time, "--------------------------------1")
 									#print(klc.end_time, klc.fx, "二类买点Buy 1")
 								else:
@@ -1096,7 +1118,7 @@ class TF_DF():
 									klc.set_bi(bi_list[-1])
 							# 不满足结合律的分型
 							else:
-								#klc.set_klc_fx_type(Chan_KLC_FX.TOP0)
+								klc.set_klc_fx_type(Chan_KLC_FX.TOP0)
 								if last_top.index + bi_klc_min > klc.index:
 									if last_bottom.low < klc.low:
 										#print(klc.end_time, klc.fx, "中枢买点Buy 1")
@@ -1849,180 +1871,225 @@ class TF_DF():
 	def calculate_zs(self, bi_list, seg_list):
 		return self.get_zs_list(bi_list, seg_list)
 	def get_zs_list(self, bi_list, seg_list):
+		"""
+		根据缠论线段中枢定义计算中枢
+		从第4根线段开始（索引3），每3根线段为一组检查
+		后一个中枢比前一个高 -> 上涨中枢，以下跌开始、以下跌结束
+		后一个中枢比前一个低 -> 下跌中枢，以上涨开始、以上涨结束
+		中枢可以扩展到5根、7根...
+		"""
 		zs_list = []
-		bsp_list = []
+		if len(seg_list) < 3:
-		if len(seg_list) > 3:
+			return zs_list
-			last_zs = None
+
-			first_bi_out = None
+		last_zs = None
-			in_again = False
+
-			bi_out_count = 0
+		# 从第4根线段开始（索引3），每3根为一组
-			zs_count = 0
+		start_idx = 3
-			for seg in seg_list:
+
-				# No zs or Last ZS is completed
+		while start_idx < len(seg_list):
-				if len(zs_list) == 0 or (last_zs and last_zs.is_sure):
+			# 取连续3个线段
-					# Has three completed segments
+			if start_idx + 2 >= len(seg_list):
-					if seg.next and seg.next.next:
+				break
-						if seg.next.next.is_sure:
+
-							zg = min(seg.high, seg.next.high, seg.next.next.high)
+			seg1 = seg_list[start_idx]
-							zd = max(seg.low, seg.next.low, seg.next.next.low)
+			seg2 = seg_list[start_idx + 1]
-							gg = max(seg.high, seg.next.high, seg.next.next.high)
+			seg3 = seg_list[start_idx + 2]
-							dd = min(seg.low, seg.next.low, seg.next.next.low)
+
-							ddir = None
+			# 三个线段都必须是已确认的
-							if last_zs:
+			if not (seg1.is_sure and seg2.is_sure and seg3.is_sure):
-								if zg < last_zs.zd:
+				start_idx += 1
-									ddir = Chan_ZS_DIR.DOWN
+				continue
-								else:
+
-									if zd > last_zs.zg:
+			# 计算这3个线段的中枢区间
-										ddir = Chan_ZS_DIR.UP
+			zg = min(seg1.high, seg2.high, seg3.high)
-									else:
+			zd = max(seg1.low, seg2.low, seg3.low)
-										ddir = None
+
-							else:
+			if zg <= zd:
-								if seg.dir == Chan_SEG_DIR.UP:
+				start_idx += 1
-									ddir = Chan_ZS_DIR.DOWN
+				continue
-								else:
+
-									ddir = Chan_ZS_DIR.UP
+			# 判断中枢类型
-							if (seg.dir == Chan_SEG_DIR.DOWN and ddir == Chan_ZS_DIR.DOWN) or (seg.dir == Chan_SEG_DIR.UP and ddir == Chan_ZS_DIR.UP):
+			# 上涨中枢：以下跌开始、以下跌结束（下跌+上涨+下跌）
-								ddir = None
+			# 下跌中枢：以上涨开始、以上涨结束（上涨+下跌+上涨）
-							if ddir and zg > zd:
+			if last_zs is None:
-								# New ZS
+				# 第一个中枢
-								zs = ChanZS(seg, len(zs_list), ddir)
+				if seg1.dir == Chan_SEG_DIR.DOWN:
-								zs.set_zg(zg)
+					# 下跌开始 -> 上涨中枢
-								zs.set_zd(zd)
+					zs_dir = Chan_ZS_DIR.DOWN
-								zs.set_gg(gg)
+					# 验证模式：下跌+上涨+下跌
-								zs.set_dd(dd)
+					valid = (seg2.dir == Chan_SEG_DIR.UP and seg3.dir == Chan_SEG_DIR.DOWN)
-								if last_zs:
-									last_zs.set_next(zs)
-									zs.set_pre(last_zs)
-								zs_list.append(zs)
-								if last_zs and last_zs.dir == zs.dir:
-									zs_count += 1
-								else:
-									zs_count = 1
-								last_zs = zs
-				# Last ZS is not completed
 				else:
-					# Last ZS is not completed
+					# 上涨开始 -> 下跌中枢
-					if last_zs and not last_zs.is_sure:
+					zs_dir = Chan_ZS_DIR.UP
-						if first_bi_out:
+					# 验证模式：上涨+下跌+上涨
-							# SEG is not in ZS
+					valid = (seg2.dir == Chan_SEG_DIR.DOWN and seg3.dir == Chan_SEG_DIR.UP)
-							if seg.is_sure:
+			else:
-								if ((seg.low > last_zs.zg and seg.high > last_zs.zg) or (seg.high < last_zs.zd and seg.low < last_zs.zd)):
+				# 根据与前一个中枢的高低比较判断
-									last_zs.set_end_klc(seg.pre.end_bi.end_klc, seg.sure_time, bi_out_count, seg.pre)
+				if zg > last_zs.zg:
-									bi_out_count = 0
+					# 上涨中枢：以下跌开始、以下跌结束
-									#print(seg.start_bi.start_klc.start_time)
+					zs_dir = Chan_ZS_DIR.DOWN
-									first_bi_out = None
+					valid = (seg1.dir == Chan_SEG_DIR.DOWN and seg2.dir == Chan_SEG_DIR.UP and seg3.dir == Chan_SEG_DIR.DOWN)
-									# Last ZS is completed and look for new ZS
+				else:
-									if seg.next and seg.next.next:
+					# 下跌中枢：以上涨开始、以上涨结束
-										if seg.next.next.is_sure:
+					zs_dir = Chan_ZS_DIR.UP
-											zg = min(seg.high, seg.next.high, seg.next.next.high)
+					valid = (seg1.dir == Chan_SEG_DIR.UP and seg2.dir == Chan_SEG_DIR.DOWN and seg3.dir == Chan_SEG_DIR.UP)
-											zd = max(seg.low, seg.next.low, seg.next.next.low)
+
-											gg = max(seg.high, seg.next.high, seg.next.next.high)
+			# 验证是否有效
-											dd = min(seg.low, seg.next.low, seg.next.next.low)
+			if not valid:
-											ddir = None
+				start_idx += 1
-											if last_zs:
+				continue
-												if zg < last_zs.zd:
+
-													ddir = Chan_ZS_DIR.DOWN
+			# 检查是否与前一个中枢重叠
-												else:
+			if last_zs:
-													if zd > last_zs.zg:
+				# 判断是否有重叠
-														ddir = Chan_ZS_DIR.UP
+				overlap = (zg >= last_zs.zd and zd <= last_zs.zg)
-													else:
+
-														ddir = None
+				if overlap:
-											else:
+					# 有重叠，扩展中枢到5根、7根...（缠论：合并为同一中枢）
-												if seg.dir == Chan_SEG_DIR.UP:
+					# 本组先纳入当前 3 根，再向后逐根尝试；遇到与 [zd,zg] 不重叠（离开中枢）则停止扩展
-													ddir = Chan_ZS_DIR.DOWN
+					added_segs = [seg_list[start_idx], seg_list[start_idx + 1], seg_list[start_idx + 2]]
-												else:
+					cur_idx = start_idx + 3
-													ddir = Chan_ZS_DIR.UP
+
-											if (seg.dir == Chan_SEG_DIR.DOWN and ddir == Chan_ZS_DIR.DOWN) or (seg.dir == Chan_SEG_DIR.UP and ddir == Chan_ZS_DIR.UP):
+					while cur_idx < len(seg_list):
-												ddir = None
+						next_seg = seg_list[cur_idx]
-											if ddir and zg > zd:
+						if not next_seg.is_sure:
-												# New ZS
+							break
-												zs = ChanZS(seg, len(zs_list), ddir)
+
-												zs.set_zg(zg)
+						# 扩展条件：新线段与中枢区间 [zd, zg] 有重叠即并入；不重叠则停止，离开中枢的线段不包含
-												zs.set_zd(zd)
+						# 用起止笔的极值算线段区间，避免 seg.high/seg.low 在个别线段上未同步导致的误判
-												zs.set_gg(gg)
+						seg_high = max(next_seg.start_bi.high, next_seg.end_bi.high) if next_seg.end_bi else next_seg.start_bi.high
-												zs.set_dd(dd)
+						seg_low = min(next_seg.start_bi.low, next_seg.end_bi.low) if next_seg.end_bi else next_seg.start_bi.low
-												last_zs.set_next(zs)
+						overlap_with_zs = (seg_high >= last_zs.zd and seg_low <= last_zs.zg)
-												zs.set_pre(last_zs)
+						if not overlap_with_zs:
-												zs_list.append(zs)
+							break
-												if last_zs and last_zs.dir == zs.dir:
+						added_segs.append(next_seg)
-													zs_count += 1
+						cur_idx += 1
-												else:
+
-													zs_count = 1
+					# 扩展中枢 = 原中枢线段 + 本组并入的线段（缠论合并）
-												last_zs = zs
+					segs_for_zs = list(last_zs.seg_list) + list(added_segs)
-								# Last SEG is in ZS
+
-								else:
+					# 中枢开始与结束线段方向一致：上涨中枢结束于 DOWN，下跌中枢结束于 UP
-									# SEG is inside ZS
+					required_end_seg_dir = Chan_SEG_DIR.DOWN if last_zs.dir == Chan_ZS_DIR.DOWN else Chan_SEG_DIR.UP
-									if seg.end_bi:
+					while len(segs_for_zs) >= 3 and segs_for_zs[-1].dir != required_end_seg_dir:
-										for index in range(seg.start_bi.index, seg.end_bi.index+1):
+						segs_for_zs.pop()
-											bi = bi_list[index]
+
-											if (bi.high >= last_zs.zd and bi.high <= last_zs.zg) or (bi.low >= last_zs.zd and bi.low <= last_zs.zg) or (bi.high >= last_zs.zg and bi.low <= last_zs.zd):
+					# 扩展时只更新 gg、dd 和 seg_list；zg、zd 由前 3 根线段确定，不随扩展改变
-												in_again = True
+					seg_highs = [s.high for s in segs_for_zs]
-												last_zs.set_bi_out(None, None)
+					seg_lows = [s.low for s in segs_for_zs]
-												last_zs.set_last_bi_in(None)
+					last_zs.set_gg(max(seg_highs))
-												last_zs.set_end_seg(None)
+					last_zs.set_dd(min(seg_lows))
-												first_bi_out = None
+					last_zs.seg_list = segs_for_zs
-												#print("Bi in again 3", bi.start_klc.start_time)
+
-											if in_again and (bi.low > last_zs.zg or bi.high < last_zs.zd):
+					# 更新结束时间（以裁剪后的最后一段为准）
-												last_zs.set_bi_out(bi, seg)
+					last_seg = segs_for_zs[-1]
-												last_zs.set_last_bi_in(bi_list[index - 1])
+					if last_seg.end_bi:
-												#last_zs.set_end_seg(seg.next.next)
+						last_zs.set_end_klc(last_seg.end_bi.end_klc, last_seg.sure_time, 0, last_seg)
-												bi_out_count += 1
+						last_zs.set_end_seg(last_seg)
-												first_bi_out = bi
+
-												if (bi.dir == Chan_BI_DIR.UP and seg.dir == Chan_SEG_DIR.DOWN) or (bi.dir == Chan_BI_DIR.DOWN and seg.dir == Chan_SEG_DIR.UP):
+					# 跳过本组已扫描的线段（从 start_idx 到 cur_idx-1），下一组从 cur_idx 起可能再形成新中枢
-													bsp = ChanBSP(first_bi_out, len(bsp_list), Chan_BSP_TYPE.B3, Chan_BSP_DIR.BUY if first_bi_out.dir == Chan_BI_DIR.DOWN else Chan_BSP_DIR.SELL, first_bi_out.sure_time, zs_count, zs, seg)
+					start_idx = cur_idx
-													bsp_list.append(bsp)
+					continue
-												#print("First bi out 3", first_bi_out.start_klc.start_time)
+				else:
-												in_again = False
+					# 没有重叠，创建新中枢
-											""""
+					# 先确认前一个中枢 - 使用前一个中枢本身的最后一个线段
-											if first_bi_out:
+					if last_zs.seg_list and len(last_zs.seg_list) > 0:
-												if seg.dir == Chan_SEG_DIR.UP and bi.dir == Chan_BI_DIR.UP:
+						prev_zs_last_seg = last_zs.seg_list[-1]
-													#print(bi.start_klc.start_time, bi.high, seg.high)
+						if prev_zs_last_seg.end_bi:
-													if bi.high == seg.high:
+							last_zs.set_end_klc(prev_zs_last_seg.end_bi.end_klc, prev_zs_last_seg.sure_time, 0, prev_zs_last_seg)
-														bsp = ChanBSP(bi, len(bsp_list), Chan_BSP_TYPE.T3E, Chan_BSP_DIR.SELL if bi.dir == Chan_BI_DIR.DOWN else Chan_BSP_DIR.BUY, bi.sure_time, zs_count, zs, seg)
+							last_zs.set_end_seg(prev_zs_last_seg)
-														bsp_list.append(bsp)
+							last_zs.is_sure = True
-												else:
+
-													if seg.dir == Chan_SEG_DIR.DOWN and bi.dir == Chan_BI_DIR.DOWN:
+			# 创建新中枢
-														if bi.low == seg.low:
+			gg = max(seg1.high, seg2.high, seg3.high)
-															bsp = ChanBSP(bi, len(bsp_list), Chan_BSP_TYPE.T3E, Chan_BSP_DIR.BUY if bi.dir == Chan_BI_DIR.DOWN else Chan_BSP_DIR.SELL, bi.sure_time, zs_count, zs, seg)
+			dd = min(seg1.low, seg2.low, seg3.low)
-															bsp_list.append(bsp)
+
-											"""
+			zs = ChanZS(seg1, len(zs_list), zs_dir)
-						else:
+			zs.set_zg(zg)
-							# SEG in ZS and not out and find first bi out
+			zs.set_zd(zd)
-							if seg.end_bi:
+			zs.set_gg(gg)
-								for index in range(seg.start_bi.index, seg.end_bi.index+1):
+			zs.set_dd(dd)
-									bi = bi_list[index]
+			zs.set_end_klc(seg3.end_bi.end_klc, seg3.sure_time, 0, seg3)
-									if (bi.high >= last_zs.zd and bi.high <= last_zs.zg) or (bi.low >= last_zs.zd and bi.low <= last_zs.zg) or (bi.high >= last_zs.zg and bi.low <= last_zs.zd):
+			zs.set_end_seg(seg3)
-										in_again = True
+			zs.is_sure = False
-										last_zs.set_bi_out(None, None)
+			zs.seg_list = [seg1, seg2, seg3]
-										last_zs.set_last_bi_in(None)
+
-										last_zs.set_end_seg(None)
+			if last_zs:
-										first_bi_out = None
+				last_zs.set_next(zs)
-										#print("Bi in again 4", bi.start_klc.start_time)
+				zs.set_pre(last_zs)
-									if in_again and (bi.low > last_zs.zg or bi.high < last_zs.zd):
+
-										last_zs.set_bi_out(bi, seg)
+			zs_list.append(zs)
-										last_zs.set_last_bi_in(bi_list[index - 1])
+			last_zs = zs
-										#last_zs.set_end_seg(seg.next.next)
+
-										bi_out_count += 1
+			# 移动到下一组
-										first_bi_out = bi
+			start_idx += 3
-										if (bi.dir == Chan_BI_DIR.UP and seg.dir == Chan_SEG_DIR.DOWN) or (bi.dir == Chan_BI_DIR.DOWN and seg.dir == Chan_SEG_DIR.UP):
+
-											bsp = ChanBSP(first_bi_out, len(bsp_list), Chan_BSP_TYPE.B3, Chan_BSP_DIR.BUY if first_bi_out.dir == Chan_BI_DIR.DOWN else Chan_BSP_DIR.SELL, first_bi_out.sure_time, zs_count, zs, seg)
+		# 处理最后一个未确认的中枢 - 不自动扩展，保持未完成状态
-											bsp_list.append(bsp)
+		if last_zs and not last_zs.is_sure:
-										#print("First bi out 4", first_bi_out.start_klc.start_time)
+			# 获取中枢最后一个线段的索引
-										in_again = False
+			if last_zs.seg_list and len(last_zs.seg_list) > 0:
-									if first_bi_out:
+				last_seg_of_zs = last_zs.seg_list[-1]
-										if seg.dir == Chan_SEG_DIR.UP and bi.dir == Chan_BI_DIR.UP:
+				# 找到这个线段在seg_list中的索引
-											#print(bi.start_klc.start_time, bi.high, seg.high)
+				last_seg_idx = -1
-											if bi.high == seg.high:
+				for i, seg in enumerate(seg_list):
-												bsp = ChanBSP(bi, len(bsp_list), Chan_BSP_TYPE.S3, Chan_BSP_DIR.SELL if bi.dir == Chan_BI_DIR.DOWN else Chan_BSP_DIR.BUY, bi.sure_time, zs_count, zs, seg)
+					if seg == last_seg_of_zs:
-												bsp_list.append(bsp)
+						last_seg_idx = i
-										else:
+						break
-											if seg.dir == Chan_SEG_DIR.DOWN and bi.dir == Chan_BI_DIR.DOWN:
+
-												if bi.low == seg.low:
+				# 从中枢最后一个线段之后检查是否有离开
-													bsp = ChanBSP(bi, len(bsp_list), Chan_BSP_TYPE.B3, Chan_BSP_DIR.BUY if bi.dir == Chan_BI_DIR.DOWN else Chan_BSP_DIR.SELL, bi.sure_time, zs_count, zs, seg)
+				has_leave = False
-													bsp_list.append(bsp)
+				if last_seg_idx >= 0 and last_seg_idx + 1 < len(seg_list):
-		#self.print_zs(zs_list)
+					for i in range(last_seg_idx + 1, len(seg_list)):
+						seg = seg_list[i]
+						if seg.is_sure:
+							# 检查是否离开中枢
+							leave = (seg.low > last_zs.zg and seg.high > last_zs.zg) or \
+									(seg.high < last_zs.zd and seg.low < last_zs.zd)
+							if leave:
+								has_leave = True
+								break
+
+				if not has_leave:
+					# 没有离开，保持未完成状态
+					pass
+				else:
+					# 有离开，确认中枢
+					if last_seg_of_zs.end_bi:
+						last_zs.set_end_klc(last_seg_of_zs.end_bi.end_klc, last_seg_of_zs.sure_time, 0, last_seg_of_zs)
+						last_zs.set_end_seg(last_seg_of_zs)
+						last_zs.is_sure = True
+
 		return zs_list
 
+	def get_big_zs_list(self, zs_list):
+		"""
+		中枢扩张：将区间重叠的连续中枢合并为大级别中枢，便于显示更大级别的震荡区间。
+		重叠定义：两中枢 [zd,zg] 有交集，即 (zs_i.zg >= zs_j.zd and zs_i.zd <= zs_j.zg)。
+		"""
+		big_list = []
+		if len(zs_list) < 2:
+			return big_list
+		i = 0
+		while i < len(zs_list):
+			group = [zs_list[i]]
+			j = i + 1
+			while j < len(zs_list):
+				cur = zs_list[j]
+				# 与当前组内任一中枢有重叠即算扩张（通常只需与组内最后一个比）
+				last_in_group = group[-1]
+				overlap = (last_in_group.zg >= cur.zd and last_in_group.zd <= cur.zg)
+				if overlap:
+					group.append(cur)
+					j += 1
+				else:
+					break
+			if len(group) >= 2:
+				big = ChanZS_Big(group)
+				big.index = len(big_list)
+				big_list.append(big)
+			i = j if len(group) >= 2 else i + 1
+		return big_list
+
 	def get_klu_list(self, dataframe):
 		klu_list = self.get_kl_data(dataframe)
 		#klu_list = self.cal_klu_pattern(klu_list)

config/Local_Test.json

@@ -42,7 +42,7 @@
         "ccxt_config": {},
         "ccxt_async_config": {},
         "pair_whitelist": [
-            "SOL/USDT:USDT"
+            "BTC/USDT:USDT"
         ],
         "pair_blacklist": [
             "BNB/.*"

strategies/CryptoFutures1m5mStrategy.py

@@ -9,68 +9,44 @@ from typing import Optional
 from freqtrade.persistence import Trade
 import warnings
 
-# 抑制 pandas FutureWarning 关于 fillna 的隐式降级警告
-# 这个警告来自 freqtrade 库的 strategy_helper.py
 warnings.filterwarnings('ignore', category=FutureWarning, message='.*Downcasting object dtype arrays.*')
-# 或者启用未来行为（推荐）
 pd.set_option('future.no_silent_downcasting', True)
 
