refactor chanlun, tf_df

TF_DF.py

@@ -1,6 +1,6 @@
 from datetime import timedelta
 from pandas import DataFrame
-from ChanEnum import Chan_FX_TYPE, Chan_KLINE_DIR, Chan_BI_DIR, Chan_SEG_DIR, Chan_ZS_DIR, Chan_BSP_DIR, Chan_BSP_TYPE, Chan_KLC_FX, Chan_PRICE_TREND
+from ChanEnum import Chan_FX_TYPE, Chan_KLINE_DIR, Chan_BI_DIR, Chan_SEG_DIR, Chan_ZS_DIR, Chan_BSP_DIR, Chan_BSP_TYPE, Chan_KLC_FX, Chan_PRICE_TREND, Chan_KLU_PATTERN
 from ChanKLU import ChanKLU
 from ChanKLC import ChanKLC
 from ChanBI import ChanBI
@@ -10,17 +10,16 @@ from ChanZS import ChanZS
 from ChanBSP import ChanBSP
 import talib.abstract as ta
 import pandas as pd
-import matplotlib.pyplot as plt
-from matplotlib.dates import DateFormatter, date2num
-import matplotlib.patches as patches
 from technical.util import resample_to_interval
 from decimal import Decimal
-import xgboost as xgb
 import numpy as np
 from ChanMACD import ChanMACD
 
 class TF_DF():
-	def __init__(self, df, interval, timeframe):
+	def __init__(self, df=None, interval=0, timeframe=None):
+		if df is not None:
+			self.init_TF_DF(df, interval, timeframe)
+	def init_TF_DF(self, df, interval, timeframe):
 		self.timeframe = timeframe
 		self.interval = interval
 		self.dataframe = resample_to_interval(df, interval)
@@ -31,12 +30,10 @@ class TF_DF():
 		self.zs_list = []
 		self.bsp_list = []
 		self.seg_list = []
-		self.init_TF_DF()
-	def init_TF_DF(self):
 		self.klu_list = self.cal_kl_data(self.dataframe)
-		self.klc_list = self.cal_klc_list(self.klu_list)
+		self.klc_list = self.get_klc_list(self.klu_list)
 		self.bi_list = self.cal_bi_list(self.klc_list)
-		self.seg_list = self.cal_seg_list(self.bi_list)
+		self.seg_list = self.get_seg_list(self.bi_list)
 		self.zs_list = self.cal_zs_list(self.bi_list, self.seg_list)
 		self.chanmacd = ChanMACD(self.klu_list)
 		self.klu_list = self.chanmacd.cal_macd_state()
@@ -97,45 +94,45 @@ class TF_DF():
 		df['macd'] = macd['macd']
 		df['macdsignal'] = macd['macdsignal']
 		df['macdhist'] = macd['macdhist']
-		df['ema5'] = self.cal_ema(df, 5)
+		df['ema5'] = ta.EMA(df, timeperiod=5)
-		df['ema10'] = self.cal_ema(df, 10)
+		df['ema10'] = ta.EMA(df, timeperiod=10)
-		df['ema24'] = self.cal_ema(df, 24)
+		df['ema24'] = ta.EMA(df, timeperiod=24)
-		df['ema26'] = self.cal_ema(df, 26)
+		df['ema52'] = ta.EMA(df, timeperiod=52)
-		df['ema52'] = self.cal_ema(df, 52)
 		df['rsi'] = ta.RSI(df, timeperiod=14)
 		df['volume_ratio'] = self.cal_volume_ratio(df)
 		return df
-	@staticmethod
+	def get_klu_state(self, dataframe):
-	def cal_ema(df, timeperiod):
+		klc_list = self.get_klc_list(dataframe)
-		"""
+		bi_list = self.cal_bi_list(klc_list)
-		计算 EMA，优先使用 pandas ewm(adjust=False) 以贴近前端/TradingView 显示；
+		klu_state_list = []
-		必要时回退到 TA-Lib（abstract）。
+		klc_index = 0
-		"""
+		for index in range(0, len(dataframe)):
-		try:
+			if klc_index == len(klc_list):
-			series = df['close'].astype(float) if isinstance(df, pd.DataFrame) else pd.Series(df).astype(float)
+				klc_index = len(klc_list) - 1
-			return series.ewm(span=int(timeperiod), adjust=False).mean()
+			klc = klc_list[klc_index]
-		except Exception:
+			if klc.end_klu and klc.end_klu.idx == index:
-			try:
+				if klc.klc_fx_type == Chan_KLC_FX.TOP4:
-				if isinstance(df, pd.DataFrame):