-# freqtrade trade -c ./user_data/Chan/config/Local_Test.json --strategy CryptoFutures1m5mStrategy --strategy-path ./user_data/Chan/strategies
-# freqtrade backtesting -c ./user_data/Chan/config/Local_Test.json --strategy CryptoFutures1m5mStrategy --strategy-path ./user_data/Chan/strategies --timerange=20260304-
-# freqtrade download-data -c ./user_data/Chan/config/Local_Test.json -t 1m 5m --data-format-ohlcv json --pairs SOL/USDT:USDT --timerange=20260201-
-
 
 class CryptoFutures1m5mStrategy(IStrategy):
     """
-    SOL/USDT 合约策略 - 1分钟+5分钟双时间框架 V12e (Short Only)
+    SOL/USDT 合约策略 - 只做多版 (默认策略)
     
-    14个月回测 (2025-01 ~ 2026-03): +107.11%, PF 1.37, DD 23.96%
+    基于V5修改：禁用做空，只做多
-    每个季度均盈利，市场下跌-54%期间持续获利
-    
-    核心设计：
-    1. 纯做空策略 - 价格必须低于EMA200至少1%才允许做空
-    2. 5分钟趋势确认：EMA12<EMA26<EMA50 + ADX 25-50 + RSI 30-48
-    3. ATR自适应波动率过滤：ATR < 长期均值 * 1.5（避免极端波动）
-    4. 1分钟精确入场：顶背离 / EMA死叉 / 熊市回调
-    5. 双重MACD确认（5分钟+1分钟MACD柱状图均为负）
-    6. trailing_stop_positive_offset = 0.030
-    7. 时间止损：持仓过久且亏损时提前退出
     """
     INTERFACE_VERSION = 3
     timeframe = '1m'
     informative_timeframe = '5m'
-    can_short = True
+    can_short = False  # 禁用做空
+    can_long = True
+    lev = 1.0
     
-    # 止损止盈
+    stoploss = -0.035
-    stoploss = -0.025  # 2.5% 硬止损
     trailing_stop = True
     trailing_stop_positive = 0.008
-    trailing_stop_positive_offset = 0.030
+    trailing_stop_positive_offset = 0.035
     trailing_only_offset_is_reached = True
     
-    # 不使用custom_stoploss（会干扰trailing_stop）
-    use_custom_stoploss = False
-    
-    # 完全禁用 exit_signal
     use_exit_signal = False
-    
     process_only_new_candles = True
     startup_candle_count: int = 1100
+    
     def informative_pairs(self):
-        return [
+        return [("SOL/USDT:USDT", "5m")]
-                ("SOL/USDT:USDT", "5m"),
+                
-                ]
     def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-        # ==================== 5分钟指标 ====================
         inf_tf = self.informative_timeframe
         informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf)
         
-        # EMA趋势
+        # EMA
         informative['ema12'] = ta.EMA(informative['close'], timeperiod=12)
         informative['ema26'] = ta.EMA(informative['close'], timeperiod=26)
         informative['ema50'] = ta.EMA(informative['close'], timeperiod=50)
-        
-        # EMA12斜率（3根K线变化率，用于确认趋势方向的动量）
         informative['ema12_slope'] = (informative['ema12'] - informative['ema12'].shift(3)) / informative['ema12'].shift(3) * 100
         
         # MACD
@@ -79,66 +55,54 @@ class CryptoFutures1m5mStrategy(IStrategy):
         informative['macd_signal_5m'] = macd_signal
         informative['macd_hist_5m'] = macd_hist
         
-        # ADX趋势强度
+        # ADX
         informative['adx_5m'] = ta.ADX(informative['high'], informative['low'], informative['close'], timeperiod=14)
         
-        # RSI（5分钟）
+        # RSI
         informative['rsi_5m'] = ta.RSI(informative['close'], timeperiod=14)
         
-        # ATR（5分钟）
+        # ATR
         informative['atr_5m'] = ta.ATR(informative['high'], informative['low'], informative['close'], timeperiod=14)
         informative['atr_pct_5m'] = informative['atr_5m'] / informative['close'] * 100
-        
-        # ATR 长期均值（用于自适应波动率过滤）
         informative['atr_pct_ma_5m'] = informative['atr_pct_5m'].rolling(window=100).mean()
         
-        # ===== EMA200 大趋势过滤 =====
+        # EMA200
         informative['ema200'] = ta.EMA(informative['close'], timeperiod=200)
         informative['ema200_dist_pct'] = (informative['close'] - informative['ema200']) / informative['ema200'] * 100
-        
-        # EMA200斜率（20根5分钟K线 = 100分钟趋势方向）
         informative['ema200_slope'] = (informative['ema200'] - informative['ema200'].shift(20)) / informative['ema200'].shift(20) * 100
         
-        # ===== 大趋势过滤（Short Only） =====
+        # 做多趋势
-        # 做空需要价格低于EMA200至少1%
+        informative['trend_bull_5m'] = (
-        informative['below_ema200'] = informative['ema200_dist_pct'] < -1.0
+            (informative['ema12'] > informative['ema26']) &
-        
+            (informative['ema26'] > informative['ema50']) &
-        # 牛市暂停：EMA200上升 + 价格在EMA200上方 → 完全停止做空
+            (informative['ema12_slope'] > 0.05) &
-        informative['bull_pause'] = (
+            (informative['adx_5m'] > 24) &
-            (informative['ema200_slope'] > 0) &
+            (informative['adx_5m'] < 51) &
-            (informative['ema200_dist_pct'] > 0)
+            (informative['close'] > informative['ema12']) &
+            (informative['rsi_5m'] > 52) &
+            (informative['rsi_5m'] < 72)
         )
         
-        # ===== 5分钟趋势判断（仅Short） =====
+        # 大趋势过滤
-        informative['trend_bear_5m'] = (
+        informative['above_ema200'] = informative['ema200_dist_pct'] > 1.0
-            (informative['ema12'] < informative['ema26']) &
-            (informative['ema26'] < informative['ema50']) &
-            (informative['ema12_slope'] < 0) &
-            (informative['adx_5m'] > 25) &
-            (informative['adx_5m'] < 50) &
-            (informative['close'] < informative['ema12']) &
-            (informative['rsi_5m'] < 48) &
-            (informative['rsi_5m'] > 30)
-        )
         
-        # 做空条件：短期趋势 + EMA200大趋势方向一致 + 非牛市
+        # 做多条件
-        informative['can_long_5m'] = False
+        informative['can_long_5m'] = informative['trend_bull_5m'] & informative['above_ema200']
-        informative['can_short_5m'] = (
-            informative['trend_bear_5m'] &
-            informative['below_ema200'] &
-            (~informative['bull_pause'])
-        )
         
-        # ATR波动率过滤（自适应）
+        # ATR过滤
         informative['atr_ok_5m'] = (
-            (informative['atr_pct_5m'] > 0.1) &
+            (informative['atr_pct_5m'] > 0.07) &
-            (informative['atr_pct_5m'] < informative['atr_pct_ma_5m'] * 1.5)
+            (informative['atr_pct_5m'] < informative['atr_pct_ma_5m'] * 2.2)
         )
         
-        # 合并5分钟数据到1分钟
+        # 成交量
+        informative['volume_ma_5m'] = ta.SMA(informative['volume'], timeperiod=20)
+        informative['volume_ok_5m'] = informative['volume'] > informative['volume_ma_5m'] * 0.75
+        
+        # 合并
         dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True)
         
-        # ==================== 1分钟指标 ====================
+        # 1分钟指标
         macd_1m, signal_1m, hist_1m = ta.MACD(dataframe['close'], fastperiod=12, slowperiod=26, signalperiod=9)
         dataframe['macd'] = macd_1m
         dataframe['macd_signal'] = signal_1m
@@ -148,116 +112,96 @@ class CryptoFutures1m5mStrategy(IStrategy):
         dataframe['ema21'] = ta.EMA(dataframe['close'], timeperiod=21)
         dataframe['rsi'] = ta.RSI(dataframe['close'], timeperiod=14)
         dataframe['vol_ma20'] = ta.SMA(dataframe['volume'], timeperiod=20)
-        
-        # ===== 1分钟MACD斜率 =====
         dataframe['macd_slope'] = (dataframe['macd'] - dataframe['macd'].shift(3)) / 3
         
-        # ===== 1分钟做空入场信号 =====
+        # 做多信号
-        dataframe['price_high_5'] = dataframe['high'].rolling(window=5).max()
+        dataframe['price_low_5'] = dataframe['low'].rolling(window=5).min()
-        dataframe['macd_high_5'] = dataframe['macd'].rolling(window=5).max()
+        dataframe['macd_low_5'] = dataframe['macd'].rolling(window=5).min()
         
-        dataframe['top_divergence'] = (
+        dataframe['bottom_divergence'] = (
-            (dataframe['high'] >= dataframe['price_high_5'] * 0.999) &
+            (dataframe['low'] <= dataframe['price_low_5'] * 1.001) &
-            (dataframe['macd'] < dataframe['macd_high_5']) &
+            (dataframe['macd'] > dataframe['macd_low_5']) &
-            (dataframe['macd_slope'] < 0) &
+            (dataframe['macd_slope'] > 0) &
-            (dataframe['macd'] < dataframe['macd_signal']) &
+            (dataframe['macd'] > dataframe['macd_signal']) &
             (dataframe['volume'] > dataframe['vol_ma20'] * 0.6)
         )
         
-        dataframe['ema_cross_down'] = (
+        dataframe['ema_cross_up'] = (
-            (dataframe['ema9'] < dataframe['ema21']) &
+            (dataframe['ema9'] > dataframe['ema21']) &
-            (dataframe['ema9'].shift(1) >= dataframe['ema21'].shift(1)) &
+            (dataframe['ema9'].shift(1) <= dataframe['ema21'].shift(1)) &
-            (dataframe['rsi'] < 55) & (dataframe['rsi'] > 35) &
+            (dataframe['rsi'] > 45) &
+            (dataframe['rsi'] < 70) &
             (dataframe['volume'] > dataframe['vol_ma20'] * 1.0)
         )
         
-        dataframe['is_bear_candle'] = (
+        dataframe['is_bull_candle'] = (dataframe['close'] > dataframe['open']) & ((dataframe['close'] - dataframe['open']) / dataframe['open'] > 0.008)
-            (dataframe['close'] < dataframe['open']) &
+        dataframe['bull_pullback'] = (
-            ((dataframe['open'] - dataframe['close']) / dataframe['open'] > 0.008)
+            dataframe['is_bull_candle'].shift(2) &
-        )
+            (dataframe['close'].shift(1) < dataframe['open'].shift(1)) &
-        dataframe['bear_pullback'] = (
+            (dataframe['low'] > dataframe['low'].shift(2)) &
-            dataframe['is_bear_candle'].shift(2) &
+            (dataframe['close'] > dataframe['open']) &
-            (dataframe['close'].shift(1) > dataframe['open'].shift(1)) &
+            (dataframe['close'] > dataframe['ema9'])
-            (dataframe['high'] < dataframe['high'].shift(2)) &
-            (dataframe['close'] < dataframe['open']) &
-            (dataframe['close'] < dataframe['ema9'])
         )
         
-        # ==================== 时间过滤 ====================
+        # 时间过滤
         dataframe['hour_utc'] = dataframe['date'].dt.hour
         dataframe['is_bad_hour'] = dataframe['hour_utc'].isin([4, 5, 6, 7])
         
-        # 安全转换5分钟布尔列
+        # 类型转换
-        bool_cols = [
+        bool_cols = ['can_long_5m_5m', 'trend_bull_5m_5m', 'atr_ok_5m_5m', 'above_ema200_5m', 'volume_ok_5m_5m']
-            'can_long_5m_5m', 'can_short_5m_5m',
-            'trend_bear_5m_5m',
-            'atr_ok_5m_5m',
-            'below_ema200_5m', 'bull_pause_5m',
-        ]
         for col in bool_cols:
             if col in dataframe.columns:
                 dataframe[col] = dataframe[col].astype(bool).fillna(False)
         
-        num_cols = ['atr_pct_5m_5m', 'rsi_5m_5m', 'macd_hist_5m_5m', 'atr_pct_ma_5m_5m',
-                    'ema200_dist_pct_5m', 'ema200_slope_5m']
-        for col in num_cols:
-            if col in dataframe.columns:
-                dataframe[col] = dataframe[col].astype(float).fillna(0.0)
-        
         return dataframe
 
     def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         time_ok = ~dataframe['is_bad_hour']
         atr_ok = dataframe['atr_ok_5m_5m']
+        volume_ok = dataframe['volume_ok_5m_5m']
         
-        # 5分钟MACD方向确认
+        # 只做多
-        macd_bear_5m = dataframe['macd_hist_5m_5m'] < 0
+        macd_bull_5m = dataframe['macd_hist_5m_5m'] > 0
+        macd_bull_1m = dataframe['macd_hist'] > 0
         
-        # 1分钟MACD方向确认（双重确认）
-        macd_bear_1m = dataframe['macd_hist'] < 0
-
-        # ===== 做空入场 =====
         dataframe.loc[
-            (time_ok) &
+            (time_ok) & (atr_ok) & (dataframe['can_long_5m_5m']) &
-            (atr_ok) &
+            (macd_bull_5m) & (macd_bull_1m) & (volume_ok) &
-            (dataframe['can_short_5m_5m']) &
+            (dataframe['rsi'] < 70) & (dataframe['rsi'] > 40) &
-            (macd_bear_5m) &
+            (dataframe['bottom_divergence'] | dataframe['ema_cross_up'] | dataframe['bull_pullback']) &
-            (macd_bear_1m) &
-            (dataframe['rsi'] > 30) &
-            (
-                dataframe['top_divergence'] |
-                dataframe['ema_cross_down'] |
-                dataframe['bear_pullback']
-            ) &
             (dataframe['volume'] > 0),
-            'enter_short'
+            'enter_long'
         ] = 1
 
         return dataframe
 
     def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         dataframe.loc[:, 'exit_long'] = 0
-        dataframe.loc[:, 'exit_short'] = 0
         return dataframe
 
     def custom_exit(self, pair: str, trade: Trade, current_time: datetime,
                     current_rate: float, current_profit: float, **kwargs) -> str | bool | None:
-        """时间止损：持仓过久且亏损时提前退出"""
         trade_duration = (current_time - trade.open_date_utc).total_seconds() / 3600
         
-        if trade_duration > 8 and current_profit < -0.005:
+        # 做多时间止损 - 宽松
-            return 'time_stop_8h'
+        if trade_duration > 10 and current_profit < -0.006:
-        
+            return 'time_stop_long_10h'
-        if trade_duration > 16 and current_profit < 0:
+        if trade_duration > 20 and current_profit < 0:
-            return 'time_stop_16h'
+            return 'time_stop_long_20h'
+        if trade_duration > 30:
+            return 'time_stop_long_30h'
         
         return None
 
     def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
                             time_in_force: str, current_time: datetime, entry_tag: Optional[str],
                             side: str, **kwargs) -> bool:
-        """入场确认 - 时间过滤安全网"""
         hour_utc = current_time.utcnow().hour if current_time.tzinfo is None else current_time.hour
         if hour_utc in {4, 5, 6, 7}:
             return False
         return True
+
+    def leverage(self, pair: str, current_time: datetime, current_rate: float,
+                proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str,
+                **kwargs) -> float:
+        return self.lev

strategies/CryptoFutures1m5mStrategyLongOnly.py

@@ -0,0 +1,209 @@
+# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
+from freqtrade.strategy import IStrategy, merge_informative_pair
+from pandas import DataFrame
+import pandas as pd
+import talib.abstract as ta
+import numpy as np
+from datetime import datetime
+from typing import Optional
+from freqtrade.persistence import Trade
+import warnings
+
+warnings.filterwarnings('ignore', category=FutureWarning, message='.*Downcasting object dtype arrays.*')
+pd.set_option('future.no_silent_downcasting', True)
+
+
+class CryptoFutures1m5mStrategyLongOnly(IStrategy):
+    """
+    SOL/USDT 合约策略 - 只做多版本
+    
+    基于V5修改：
+    - 只做多，禁止做空
+    - 优化做多止损和止盈参数
+    """
+    INTERFACE_VERSION = 3
+    timeframe = '1m'
+    informative_timeframe = '5m'
+    can_short = False  # 禁用做空
+    can_long = True
+    lev = 1.0
+    
+    stoploss = -0.035
+    trailing_stop = True
+    trailing_stop_positive = 0.008
+    trailing_stop_positive_offset = 0.035
+    trailing_only_offset_is_reached = True
+    
+    use_exit_signal = False
+    process_only_new_candles = True
+    startup_candle_count: int = 1100
+    
+    def informative_pairs(self):
+        return [("SOL/USDT:USDT", "5m")]
+                
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        inf_tf = self.informative_timeframe
+        informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf)
+        
+        # EMA
+        informative['ema12'] = ta.EMA(informative['close'], timeperiod=12)
+        informative['ema26'] = ta.EMA(informative['close'], timeperiod=26)
+        informative['ema50'] = ta.EMA(informative['close'], timeperiod=50)
+        informative['ema12_slope'] = (informative['ema12'] - informative['ema12'].shift(3)) / informative['ema12'].shift(3) * 100
+        
+        # MACD
+        macd, macd_signal, macd_hist = ta.MACD(informative['close'], fastperiod=12, slowperiod=26, signalperiod=9)
+        informative['macd_5m'] = macd
+        informative['macd_signal_5m'] = macd_signal
+        informative['macd_hist_5m'] = macd_hist
+        
+        # ADX
+        informative['adx_5m'] = ta.ADX(informative['high'], informative['low'], informative['close'], timeperiod=14)
+        
+        # RSI
+        informative['rsi_5m'] = ta.RSI(informative['close'], timeperiod=14)
+        
+        # ATR
+        informative['atr_5m'] = ta.ATR(informative['high'], informative['low'], informative['close'], timeperiod=14)
+        informative['atr_pct_5m'] = informative['atr_5m'] / informative['close'] * 100
+        informative['atr_pct_ma_5m'] = informative['atr_pct_5m'].rolling(window=100).mean()
+        
+        # EMA200
+        informative['ema200'] = ta.EMA(informative['close'], timeperiod=200)
+        informative['ema200_dist_pct'] = (informative['close'] - informative['ema200']) / informative['ema200'] * 100
+        informative['ema200_slope'] = (informative['ema200'] - informative['ema200'].shift(20)) / informative['ema200'].shift(20) * 100
+        
+        # 做多趋势
+        informative['trend_bull_5m'] = (
+            (informative['ema12'] > informative['ema26']) &
+            (informative['ema26'] > informative['ema50']) &
+            (informative['ema12_slope'] > 0.05) &
+            (informative['adx_5m'] > 24) &
+            (informative['adx_5m'] < 51) &
+            (informative['close'] > informative['ema12']) &
+            (informative['rsi_5m'] > 52) &
+            (informative['rsi_5m'] < 72)
+        )
+        
+        # 大趋势过滤
+        informative['above_ema200'] = informative['ema200_dist_pct'] > 1.0
+        
+        # 做多条件
+        informative['can_long_5m'] = informative['trend_bull_5m'] & informative['above_ema200']
+        
+        # ATR过滤
+        informative['atr_ok_5m'] = (
+            (informative['atr_pct_5m'] > 0.07) &
+            (informative['atr_pct_5m'] < informative['atr_pct_ma_5m'] * 2.2)
+        )
+        
+        # 成交量
+        informative['volume_ma_5m'] = ta.SMA(informative['volume'], timeperiod=20)
+        informative['volume_ok_5m'] = informative['volume'] > informative['volume_ma_5m'] * 0.75
+        
+        # 合并
+        dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True)
+        
+        # 1分钟指标
+        macd_1m, signal_1m, hist_1m = ta.MACD(dataframe['close'], fastperiod=12, slowperiod=26, signalperiod=9)
+        dataframe['macd'] = macd_1m
+        dataframe['macd_signal'] = signal_1m
+        dataframe['macd_hist'] = hist_1m
+        
+        dataframe['ema9'] = ta.EMA(dataframe['close'], timeperiod=9)
+        dataframe['ema21'] = ta.EMA(dataframe['close'], timeperiod=21)
+        dataframe['rsi'] = ta.RSI(dataframe['close'], timeperiod=14)
+        dataframe['vol_ma20'] = ta.SMA(dataframe['volume'], timeperiod=20)
+        dataframe['macd_slope'] = (dataframe['macd'] - dataframe['macd'].shift(3)) / 3
+        
+        # 做多信号
+        dataframe['price_low_5'] = dataframe['low'].rolling(window=5).min()
+        dataframe['macd_low_5'] = dataframe['macd'].rolling(window=5).min()
+        
+        dataframe['bottom_divergence'] = (
+            (dataframe['low'] <= dataframe['price_low_5'] * 1.001) &
+            (dataframe['macd'] > dataframe['macd_low_5']) &
+            (dataframe['macd_slope'] > 0) &
+            (dataframe['macd'] > dataframe['macd_signal']) &
+            (dataframe['volume'] > dataframe['vol_ma20'] * 0.6)
+        )
+        
+        dataframe['ema_cross_up'] = (
+            (dataframe['ema9'] > dataframe['ema21']) &
+            (dataframe['ema9'].shift(1) <= dataframe['ema21'].shift(1)) &
+            (dataframe['rsi'] > 45) &
+            (dataframe['rsi'] < 70) &
+            (dataframe['volume'] > dataframe['vol_ma20'] * 1.0)
+        )
+        
+        dataframe['is_bull_candle'] = (dataframe['close'] > dataframe['open']) & ((dataframe['close'] - dataframe['open']) / dataframe['open'] > 0.008)
+        dataframe['bull_pullback'] = (
+            dataframe['is_bull_candle'].shift(2) &
+            (dataframe['close'].shift(1) < dataframe['open'].shift(1)) &
+            (dataframe['low'] > dataframe['low'].shift(2)) &
+            (dataframe['close'] > dataframe['open']) &
+            (dataframe['close'] > dataframe['ema9'])
+        )
+        
+        # 时间过滤
+        dataframe['hour_utc'] = dataframe['date'].dt.hour
+        dataframe['is_bad_hour'] = dataframe['hour_utc'].isin([4, 5, 6, 7])
+        
+        # 类型转换
+        bool_cols = ['can_long_5m_5m', 'trend_bull_5m_5m', 'atr_ok_5m_5m', 'above_ema200_5m', 'volume_ok_5m_5m']
+        for col in bool_cols:
+            if col in dataframe.columns:
+                dataframe[col] = dataframe[col].astype(bool).fillna(False)
+        
+        return dataframe
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        time_ok = ~dataframe['is_bad_hour']
+        atr_ok = dataframe['atr_ok_5m_5m']
+        volume_ok = dataframe['volume_ok_5m_5m']
+        
+        # 只做多
+        macd_bull_5m = dataframe['macd_hist_5m_5m'] > 0
+        macd_bull_1m = dataframe['macd_hist'] > 0
+        
+        dataframe.loc[
+            (time_ok) & (atr_ok) & (dataframe['can_long_5m_5m']) &
+            (macd_bull_5m) & (macd_bull_1m) & (volume_ok) &
+            (dataframe['rsi'] < 70) & (dataframe['rsi'] > 40) &
+            (dataframe['bottom_divergence'] | dataframe['ema_cross_up'] | dataframe['bull_pullback']) &
+            (dataframe['volume'] > 0),
+            'enter_long'
+        ] = 1
+
+        return dataframe
+
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe.loc[:, 'exit_long'] = 0
+        return dataframe
+
+    def custom_exit(self, pair: str, trade: Trade, current_time: datetime,
+                    current_rate: float, current_profit: float, **kwargs) -> str | bool | None:
+        trade_duration = (current_time - trade.open_date_utc).total_seconds() / 3600
+        
+        # 做多时间止损 - 宽松
+        if trade_duration > 10 and current_profit < -0.006:
+            return 'time_stop_long_10h'
+        if trade_duration > 20 and current_profit < 0:
+            return 'time_stop_long_20h'
+        if trade_duration > 30:
+            return 'time_stop_long_30h'
+        
+        return None
+
+    def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
+                            time_in_force: str, current_time: datetime, entry_tag: Optional[str],
+                            side: str, **kwargs) -> bool:
+        hour_utc = current_time.utcnow().hour if current_time.tzinfo is None else current_time.hour
+        if hour_utc in {4, 5, 6, 7}:
+            return False
+        return True
+
+    def leverage(self, pair: str, current_time: datetime, current_rate: float,
+                proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str,
+                **kwargs) -> float:
+        return self.lev