+					klu_state_list.append("10")
-					return ta.EMA(df, timeperiod=int(timeperiod))
+				elif klc.klc_fx_type == Chan_KLC_FX.BOTTOM4:
-			except Exception:
+					klu_state_list.append("-10")
-				pass
+				else:
-			# 最后回退：返回同索引的 NaN 序列
+					klu_state_list.append("00")
-			if isinstance(df, pd.DataFrame) and 'close' in df:
+				klc_index += 1
-				return pd.Series(np.nan, index=df.index)
+			else:
-			return pd.Series(dtype=float)
+				klu_state_list.append("00")
+		return klu_state_list
 	def check_fx(self, klc):
 		if klc.pre and klc.next:
-			if klc.high > klc.pre.high and klc.high > klc.next.high:
+			if klc.high > klc.pre.high and klc.high > klc.next.high and klc.low > klc.pre.low and klc.low > klc.next.low:
-				if klc.macd > 0 and klc.signal > klc.macdhist:
+				#if (klc.close > klc.ema52 or klc.next.close > klc.next.ema52) and klc.macd > 0 and klc.macd > klc.macdhist:
-					klc.set_fx(Chan_FX_TYPE.TOP)
+				klc.set_fx(Chan_FX_TYPE.TOP)
-					# print(klc.start_time, klc.end_time,klc.next.start_time, klc.next.end_time,klc.fx, "TOP")
+				#print(klc.start_time, klc.end_time,klc.next.start_time, klc.next.end_time, klc.macd, klc.state, klc.fx, "TOP")
-					return Chan_FX_TYPE.TOP
+				return Chan_FX_TYPE.TOP
-			elif klc.low < klc.pre.low and klc.low < klc.next.low:
+			elif klc.low < klc.pre.low and klc.low < klc.next.low and klc.high < klc.pre.high and klc.high < klc.next.high:
-				if klc.macd < 0 and klc.signal < klc.macdhist:
+				#if (klc.close < klc.ema52 or klc.next.close < klc.next.ema52) and klc.macd < 0 and klc.macd < klc.macdhist:
-					klc.set_fx(Chan_FX_TYPE.BOTTOM)
+				klc.set_fx(Chan_FX_TYPE.BOTTOM)
-					# print(klc.start_time, klc.end_time,klc.next.start_time, klc.next.end_time,klc.fx, "BOTTOM")
+				#print(klc.start_time, klc.end_time,klc.next.start_time, klc.next.end_time, klc.macd, klc.state, klc.fx, "BOTTOM")
-					return Chan_FX_TYPE.BOTTOM
+				return Chan_FX_TYPE.BOTTOM
 		return Chan_FX_TYPE.UNKNOWN
 	def cal_volume_ratio(self, dataframe, window=10):
 		df = dataframe.copy()
@@ -155,6 +152,9 @@ class TF_DF():
 		if not klc_list:
 			return klc_list
 		last_trend = Chan_PRICE_TREND.UNKNOWN
+		# 趋势延续性：参考近 N 根已完成的KLC
+		lookback_n = 5
+		prev_klcs = []
 		for klc in klc_list:
 			price = getattr(klc, 'close', None)
 			ema24 = getattr(klc, 'ema24', None)
@@ -164,13 +164,14 @@ class TF_DF():
 			hist = getattr(klc, 'macdhist', 0) if getattr(klc, 'macdhist', None) is not None else 0
 			rsi = getattr(klc, 'rsi', None)
 			trend = Chan_PRICE_TREND.UNKNOWN
+			score = 0
 			try:
 				# 有效性
 				price_valid = price is not None and price != 0
 				ema24_valid = ema24 is not None and ema24 != 0
 				ema52_valid = ema52 is not None and ema52 != 0
 				# 多因子投票
-				score = 0
+				
 				# 1) 均线结构 + 价位
 				if ema24_valid and ema52_valid:
 					score += 1 if ema24 > ema52 else -1
@@ -194,6 +195,76 @@ class TF_DF():
 						spread_now = ema24 - ema52
 						spread_pre = pre_ema24 - pre_ema52
 						score += 1 if spread_now >= spread_pre else -1
+					# 3.1) MACD柱体动量趋势：考虑 macdhist 的斜率与过零
+					pre_hist = getattr(pre, 'macdhist', None)
+					if pre_hist is not None and hist is not None:
+						# 柱体斜率：上升加分，下降减分
+						if hist > pre_hist:
+							score += 1
+						elif hist < pre_hist:
+							score -= 1
+						# 过零加权：负转正更偏多，正转负更偏空
+						if pre_hist < 0 and hist > 0:
+							score += 1
+						elif pre_hist > 0 and hist < 0:
+							score -= 1
+					# 3.2) EMA52 突破/跌破加权
+					if ema52_valid and price_valid and pre_close is not None and pre_ema52 not in (None, 0):
+						# 看多突破：从均线下方上破且动量配合
+						if pre_close <= pre_ema52 and price > ema52 and (hist is None or pre_hist is None or hist >= pre_hist):
+							score += 1
+						# 看空跌破：从均线上方下破且动量配合
+						if pre_close >= pre_ema52 and price < ema52 and (hist is None or pre_hist is None or hist <= pre_hist):
+							score -= 1
+				# 3.3) EMA52 支撑/阻力触碰（非强穿越）
+				if ema52_valid and price_valid:
+					low_v = getattr(klc, 'low', None)
+					high_v = getattr(klc, 'high', None)
+					if low_v is not None and high_v is not None and ema52 not in (None, 0):
+						# 触碰容差（相对EMA52的0.15%）
+						touch_tol = 0.0015
+						# 作为支撑：收盘在上，最低靠近EMA52
+						near_support_touch = (price > ema52) and (abs(low_v - ema52) / abs(ema52) <= touch_tol)
+						# 作为阻力：收盘在下，最高靠近EMA52
+						near_resistance_touch = (price < ema52) and (abs(high_v - ema52) / abs(ema52) <= touch_tol)
+						if near_support_touch:
+							# 若动量不弱，则更偏多
+							score += 1 if (hist is None or pre_hist is None or hist >= pre_hist) else 0
+						if near_resistance_touch:
+							# 若动量不强，则更偏空
+							score -= 1 if (hist is None or pre_hist is None or hist <= pre_hist) else 0
+				# 3.4) 多次对 EMA52 的"拒绝"配合 MACD 逆向：易形成压/支并反向
+				# 统计近窗口内的上/下拒绝次数：
+				# - 上拒绝：价格位于 EMA52 下方，最高触及/越过 EMA52 但收盘仍在下方
+				# - 下拒绝：价格位于 EMA52 上方，最低触及/跌破 EMA52 但收盘仍在上方
+				recent_up_rejects = 0
+				recent_down_rejects = 0
+				if ema52_valid:
+					window_rej = prev_klcs[-lookback_n:] if len(prev_klcs) > 0 else []
+					rej_tol = 0.0015
+					for wk in window_rej:
+						wk_close = getattr(wk, 'close', None)
+						wk_ema52 = getattr(wk, 'ema52', None)
+						wk_high = getattr(wk, 'high', None)
+						wk_low = getattr(wk, 'low', None)
+						if wk_close is None or wk_ema52 in (None, 0):
+							continue
+						# 上拒绝（阻力）：下方多次试图上破但未站上
+						if wk_close < wk_ema52 and wk_high is not None:
+							if wk_high >= wk_ema52 or abs(wk_high - wk_ema52) / abs(wk_ema52) <= rej_tol:
+								recent_up_rejects += 1
+						# 下拒绝（支撑）：上方多次试图下破但未跌破
+						if wk_close > wk_ema52 and wk_low is not None:
+							if wk_low <= wk_ema52 or abs(wk_low - wk_ema52) / abs(wk_ema52) <= rej_tol:
+								recent_down_rejects += 1
+				# 定义 MACD 的方向偏好
+				macd_bias_up = (macd >= signal) and (hist is None or pre_hist is None or hist >= pre_hist)
+				macd_bias_down = (macd <= signal) and (hist is None or pre_hist is None or hist <= pre_hist)
+				# 若多次上拒绝且 MACD 偏空，则更偏向下行；若多次下拒绝且 MACD 偏多，则更偏向上行
+				if recent_up_rejects >= 2 and macd_bias_down:
+					score -= 2
+				if recent_down_rejects >= 2 and macd_bias_up:
+					score += 2
 				# 4) RSI 辅助
 				if rsi is not None:
 					if rsi >= 55:
@@ -231,17 +302,121 @@ class TF_DF():
 						near_macd = abs(macd - signal) <= (abs(price) * 0.00005 if price_valid else 0)
 						near_flat = near_ema52 and near_macd
 					# 7) 动态阈值 + 趋势记忆（更强粘滞：趋势中容忍小幅反分）
+					# 引入过去 N 根KLC 的趋势延续性来动态调整翻转阈值，并结合 EMA52 支撑/阻力触碰强化门槛
+					force_flip_down = False
+					force_flip_up = False
 					if near_flat:
 						trend = Chan_PRICE_TREND.FLAT
 					else:
-						if last_trend == Chan_PRICE_TREND.UP:
+						# 计算过去窗口的趋势一致性
-							# 仅当出现明显反向才翻转，否则维持UP
+						window = prev_klcs[-lookback_n:] if len(prev_klcs) > 0 else []
-							if score <= -2:
+						persist_up = 0
+						persist_down = 0
+						for wk in window:
+							if getattr(wk, 'trend', None) == Chan_PRICE_TREND.UP:
+								persist_up += 1
+							elif getattr(wk, 'trend', None) == Chan_PRICE_TREND.DOWN:
+								persist_down += 1
+						persist_ratio_up = (persist_up / len(window)) if len(window) > 0 else 0
+						persist_ratio_down = (persist_down / len(window)) if len(window) > 0 else 0
+						# 基准阈值
+						down_flip_threshold = -2
+						up_flip_threshold = 2
+						# 若最近多为UP，则从UP翻转需更强反向信号；同理对DOWN
+						if last_trend == Chan_PRICE_TREND.UP and persist_ratio_up >= 0.6:
+							down_flip_threshold = -3
+						elif last_trend == Chan_PRICE_TREND.DOWN and persist_ratio_down >= 0.6:
+							up_flip_threshold = 3
+						# EMA52 触碰强化门槛：UP时若出现支撑触碰，下翻更难；DOWN时若出现阻力触碰，上翻更难
+						if ema52_valid and price_valid:
+							low_v = getattr(klc, 'low', None)
+							high_v = getattr(klc, 'high', None)
+							if low_v is not None and high_v is not None and ema52 not in (None, 0):
+								touch_tol = 0.0015
+								near_support_touch = (price > ema52) and (abs(low_v - ema52) / abs(ema52) <= touch_tol)
+								near_resistance_touch = (price < ema52) and (abs(high_v - ema52) / abs(ema52) <= touch_tol)
+								if last_trend == Chan_PRICE_TREND.UP and near_support_touch:
+									# 强化维持UP：进一步降低向下翻转阈值
+									down_flip_threshold = min(down_flip_threshold - 1, -3)
+								if last_trend == Chan_PRICE_TREND.DOWN and near_resistance_touch:
+									# 强化维持DOWN：进一步提高向上翻转阈值
+									up_flip_threshold = max(up_flip_threshold + 1, 3)
+						# 7.1) 复合拐头信号：MACD/Signal 同向拐头 + hist 连续减弱 + 多次未能越过 EMA52
+						pre_macd = getattr(pre, 'macd', None) if pre else None
+						pre_signal = getattr(pre, 'signal', None) if pre else None
+						macd_slope = (macd - pre_macd) if (pre_macd is not None and macd is not None) else 0
+						signal_slope = (signal - pre_signal) if (pre_signal is not None and signal is not None) else 0
+						# hist 连续减弱（绝对值缩小）
+						hist_seq = []
+						for wk in prev_klcs[-2:]:
+							val = getattr(wk, 'macdhist', None)
+							if val is not None:
+								hist_seq.append(val)
+						if hist is not None:
+							hist_seq.append(hist)
+						weaken_steps = 0
+						for i in range(1, len(hist_seq)):
+							if abs(hist_seq[i]) < abs(hist_seq[i-1]):
+								weaken_steps += 1
+						# 近窗口对 EMA52 的"未能站上/跌破"统计（放宽窗口与条件）
+						window_ema = prev_klcs[-4:] if len(prev_klcs) > 0 else []
+						no_up_break = False
+						no_down_break = False
+						if ema52_valid:
+							# 未能有效上破：最近若干根收盘大多数不在 EMA52 上方，且高点多次触及/接近
+							cnt_touch_up = 0
+							cnt_close_above = 0
+							for wk in window_ema:
+								wk_close = getattr(wk, 'close', None)
+								wk_high = getattr(wk, 'high', None)
+								wk_ema = getattr(wk, 'ema52', None)
+								if wk_close is not None and wk_ema not in (None, 0):
+									if wk_close > wk_ema:
+										cnt_close_above += 1
+									if wk_high is not None and (wk_high >= wk_ema or abs(wk_high - wk_ema) / abs(wk_ema) <= 0.0015):
+										cnt_touch_up += 1