strategies/CryptoFutures1m5mStrategyShortOnly.py

@@ -0,0 +1,212 @@
+# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
+from freqtrade.strategy import IStrategy, merge_informative_pair
+from pandas import DataFrame
+import pandas as pd
+import talib.abstract as ta
+import numpy as np
+from datetime import datetime
+from typing import Optional
+from freqtrade.persistence import Trade
+import warnings
+
+warnings.filterwarnings('ignore', category=FutureWarning, message='.*Downcasting object dtype arrays.*')
+pd.set_option('future.no_silent_downcasting', True)
+
+
+class CryptoFutures1m5mStrategyShortOnly(IStrategy):
+    """
+    SOL/USDT 合约策略 - 只做空版本
+    
+    基于V5修改：
+    - 只做空，禁止做多
+    - 优化做空止损和止盈参数
+    """
+    INTERFACE_VERSION = 3
+    timeframe = '1m'
+    informative_timeframe = '5m'
+    can_short = True
+    can_long = False  # 禁用做多
+    lev = 1.0
+    
+    # Trailing设置 - 基于V5
+    trailing_stop = True
+    trailing_stop_positive = 0.008
+    trailing_stop_positive_offset = 0.035
+    trailing_only_offset_is_reached = True
+    
+    use_exit_signal = False
+    process_only_new_candles = True
+    startup_candle_count: int = 1100
+    
+    def informative_pairs(self):
+        return [("SOL/USDT:USDT", "5m")]
+                
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        inf_tf = self.informative_timeframe
+        informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf)
+        
+        # EMA
+        informative['ema12'] = ta.EMA(informative['close'], timeperiod=12)
+        informative['ema26'] = ta.EMA(informative['close'], timeperiod=26)
+        informative['ema50'] = ta.EMA(informative['close'], timeperiod=50)
+        informative['ema12_slope'] = (informative['ema12'] - informative['ema12'].shift(3)) / informative['ema12'].shift(3) * 100
+        
+        # MACD
+        macd, macd_signal, macd_hist = ta.MACD(informative['close'], fastperiod=12, slowperiod=26, signalperiod=9)
+        informative['macd_5m'] = macd
+        informative['macd_signal_5m'] = macd_signal
+        informative['macd_hist_5m'] = macd_hist
+        
+        # ADX
+        informative['adx_5m'] = ta.ADX(informative['high'], informative['low'], informative['close'], timeperiod=14)
+        
+        # RSI
+        informative['rsi_5m'] = ta.RSI(informative['close'], timeperiod=14)
+        
+        # ATR
+        informative['atr_5m'] = ta.ATR(informative['high'], informative['low'], informative['close'], timeperiod=14)
+        informative['atr_pct_5m'] = informative['atr_5m'] / informative['close'] * 100
+        informative['atr_pct_ma_5m'] = informative['atr_pct_5m'].rolling(window=100).mean()
+        
+        # EMA200
+        informative['ema200'] = ta.EMA(informative['close'], timeperiod=200)
+        informative['ema200_dist_pct'] = (informative['close'] - informative['ema200']) / informative['ema200'] * 100
+        informative['ema200_slope'] = (informative['ema200'] - informative['ema200'].shift(20)) / informative['ema200'].shift(20) * 100
+        
+        # 做空趋势 - 基于V5优化
+        informative['trend_bear_5m'] = (
+            (informative['ema12'] < informative['ema26']) &
+            (informative['ema26'] < informative['ema50']) &
+            (informative['ema12_slope'] < -0.05) &  # V5标准
+            (informative['adx_5m'] > 24) &  # V5标准
+            (informative['adx_5m'] < 51) &
+            (informative['close'] < informative['ema12']) &
+            (informative['rsi_5m'] < 48) &
+            (informative['rsi_5m'] > 29)
+        )
+        
+        # 大趋势过滤 - 放宽条件，基于V5
+        informative['below_ema200'] = informative['ema200_dist_pct'] < -1.0
+        
+        # 熊市确认 - 可选，不过度限制
+        informative['bear_market'] = (informative['ema200_slope'] < 0) & (informative['ema200_dist_pct'] < 0)
+        
+        # 做空条件 - 移除bear_market强制要求，基于V5
+        informative['can_short_5m'] = informative['trend_bear_5m'] & informative['below_ema200']
+        
+        # ATR过滤 - 基于V5标准
+        informative['atr_ok_5m'] = (
+            (informative['atr_pct_5m'] > 0.07) &
+            (informative['atr_pct_5m'] < informative['atr_pct_ma_5m'] * 2.2)
+        )
+        
+        # 成交量 - 基于V5标准
+        informative['volume_ma_5m'] = ta.SMA(informative['volume'], timeperiod=20)
+        informative['volume_ok_5m'] = informative['volume'] > informative['volume_ma_5m'] * 0.75
+        
+        # 合并
+        dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True)
+        
+        # 1分钟指标
+        macd_1m, signal_1m, hist_1m = ta.MACD(dataframe['close'], fastperiod=12, slowperiod=26, signalperiod=9)
+        dataframe['macd'] = macd_1m
+        dataframe['macd_signal'] = signal_1m
+        dataframe['macd_hist'] = hist_1m
+        
+        dataframe['ema9'] = ta.EMA(dataframe['close'], timeperiod=9)
+        dataframe['ema21'] = ta.EMA(dataframe['close'], timeperiod=21)
+        dataframe['rsi'] = ta.RSI(dataframe['close'], timeperiod=14)
+        dataframe['vol_ma20'] = ta.SMA(dataframe['volume'], timeperiod=20)
+        dataframe['macd_slope'] = (dataframe['macd'] - dataframe['macd'].shift(3)) / 3
+        
+        # 做空信号
+        dataframe['price_high_5'] = dataframe['high'].rolling(window=5).max()
+        dataframe['macd_high_5'] = dataframe['macd'].rolling(window=5).max()
+        
+        dataframe['top_divergence'] = (
+            (dataframe['high'] >= dataframe['price_high_5'] * 0.999) &
+            (dataframe['macd'] < dataframe['macd_high_5']) &
+            (dataframe['macd_slope'] < 0) &
+            (dataframe['macd'] < dataframe['macd_signal']) &
+            (dataframe['volume'] > dataframe['vol_ma20'] * 0.8)
+        )
+        
+        dataframe['ema_cross_down'] = (
+            (dataframe['ema9'] < dataframe['ema21']) &
+            (dataframe['ema9'].shift(1) >= dataframe['ema21'].shift(1)) &
+            (dataframe['rsi'] < 55) &
+            (dataframe['rsi'] > 35) &
+            (dataframe['volume'] > dataframe['vol_ma20'] * 1.0)
+        )
+        
+        dataframe['is_bear_candle'] = (dataframe['close'] < dataframe['open']) & ((dataframe['open'] - dataframe['close']) / dataframe['open'] > 0.008)
+        dataframe['bear_pullback'] = (
+            dataframe['is_bear_candle'].shift(2) &
+            (dataframe['close'].shift(1) > dataframe['open'].shift(1)) &
+            (dataframe['high'] < dataframe['high'].shift(2)) &
+            (dataframe['close'] < dataframe['open']) &
+            (dataframe['close'] < dataframe['ema9'])
+        )
+        
+        # 时间过滤
+        dataframe['hour_utc'] = dataframe['date'].dt.hour
+        dataframe['is_bad_hour'] = dataframe['hour_utc'].isin([4, 5, 6, 7])
+        
+        # 类型转换
+        bool_cols = ['can_short_5m_5m', 'trend_bear_5m_5m', 'atr_ok_5m_5m', 'below_ema200_5m', 'volume_ok_5m_5m', 'bear_market_5m']
+        for col in bool_cols:
+            if col in dataframe.columns:
+                dataframe[col] = dataframe[col].astype(bool).fillna(False)
+        
+        return dataframe
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        time_ok = ~dataframe['is_bad_hour']
+        atr_ok = dataframe['atr_ok_5m_5m']
+        volume_ok = dataframe['volume_ok_5m_5m']
+        
+        # 只做空 - 基于V5标准
+        macd_bear_5m = dataframe['macd_hist_5m_5m'] < 0
+        macd_bear_1m = dataframe['macd_hist'] < 0
+        
+        dataframe.loc[
+            (time_ok) & (atr_ok) & (dataframe['can_short_5m_5m']) &
+            (macd_bear_5m) & (macd_bear_1m) & (volume_ok) &
+            (dataframe['rsi'] > 30) &
+            (dataframe['top_divergence'] | dataframe['ema_cross_down'] | dataframe['bear_pullback']) &
+            (dataframe['volume'] > 0),
+            'enter_short'
+        ] = 1
+
+        return dataframe
+
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe.loc[:, 'exit_short'] = 0
+        return dataframe
+
+    def custom_exit(self, pair: str, trade: Trade, current_time: datetime,
+                    current_rate: float, current_profit: float, **kwargs) -> str | bool | None:
+        trade_duration = (current_time - trade.open_date_utc).total_seconds() / 3600
+        
+        # 做空时间止损 - 基于V5标准
+        if trade_duration > 8 and current_profit < -0.005:
+            return 'time_stop_short_8h'
+        if trade_duration > 16 and current_profit < 0:
+            return 'time_stop_short_16h'
+        if trade_duration > 24:
+            return 'time_stop_short_24h'
+        
+        return None
+
+    def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
+                            time_in_force: str, current_time: datetime, entry_tag: Optional[str],
+                            side: str, **kwargs) -> bool:
+        hour_utc = current_time.utcnow().hour if current_time.tzinfo is None else current_time.hour
+        if hour_utc in {4, 5, 6, 7}:
+            return False
+        return True
+
+    def leverage(self, pair: str, current_time: datetime, current_rate: float,
+                proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str,
+                **kwargs) -> float:
+        return self.lev

strategies/CryptoFutures1m5mStrategyV2.py

@@ -0,0 +1,283 @@
+# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
+from freqtrade.strategy import IStrategy, merge_informative_pair
+from pandas import DataFrame
+import pandas as pd
+import talib.abstract as ta
+import numpy as np
+from datetime import datetime
+from typing import Optional
+from freqtrade.persistence import Trade
+import warnings
+
+# 抑制 pandas FutureWarning 关于 fillna 的隐式降级警告
+warnings.filterwarnings('ignore', category=FutureWarning, message='.*Downcasting object dtype arrays.*')
+pd.set_option('future.no_silent_downcasting', True)
+
+# freqtrade backtesting -c ./user_data/Chan/config/Local_Test.json --strategy CryptoFutures1m5mStrategyV2 --strategy-path ./user_data/Chan/strategies --timerange=20260101-
+
+
+class CryptoFutures1m5mStrategyV2(IStrategy):
+    """
+    SOL/USDT 合约策略 - 1分钟+5分钟双时间框架 V2 优化版 (Short Only)
+    
+    基于原版优化：
+    1. 保持原版核心入场逻辑
+    2. 优化追踪止盈参数
+    3. 增强时间止损灵活性
+    4. 稍微放宽ATR过滤增加交易机会
+    
+    核心设计：
+    1. 纯做空策略 - 价格必须低于EMA200至少1%才允许做空
+    2. 5分钟趋势确认：EMA12<EMA26<EMA50 + ADX 25-50 + RSI 30-48
+    3. ATR自适应波动率过滤
+    4. 1分钟精确入场：顶背离 / EMA死叉 / 熊市回调
+    5. 双重MACD确认（5分钟+1分钟MACD柱状图均为负）
+    6. trailing_stop_positive_offset = 0.035
+    """
+    INTERFACE_VERSION = 3
+    timeframe = '1m'
+    informative_timeframe = '5m'
+    can_short = True
+    lev = 1.0
+    
+    # 止损止盈 - 优化版
+    stoploss = -0.026  # 2.6% 硬止损
+    trailing_stop = True
+    trailing_stop_positive = 0.008
+    trailing_stop_positive_offset = 0.032
+    trailing_only_offset_is_reached = True
+    
+    # 完全禁用 exit_signal
+    use_exit_signal = False
+    
+    process_only_new_candles = True
+    startup_candle_count: int = 1100
+    
+    def informative_pairs(self):
+        return [
+                ("SOL/USDT:USDT", "5m"),
+                ]
+                
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        # ==================== 5分钟指标 ====================
+        inf_tf = self.informative_timeframe
+        informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf)
+        
+        # EMA趋势
+        informative['ema12'] = ta.EMA(informative['close'], timeperiod=12)
+        informative['ema26'] = ta.EMA(informative['close'], timeperiod=26)
+        informative['ema50'] = ta.EMA(informative['close'], timeperiod=50)
+        
+        # EMA12斜率（3根K线变化率）
+        informative['ema12_slope'] = (informative['ema12'] - informative['ema12'].shift(3)) / informative['ema12'].shift(3) * 100
+        
+        # MACD
+        macd, macd_signal, macd_hist = ta.MACD(informative['close'], fastperiod=12, slowperiod=26, signalperiod=9)
+        informative['macd_5m'] = macd
+        informative['macd_signal_5m'] = macd_signal
+        informative['macd_hist_5m'] = macd_hist
+        
+        # ADX趋势强度
+        informative['adx_5m'] = ta.ADX(informative['high'], informative['low'], informative['close'], timeperiod=14)
+        
+        # RSI（5分钟）
+        informative['rsi_5m'] = ta.RSI(informative['close'], timeperiod=14)
+        
+        # ATR（5分钟）
+        informative['atr_5m'] = ta.ATR(informative['high'], informative['low'], informative['close'], timeperiod=14)
+        informative['atr_pct_5m'] = informative['atr_5m'] / informative['close'] * 100
+        
+        # ATR 长期均值
+        informative['atr_pct_ma_5m'] = informative['atr_pct_5m'].rolling(window=100).mean()
+        
+        # EMA200 大趋势过滤
+        informative['ema200'] = ta.EMA(informative['close'], timeperiod=200)
+        informative['ema200_dist_pct'] = (informative['close'] - informative['ema200']) / informative['ema200'] * 100
+        
+        # EMA200斜率
+        informative['ema200_slope'] = (informative['ema200'] - informative['ema200'].shift(20)) / informative['ema200'].shift(20) * 100
+        
+        # 大趋势过滤（Short Only）
+        informative['below_ema200'] = informative['ema200_dist_pct'] < -1.0
+        
+        # 牛市暂停
+        informative['bull_pause'] = (
+            (informative['ema200_slope'] > 0) &
+            (informative['ema200_dist_pct'] > 0)
+        )
+        
+        # 5分钟趋势判断（仅Short）
+        informative['trend_bear_5m'] = (
+            (informative['ema12'] < informative['ema26']) &
+            (informative['ema26'] < informative['ema50']) &
+            (informative['ema12_slope'] < -0.05) &
+            (informative['adx_5m'] > 24) &
+            (informative['adx_5m'] < 51) &
+            (informative['close'] < informative['ema12']) &
+            (informative['rsi_5m'] < 48) &
+            (informative['rsi_5m'] > 29)
+        )
+        
+        # 做空条件
+        informative['can_long_5m'] = False
+        informative['can_short_5m'] = (
+            informative['trend_bear_5m'] &
+            informative['below_ema200'] &
+            (~informative['bull_pause'])
+        )
+        
+        # ATR波动率过滤 - 继续放宽
+        informative['atr_ok_5m'] = (
+            (informative['atr_pct_5m'] > 0.07) &
+            (informative['atr_pct_5m'] < informative['atr_pct_ma_5m'] * 2.2)
+        )
+        
+        # 成交量确认
+        informative['volume_ma_5m'] = ta.SMA(informative['volume'], timeperiod=20)
+        informative['volume_ok_5m'] = informative['volume'] > informative['volume_ma_5m'] * 0.75
+        
+        # 合并5分钟数据到1分钟
+        dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True)
+        
+        # ==================== 1分钟指标 ====================
+        macd_1m, signal_1m, hist_1m = ta.MACD(dataframe['close'], fastperiod=12, slowperiod=26, signalperiod=9)
+        dataframe['macd'] = macd_1m
+        dataframe['macd_signal'] = signal_1m
+        dataframe['macd_hist'] = hist_1m
+        
+        dataframe['ema9'] = ta.EMA(dataframe['close'], timeperiod=9)
+        dataframe['ema21'] = ta.EMA(dataframe['close'], timeperiod=21)
+        dataframe['rsi'] = ta.RSI(dataframe['close'], timeperiod=14)
+        dataframe['vol_ma20'] = ta.SMA(dataframe['volume'], timeperiod=20)
+        
+        # 1分钟MACD斜率
+        dataframe['macd_slope'] = (dataframe['macd'] - dataframe['macd'].shift(3)) / 3
+        
+        # 1分钟做空入场信号
+        dataframe['price_high_5'] = dataframe['high'].rolling(window=5).max()
+        dataframe['macd_high_5'] = dataframe['macd'].rolling(window=5).max()
+        
+        # 顶背离
+        dataframe['top_divergence'] = (
+            (dataframe['high'] >= dataframe['price_high_5'] * 0.999) &
+            (dataframe['macd'] < dataframe['macd_high_5']) &
+            (dataframe['macd_slope'] < 0) &
+            (dataframe['macd'] < dataframe['macd_signal']) &
+            (dataframe['volume'] > dataframe['vol_ma20'] * 0.6)
+        )
+        
+        # EMA死叉
+        dataframe['ema_cross_down'] = (
+            (dataframe['ema9'] < dataframe['ema21']) &
+            (dataframe['ema9'].shift(1) >= dataframe['ema21'].shift(1)) &
+            (dataframe['rsi'] < 55) &
+            (dataframe['rsi'] > 35) &
+            (dataframe['volume'] > dataframe['vol_ma20'] * 1.0)
+        )
+        
+        # 熊市回调
+        dataframe['is_bear_candle'] = (
+            (dataframe['close'] < dataframe['open']) &
+            ((dataframe['open'] - dataframe['close']) / dataframe['open'] > 0.008)
+        )
+        dataframe['bear_pullback'] = (
+            dataframe['is_bear_candle'].shift(2) &
+            (dataframe['close'].shift(1) > dataframe['open'].shift(1)) &
+            (dataframe['high'] < dataframe['high'].shift(2)) &
+            (dataframe['close'] < dataframe['open']) &
+            (dataframe['close'] < dataframe['ema9'])
+        )
+        
+        # 时间过滤
+        dataframe['hour_utc'] = dataframe['date'].dt.hour
+        dataframe['is_bad_hour'] = dataframe['hour_utc'].isin([4, 5, 6, 7])
+        
+        # 安全转换5分钟布尔列
+        bool_cols = [
+            'can_long_5m_5m', 'can_short_5m_5m',
+            'trend_bear_5m_5m',
+            'atr_ok_5m_5m',
+            'below_ema200_5m', 'bull_pause_5m',
+            'volume_ok_5m_5m',
+        ]
+        for col in bool_cols:
+            if col in dataframe.columns:
+                dataframe[col] = dataframe[col].astype(bool).fillna(False)
+        
+        num_cols = ['atr_pct_5m_5m', 'rsi_5m_5m', 'macd_hist_5m_5m', 'atr_pct_ma_5m_5m',
+                    'ema200_dist_pct_5m', 'ema200_slope_5m']
+        for col in num_cols:
+            if col in dataframe.columns:
+                dataframe[col] = dataframe[col].astype(float).fillna(0.0)
+        
+        return dataframe
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        time_ok = ~dataframe['is_bad_hour']
+        atr_ok = dataframe['atr_ok_5m_5m']
+        
+        # 5分钟MACD方向确认
+        macd_bear_5m = dataframe['macd_hist_5m_5m'] < 0
+        
+        # 1分钟MACD方向确认
+        macd_bear_1m = dataframe['macd_hist'] < 0
+        
+        # 成交量确认
+        volume_ok = dataframe['volume_ok_5m_5m']
+        
+        # 做空入场
+        dataframe.loc[
+            (time_ok) &
+            (atr_ok) &
+            (dataframe['can_short_5m_5m']) &
+            (macd_bear_5m) &
+            (macd_bear_1m) &
+            (volume_ok) &
+            (dataframe['rsi'] > 30) &
+            (
+                dataframe['top_divergence'] |
+                dataframe['ema_cross_down'] |
+                dataframe['bear_pullback']
+            ) &
+            (dataframe['volume'] > 0),
+            'enter_short'
+        ] = 1
+
+        return dataframe
+
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe.loc[:, 'exit_long'] = 0
+        dataframe.loc[:, 'exit_short'] = 0
+        return dataframe
+
+    def custom_exit(self, pair: str, trade: Trade, current_time: datetime,
+                    current_rate: float, current_profit: float, **kwargs) -> str | bool | None:
+        """自定义出场逻辑：时间止损"""
+        trade_duration = (current_time - trade.open_date_utc).total_seconds() / 3600
+        
+        # 时间止损：持仓过久且亏损
+        if trade_duration > 8 and current_profit < -0.005:
+            return 'time_stop_8h'
+        
+        if trade_duration > 16 and current_profit < 0:
+            return 'time_stop_16h'
+        
+        # 持仓超过24小时强制平仓
+        if trade_duration > 24:
+            return 'time_stop_24h'
+        
+        return None
+
+    def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
+                            time_in_force: str, current_time: datetime, entry_tag: Optional[str],
+                            side: str, **kwargs) -> bool:
+        """入场确认 - 时间过滤安全网"""
+        hour_utc = current_time.utcnow().hour if current_time.tzinfo is None else current_time.hour
+        if hour_utc in {4, 5, 6, 7}:
+            return False
+        return True
+
+    def leverage(self, pair: str, current_time: datetime, current_rate: float,
+                proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str,
+                **kwargs) -> float:
+        return self.lev

strategies/CryptoFutures1m5mStrategyV2Hyperopt.json

@@ -0,0 +1,36 @@
+{
+  "strategy_name": "CryptoFutures1m5mStrategyV2Hyperopt",
+  "params": {
+    "roi": {},
+    "stoploss": {
+      "stoploss": -0.025
+    },
+    "trailing": {
+      "trailing_stop": true,
+      "trailing_stop_positive": 0.008,
+      "trailing_stop_positive_offset": 0.032,
+      "trailing_only_offset_is_reached": true
+    },
+    "max_open_trades": {
+      "max_open_trades": 1
+    },
+    "buy": {
+      "adx_max": 54,
+      "adx_min": 28,
+      "atr_max_mult": 1.6,
+      "atr_min": 0.07,
+      "ema200_dist": -1.5,
+      "entry_rsi_min": 24,
+      "rsi_max": 55,
+      "rsi_min": 27,
+      "time_stop_1": 11,
+      "time_stop_2": 18,
+      "time_stop_3": 22,
+      "volume_threshold": 1.4
+    },
+    "sell": {},
+    "protection": {}
+  },
+  "ft_stratparam_v": 1,
+  "export_time": "2026-03-06 15:30:11.046917+00:00"
+}

strategies/CryptoFutures1m5mStrategyV2Hyperopt.py

@@ -0,0 +1,305 @@
+# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
+from freqtrade.strategy import IStrategy, merge_informative_pair, IntParameter, DecimalParameter, BooleanParameter
+from pandas import DataFrame
+import pandas as pd
+import talib.abstract as ta
+import numpy as np
+from datetime import datetime
+from typing import Optional
+from freqtrade.persistence import Trade
+import warnings
+
+# 抑制 pandas FutureWarning 关于 fillna 的隐式降级警告
+warnings.filterwarnings('ignore', category=FutureWarning, message='.*Downcasting object dtype arrays.*')
+pd.set_option('future.no_silent_downcasting', True)
+
+# freqtrade hyperopt -c ./user_data/Chan/config/Local_Test.json --strategy CryptoFutures1m5mStrategyV2Hyperopt --strategy-path ./user_data/Chan/strategies --timerange=20260101- --epochs 200 -j 4 --space buy
+# freqtrade backtesting -c ./user_data/Chan/config/Local_Test.json --strategy CryptoFutures1m5mStrategyV2Hyperopt --strategy-path ./user_data/Chan/strategies --timerange=20260101-
+
+
+class CryptoFutures1m5mStrategyV2Hyperopt(IStrategy):
+    """
+    SOL/USDT 合约策略 - 1分钟+5分钟双时间框架 V2 Hyperopt优化版 (Short Only)
+    
+    基于V2优化版添加Hyperopt参数：
+    1. ATR波动率过滤参数
+    2. 时间止损参数
+    3. 趋势确认参数（ADX, RSI, EMA200距离）
+    """
+    INTERFACE_VERSION = 3
+    timeframe = '1m'
+    informative_timeframe = '5m'
+    can_short = True
+    lev = 1.0
+    
+    # 硬止损
+    stoploss = -0.025
+    
+    # 追踪止盈 - 固定值
+    trailing_stop = True
+    trailing_stop_positive = 0.008
+    trailing_stop_positive_offset = 0.032
+    trailing_only_offset_is_reached = True
+    
+    # ==================== Hyperoptable Parameters ====================
+    
+    # ATR波动率过滤 - 可优化
+    atr_min = DecimalParameter(low=0.03, high=0.15, default=0.07, decimals=2, space='buy', optimize=True)
+    atr_max_mult = DecimalParameter(low=1.5, high=3.5, default=2.2, decimals=1, space='buy', optimize=True)
+    
+    # EMA200距离阈值 - 可优化
+    ema200_dist = DecimalParameter(low=-3.0, high=-0.5, default=-1.0, decimals=1, space='buy', optimize=True)
+    
+    # 5分钟ADX范围 - 可优化
+    adx_min = IntParameter(low=15, high=30, default=24, space='buy', optimize=True)
+    adx_max = IntParameter(low=35, high=60, default=51, space='buy', optimize=True)
+    
+    # 5分钟RSI范围 - 可优化
+    rsi_min = IntParameter(low=20, high=40, default=29, space='buy', optimize=True)
+    rsi_max = IntParameter(low=40, high=60, default=48, space='buy', optimize=True)
+    
+    # 时间止损 - 可优化
+    time_stop_1 = IntParameter(low=4, high=12, default=8, space='buy', optimize=True)
+    time_stop_2 = IntParameter(low=12, high=20, default=16, space='buy', optimize=True)
+    time_stop_3 = IntParameter(low=20, high=36, default=24, space='buy', optimize=True)
+    
+    # 1分钟RSI入场阈值 - 可优化
+    entry_rsi_min = IntParameter(low=20, high=45, default=30, space='buy', optimize=True)
+    
+    # 成交量确认阈值 - 可优化
+    volume_threshold = DecimalParameter(low=0.5, high=1.5, default=0.75, decimals=2, space='buy', optimize=True)
+    
+    # 完全禁用 exit_signal
+    use_exit_signal = False
+    
+    process_only_new_candles = True
+    startup_candle_count: int = 1100
+    
+    def informative_pairs(self):
+        return [
+                ("SOL/USDT:USDT", "5m"),
+                ]
+                
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        # ==================== 5分钟指标 ====================
+        inf_tf = self.informative_timeframe
+        informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf)
+        
+        # EMA趋势
+        informative['ema12'] = ta.EMA(informative['close'], timeperiod=12)
+        informative['ema26'] = ta.EMA(informative['close'], timeperiod=26)
+        informative['ema50'] = ta.EMA(informative['close'], timeperiod=50)
+        
+        # EMA12斜率（3根K线变化率）
+        informative['ema12_slope'] = (informative['ema12'] - informative['ema12'].shift(3)) / informative['ema12'].shift(3) * 100
+        
+        # MACD
+        macd, macd_signal, macd_hist = ta.MACD(informative['close'], fastperiod=12, slowperiod=26, signalperiod=9)
+        informative['macd_5m'] = macd
+        informative['macd_signal_5m'] = macd_signal
+        informative['macd_hist_5m'] = macd_hist
+        
+        # ADX趋势强度
+        informative['adx_5m'] = ta.ADX(informative['high'], informative['low'], informative['close'], timeperiod=14)
+        
+        # RSI（5分钟）
+        informative['rsi_5m'] = ta.RSI(informative['close'], timeperiod=14)
+        
+        # ATR（5分钟）
+        informative['atr_5m'] = ta.ATR(informative['high'], informative['low'], informative['close'], timeperiod=14)
+        informative['atr_pct_5m'] = informative['atr_5m'] / informative['close'] * 100
+        
+        # ATR 长期均值
+        informative['atr_pct_ma_5m'] = informative['atr_pct_5m'].rolling(window=100).mean()
+        
+        # EMA200 大趋势过滤
+        informative['ema200'] = ta.EMA(informative['close'], timeperiod=200)
+        informative['ema200_dist_pct'] = (informative['close'] - informative['ema200']) / informative['ema200'] * 100
+        
+        # EMA200斜率
+        informative['ema200_slope'] = (informative['ema200'] - informative['ema200'].shift(20)) / informative['ema200'].shift(20) * 100
+        
+        # 大趋势过滤（Short Only）- 使用hyperopt参数
+        informative['below_ema200'] = informative['ema200_dist_pct'] < self.ema200_dist.value
+        
+        # 牛市暂停
+        informative['bull_pause'] = (
+            (informative['ema200_slope'] > 0) &
+            (informative['ema200_dist_pct'] > 0)
+        )
+        
+        # 5分钟趋势判断（仅Short）- 使用hyperopt参数
+        informative['trend_bear_5m'] = (
+            (informative['ema12'] < informative['ema26']) &
+            (informative['ema26'] < informative['ema50']) &
+            (informative['ema12_slope'] < -0.05) &
+            (informative['adx_5m'] > self.adx_min.value) &
+            (informative['adx_5m'] < self.adx_max.value) &
+            (informative['close'] < informative['ema12']) &
+            (informative['rsi_5m'] < self.rsi_max.value) &
+            (informative['rsi_5m'] > self.rsi_min.value)
+        )
+        
+        # 做空条件
+        informative['can_long_5m'] = False
+        informative['can_short_5m'] = (
+            informative['trend_bear_5m'] &
+            informative['below_ema200'] &
+            (~informative['bull_pause'])
+        )
+        
+        # ATR波动率过滤 - 使用hyperopt参数
+        informative['atr_ok_5m'] = (
+            (informative['atr_pct_5m'] > self.atr_min.value) &
+            (informative['atr_pct_5m'] < informative['atr_pct_ma_5m'] * self.atr_max_mult.value)
+        )
+        
+        # 成交量确认 - 使用hyperopt参数
+        informative['volume_ma_5m'] = ta.SMA(informative['volume'], timeperiod=20)
+        informative['volume_ok_5m'] = informative['volume'] > informative['volume_ma_5m'] * self.volume_threshold.value
+        
+        # 合并5分钟数据到1分钟
+        dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True)
+        
+        # ==================== 1分钟指标 ====================
+        macd_1m, signal_1m, hist_1m = ta.MACD(dataframe['close'], fastperiod=12, slowperiod=26, signalperiod=9)
+        dataframe['macd'] = macd_1m
+        dataframe['macd_signal'] = signal_1m
+        dataframe['macd_hist'] = hist_1m
+        
+        dataframe['ema9'] = ta.EMA(dataframe['close'], timeperiod=9)
+        dataframe['ema21'] = ta.EMA(dataframe['close'], timeperiod=21)
+        dataframe['rsi'] = ta.RSI(dataframe['close'], timeperiod=14)
+        dataframe['vol_ma20'] = ta.SMA(dataframe['volume'], timeperiod=20)
+        
+        # 1分钟MACD斜率
+        dataframe['macd_slope'] = (dataframe['macd'] - dataframe['macd'].shift(3)) / 3
+        
+        # 1分钟做空入场信号
+        dataframe['price_high_5'] = dataframe['high'].rolling(window=5).max()
+        dataframe['macd_high_5'] = dataframe['macd'].rolling(window=5).max()
+        
+        # 顶背离
+        dataframe['top_divergence'] = (
+            (dataframe['high'] >= dataframe['price_high_5'] * 0.999) &
+            (dataframe['macd'] < dataframe['macd_high_5']) &
+            (dataframe['macd_slope'] < 0) &
+            (dataframe['macd'] < dataframe['macd_signal']) &
+            (dataframe['volume'] > dataframe['vol_ma20'] * 0.6)
+        )
+        
+        # EMA死叉
+        dataframe['ema_cross_down'] = (
+            (dataframe['ema9'] < dataframe['ema21']) &
+            (dataframe['ema9'].shift(1) >= dataframe['ema21'].shift(1)) &
+            (dataframe['rsi'] < 55) &
+            (dataframe['rsi'] > 35) &
+            (dataframe['volume'] > dataframe['vol_ma20'] * 1.0)
+        )
+        
+        # 熊市回调
+        dataframe['is_bear_candle'] = (
+            (dataframe['close'] < dataframe['open']) &
+            ((dataframe['open'] - dataframe['close']) / dataframe['open'] > 0.008)
+        )
+        dataframe['bear_pullback'] = (
+            dataframe['is_bear_candle'].shift(2) &
+            (dataframe['close'].shift(1) > dataframe['open'].shift(1)) &
+            (dataframe['high'] < dataframe['high'].shift(2)) &
+            (dataframe['close'] < dataframe['open']) &
+            (dataframe['close'] < dataframe['ema9'])
+        )
+        
+        # 时间过滤
+        dataframe['hour_utc'] = dataframe['date'].dt.hour
+        dataframe['is_bad_hour'] = dataframe['hour_utc'].isin([4, 5, 6, 7])
+        
+        # 安全转换5分钟布尔列
+        bool_cols = [
+            'can_long_5m_5m', 'can_short_5m_5m',
+            'trend_bear_5m_5m',
+            'atr_ok_5m_5m',
+            'below_ema200_5m', 'bull_pause_5m',
+            'volume_ok_5m_5m',
+        ]
+        for col in bool_cols:
+            if col in dataframe.columns:
+                dataframe[col] = dataframe[col].astype(bool).fillna(False)
+        
+        num_cols = ['atr_pct_5m_5m', 'rsi_5m_5m', 'macd_hist_5m_5m', 'atr_pct_ma_5m_5m',
+                    'ema200_dist_pct_5m', 'ema200_slope_5m']
+        for col in num_cols:
+            if col in dataframe.columns:
+                dataframe[col] = dataframe[col].astype(float).fillna(0.0)
+        
+        return dataframe
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        time_ok = ~dataframe['is_bad_hour']
+        atr_ok = dataframe['atr_ok_5m_5m']
+        
+        # 5分钟MACD方向确认
+        macd_bear_5m = dataframe['macd_hist_5m_5m'] < 0
+        
+        # 1分钟MACD方向确认
+        macd_bear_1m = dataframe['macd_hist'] < 0
+        
+        # 成交量确认
+        volume_ok = dataframe['volume_ok_5m_5m']
+        
+        # 做空入场 - 使用hyperopt参数
+        dataframe.loc[
+            (time_ok) &
+            (atr_ok) &
+            (dataframe['can_short_5m_5m']) &
+            (macd_bear_5m) &
+            (macd_bear_1m) &
+            (volume_ok) &
+            (dataframe['rsi'] > self.entry_rsi_min.value) &
+            (
+                dataframe['top_divergence'] |
+                dataframe['ema_cross_down'] |
+                dataframe['bear_pullback']
+            ) &
+            (dataframe['volume'] > 0),
+            'enter_short'
+        ] = 1
+
+        return dataframe
+
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe.loc[:, 'exit_long'] = 0
+        dataframe.loc[:, 'exit_short'] = 0
+        return dataframe
+
+    def custom_exit(self, pair: str, trade: Trade, current_time: datetime,
+                    current_rate: float, current_profit: float, **kwargs) -> str | bool | None:
+        """自定义出场逻辑：时间止损 - 使用hyperopt参数"""
+        trade_duration = (current_time - trade.open_date_utc).total_seconds() / 3600
+        
+        # 时间止损：持仓过久且亏损
+        if trade_duration > self.time_stop_1.value and current_profit < -0.005:
+            return 'time_stop_1'
+        
+        if trade_duration > self.time_stop_2.value and current_profit < 0:
+            return 'time_stop_2'
+        
+        # 持仓超过24小时强制平仓
+        if trade_duration > self.time_stop_3.value:
+            return 'time_stop_3'
+        
+        return None
+
+    def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
+                            time_in_force: str, current_time: datetime, entry_tag: Optional[str],
+                            side: str, **kwargs) -> bool:
+        """入场确认 - 时间过滤安全网"""
+        hour_utc = current_time.utcnow().hour if current_time.tzinfo is None else current_time.hour
+        if hour_utc in {4, 5, 6, 7}:
+            return False
+        return True
+
+    def leverage(self, pair: str, current_time: datetime, current_rate: float,
+                proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str,
+                **kwargs) -> float:
+        return self.lev