+							no_up_break = (cnt_close_above <= 1 and cnt_touch_up >= 1 and price <= ema52)
+							# 未能有效下破：最近若干根收盘大多数不在 EMA52 下方，且低点多次触及/接近
+							cnt_touch_down = 0
+							cnt_close_below = 0
+							for wk in window_ema:
+								wk_close = getattr(wk, 'close', None)
+								wk_low = getattr(wk, 'low', None)
+								wk_ema = getattr(wk, 'ema52', None)
+								if wk_close is not None and wk_ema not in (None, 0):
+									if wk_close < wk_ema:
+										cnt_close_below += 1
+									if wk_low is not None and (wk_low <= wk_ema or abs(wk_low - wk_ema) / abs(wk_ema) <= 0.0015):
+										cnt_touch_down += 1
+							no_down_break = (cnt_close_below <= 1 and cnt_touch_down >= 1 and price >= ema52)
+						# 若当前为UP趋势，出现明显拐头+hist减弱+未能上破EMA52，则加速看空
+						if last_trend == Chan_PRICE_TREND.UP and macd_slope < 0 and signal_slope < 0 and weaken_steps >= 1 and no_up_break and macd_bias_down:
+							score -= 3
+							down_flip_threshold = max(down_flip_threshold, 0)
+							force_flip_down = True
+						# 若当前为DOWN趋势，出现明显拐头+hist减弱+未能下破EMA52，则加速看多
+						if last_trend == Chan_PRICE_TREND.DOWN and macd_slope > 0 and signal_slope > 0 and weaken_steps >= 1 and no_down_break and macd_bias_up:
+							score += 3
+							up_flip_threshold = min(up_flip_threshold, 0)
+							force_flip_up = True
+						# 多次对 EMA52 的拒绝配合 MACD 逆向：加速反向翻转（降低相反方向阈值）
+						if recent_up_rejects >= 2 and macd_bias_down:
+							# 从 UP 向 DOWN 的翻转更容易
+							down_flip_threshold = max(down_flip_threshold, -1)
+						if recent_down_rejects >= 2 and macd_bias_up:
+							# 从 DOWN 向 UP 的翻转更容易
+							up_flip_threshold = min(up_flip_threshold, 1)
+						if force_flip_down:
+							trend = Chan_PRICE_TREND.DOWN
+						elif force_flip_up:
+							trend = Chan_PRICE_TREND.UP
+						elif last_trend == Chan_PRICE_TREND.UP:
+							if score <= down_flip_threshold:
 								trend = Chan_PRICE_TREND.DOWN
 							else:
 								trend = Chan_PRICE_TREND.UP
 						elif last_trend == Chan_PRICE_TREND.DOWN:
-							if score >= 2:
+							if score >= up_flip_threshold:
 								trend = Chan_PRICE_TREND.UP
 							else:
 								trend = Chan_PRICE_TREND.DOWN
@@ -256,14 +431,19 @@ class TF_DF():
 			except Exception:
 				trend = Chan_PRICE_TREND.UNKNOWN
 			# 写回趋势
+			if klc.end_time is None:
+				trend = Chan_PRICE_TREND.FLAT
 			if hasattr(klc, 'set_trend'):
 				klc.set_trend(trend)
 			else:
 				setattr(klc, 'trend', trend)
 			last_trend = trend
+			# 更新滑窗：仅向后看
+			prev_klcs.append(klc)
 			price_diff = klc.close - klc.pre.close if klc.pre else 0
-			#print(klc.start_time, klc.end_time, klc.close, klc.ema24, klc.ema52, klc.macd, klc.signal, klc.macdhist, klc.trend, price_diff)
+			#if klc.index > len(klc_list) - 10:
-			#print(klc.start_time, klc.end_time, klc.trend, price_diff)
+				#print(klc.start_time, klc.end_time, klc.close, klc.ema24, klc.ema52, klc.macd, klc.signal, klc.macdhist, klc.trend, price_diff, score)
+				#print(klc.start_time, klc.end_time, klc.trend, price_diff, score)
 		return klc_list
 	def cal_kl_data(self, dataframe:DataFrame):
 		fields = "time,open,high,low,close,volume"
@@ -300,8 +480,46 @@ class TF_DF():
 			if 'macd' in item:
 				klu.set_indicators(item)
 		return klu_list
-
+	def get_bi_list(self, dataframe):
-	def cal_klc_list(self, klu_list):