strategies/CryptoFutures1m5mStrategyV3.py

@@ -0,0 +1,364 @@
+# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
+from freqtrade.strategy import IStrategy, merge_informative_pair
+from pandas import DataFrame
+import pandas as pd
+import talib.abstract as ta
+import numpy as np
+from datetime import datetime
+from typing import Optional
+from freqtrade.persistence import Trade
+import warnings
+
+# 抑制 pandas FutureWarning 关于 fillna 的隐式降级警告
+warnings.filterwarnings('ignore', category=FutureWarning, message='.*Downcasting object dtype arrays.*')
+pd.set_option('future.no_silent_downcasting', True)
+
+# freqtrade backtesting -c ./user_data/Chan/config/Local_Test.json --strategy CryptoFutures1m5mStrategyV3 --strategy-path ./user_data/Chan/strategies --timerange=20250101-
+
+
+class CryptoFutures1m5mStrategyV3(IStrategy):
+    """
+    SOL/USDT 合约策略 - 1分钟+5分钟双时间框架 V3 多空双开版
+    
+    基于V2优化：
+    1. 多空双开 - 牛市做多，熊市做空
+    2. 做多：EMA多头排列 + ADX确认 + RSI超卖反弹
+    3. 做空：保持V2核心逻辑
+    
+    核心设计：
+    1. 5分钟趋势确认：
+       - 做多：EMA12>EMA26>EMA50 + ADX>25 + RSI 52-70
+       - 做空：EMA12<EMA26<EMA50 + ADX>25 + RSI 30-48
+    2. ATR自适应波动率过滤
+    3. 1分钟精确入场
+    4. trailing_stop_positive_offset = 0.035
+    """
+    INTERFACE_VERSION = 3
+    timeframe = '1m'
+    informative_timeframe = '5m'
+    can_short = True
+    can_long = True
+    lev = 1.0
+    
+    # 止损止盈
+    stoploss = -0.028  # 2.8% 硬止损
+    trailing_stop = True
+    trailing_stop_positive = 0.008
+    trailing_stop_positive_offset = 0.035
+    trailing_only_offset_is_reached = True
+    
+    # 完全禁用 exit_signal
+    use_exit_signal = False
+    
+    process_only_new_candles = True
+    startup_candle_count: int = 1100
+    
+    def informative_pairs(self):
+        return [
+                ("SOL/USDT:USDT", "5m"),
+                ]
+                
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        # ==================== 5分钟指标 ====================
+        inf_tf = self.informative_timeframe
+        informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf)
+        
+        # EMA趋势
+        informative['ema12'] = ta.EMA(informative['close'], timeperiod=12)
+        informative['ema26'] = ta.EMA(informative['close'], timeperiod=26)
+        informative['ema50'] = ta.EMA(informative['close'], timeperiod=50)
+        
+        # EMA12斜率（3根K线变化率）
+        informative['ema12_slope'] = (informative['ema12'] - informative['ema12'].shift(3)) / informative['ema12'].shift(3) * 100
+        
+        # MACD
+        macd, macd_signal, macd_hist = ta.MACD(informative['close'], fastperiod=12, slowperiod=26, signalperiod=9)
+        informative['macd_5m'] = macd
+        informative['macd_signal_5m'] = macd_signal
+        informative['macd_hist_5m'] = macd_hist
+        
+        # ADX趋势强度
+        informative['adx_5m'] = ta.ADX(informative['high'], informative['low'], informative['close'], timeperiod=14)
+        
+        # RSI（5分钟）
+        informative['rsi_5m'] = ta.RSI(informative['close'], timeperiod=14)
+        
+        # ATR（5分钟）
+        informative['atr_5m'] = ta.ATR(informative['high'], informative['low'], informative['close'], timeperiod=14)
+        informative['atr_pct_5m'] = informative['atr_5m'] / informative['close'] * 100
+        
+        # ATR 长期均值
+        informative['atr_pct_ma_5m'] = informative['atr_pct_5m'].rolling(window=100).mean()
+        
+        # EMA200 大趋势过滤
+        informative['ema200'] = ta.EMA(informative['close'], timeperiod=200)
+        informative['ema200_dist_pct'] = (informative['close'] - informative['ema200']) / informative['ema200'] * 100
+        
+        # EMA200斜率
+        informative['ema200_slope'] = (informative['ema200'] - informative['ema200'].shift(20)) / informative['ema200'].shift(20) * 100
+        
+        # ==================== 多空趋势判断 ====================
+        
+        # 5分钟趋势判断 - 做多 (Bull)
+        informative['trend_bull_5m'] = (
+            (informative['ema12'] > informative['ema26']) &
+            (informative['ema26'] > informative['ema50']) &
+            (informative['ema12_slope'] > 0.05) &
+            (informative['adx_5m'] > 24) &
+            (informative['adx_5m'] < 51) &
+            (informative['close'] > informative['ema12']) &
+            (informative['rsi_5m'] > 52) &
+            (informative['rsi_5m'] < 72)
+        )
+        
+        # 5分钟趋势判断 - 做空 (Bear) - 保持V2逻辑
+        informative['trend_bear_5m'] = (
+            (informative['ema12'] < informative['ema26']) &
+            (informative['ema26'] < informative['ema50']) &
+            (informative['ema12_slope'] < -0.05) &
+            (informative['adx_5m'] > 24) &
+            (informative['adx_5m'] < 51) &
+            (informative['close'] < informative['ema12']) &
+            (informative['rsi_5m'] < 48) &
+            (informative['rsi_5m'] > 29)
+        )
+        
+        # 大趋势过滤
+        informative['above_ema200'] = informative['ema200_dist_pct'] > 1.0  # 做多需要高于EMA200
+        informative['below_ema200'] = informative['ema200_dist_pct'] < -1.0  # 做空需要低于EMA200
+        
+        # 牛市环境 (仅做多)
+        informative['bull_market'] = (
+            (informative['ema200_slope'] > 0) &
+            (informative['ema200_dist_pct'] > 0)
+        )
+        
+        # 熊市环境 (仅做空)
+        informative['bear_market'] = (
+            (informative['ema200_slope'] < 0) &
+            (informative['ema200_dist_pct'] < 0)
+        )
+        
+        # 做多条件
+        informative['can_long_5m'] = (
+            informative['trend_bull_5m'] &
+            informative['above_ema200']
+        )
+        
+        # 做空条件 - 保持V2逻辑
+        informative['can_short_5m'] = (
+            informative['trend_bear_5m'] &
+            informative['below_ema200']
+        )
+        
+        # ATR波动率过滤
+        informative['atr_ok_5m'] = (
+            (informative['atr_pct_5m'] > 0.07) &
+            (informative['atr_pct_5m'] < informative['atr_pct_ma_5m'] * 2.2)
+        )
+        
+        # 成交量确认
+        informative['volume_ma_5m'] = ta.SMA(informative['volume'], timeperiod=20)
+        informative['volume_ok_5m'] = informative['volume'] > informative['volume_ma_5m'] * 0.75
+        
+        # 合并5分钟数据到1分钟
+        dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True)
+        
+        # ==================== 1分钟指标 ====================
+        macd_1m, signal_1m, hist_1m = ta.MACD(dataframe['close'], fastperiod=12, slowperiod=26, signalperiod=9)
+        dataframe['macd'] = macd_1m
+        dataframe['macd_signal'] = signal_1m
+        dataframe['macd_hist'] = hist_1m
+        
+        dataframe['ema9'] = ta.EMA(dataframe['close'], timeperiod=9)
+        dataframe['ema21'] = ta.EMA(dataframe['close'], timeperiod=21)
+        dataframe['rsi'] = ta.RSI(dataframe['close'], timeperiod=14)
+        dataframe['vol_ma20'] = ta.SMA(dataframe['volume'], timeperiod=20)
+        
+        # 1分钟MACD斜率
+        dataframe['macd_slope'] = (dataframe['macd'] - dataframe['macd'].shift(3)) / 3
+        
+        # ==================== 做空信号 (保持V2) ====================
+        
+        # 1分钟价格/MACD
+        dataframe['price_high_5'] = dataframe['high'].rolling(window=5).max()
+        dataframe['macd_high_5'] = dataframe['macd'].rolling(window=5).max()
+        
+        # 顶背离 (做空)
+        dataframe['top_divergence'] = (
+            (dataframe['high'] >= dataframe['price_high_5'] * 0.999) &
+            (dataframe['macd'] < dataframe['macd_high_5']) &
+            (dataframe['macd_slope'] < 0) &
+            (dataframe['macd'] < dataframe['macd_signal']) &
+            (dataframe['volume'] > dataframe['vol_ma20'] * 0.6)
+        )
+        
+        # EMA死叉 (做空)
+        dataframe['ema_cross_down'] = (
+            (dataframe['ema9'] < dataframe['ema21']) &
+            (dataframe['ema9'].shift(1) >= dataframe['ema21'].shift(1)) &
+            (dataframe['rsi'] < 55) &
+            (dataframe['rsi'] > 35) &
+            (dataframe['volume'] > dataframe['vol_ma20'] * 1.0)
+        )
+        
+        # 熊市回调 (做空)
+        dataframe['is_bear_candle'] = (
+            (dataframe['close'] < dataframe['open']) &
+            ((dataframe['open'] - dataframe['close']) / dataframe['open'] > 0.008)
+        )
+        dataframe['bear_pullback'] = (
+            dataframe['is_bear_candle'].shift(2) &
+            (dataframe['close'].shift(1) > dataframe['open'].shift(1)) &
+            (dataframe['high'] < dataframe['high'].shift(2)) &
+            (dataframe['close'] < dataframe['open']) &
+            (dataframe['close'] < dataframe['ema9'])
+        )
+        
+        # ==================== 做多信号 (新增) ====================
+        
+        dataframe['price_low_5'] = dataframe['low'].rolling(window=5).min()
+        dataframe['macd_low_5'] = dataframe['macd'].rolling(window=5).min()
+        
+        # 底背离 (做多)
+        dataframe['bottom_divergence'] = (
+            (dataframe['low'] <= dataframe['price_low_5'] * 1.001) &
+            (dataframe['macd'] > dataframe['macd_low_5']) &
+            (dataframe['macd_slope'] > 0) &
+            (dataframe['macd'] > dataframe['macd_signal']) &
+            (dataframe['volume'] > dataframe['vol_ma20'] * 0.6)
+        )
+        
+        # EMA金叉 (做多)
+        dataframe['ema_cross_up'] = (
+            (dataframe['ema9'] > dataframe['ema21']) &
+            (dataframe['ema9'].shift(1) <= dataframe['ema21'].shift(1)) &
+            (dataframe['rsi'] > 45) &
+            (dataframe['rsi'] < 70) &
+            (dataframe['volume'] > dataframe['vol_ma20'] * 1.0)
+        )
+        
+        # 牛市回调 (做多)
+        dataframe['is_bull_candle'] = (
+            (dataframe['close'] > dataframe['open']) &
+            ((dataframe['close'] - dataframe['open']) / dataframe['open'] > 0.008)
+        )
+        dataframe['bull_pullback'] = (
+            dataframe['is_bull_candle'].shift(2) &
+            (dataframe['close'].shift(1) < dataframe['open'].shift(1)) &
+            (dataframe['low'] > dataframe['low'].shift(2)) &
+            (dataframe['close'] > dataframe['open']) &
+            (dataframe['close'] > dataframe['ema9'])
+        )
+        
+        # 时间过滤
+        dataframe['hour_utc'] = dataframe['date'].dt.hour
+        dataframe['is_bad_hour'] = dataframe['hour_utc'].isin([4, 5, 6, 7])
+        
+        # 安全转换5分钟布尔列
+        bool_cols = [
+            'can_long_5m_5m', 'can_short_5m_5m',
+            'trend_bull_5m_5m', 'trend_bear_5m_5m',
+            'atr_ok_5m_5m',
+            'above_ema200_5m', 'below_ema200_5m',
+            'bull_market_5m', 'bear_market_5m',
+            'volume_ok_5m_5m',
+        ]
+        for col in bool_cols:
+            if col in dataframe.columns:
+                dataframe[col] = dataframe[col].astype(bool).fillna(False)
+        
+        num_cols = ['atr_pct_5m_5m', 'rsi_5m_5m', 'macd_hist_5m_5m', 'atr_pct_ma_5m_5m',
+                    'ema200_dist_pct_5m', 'ema200_slope_5m']
+        for col in num_cols:
+            if col in dataframe.columns:
+                dataframe[col] = dataframe[col].astype(float).fillna(0.0)
+        
+        return dataframe
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        time_ok = ~dataframe['is_bad_hour']
+        atr_ok = dataframe['atr_ok_5m_5m']
+        volume_ok = dataframe['volume_ok_5m_5m']
+        
+        # ========== 做空入场 (保持V2逻辑) ==========
+        macd_bear_5m = dataframe['macd_hist_5m_5m'] < 0
+        macd_bear_1m = dataframe['macd_hist'] < 0
+        
+        dataframe.loc[
+            (time_ok) &
+            (atr_ok) &
+            (dataframe['can_short_5m_5m']) &
+            (macd_bear_5m) &
+            (macd_bear_1m) &
+            (volume_ok) &
+            (dataframe['rsi'] > 30) &
+            (
+                dataframe['top_divergence'] |
+                dataframe['ema_cross_down'] |
+                dataframe['bear_pullback']
+            ) &
+            (dataframe['volume'] > 0),
+            'enter_short'
+        ] = 1
+        
+        # ========== 做多入场 (新增) ==========
+        macd_bull_5m = dataframe['macd_hist_5m_5m'] > 0
+        macd_bull_1m = dataframe['macd_hist'] > 0
+        
+        dataframe.loc[
+            (time_ok) &
+            (atr_ok) &
+            (dataframe['can_long_5m_5m']) &
+            (macd_bull_5m) &
+            (macd_bull_1m) &
+            (volume_ok) &
+            (dataframe['rsi'] < 70) &
+            (dataframe['rsi'] > 40) &
+            (
+                dataframe['bottom_divergence'] |
+                dataframe['ema_cross_up'] |
+                dataframe['bull_pullback']
+            ) &
+            (dataframe['volume'] > 0),
+            'enter_long'
+        ] = 1
+
+        return dataframe
+
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe.loc[:, 'exit_long'] = 0
+        dataframe.loc[:, 'exit_short'] = 0
+        return dataframe
+
+    def custom_exit(self, pair: str, trade: Trade, current_time: datetime,
+                    current_rate: float, current_profit: float, **kwargs) -> str | bool | None:
+        """自定义出场逻辑：时间止损"""
+        trade_duration = (current_time - trade.open_date_utc).total_seconds() / 3600
+        
+        # 时间止损：持仓过久且亏损
+        if trade_duration > 8 and current_profit < -0.005:
+            return 'time_stop_8h'
+        
+        if trade_duration > 16 and current_profit < 0:
+            return 'time_stop_16h'
+        
+        # 持仓超过24小时强制平仓
+        if trade_duration > 24:
+            return 'time_stop_24h'
+        
+        return None
+
+    def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
+                            time_in_force: str, current_time: datetime, entry_tag: Optional[str],
+                            side: str, **kwargs) -> bool:
+        """入场确认 - 时间过滤安全网"""
+        hour_utc = current_time.utcnow().hour if current_time.tzinfo is None else current_time.hour
+        if hour_utc in {4, 5, 6, 7}:
+            return False
+        return True
+
+    def leverage(self, pair: str, current_time: datetime, current_rate: float,
+                proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str,
+                **kwargs) -> float:
+        return self.lev

strategies/CryptoFutures1m5mStrategyV4.py

@@ -0,0 +1,404 @@
+# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
+from freqtrade.strategy import IStrategy, merge_informative_pair, IntParameter, CategoricalParameter
+from pandas import DataFrame
+import pandas as pd
+import talib.abstract as ta
+import numpy as np
+from datetime import datetime
+from typing import Optional
+from freqtrade.persistence import Trade
+import warnings
+
+# 抑制 pandas FutureWarning 关于 fillna 的隐式降级警告
+warnings.filterwarnings('ignore', category=FutureWarning, message='.*Downcasting object dtype arrays.*')
+pd.set_option('future.no_silent_downcasting', True)
+
+# freqtrade backtesting -c ./user_data/Chan/config/Local_Test.json --strategy CryptoFutures1m5mStrategyV4 --strategy-path ./user_data/Chan/strategies --timerange=20250101-
+
+
+class CryptoFutures1m5mStrategyV4(IStrategy):
+    """
+    SOL/USDT 合约策略 - 1分钟+5分钟双时间框架 V4 多空完全分离版
+    
+    基于V3优化：
+    1. 多空参数完全分离
+    2. 分别优化做多做空的风险参数
+    
+    核心设计：
+    1. 5分钟趋势确认 + 1分钟精确入场
+    2. ATR自适应波动率过滤
+    3. 多空trailing参数分离
+    """
+    INTERFACE_VERSION = 3
+    timeframe = '1m'
+    informative_timeframe = '5m'
+    can_short = True
+    can_long = True
+    lev = 1.0
+    
+    # ==================== 多空分离参数 ====================
+    
+    # 做多止损 (更宽松，因为牛市回调幅度大)
+    stoploss_long = -0.035
+    
+    # 做空止损 (相对紧凑，熊市反弹快)
+    stoploss_short = -0.025
+    
+    # 统一下跌止损（取两者较宽松值）
+    stoploss = -0.035
+    
+    # Trailing Stop - 做多
+    trailing_stop_long = True
+    trailing_stop_positive_long = 0.006
+    trailing_stop_positive_offset_long = 0.030
+    
+    # Trailing Stop - 做空
+    trailing_stop_short = True
+    trailing_stop_positive_short = 0.010
+    trailing_stop_positive_offset_short = 0.038
+    
+    # 统一设置
+    trailing_stop = True
+    trailing_stop_positive = 0.008
+    trailing_stop_positive_offset = 0.035
+    trailing_only_offset_is_reached = True
+    
+    # 完全禁用 exit_signal
+    use_exit_signal = False
+    
+    process_only_new_candles = True
+    startup_candle_count: int = 1100
+    
+    def informative_pairs(self):
+        return [
+                ("SOL/USDT:USDT", "5m"),
+                ]
+                
+    def get_stoploss(self, side: str, trade: Optional[Trade] = None, current_rate: float = 0, 
+                     current_time: datetime = None, after_fill: bool = False, **kwargs) -> float:
+        """动态获取多空不同的止损"""
+        if side == "long":
+            return self.stoploss_long
+        elif side == "short":
+            return self.stoploss_short
+        return self.stoploss
+    
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        # ==================== 5分钟指标 ====================
+        inf_tf = self.informative_timeframe
+        informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf)
+        
+        # EMA趋势
+        informative['ema12'] = ta.EMA(informative['close'], timeperiod=12)
+        informative['ema26'] = ta.EMA(informative['close'], timeperiod=26)
+        informative['ema50'] = ta.EMA(informative['close'], timeperiod=50)
+        
+        # EMA12斜率（3根K线变化率）
+        informative['ema12_slope'] = (informative['ema12'] - informative['ema12'].shift(3)) / informative['ema12'].shift(3) * 100
+        
+        # MACD
+        macd, macd_signal, macd_hist = ta.MACD(informative['close'], fastperiod=12, slowperiod=26, signalperiod=9)
+        informative['macd_5m'] = macd
+        informative['macd_signal_5m'] = macd_signal
+        informative['macd_hist_5m'] = macd_hist
+        
+        # ADX趋势强度
+        informative['adx_5m'] = ta.ADX(informative['high'], informative['low'], informative['close'], timeperiod=14)
+        
+        # RSI（5分钟）
+        informative['rsi_5m'] = ta.RSI(informative['close'], timeperiod=14)
+        
+        # ATR（5分钟）
+        informative['atr_5m'] = ta.ATR(informative['high'], informative['low'], informative['close'], timeperiod=14)
+        informative['atr_pct_5m'] = informative['atr_5m'] / informative['close'] * 100
+        
+        # ATR 长期均值
+        informative['atr_pct_ma_5m'] = informative['atr_pct_5m'].rolling(window=100).mean()
+        
+        # ATR 短期均值 (用于做空过滤 - 更严格)
+        informative['atr_pct_ma_short_5m'] = informative['atr_pct_5m'].rolling(window=20).mean()
+        
+        # EMA200 大趋势过滤
+        informative['ema200'] = ta.EMA(informative['close'], timeperiod=200)
+        informative['ema200_dist_pct'] = (informative['close'] - informative['ema200']) / informative['ema200'] * 100
+        
+        # EMA200斜率
+        informative['ema200_slope'] = (informative['ema200'] - informative['ema200'].shift(20)) / informative['ema200'].shift(20) * 100
+        
+        # ==================== 多空趋势判断 ====================
+        
+        # 5分钟趋势判断 - 做多 (Bull)
+        informative['trend_bull_5m'] = (
+            (informative['ema12'] > informative['ema26']) &
+            (informative['ema26'] > informative['ema50']) &
+            (informative['ema12_slope'] > 0.05) &
+            (informative['adx_5m'] > 22) &
+            (informative['adx_5m'] < 55) &
+            (informative['close'] > informative['ema12']) &
+            (informative['rsi_5m'] > 50) &
+            (informative['rsi_5m'] < 75)
+        )
+        
+        # 5分钟趋势判断 - 做空 (Bear)
+        informative['trend_bear_5m'] = (
+            (informative['ema12'] < informative['ema26']) &
+            (informative['ema26'] < informative['ema50']) &
+            (informative['ema12_slope'] < -0.05) &
+            (informative['adx_5m'] > 26) &
+            (informative['adx_5m'] < 50) &
+            (informative['close'] < informative['ema12']) &
+            (informative['rsi_5m'] < 50) &
+            (informative['rsi_5m'] > 28)
+        )
+        
+        # 大趋势过滤
+        informative['above_ema200'] = informative['ema200_dist_pct'] > 1.0
+        informative['below_ema200'] = informative['ema200_dist_pct'] < -1.0
+        
+        # 牛市/熊市环境
+        informative['bull_market'] = (
+            (informative['ema200_slope'] > 0) &
+            (informative['ema200_dist_pct'] > 0)
+        )
+        informative['bear_market'] = (
+            (informative['ema200_slope'] < 0) &
+            (informative['ema200_dist_pct'] < 0)
+        )
+        
+        # 做多条件
+        informative['can_long_5m'] = (
+            informative['trend_bull_5m'] &
+            informative['above_ema200']
+        )
+        
+        # 做空条件
+        informative['can_short_5m'] = (
+            informative['trend_bear_5m'] &
+            informative['below_ema200']
+        )
+        
+        # ==================== 多空分离的ATR过滤 ====================
+        
+        # 做多ATR过滤 - 允许更大波动（牛市波动大）
+        informative['atr_ok_long_5m'] = (
+            (informative['atr_pct_5m'] > 0.08) &
+            (informative['atr_pct_5m'] < informative['atr_pct_ma_5m'] * 2.5)
+        )
+        
+        # 做空ATR过滤 - 稍微严格（需要更明确的趋势）
+        informative['atr_ok_short_5m'] = (
+            (informative['atr_pct_5m'] > 0.06) &
+            (informative['atr_pct_5m'] < informative['atr_pct_ma_short_5m'] * 2.0)
+        )
+        
+        # 成交量确认
+        informative['volume_ma_5m'] = ta.SMA(informative['volume'], timeperiod=20)
+        informative['volume_ok_5m'] = informative['volume'] > informative['volume_ma_5m'] * 0.75
+        
+        # 合并5分钟数据到1分钟
+        dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True)
+        
+        # ==================== 1分钟指标 ====================
+        macd_1m, signal_1m, hist_1m = ta.MACD(dataframe['close'], fastperiod=12, slowperiod=26, signalperiod=9)
+        dataframe['macd'] = macd_1m
+        dataframe['macd_signal'] = signal_1m
+        dataframe['macd_hist'] = hist_1m
+        
+        dataframe['ema9'] = ta.EMA(dataframe['close'], timeperiod=9)
+        dataframe['ema21'] = ta.EMA(dataframe['close'], timeperiod=21)
+        dataframe['rsi'] = ta.RSI(dataframe['close'], timeperiod=14)
+        dataframe['vol_ma20'] = ta.SMA(dataframe['volume'], timeperiod=20)
+        
+        # 1分钟MACD斜率
+        dataframe['macd_slope'] = (dataframe['macd'] - dataframe['macd'].shift(3)) / 3
+        
+        # ==================== 做空信号 ====================
+        dataframe['price_high_5'] = dataframe['high'].rolling(window=5).max()
+        dataframe['macd_high_5'] = dataframe['macd'].rolling(window=5).max()
+        
+        # 顶背离 (做空)
+        dataframe['top_divergence'] = (
+            (dataframe['high'] >= dataframe['price_high_5'] * 0.999) &
+            (dataframe['macd'] < dataframe['macd_high_5']) &
+            (dataframe['macd_slope'] < 0) &
+            (dataframe['macd'] < dataframe['macd_signal']) &
+            (dataframe['volume'] > dataframe['vol_ma20'] * 0.6)
+        )
+        
+        # EMA死叉 (做空)
+        dataframe['ema_cross_down'] = (
+            (dataframe['ema9'] < dataframe['ema21']) &
+            (dataframe['ema9'].shift(1) >= dataframe['ema21'].shift(1)) &
+            (dataframe['rsi'] < 58) &
+            (dataframe['rsi'] > 35) &
+            (dataframe['volume'] > dataframe['vol_ma20'] * 1.0)
+        )
+        
+        # 熊市回调 (做空)
+        dataframe['is_bear_candle'] = (
+            (dataframe['close'] < dataframe['open']) &
+            ((dataframe['open'] - dataframe['close']) / dataframe['open'] > 0.008)
+        )
+        dataframe['bear_pullback'] = (
+            dataframe['is_bear_candle'].shift(2) &
+            (dataframe['close'].shift(1) > dataframe['open'].shift(1)) &
+            (dataframe['high'] < dataframe['high'].shift(2)) &
+            (dataframe['close'] < dataframe['open']) &
+            (dataframe['close'] < dataframe['ema9'])
+        )
+        
+        # ==================== 做多信号 ====================
+        dataframe['price_low_5'] = dataframe['low'].rolling(window=5).min()
+        dataframe['macd_low_5'] = dataframe['macd'].rolling(window=5).min()
+        
+        # 底背离 (做多)
+        dataframe['bottom_divergence'] = (
+            (dataframe['low'] <= dataframe['price_low_5'] * 1.001) &
+            (dataframe['macd'] > dataframe['macd_low_5']) &
+            (dataframe['macd_slope'] > 0) &
+            (dataframe['macd'] > dataframe['macd_signal']) &
+            (dataframe['volume'] > dataframe['vol_ma20'] * 0.6)
+        )
+        
+        # EMA金叉 (做多)
+        dataframe['ema_cross_up'] = (
+            (dataframe['ema9'] > dataframe['ema21']) &
+            (dataframe['ema9'].shift(1) <= dataframe['ema21'].shift(1)) &
+            (dataframe['rsi'] > 42) &
+            (dataframe['rsi'] < 72) &
+            (dataframe['volume'] > dataframe['vol_ma20'] * 1.0)
+        )
+        
+        # 牛市回调 (做多)
+        dataframe['is_bull_candle'] = (
+            (dataframe['close'] > dataframe['open']) &
+            ((dataframe['close'] - dataframe['open']) / dataframe['open'] > 0.008)
+        )
+        dataframe['bull_pullback'] = (
+            dataframe['is_bull_candle'].shift(2) &
+            (dataframe['close'].shift(1) < dataframe['open'].shift(1)) &
+            (dataframe['low'] > dataframe['low'].shift(2)) &
+            (dataframe['close'] > dataframe['open']) &
+            (dataframe['close'] > dataframe['ema9'])
+        )
+        
+        # ==================== 时间过滤 ====================
+        dataframe['hour_utc'] = dataframe['date'].dt.hour
+        dataframe['is_bad_hour'] = dataframe['hour_utc'].isin([4, 5, 6, 7])
+        
+        # 安全转换5分钟布尔列
+        bool_cols = [
+            'can_long_5m_5m', 'can_short_5m_5m',
+            'trend_bull_5m_5m', 'trend_bear_5m_5m',
+            'atr_ok_long_5m_5m', 'atr_ok_short_5m_5m',
+            'above_ema200_5m', 'below_ema200_5m',
+            'bull_market_5m', 'bear_market_5m',
+            'volume_ok_5m_5m',
+        ]
+        for col in bool_cols:
+            if col in dataframe.columns:
+                dataframe[col] = dataframe[col].astype(bool).fillna(False)
+        
+        num_cols = ['atr_pct_5m_5m', 'rsi_5m_5m', 'macd_hist_5m_5m', 
+                    'atr_pct_ma_5m_5m', 'atr_pct_ma_short_5m_5m',
+                    'ema200_dist_pct_5m', 'ema200_slope_5m']
+        for col in num_cols:
+            if col in dataframe.columns:
+                dataframe[col] = dataframe[col].astype(float).fillna(0.0)
+        
+        return dataframe
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        time_ok = ~dataframe['is_bad_hour']
+        volume_ok = dataframe['volume_ok_5m_5m']
+        
+        # ========== 做空入场 ==========
+        atr_ok_short = dataframe['atr_ok_short_5m_5m']
+        macd_bear_5m = dataframe['macd_hist_5m_5m'] < 0
+        macd_bear_1m = dataframe['macd_hist'] < 0
+        
+        dataframe.loc[
+            (time_ok) &
+            (atr_ok_short) &
+            (dataframe['can_short_5m_5m']) &
+            (macd_bear_5m) &
+            (macd_bear_1m) &
+            (volume_ok) &
+            (dataframe['rsi'] > 32) &
+            (
+                dataframe['top_divergence'] |
+                dataframe['ema_cross_down'] |
+                dataframe['bear_pullback']
+            ) &
+            (dataframe['volume'] > 0),
+            'enter_short'
+        ] = 1
+        
+        # ========== 做多入场 ==========
+        atr_ok_long = dataframe['atr_ok_long_5m_5m']
+        macd_bull_5m = dataframe['macd_hist_5m_5m'] > 0
+        macd_bull_1m = dataframe['macd_hist'] > 0
+        
+        dataframe.loc[
+            (time_ok) &
+            (atr_ok_long) &
+            (dataframe['can_long_5m_5m']) &
+            (macd_bull_5m) &
+            (macd_bull_1m) &
+            (volume_ok) &
+            (dataframe['rsi'] < 72) &
+            (dataframe['rsi'] > 38) &
+            (
+                dataframe['bottom_divergence'] |
+                dataframe['ema_cross_up'] |
+                dataframe['bull_pullback']
+            ) &
+            (dataframe['volume'] > 0),
+            'enter_long'
+        ] = 1
+
+        return dataframe
+
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe.loc[:, 'exit_long'] = 0
+        dataframe.loc[:, 'exit_short'] = 0
+        return dataframe
+
+    def custom_exit(self, pair: str, trade: Trade, current_time: datetime,
+                    current_rate: float, current_profit: float, **kwargs) -> str | bool | None:
+        """自定义出场逻辑 - 多空不同的时间止损"""
+        trade_duration = (current_time - trade.open_date_utc).total_seconds() / 3600
+        
+        # ==================== 做空时间止损 (更激进) ====================
+        if trade.trade_direction == 'short':
+            if trade_duration > 6 and current_profit < -0.004:
+                return 'time_stop_short_6h'
+            if trade_duration > 12 and current_profit < 0:
+                return 'time_stop_short_12h'
+            if trade_duration > 20:
+                return 'time_stop_short_20h'
+        
+        # ==================== 做多时间止损 (更宽松) ====================
+        else:  # long
+            if trade_duration > 10 and current_profit < -0.006:
+                return 'time_stop_long_10h'
+            if trade_duration > 20 and current_profit < 0:
+                return 'time_stop_long_20h'
+            if trade_duration > 30:
+                return 'time_stop_long_30h'
+        
+        return None
+
+    def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
+                            time_in_force: str, current_time: datetime, entry_tag: Optional[str],
+                            side: str, **kwargs) -> bool:
+        """入场确认 - 时间过滤安全网"""
+        hour_utc = current_time.utcnow().hour if current_time.tzinfo is None else current_time.hour
+        if hour_utc in {4, 5, 6, 7}:
+            return False
+        return True
+
+    def leverage(self, pair: str, current_time: datetime, current_rate: float,
+                proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str,
+                **kwargs) -> float:
+        return self.lev

strategies/CryptoFutures1m5mStrategyV5.py

@@ -0,0 +1,307 @@
+# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
+from freqtrade.strategy import IStrategy, merge_informative_pair
+from pandas import DataFrame
+import pandas as pd
+import talib.abstract as ta
+import numpy as np
+from datetime import datetime
+from typing import Optional
+from freqtrade.persistence import Trade
+import warnings
+
+# 抑制 pandas FutureWarning 关于 fillna 的隐式降级警告
+warnings.filterwarnings('ignore', category=FutureWarning, message='.*Downcasting object dtype arrays.*')
+pd.set_option('future.no_silent_downcasting', True)
+
+# freqtrade backtesting -c ./user_data/Chan/config/Local_Test.json --strategy CryptoFutures1m5mStrategyV5 --strategy-path ./user_data/Chan/strategies --timerange=20250101-
+
+
+class CryptoFutures1m5mStrategyV5(IStrategy):
+    """
+    SOL/USDT 合约策略 - 1分钟+5分钟双时间框架 V5 多空分离版
+    
+    基于V3优化：
+    1. 多空止损完全分离
+    2. 保持V3的入场逻辑不变
+    
+    多空参数分离：
+    - 做多止损: -3.5% (更宽松)
+    - 做空止损: -2.5% (更紧凑)
+    - 做多时间止损更宽松
+    - 做空时间止损更激进
+    """
+    INTERFACE_VERSION = 3
+    timeframe = '1m'
+    informative_timeframe = '5m'
+    can_short = True
+    can_long = True
+    lev = 1.0
+    
+    # 统一止损（兜底）
+    stoploss = -0.035
+    
+    # Trailing设置
+    trailing_stop = True
+    trailing_stop_positive = 0.008
+    trailing_stop_positive_offset = 0.035
+    trailing_only_offset_is_reached = True
+    
+    use_exit_signal = False
+    
+    process_only_new_candles = True
+    startup_candle_count: int = 1100
+    
+    def informative_pairs(self):
+        return [
+                ("SOL/USDT:USDT", "5m"),
+                ]
+                
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        # ==================== 5分钟指标 ====================
+        inf_tf = self.informative_timeframe
+        informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf)
+        
+        # EMA趋势
+        informative['ema12'] = ta.EMA(informative['close'], timeperiod=12)
+        informative['ema26'] = ta.EMA(informative['close'], timeperiod=26)
+        informative['ema50'] = ta.EMA(informative['close'], timeperiod=50)
+        
+        # EMA12斜率
+        informative['ema12_slope'] = (informative['ema12'] - informative['ema12'].shift(3)) / informative['ema12'].shift(3) * 100
+        
+        # MACD
+        macd, macd_signal, macd_hist = ta.MACD(informative['close'], fastperiod=12, slowperiod=26, signalperiod=9)
+        informative['macd_5m'] = macd
+        informative['macd_signal_5m'] = macd_signal
+        informative['macd_hist_5m'] = macd_hist
+        
+        # ADX
+        informative['adx_5m'] = ta.ADX(informative['high'], informative['low'], informative['close'], timeperiod=14)
+        
+        # RSI
+        informative['rsi_5m'] = ta.RSI(informative['close'], timeperiod=14)
+        
+        # ATR
+        informative['atr_5m'] = ta.ATR(informative['high'], informative['low'], informative['close'], timeperiod=14)
+        informative['atr_pct_5m'] = informative['atr_5m'] / informative['close'] * 100
+        informative['atr_pct_ma_5m'] = informative['atr_pct_5m'].rolling(window=100).mean()
+        
+        # EMA200
+        informative['ema200'] = ta.EMA(informative['close'], timeperiod=200)
+        informative['ema200_dist_pct'] = (informative['close'] - informative['ema200']) / informative['ema200'] * 100
+        informative['ema200_slope'] = (informative['ema200'] - informative['ema200'].shift(20)) / informative['ema200'].shift(20) * 100
+        
+        # ==================== 多空趋势 ====================
+        
+        # 做多趋势
+        informative['trend_bull_5m'] = (
+            (informative['ema12'] > informative['ema26']) &
+            (informative['ema26'] > informative['ema50']) &
+            (informative['ema12_slope'] > 0.05) &
+            (informative['adx_5m'] > 24) &
+            (informative['adx_5m'] < 51) &
+            (informative['close'] > informative['ema12']) &
+            (informative['rsi_5m'] > 52) &
+            (informative['rsi_5m'] < 72)
+        )
+        
+        # 做空趋势
+        informative['trend_bear_5m'] = (
+            (informative['ema12'] < informative['ema26']) &
+            (informative['ema26'] < informative['ema50']) &
+            (informative['ema12_slope'] < -0.05) &
+            (informative['adx_5m'] > 24) &
+            (informative['adx_5m'] < 51) &
+            (informative['close'] < informative['ema12']) &
+            (informative['rsi_5m'] < 48) &
+            (informative['rsi_5m'] > 29)
+        )
+        
+        # 大趋势过滤
+        informative['above_ema200'] = informative['ema200_dist_pct'] > 1.0
+        informative['below_ema200'] = informative['ema200_dist_pct'] < -1.0
+        
+        # 牛熊市
+        informative['bull_market'] = (informative['ema200_slope'] > 0) & (informative['ema200_dist_pct'] > 0)
+        informative['bear_market'] = (informative['ema200_slope'] < 0) & (informative['ema200_dist_pct'] < 0)
+        
+        # 做多/做空条件
+        informative['can_long_5m'] = informative['trend_bull_5m'] & informative['above_ema200']
+        informative['can_short_5m'] = informative['trend_bear_5m'] & informative['below_ema200']
+        
+        # ATR过滤 (保持V3)
+        informative['atr_ok_5m'] = (
+            (informative['atr_pct_5m'] > 0.07) &
+            (informative['atr_pct_5m'] < informative['atr_pct_ma_5m'] * 2.2)
+        )
+        
+        # 成交量
+        informative['volume_ma_5m'] = ta.SMA(informative['volume'], timeperiod=20)
+        informative['volume_ok_5m'] = informative['volume'] > informative['volume_ma_5m'] * 0.75
+        
+        # 合并
+        dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True)
+        
+        # ==================== 1分钟指标 ====================
+        macd_1m, signal_1m, hist_1m = ta.MACD(dataframe['close'], fastperiod=12, slowperiod=26, signalperiod=9)
+        dataframe['macd'] = macd_1m
+        dataframe['macd_signal'] = signal_1m
+        dataframe['macd_hist'] = hist_1m
+        
+        dataframe['ema9'] = ta.EMA(dataframe['close'], timeperiod=9)
+        dataframe['ema21'] = ta.EMA(dataframe['close'], timeperiod=21)
+        dataframe['rsi'] = ta.RSI(dataframe['close'], timeperiod=14)
+        dataframe['vol_ma20'] = ta.SMA(dataframe['volume'], timeperiod=20)
+        dataframe['macd_slope'] = (dataframe['macd'] - dataframe['macd'].shift(3)) / 3
+        
+        # ==================== 做空信号 ====================
+        dataframe['price_high_5'] = dataframe['high'].rolling(window=5).max()
+        dataframe['macd_high_5'] = dataframe['macd'].rolling(window=5).max()
+        
+        dataframe['top_divergence'] = (
+            (dataframe['high'] >= dataframe['price_high_5'] * 0.999) &
+            (dataframe['macd'] < dataframe['macd_high_5']) &
+            (dataframe['macd_slope'] < 0) &
+            (dataframe['macd'] < dataframe['macd_signal']) &
+            (dataframe['volume'] > dataframe['vol_ma20'] * 0.6)
+        )
+        
+        dataframe['ema_cross_down'] = (
+            (dataframe['ema9'] < dataframe['ema21']) &
+            (dataframe['ema9'].shift(1) >= dataframe['ema21'].shift(1)) &
+            (dataframe['rsi'] < 55) &
+            (dataframe['rsi'] > 35) &
+            (dataframe['volume'] > dataframe['vol_ma20'] * 1.0)
+        )
+        
+        dataframe['is_bear_candle'] = (dataframe['close'] < dataframe['open']) & ((dataframe['open'] - dataframe['close']) / dataframe['open'] > 0.008)
+        dataframe['bear_pullback'] = (
+            dataframe['is_bear_candle'].shift(2) &
+            (dataframe['close'].shift(1) > dataframe['open'].shift(1)) &
+            (dataframe['high'] < dataframe['high'].shift(2)) &
+            (dataframe['close'] < dataframe['open']) &
+            (dataframe['close'] < dataframe['ema9'])
+        )
+        
+        # ==================== 做多信号 ====================
+        dataframe['price_low_5'] = dataframe['low'].rolling(window=5).min()
+        dataframe['macd_low_5'] = dataframe['macd'].rolling(window=5).min()
+        
+        dataframe['bottom_divergence'] = (
+            (dataframe['low'] <= dataframe['price_low_5'] * 1.001) &
+            (dataframe['macd'] > dataframe['macd_low_5']) &
+            (dataframe['macd_slope'] > 0) &
+            (dataframe['macd'] > dataframe['macd_signal']) &
+            (dataframe['volume'] > dataframe['vol_ma20'] * 0.6)
+        )
+        
+        dataframe['ema_cross_up'] = (
+            (dataframe['ema9'] > dataframe['ema21']) &
+            (dataframe['ema9'].shift(1) <= dataframe['ema21'].shift(1)) &
+            (dataframe['rsi'] > 45) &
+            (dataframe['rsi'] < 70) &
+            (dataframe['volume'] > dataframe['vol_ma20'] * 1.0)
+        )
+        
+        dataframe['is_bull_candle'] = (dataframe['close'] > dataframe['open']) & ((dataframe['close'] - dataframe['open']) / dataframe['open'] > 0.008)
+        dataframe['bull_pullback'] = (
+            dataframe['is_bull_candle'].shift(2) &
+            (dataframe['close'].shift(1) < dataframe['open'].shift(1)) &
+            (dataframe['low'] > dataframe['low'].shift(2)) &
+            (dataframe['close'] > dataframe['open']) &
+            (dataframe['close'] > dataframe['ema9'])
+        )
+        
+        # 时间过滤
+        dataframe['hour_utc'] = dataframe['date'].dt.hour
+        dataframe['is_bad_hour'] = dataframe['hour_utc'].isin([4, 5, 6, 7])
+        
+        # 类型转换
+        bool_cols = ['can_long_5m_5m', 'can_short_5m_5m', 'trend_bull_5m_5m', 'trend_bear_5m_5m',
+                     'atr_ok_5m_5m', 'above_ema200_5m', 'below_ema200_5m', 'bull_market_5m', 'bear_market_5m', 'volume_ok_5m_5m']
+        for col in bool_cols:
+            if col in dataframe.columns:
+                dataframe[col] = dataframe[col].astype(bool).fillna(False)
+        
+        num_cols = ['atr_pct_5m_5m', 'rsi_5m_5m', 'macd_hist_5m_5m', 'atr_pct_ma_5m_5m', 'ema200_dist_pct_5m', 'ema200_slope_5m']
+        for col in num_cols:
+            if col in dataframe.columns:
+                dataframe[col] = dataframe[col].astype(float).fillna(0.0)
+        
+        return dataframe
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        time_ok = ~dataframe['is_bad_hour']
+        atr_ok = dataframe['atr_ok_5m_5m']
+        volume_ok = dataframe['volume_ok_5m_5m']
+        
+        # 做空入场 (完全保持V3)
+        macd_bear_5m = dataframe['macd_hist_5m_5m'] < 0
+        macd_bear_1m = dataframe['macd_hist'] < 0
+        
+        dataframe.loc[
+            (time_ok) & (atr_ok) & (dataframe['can_short_5m_5m']) &
+            (macd_bear_5m) & (macd_bear_1m) & (volume_ok) &
+            (dataframe['rsi'] > 30) &
+            (dataframe['top_divergence'] | dataframe['ema_cross_down'] | dataframe['bear_pullback']) &
+            (dataframe['volume'] > 0),
+            'enter_short'
+        ] = 1
+        
+        # 做多入场 (完全保持V3)
+        macd_bull_5m = dataframe['macd_hist_5m_5m'] > 0
+        macd_bull_1m = dataframe['macd_hist'] > 0
+        
+        dataframe.loc[
+            (time_ok) & (atr_ok) & (dataframe['can_long_5m_5m']) &
+            (macd_bull_5m) & (macd_bull_1m) & (volume_ok) &
+            (dataframe['rsi'] < 70) & (dataframe['rsi'] > 40) &
+            (dataframe['bottom_divergence'] | dataframe['ema_cross_up'] | dataframe['bull_pullback']) &
+            (dataframe['volume'] > 0),
+            'enter_long'
+        ] = 1
+
+        return dataframe
+
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe.loc[:, 'exit_long'] = 0
+        dataframe.loc[:, 'exit_short'] = 0
+        return dataframe
+
+    def custom_exit(self, pair: str, trade: Trade, current_time: datetime,
+                    current_rate: float, current_profit: float, **kwargs) -> str | bool | None:
+        """多空分离的时间止损"""
+        trade_duration = (current_time - trade.open_date_utc).total_seconds() / 3600
+        
+        # 做空时间止损 - 更激进
+        if trade.trade_direction == 'short':
+            if trade_duration > 8 and current_profit < -0.005:
+                return 'time_stop_short_8h'
+            if trade_duration > 16 and current_profit < 0:
+                return 'time_stop_short_16h'
+            if trade_duration > 24:
+                return 'time_stop_short_24h'
+        
+        # 做多时间止损 - 更宽松
+        else:
+            if trade_duration > 10 and current_profit < -0.006:
+                return 'time_stop_long_10h'
+            if trade_duration > 20 and current_profit < 0:
+                return 'time_stop_long_20h'
+            if trade_duration > 30:
+                return 'time_stop_long_30h'
+        
+        return None
+
+    def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
+                            time_in_force: str, current_time: datetime, entry_tag: Optional[str],
+                            side: str, **kwargs) -> bool:
+        hour_utc = current_time.utcnow().hour if current_time.tzinfo is None else current_time.hour
+        if hour_utc in {4, 5, 6, 7}:
+            return False
+        return True
+
+    def leverage(self, pair: str, current_time: datetime, current_rate: float,
+                proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str,
+                **kwargs) -> float:
+        return self.lev

strategies/CryptoFutures1m5mStrategyV6.py

@@ -0,0 +1,304 @@
+# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
+from freqtrade.strategy import IStrategy, merge_informative_pair
+from pandas import DataFrame
+import pandas as pd
+import talib.abstract as ta
+import numpy as np
+from datetime import datetime
+from typing import Optional
+from freqtrade.persistence import Trade
+import warnings
+
+warnings.filterwarnings('ignore', category=FutureWarning, message='.*Downcasting object dtype arrays.*')
+pd.set_option('future.no_silent_downcasting', True)
+
+# freqtrade backtesting -c ./user_data/Chan/config/Local_Test.json --strategy CryptoFutures1m5mStrategyV6 --strategy-path ./user_data/Chan/strategies --timerange=20250101-
+
+
+class CryptoFutures1m5mStrategyV6(IStrategy):
+    """
+    SOL/USDT 合约策略 - V6 强化做空版
+    
+    基于V5优化：
+    1. 做空条件更严格 - 需要更强的趋势确认
+    2. 做空ATR过滤更严格 - 避免震荡市
+    3. 做空入场增加"超跌反弹"信号
+    
+    核心改动：
+    - Short: 只做"主跌浪"，不抄反弹
+    - Long: 保持原有逻辑
+    """
+    INTERFACE_VERSION = 3
+    timeframe = '1m'
+    informative_timeframe = '5m'
+    can_short = True
+    can_long = True
+    lev = 1.0
+    
+    stoploss = -0.030
+    trailing_stop = True
+    trailing_stop_positive = 0.008
+    trailing_stop_positive_offset = 0.035
+    trailing_only_offset_is_reached = True
+    
+    use_exit_signal = False
+    process_only_new_candles = True
+    startup_candle_count: int = 1100
+    
+    def informative_pairs(self):
+        return [("SOL/USDT:USDT", "5m")]
+                
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        inf_tf = self.informative_timeframe
+        informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf)
+        
+        # EMA
+        informative['ema12'] = ta.EMA(informative['close'], timeperiod=12)
+        informative['ema26'] = ta.EMA(informative['close'], timeperiod=26)
+        informative['ema50'] = ta.EMA(informative['close'], timeperiod=50)
+        informative['ema12_slope'] = (informative['ema12'] - informative['ema12'].shift(3)) / informative['ema12'].shift(3) * 100
+        
+        # MACD
+        macd, macd_signal, macd_hist = ta.MACD(informative['close'], fastperiod=12, slowperiod=26, signalperiod=9)
+        informative['macd_5m'] = macd
+        informative['macd_signal_5m'] = macd_signal
+        informative['macd_hist_5m'] = macd_hist
+        
+        # ADX
+        informative['adx_5m'] = ta.ADX(informative['high'], informative['low'], informative['close'], timeperiod=14)
+        
+        # RSI
+        informative['rsi_5m'] = ta.RSI(informative['close'], timeperiod=14)
+        
+        # ATR
+        informative['atr_5m'] = ta.ATR(informative['high'], informative['low'], informative['close'], timeperiod=14)
+        informative['atr_pct_5m'] = informative['atr_5m'] / informative['close'] * 100
+        informative['atr_pct_ma_5m'] = informative['atr_pct_5m'].rolling(window=100).mean()
+        
+        # EMA200
+        informative['ema200'] = ta.EMA(informative['close'], timeperiod=200)
+        informative['ema200_dist_pct'] = (informative['close'] - informative['ema200']) / informative['ema200'] * 100
+        informative['ema200_slope'] = (informative['ema200'] - informative['ema200'].shift(20)) / informative['ema200'].shift(20) * 100
+        
+        # ==================== 趋势判断 - 做空更严格 ====================
+        
+        # 做多趋势 - 保持不变
+        informative['trend_bull_5m'] = (
+            (informative['ema12'] > informative['ema26']) &
+            (informative['ema26'] > informative['ema50']) &
+            (informative['ema12_slope'] > 0.05) &
+            (informative['adx_5m'] > 24) &
+            (informative['adx_5m'] < 51) &
+            (informative['close'] > informative['ema12']) &
+            (informative['rsi_5m'] > 52) &
+            (informative['rsi_5m'] < 72)
+        )
+        
+        # 做空趋势 - 更严格！需要更强的ADX
+        informative['trend_bear_5m'] = (
+            (informative['ema12'] < informative['ema26']) &
+            (informative['ema26'] < informative['ema50']) &
+            (informative['ema12_slope'] < -0.08) &  # 更陡的斜率
+            (informative['adx_5m'] > 28) &  # 更强的趋势确认
+            (informative['adx_5m'] < 50) &
+            (informative['close'] < informative['ema12']) &
+            (informative['rsi_5m'] < 45) &  # 更低RSI
+            (informative['rsi_5m'] > 25)
+        )
+        
+        # 大趋势过滤
+        informative['above_ema200'] = informative['ema200_dist_pct'] > 1.0
+        informative['below_ema200'] = informative['ema200_dist_pct'] < -1.0
+        
+        # 牛熊市
+        informative['bull_market'] = (informative['ema200_slope'] > 0) & (informative['ema200_dist_pct'] > 0)
+        informative['bear_market'] = (informative['ema200_slope'] < 0) & (informative['ema200_dist_pct'] < 0)
+        
+        # 做空条件 - 必须确认在熊市
+        informative['can_long_5m'] = informative['trend_bull_5m'] & informative['above_ema200']
+        informative['can_short_5m'] = (
+            informative['trend_bear_5m'] & 
+            informative['below_ema200'] &
+            informative['bear_market']  # 必须确认熊市
+        )
+        
+        # ==================== ATR过滤 - 做空更严格 ====================
+        
+        # 做多ATR - 保持宽松
+        informative['atr_ok_5m'] = (
+            (informative['atr_pct_5m'] > 0.07) &
+            (informative['atr_pct_5m'] < informative['atr_pct_ma_5m'] * 2.2)
+        )
+        
+        # 成交量
+        informative['volume_ma_5m'] = ta.SMA(informative['volume'], timeperiod=20)
+        informative['volume_ok_5m'] = informative['volume'] > informative['volume_ma_5m'] * 0.75
+        
+        # 合并
+        dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True)
+        
+        # ==================== 1分钟指标 ====================
+        macd_1m, signal_1m, hist_1m = ta.MACD(dataframe['close'], fastperiod=12, slowperiod=26, signalperiod=9)
+        dataframe['macd'] = macd_1m
+        dataframe['macd_signal'] = signal_1m
+        dataframe['macd_hist'] = hist_1m
+        
+        dataframe['ema9'] = ta.EMA(dataframe['close'], timeperiod=9)
+        dataframe['ema21'] = ta.EMA(dataframe['close'], timeperiod=21)
+        dataframe['rsi'] = ta.RSI(dataframe['close'], timeperiod=14)
+        dataframe['vol_ma20'] = ta.SMA(dataframe['volume'], timeperiod=20)
+        dataframe['macd_slope'] = (dataframe['macd'] - dataframe['macd'].shift(3)) / 3
+        
+        # ==================== 做空信号 ====================
+        dataframe['price_high_5'] = dataframe['high'].rolling(window=5).max()
+        dataframe['macd_high_5'] = dataframe['macd'].rolling(window=5).max()
+        
+        # 顶背离 - 强化版
+        dataframe['top_divergence'] = (
+            (dataframe['high'] >= dataframe['price_high_5'] * 0.999) &
+            (dataframe['macd'] < dataframe['macd_high_5']) &
+            (dataframe['macd_slope'] < 0) &
+            (dataframe['macd'] < dataframe['macd_signal']) &
+            (dataframe['volume'] > dataframe['vol_ma20'] * 0.8)  # 更强成交量确认
+        )
+        
+        # EMA死叉
+        dataframe['ema_cross_down'] = (
+            (dataframe['ema9'] < dataframe['ema21']) &
+            (dataframe['ema9'].shift(1) >= dataframe['ema21'].shift(1)) &
+            (dataframe['rsi'] < 55) &
+            (dataframe['rsi'] > 35) &
+            (dataframe['volume'] > dataframe['vol_ma20'] * 1.0)
+        )
+        
+        # 熊市回调
+        dataframe['is_bear_candle'] = (dataframe['close'] < dataframe['open']) & ((dataframe['open'] - dataframe['close']) / dataframe['open'] > 0.008)
+        dataframe['bear_pullback'] = (
+            dataframe['is_bear_candle'].shift(2) &
+            (dataframe['close'].shift(1) > dataframe['open'].shift(1)) &
+            (dataframe['high'] < dataframe['high'].shift(2)) &
+            (dataframe['close'] < dataframe['open']) &
+            (dataframe['close'] < dataframe['ema9'])
+        )
+        
+        # ==================== 做多信号 ====================
+        dataframe['price_low_5'] = dataframe['low'].rolling(window=5).min()
+        dataframe['macd_low_5'] = dataframe['macd'].rolling(window=5).min()
+        
+        dataframe['bottom_divergence'] = (
+            (dataframe['low'] <= dataframe['price_low_5'] * 1.001) &
+            (dataframe['macd'] > dataframe['macd_low_5']) &
+            (dataframe['macd_slope'] > 0) &
+            (dataframe['macd'] > dataframe['macd_signal']) &
+            (dataframe['volume'] > dataframe['vol_ma20'] * 0.6)
+        )
+        
+        dataframe['ema_cross_up'] = (
+            (dataframe['ema9'] > dataframe['ema21']) &
+            (dataframe['ema9'].shift(1) <= dataframe['ema21'].shift(1)) &
+            (dataframe['rsi'] > 45) &
+            (dataframe['rsi'] < 70) &
+            (dataframe['volume'] > dataframe['vol_ma20'] * 1.0)
+        )
+        
+        dataframe['is_bull_candle'] = (dataframe['close'] > dataframe['open']) & ((dataframe['close'] - dataframe['open']) / dataframe['open'] > 0.008)
+        dataframe['bull_pullback'] = (
+            dataframe['is_bull_candle'].shift(2) &
+            (dataframe['close'].shift(1) < dataframe['open'].shift(1)) &
+            (dataframe['low'] > dataframe['low'].shift(2)) &
+            (dataframe['close'] > dataframe['open']) &
+            (dataframe['close'] > dataframe['ema9'])
+        )
+        
+        # 时间过滤
+        dataframe['hour_utc'] = dataframe['date'].dt.hour
+        dataframe['is_bad_hour'] = dataframe['hour_utc'].isin([4, 5, 6, 7])
+        
+        # 类型转换
+        bool_cols = ['can_long_5m_5m', 'can_short_5m_5m', 'trend_bull_5m_5m', 'trend_bear_5m_5m',
+                     'atr_ok_5m_5m', 'above_ema200_5m', 'below_ema200_5m', 'bull_market_5m', 'bear_market_5m', 'volume_ok_5m_5m']
+        for col in bool_cols:
+            if col in dataframe.columns:
+                dataframe[col] = dataframe[col].astype(bool).fillna(False)
+        
+        num_cols = ['atr_pct_5m_5m', 'rsi_5m_5m', 'macd_hist_5m_5m', 'atr_pct_ma_5m_5m', 'ema200_dist_pct_5m', 'ema200_slope_5m']
+        for col in num_cols:
+            if col in dataframe.columns:
+                dataframe[col] = dataframe[col].astype(float).fillna(0.0)
+        
+        return dataframe
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        time_ok = ~dataframe['is_bad_hour']
+        atr_ok = dataframe['atr_ok_5m_5m']
+        volume_ok = dataframe['volume_ok_5m_5m']
+        
+        # 做空入场 - 更严格的熊市条件
+        macd_bear_5m = dataframe['macd_hist_5m_5m'] < 0
+        macd_bear_1m = dataframe['macd_hist'] < 0
+        
+        dataframe.loc[
+            (time_ok) & (atr_ok) & (dataframe['can_short_5m_5m']) &
+            (macd_bear_5m) & (macd_bear_1m) & (volume_ok) &
+            (dataframe['rsi'] > 28) &  # 更低RSI
+            (dataframe['top_divergence'] | dataframe['ema_cross_down'] | dataframe['bear_pullback']) &
+            (dataframe['volume'] > 0),
+            'enter_short'
+        ] = 1
+        
+        # 做多入场
+        macd_bull_5m = dataframe['macd_hist_5m_5m'] > 0
+        macd_bull_1m = dataframe['macd_hist'] > 0
+        
+        dataframe.loc[
+            (time_ok) & (atr_ok) & (dataframe['can_long_5m_5m']) &
+            (macd_bull_5m) & (macd_bull_1m) & (volume_ok) &
+            (dataframe['rsi'] < 70) & (dataframe['rsi'] > 40) &
+            (dataframe['bottom_divergence'] | dataframe['ema_cross_up'] | dataframe['bull_pullback']) &
+            (dataframe['volume'] > 0),
+            'enter_long'
+        ] = 1
+
+        return dataframe
+
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe.loc[:, 'exit_long'] = 0
+        dataframe.loc[:, 'exit_short'] = 0
+        return dataframe
+
+    def custom_exit(self, pair: str, trade: Trade, current_time: datetime,
+                    current_rate: float, current_profit: float, **kwargs) -> str | bool | None:
+        trade_duration = (current_time - trade.open_date_utc).total_seconds() / 3600
+        
+        # 做空 - 更激进的时间止损
+        if trade.trade_direction == 'short':
+            if trade_duration > 6 and current_profit < -0.004:
+                return 'time_stop_short_6h'
+            if trade_duration > 12 and current_profit < 0:
+                return 'time_stop_short_12h'
+            if trade_duration > 20:
+                return 'time_stop_short_20h'
+        
+        # 做多 - 保持宽松
+        else:
+            if trade_duration > 10 and current_profit < -0.006:
+                return 'time_stop_long_10h'
+            if trade_duration > 20 and current_profit < 0:
+                return 'time_stop_long_20h'
+            if trade_duration > 30:
+                return 'time_stop_long_30h'
+        
+        return None
+
+    def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
+                            time_in_force: str, current_time: datetime, entry_tag: Optional[str],
+                            side: str, **kwargs) -> bool:
+        hour_utc = current_time.utcnow().hour if current_time.tzinfo is None else current_time.hour
+        if hour_utc in {4, 5, 6, 7}:
+            return False
+        return True
+
+    def leverage(self, pair: str, current_time: datetime, current_rate: float,
+                proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str,
+                **kwargs) -> float:
+        return self.lev

strategies/PanZhengBeiChiStrategy.py

@@ -0,0 +1,444 @@
+"""
+盘整背驰策略 (PanZhengBeiChi Strategy)
+
+基于缠论的盘整背驰进行交易：
+- 盘整背驰：同级别走势中，Ai与Ai+2比较力度减弱
+- 顶背驰（卖点）：价格创新高或接近，但MACD力度明显减弱
+- 底背驰（买点）：价格创新低或接近，但MACD力度明显减弱
+
+使用命令：
+    freqtrade backtesting -c ./user_data/Chan/config/Local_Test.json \
+        --strategy PanZhengBeiChiStrategy --strategy-path ./user_data/Chan/strategies \
+        --timerange=20250301-
+"""
+
+import logging
+from datetime import datetime
+from typing import Optional
+import numpy as np
+import pandas as pd
+import talib.abstract as ta
+from pandas import DataFrame
+from technical.util import resample_to_interval, resampled_merge
+from freqtrade.strategy import IStrategy
+
+logger = logging.getLogger(__name__)
+
+
+class PanZhengBeiChiStrategy(IStrategy):
+    """
+    盘整背驰策略
+    
+    核心逻辑：
+    1. 在5分钟级别识别同级别走势段（Ai）
+    2. 比较Ai与Ai+2的MACD力度，判断盘整背驰
+    3. 盘整顶背驰（i+2为偶数）-> 卖出
+    4. 盘整底背驰（i+2为奇数）-> 买入
+    """
+    INTERFACE_VERSION: int = 3
+
+    # === 基础配置 ===
+    timeframe = '1m'
+    informative_timeframe = '5m'
+    can_short = True
+    can_long = True
+    
+    startup_candle_count: int = 2000  # 需要足够的数据来识别走势段
+
+    # === 止损止盈配置 ===
+    stoploss = -0.02  # 2% 硬止损
+    use_custom_stoploss = False
+
+    # Trailing stop
+    trailing_stop = True
+    trailing_stop_positive = 0.008  # 回撤 0.8% 触发退出
+    trailing_stop_positive_offset = 0.015  # 盈利 1.5% 后才开始追踪
+    trailing_only_offset_is_reached = True
+
+    # ROI - 调整止盈策略
+    minimal_roi = {
+        "0": 0.015,   # 1.5% 立即止盈（更保守）
+        "30": 0.01,   # 30分钟后 1%
+        "120": 0.008, # 2小时后 0.8%
+    }
+
+    order_types = {
+        "entry": "market",
+        "exit": "market",
+        "stoploss": "market",
+        "stoploss_on_exchange": False,
+    }
+
+    # === 盘整背驰参数 ===
+    same_level_timeframe = 5  # 5分钟级别
+    pivot_window = 4          # 转折点确认窗口（增大减少噪音）
+    min_segment_length = 5    # 最小段长度（K线数）（增大减少假信号）
+    
+    # 背驰判断参数（更严格）
+    beichi_price_threshold = 1.10   # 价格涨幅/跌幅阈值（允许10%范围内，更严格）
+    beichi_macd_threshold = 0.75    # MACD力度阈值（低于75%即背驰，更严格）
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """计算指标并识别盘整背驰"""
+        ticker = self.get_ticker_indicator()
+        
+        # Resample 到 5m 进行同级别分解
+        dataframe_5m = resample_to_interval(dataframe, ticker * self.same_level_timeframe)
+        
+        # 在 5m 上计算指标
+        dataframe_5m = self.add_indicators_5m(dataframe_5m)
+        
+        # 识别盘整背驰
+        dataframe_5m = self.identify_panzheng_beichi(dataframe_5m)
+        
+        # 合并回 1m dataframe
+        dataframe = resampled_merge(dataframe, dataframe_5m)
+        
+        # 在 1m 上也计算基础指标
+        dataframe = self.add_indicators_1m(dataframe)
+        
+        return dataframe
+
+    def add_indicators_5m(self, dataframe: DataFrame) -> DataFrame:
+        """在5m级别计算指标"""
+        # MACD 用于识别背驰
+        macd = ta.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9)
+        dataframe['macd'] = macd['macd']
+        dataframe['macdsignal'] = macd['macdsignal']
+        dataframe['macdhist'] = macd['macdhist']
+        
+        # EMA 用于识别趋势
+        dataframe['ema12'] = ta.EMA(dataframe, timeperiod=12)
+        dataframe['ema26'] = ta.EMA(dataframe, timeperiod=26)
+        dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
+        dataframe['ema200'] = ta.EMA(dataframe, timeperiod=200)
+        
+        # EMA趋势方向
+        dataframe['ema_trend_up'] = (dataframe['ema12'] > dataframe['ema26']) & (dataframe['ema26'] > dataframe['ema50'])
+        dataframe['ema_trend_dn'] = (dataframe['ema12'] < dataframe['ema26']) & (dataframe['ema26'] < dataframe['ema50'])
+        
+        # 价格与EMA200关系
+        dataframe['price_above_ema200'] = dataframe['close'] > dataframe['ema200']
+        dataframe['price_below_ema200'] = dataframe['close'] < dataframe['ema200']
+        
+        # 趋势强度
+        dataframe['ema12_slope'] = dataframe['ema12'].diff(5) / dataframe['ema12'].shift(5)
+        dataframe['ema26_slope'] = dataframe['ema26'].diff(5) / dataframe['ema26'].shift(5)
+        
+        # 强趋势判断
+        dataframe['strong_uptrend'] = (
+            (dataframe['ema12_slope'] > 0) & 
+            (dataframe['ema26_slope'] > 0) & 
+            (dataframe['price_above_ema200'])
+        )
+        dataframe['strong_downtrend'] = (
+            (dataframe['ema12_slope'] < 0) & 
+            (dataframe['ema26_slope'] < 0) & 
+            (dataframe['price_below_ema200'])
+        )
+        
+        # RSI
+        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
+        
+        # ATR 用于波动率过滤
+        dataframe['atr'] = ta.ATR(dataframe, timeperiod=14)
+        dataframe['atr_mean'] = dataframe['atr'].rolling(window=20).mean()
+        dataframe['volatility_ok'] = dataframe['atr'] > dataframe['atr_mean'] * 0.8
+        
+        return dataframe
+
+    def add_indicators_1m(self, dataframe: DataFrame) -> DataFrame:
+        """在1m级别计算基础指标"""
+        dataframe['rsi_1m'] = ta.RSI(dataframe, timeperiod=14)
+        dataframe['volume_mean'] = dataframe['volume'].rolling(window=20).mean()
+        
+        # MACD 用于1m级别确认
+        macd_1m = ta.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9)
+        dataframe['macd_1m'] = macd_1m['macd']
+        dataframe['macdsignal_1m'] = macd_1m['macdsignal']
+        dataframe['macdhist_1m'] = macd_1m['macdhist']
+        
+        # MACD交叉
+        dataframe['macd_cross_up'] = (
+            (dataframe['macd_1m'] > dataframe['macdsignal_1m']) &
+            (dataframe['macd_1m'].shift(1) <= dataframe['macdsignal_1m'].shift(1))
+        )
+        dataframe['macd_cross_dn'] = (
+            (dataframe['macd_1m'] < dataframe['macdsignal_1m']) &
+            (dataframe['macd_1m'].shift(1) >= dataframe['macdsignal_1m'].shift(1))
+        )
+        
+        return dataframe
+
+    def identify_panzheng_beichi(self, dataframe: DataFrame) -> DataFrame:
+        """
+        识别盘整背驰
+        
+        核心逻辑：
+        1. 识别局部转折点（高低点）
+        2. 构建同级别走势段（Ai）
+        3. 比较Ai与Ai+2的MACD力度
+        4. 判断盘整背驰：价格涨幅相近但MACD力度减弱
+        """
+        df = dataframe.copy()
+        window = self.pivot_window
+        lookback = window + 1
+        
+        # 初始化列
+        df['ai_index'] = -1
+        df['ai_type'] = 0  # 1: 上涨, -1: 下跌
+        df['ai_high'] = np.nan
+        df['ai_low'] = np.nan
+        df['ai_macd_max'] = np.nan
+        df['ai_macd_min'] = np.nan
+        df['beichi_long'] = False   # 盘整底背驰（买入信号）
+        df['beichi_short'] = False  # 盘整顶背驰（卖出信号）
+        
+        # 识别局部高点
+        df['temp_high'] = df['high'].shift(window)
+        df['is_pivot_high'] = (
+            (df['temp_high'] == df['temp_high'].rolling(window=lookback).max()) &
+            (df['temp_high'].notna())
+        )
+        
+        # 识别局部低点
+        df['temp_low'] = df['low'].shift(window)
+        df['is_pivot_low'] = (
+            (df['temp_low'] == df['temp_low'].rolling(window=lookback).min()) &
+            (df['temp_low'].notna())
+        )
+        
+        # 逐行处理，识别走势段和背驰
+        ai_list = []
+        current_ai_start = None
+        current_ai_type = None
+        last_pivot_idx = None
+        
+        for i in range(window, len(df)):
+            # 检查新的转折点
+            is_new_pivot = False
+            pivot_type = None
+            
+            if df.iloc[i]['is_pivot_high']:
+                is_new_pivot = True
+                pivot_type = 'high'
+            elif df.iloc[i]['is_pivot_low']:
+                is_new_pivot = True
+                pivot_type = 'low'
+            
+            if is_new_pivot and last_pivot_idx is not None:
+                # 完成一个走势段
+                if current_ai_start is not None:
+                    seg_df = df.iloc[current_ai_start:last_pivot_idx]
+                    if len(seg_df) >= self.min_segment_length:
+                        high_val = seg_df['high'].max()
+                        low_val = seg_df['low'].min()
+                        macd_max = seg_df['macd'].max()
+                        macd_min = seg_df['macd'].min()
+                        
+                        # 判断走势类型
+                        if current_ai_type is None:
+                            if high_val > df.iloc[current_ai_start]['close']:
+                                current_ai_type = 1
+                            else:
+                                current_ai_type = -1
+                        
+                        ai_info = {
+                            'start': current_ai_start,
+                            'end': last_pivot_idx,
+                            'type': current_ai_type,
+                            'high': high_val,
+                            'low': low_val,
+                            'macd_max': macd_max,
+                            'macd_min': macd_min,
+                        }
+                        ai_list.append(ai_info)
+                        
+                        # 标记该段
+                        df.iloc[current_ai_start:last_pivot_idx, df.columns.get_loc('ai_index')] = len(ai_list) - 1
+                        df.iloc[current_ai_start:last_pivot_idx, df.columns.get_loc('ai_type')] = current_ai_type
+                        df.iloc[current_ai_start:last_pivot_idx, df.columns.get_loc('ai_high')] = high_val
+                        df.iloc[current_ai_start:last_pivot_idx, df.columns.get_loc('ai_low')] = low_val
+                        df.iloc[current_ai_start:last_pivot_idx, df.columns.get_loc('ai_macd_max')] = macd_max
+                        df.iloc[current_ai_start:last_pivot_idx, df.columns.get_loc('ai_macd_min')] = macd_min
+                        
+                        # 判断背驰（Ai与Ai+2比较）
+                        if len(ai_list) >= 3:
+                            ai = ai_list[-3]      # Ai
+                            ai_plus_2 = ai_list[-1]  # Ai+2
+                            
+                            if ai['type'] == ai_plus_2['type']:
+                                # 上涨段：比较向上力度
+                                if ai['type'] == 1:
+                                    price_chg = (ai_plus_2['high'] - ai_plus_2['low']) / ai_plus_2['low'] if ai_plus_2['low'] > 0 else 0
+                                    price_chg_prev = (ai['high'] - ai['low']) / ai['low'] if ai['low'] > 0 else 0
+                                    macd_chg = ai_plus_2['macd_max']
+                                    macd_chg_prev = ai['macd_max']
+                                    
+                                    # 顶背驰：价格涨幅相近但MACD力度减弱
+                                    if price_chg <= price_chg_prev * self.beichi_price_threshold and \
+                                       macd_chg < macd_chg_prev * self.beichi_macd_threshold:
+                                        idx = len(ai_list) - 1  # i+2的索引
+                                        if idx % 2 == 0:  # 偶数 -> 卖出
+                                            df.iloc[last_pivot_idx, df.columns.get_loc('beichi_short')] = True
+                                        else:  # 奇数 -> 买入
+                                            df.iloc[last_pivot_idx, df.columns.get_loc('beichi_long')] = True
+                                
+                                # 下跌段：比较向下力度
+                                else:
+                                    price_chg = abs((ai_plus_2['high'] - ai_plus_2['low']) / ai_plus_2['low']) if ai_plus_2['low'] > 0 else 0
+                                    price_chg_prev = abs((ai['high'] - ai['low']) / ai['low']) if ai['low'] > 0 else 0
+                                    macd_chg = abs(ai_plus_2['macd_min'])
+                                    macd_chg_prev = abs(ai['macd_min'])
+                                    
+                                    # 底背驰：价格跌幅相近但MACD力度减弱
+                                    if price_chg <= price_chg_prev * self.beichi_price_threshold and \
+                                       macd_chg < macd_chg_prev * self.beichi_macd_threshold:
+                                        idx = len(ai_list) - 1
+                                        if idx % 2 == 0:  # 偶数 -> 卖出
+                                            df.iloc[last_pivot_idx, df.columns.get_loc('beichi_short')] = True
+                                        else:  # 奇数 -> 买入
+                                            df.iloc[last_pivot_idx, df.columns.get_loc('beichi_long')] = True
+                
+                # 更新当前段信息
+                if pivot_type == 'high':
+                    current_ai_type = -1  # 高点后向下
+                else:
+                    current_ai_type = 1   # 低点后向上
+                current_ai_start = last_pivot_idx
+            
+            if is_new_pivot:
+                last_pivot_idx = i
+        
+        return df
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        ticker = self.get_ticker_indicator()
+        resample_col = f"resample_{ticker * self.same_level_timeframe}_"
+        
+        # 5m 级别指标列名
+        beichi_long_col = f"{resample_col}beichi_long"
+        beichi_short_col = f"{resample_col}beichi_short"
+        ai_type_col = f"{resample_col}ai_type"
+        rsi_5m_col = f"{resample_col}rsi"
+        ema_trend_up_col = f"{resample_col}ema_trend_up"
+        ema_trend_dn_col = f"{resample_col}ema_trend_dn"
+        volatility_ok_col = f"{resample_col}volatility_ok"
+        strong_uptrend_col = f"{resample_col}strong_uptrend"
+        strong_downtrend_col = f"{resample_col}strong_downtrend"
+        price_above_ema200_col = f"{resample_col}price_above_ema200"
+        price_below_ema200_col = f"{resample_col}price_below_ema200"
+        
+        # === 做多入场 ===
+        # 条件：盘整底背驰 + 强上升趋势确认
+        dataframe.loc[
+            (
+                # 核心信号：盘整底背驰
+                (dataframe[beichi_long_col] == True) &
+                
+                # 强上升趋势确认（更严格）
+                (dataframe[strong_uptrend_col] == True) &
+                
+                # RSI 确认（更严格：只在大趋势中操作）
+                (dataframe[rsi_5m_col] > 40) &
+                (dataframe[rsi_5m_col] < 60) &
+                
+                # 1m 指标确认
+                (dataframe['macd_1m'] > dataframe['macdsignal_1m']) &
+                
+                # 成交量确认
+                (dataframe['volume'] > dataframe['volume_mean'] * 1.5)
+            ),
+            ['enter_long', 'enter_tag']
+        ] = (1, "pzbc_long")
+        
+        # === 做空入场 ===
+        # 条件：盘整顶背驰 + 强下降趋势确认（更严格）
+        dataframe.loc[
+            (
+                # 核心信号：盘整顶背驰
+                (dataframe[beichi_short_col] == True) &
+                
+                # 强下降趋势确认
+                (dataframe[strong_downtrend_col] == True) &
+                
+                # RSI 确认
+                (dataframe[rsi_5m_col] > 40) &
+                (dataframe[rsi_5m_col] < 60) &
+                
+                # 1m 指标确认
+                (dataframe['macd_1m'] < dataframe['macdsignal_1m']) &
+                
+                # 成交量确认
+                (dataframe['volume'] > dataframe['volume_mean'] * 1.5)
+            ),
+            ['enter_short', 'enter_tag']
+        ] = (1, "pzbc_short")
+        
+        return dataframe
+
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        出场逻辑
+        
+        多头出场：
+        1. 出现盘整顶背驰
+        2. 趋势转弱
+        
+        空头出场：
+        1. 出现盘整底背驰
+        2. 趋势转弱
+        """
+        ticker = self.get_ticker_indicator()
+        resample_col = f"resample_{ticker * self.same_level_timeframe}_"
+        
+        beichi_long_col = f"{resample_col}beichi_long"
+        beichi_short_col = f"{resample_col}beichi_short"
+        ai_type_col = f"{resample_col}ai_type"
+        rsi_5m_col = f"{resample_col}rsi"
+        ema_trend_dn_col = f"{resample_col}ema_trend_dn"
+        strong_downtrend_col = f"{resample_col}strong_downtrend"
+        strong_uptrend_col = f"{resample_col}strong_uptrend"
+        
+        # === 多头出场 ===
+        dataframe.loc[
+            (
+                # 出现盘整顶背驰 -> 退出多头
+                (dataframe[beichi_short_col] == True) |
+                
+                # 趋势转弱
+                (
+                    (dataframe[ai_type_col] == -1) &
+                    (dataframe[rsi_5m_col] > 55)
+                ) |
+                
+                # 强下跌趋势
+                (dataframe[strong_downtrend_col] == True)
+            ),
+            ['exit_long', 'exit_tag']
+        ] = (1, "pzbc_exit_long")
+        
+        # === 空头出场 ===
+        dataframe.loc[
+            (
+                # 出现盘整底背驰 -> 退出空头
+                (dataframe[beichi_long_col] == True) |
+                
+                # 趋势转弱
+                (
+                    (dataframe[ai_type_col] == 1) &
+                    (dataframe[rsi_5m_col] < 45)
+                ) |
+                
+                # 强上涨趋势
+                (dataframe[strong_uptrend_col] == True)
+            ),
+            ['exit_short', 'exit_tag']
+        ] = (1, "pzbc_exit_short")
+        
+        return dataframe
+
+    def get_ticker_indicator(self) -> int:
+        """获取 timeframe 的分钟数"""
+        return int(self.timeframe[:-1])
+

strategies/PureRandomRuleStrategy.py

@@ -0,0 +1,204 @@
+"""
+纯随机规则策略 (PureRandomRuleStrategy)
+
+完全不看 K 线、不看指标、不看量价、不看趋势、不看形态的纯规则交易系统。
+
+规则：
+- 固定时间周期开仓（例如：每 4 小时一单）
+- 方向随机多空，不做任何行情判断
+- 每次只开1个方向，不对冲
+- 固定止盈：2%
+- 固定止损：1%
+- 到价立即平仓，不移动、不修改
+- 单笔仓位：总资金的 5%
+- 单笔最大风险：总资金的 0.05%
+- 连续止损 3 次，当天停止交易
+- 总持仓不超过 20%
+
+使用命令：
+    freqtrade backtesting -c ./user_data/Chan/config/Local_Test.json --strategy PureRandomRuleStrategy --strategy-path ./user_data/Chan/strategies --timerange=20260101-
+
+实盘命令：
+    freqtrade trade -c ./user_data/Chan/config/Chan.json \
+        --strategy PureRandomRuleStrategy --strategy-path ./user_data/Chan/strategies
+"""
+
+import logging
+from datetime import datetime
+from typing import Optional
+import random
+import pandas as pd
+import talib.abstract as ta
+from pandas import DataFrame
+from freqtrade.strategy import IStrategy
+
+logger = logging.getLogger(__name__)
+
+
+class PureRandomRuleStrategy(IStrategy):
+    """
+    纯随机规则策略
+    
+    核心特点：
+    1. 不看任何行情数据
+    2. 固定时间开仓（可配置间隔）
+    3. 随机选择多空方向
+    4. 固定止盈止损
+    5. 风险控制（连续止损、持仓限制）
+    """
+    INTERFACE_VERSION: int = 3
+
+    # === 基础配置 ===
+    timeframe = '1m'  # 主时间框架
+    informative_timeframe = '1h'  # 1小时作为参考（需要数据支持）
+    can_short = True
+    can_long = True
+    
+    startup_candle_count = 200  # 需要更多数据计算 EMA
+
+    # === 交易时间间隔配置 ===
+    trade_interval_hours = 4
+    
+    # === 止盈止损配置 ===
+    take_profit_pct = 0.024
+    stop_loss_pct = 0.01
+    
+    # === 仓位配置 ===
+    entry_percent = 0.05
+    max_position_pct = 0.20
+    
+    # === 风险控制 ===
+    max_consecutive_losses = 3
+
+    # === 订单类型 ===
+    order_types = {
+        "entry": "market",
+        "exit": "market",
+        "stoploss": "market",
+        "stoploss_on_exchange": False,
+    }
+
+    # === 最小 ROI ===
+    minimal_roi = {
+        "0": take_profit_pct,
+    }
+
+    # === 止损 ===
+    stoploss = -stop_loss_pct
+
+    # === 追踪止损 ===
+    trailing_stop = False
+
+    # === 策略状态 ===
+    _last_entry_time: Optional[datetime] = None
+    _consecutive_losses: int = 0
+    _last_loss_date: Optional[datetime] = None
+    _today_loss_count: int = 0
+
+    def __init__(self, config: dict) -> None:
+        super().__init__(config)
+        random.seed()
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        计算 1h EMA26 和波动幅度
+        使用 resampled_merge 合并 1h 数据
+        """
+        from technical.util import resample_to_interval, resampled_merge
+        
+        # 重采样到 1h (1m * 60 = 60)
+        dataframe_1h = resample_to_interval(dataframe, 60)
+        
+        # 计算 1h EMA26
+        dataframe_1h['ema26'] = ta.EMA(dataframe_1h, timeperiod=26)
+        
+        # 计算 1h 波动幅度: (high - low) / open * 100%
+        dataframe_1h['volatility'] = (dataframe_1h['high'] - dataframe_1h['low']) / dataframe_1h['open']
+        
+        # 合并到主 dataframe
+        # 列名格式: resample_60_ema26, resample_60_volatility
+        dataframe = resampled_merge(dataframe, dataframe_1h)
+        
+        return dataframe
+    
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        入场逻辑：固定时间 + 1h EMA26方向过滤 + 波动过滤 + 时间过滤
+        
+        规则：
+        1. 1h EMA26 上方 -> 只做多
+        2. 1h EMA26 下方 -> 只做空
+        3. 固定时间间隔开仓（4小时）
+        4. 1h 波动幅度 > 0.5% 且 < 5%
+        5. UTC 8:00-20:00
+        """
+        dataframe['enter_long'] = 0
+        dataframe['enter_short'] = 0
+        dataframe['enter_tag'] = ''
+        
+        last_entry_idx = None
+        
+        # 1h EMA26 列名
+        ema26_col = 'resample_60_ema26'
+        # 1h 波动幅度列名
+        volatility_col = 'resample_60_volatility'
+        
+        # 波动幅度阈值
+        min_volatility = 0.005   # 0.5%
+        max_volatility = 0.05    # 5%
+        
+        for i in range(len(dataframe)):
+            current_time = dataframe['date'].iloc[i]
+            current_price = dataframe['close'].iloc[i]
+            
+            # 使用 resample 后的 EMA26 列
+            ema26_1h = dataframe[ema26_col].iloc[i]
+            # 波动幅度
+            volatility = dataframe[volatility_col].iloc[i]
+            
+            # 跳过没有 EMA 数据的情况
+            if pd.isna(ema26_1h):
+                continue
+            
+            # 检查时间间隔（4小时）
+            can_entry = True
+            if last_entry_idx is not None:
+                hours_since_last = (current_time - dataframe['date'].iloc[last_entry_idx]).total_seconds() / 3600
+                if hours_since_last < self.trade_interval_hours:
+                    can_entry = False
+            
+            # 检查当天连续止损
+            if self._today_loss_count >= self.max_consecutive_losses:
+                can_entry = False
+            
+            # 检查波动幅度（>0.5% 且 <5%）
+            if not pd.isna(volatility):
+                if volatility < min_volatility or volatility > max_volatility:
+                    can_entry = False
+            else:
+                can_entry = False
+            
+            # 检查时间过滤（UTC 8:00-20:00）
+            utc_hour = current_time.hour
+            #if utc_hour < 8 or utc_hour >= 20:
+                #can_entry = False
+            
+            if can_entry:
+                # 判断方向：价格 > 1h EMA26 做多，价格 < 1h EMA26 做空
+                if current_price > ema26_1h:
+                    dataframe.loc[dataframe.index[i], 'enter_long'] = 1
+                    dataframe.loc[dataframe.index[i], 'enter_tag'] = 'long_above_ema'
+                elif current_price < ema26_1h:
+                    dataframe.loc[dataframe.index[i], 'enter_short'] = 1
+                    dataframe.loc[dataframe.index[i], 'enter_tag'] = 'short_below_ema'
+                
+                if dataframe.loc[dataframe.index[i], 'enter_long'] == 1 or dataframe.loc[dataframe.index[i], 'enter_short'] == 1:
+                    last_entry_idx = i
+                    self._last_entry_time = current_time
+        
+        return dataframe
+
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe['exit_long'] = 0
+        dataframe['exit_short'] = 0
+        return dataframe

web/app.py

@@ -438,6 +438,7 @@ def add_indicators(df):
 	df['ema10'] = (ta.EMA(df, timeperiod=10)).fillna(0)
 	df['ema24'] = (ta.EMA(df, timeperiod=24)).fillna(0)
 	df['ema52'] = (ta.EMA(df, timeperiod=52)).fillna(0)
+	df['ema26'] = (ta.EMA(df, timeperiod=26)).fillna(0)
 	# 常用SMA 24/52
 	try:
 		df['sma24'] = (ta.SMA(df, timeperiod=24)).fillna(0)

web/templates/index.html

@@ -5477,7 +5477,7 @@
                             if (fx.fx_strength < 1.0) { // 降低阈值，让更多分型显示
                                 displayText = fx.fx_strength >= 0.8 ? '' : '' // 0.8以上显示点，0.8以下不显示文本
                             }
-                            displayText = fx.fx_type.replace("TOP", "").replace("BOTTOM", "").replace("11", "").replace("21", "").replace("3", "").replace("41", "").replace("51", "").replace("01", "");
+                            displayText = fx.fx_type.replace("TOP", "").replace("BOTTOM", "").replace("11", "").replace("21", "").replace("31", "").replace("41", "").replace("51", "").replace("01", "");
                             // 添加标记配置
                             const markerConfig = {
                                 time: timestamp,