+		bi_list = self.cal_bi_list(self.get_klc_list(dataframe))
+		return bi_list
+	def get_kl_data(self, dataframe:DataFrame):
+		fields = "time,open,high,low,close,volume"
+		klu_list = []
+		last_klu = None
+		for i in range(0, len(dataframe)):
+			item = dataframe.iloc[i]
+			date = item['date']
+			o = item['open']
+			h = item['high']
+			l = item['low']
+			c = item['close']
+			v = item['volume']
+			#time_obj = date.fromtimestamp(date)
+			#date = date + timedelta(hours=8)
+			time_str = date.strftime('%Y-%m-%d %H:%M:%S')
+			item_data = [
+				time_str,
+				o,
+				h,
+				l,
+				c,
+				v
+			]
+			#klu =  KLU(self.create_item_dict(item_data, GetColumnNameFromFieldList(fields)))
+			klu = ChanKLU(time_str, o, h, l, c, v)
+			#print(klu.time, klu.open, klu.high, klu.low, klu.close, klu.volume)	
+			klu.set_idx(i)
+			klu_list.append(klu)
+			if last_klu:
+				last_klu.set_next(klu)
+				klu.set_pre(last_klu)
+			last_klu = klu
+			if 'macd' in item:
+				klu.set_indicators(item)
+		return klu_list
+	def get_klc_list(self, dataframe):
+		klu_list = self.get_klu_list(dataframe)
 		klc_list = []
 		last_klu = None
 		macd = ChanMACD(klu_list)
@@ -309,19 +527,33 @@ class TF_DF():
 		for klu in klu_list:
 			if len(klc_list) > 0:
 				last_klc = klc_list[-1]
-				included = last_klc.check_klu_included(klu)
+				if klu.exception:
-				if not included:
 					ddir = Chan_KLINE_DIR.DOWN
 					if last_klc.high < klu.high:
 						ddir = Chan_KLINE_DIR.UP
 					klc = ChanKLC(klu, index=len(klc_list), ddir=ddir)
+					klc.high = klu.close if klu.close > klu.open else klu.open
+					klc.low = klu.open if klu.close > klu.open else klu.close
 					klc_list.append(klc)
 					last_klc.set_next(klc)
 					klc.set_pre(last_klc)
 					last_klc.set_end_klu(last_klu)
 					klc.set_pre_fx()
+					#print(klu.time, klu.high, klu.low, klu.close, klu.open, klu.exception)
 				else:
-					last_klc.add_klu(klu)
+					included = last_klc.check_klu_included(klu)
+					if not included:
+						ddir = Chan_KLINE_DIR.DOWN
+						if last_klc.high < klu.high:
+							ddir = Chan_KLINE_DIR.UP
+						klc = ChanKLC(klu, index=len(klc_list), ddir=ddir)
+						klc_list.append(klc)
+						last_klc.set_next(klc)
+						klc.set_pre(last_klc)
+						last_klc.set_end_klu(last_klu)
+						klc.set_pre_fx()
+					else:
+						last_klc.add_klu(klu)
 			else:
 				ddir = Chan_KLINE_DIR.UP
 				if klu.open > klu.close:
@@ -332,7 +564,7 @@ class TF_DF():
 		klc_list = self.cal_trend(klc_list)
 		return klc_list
 
-	def cal_seg_list(self, bi_list):
+	def get_seg_list(self, bi_list):
 		seg_list = []
 		up_bi_list = []
 		down_bi_list = []
@@ -371,7 +603,7 @@ class TF_DF():
 									if last_down_sbi.has_fx_gap:
 										look_for_bottom = True
 										last_seg.pre_set_end_bi(bi_list[last_down_sbi.start_bi.index - 1])
-										seg = ChanSEG(last_down_sbi.start_bi, len(seg_list), Chan_SEG_DIR.DOWN)
+										seg = ChanSEG(last_down_sbi.start_bi, len(seg_list), Chan_SEG_DIR.DOWN, bi)
 										seg_list.append(seg)
 										last_seg.set_next(seg)
 										seg.set_pre(last_seg)
@@ -397,7 +629,7 @@ class TF_DF():
 											#print(bi.start_time, look_for_top, "UP 3")
 										else:
 											last_seg.set_end_bi(bi_list[last_down_sbi.start_bi.index - 1], bi)
-											seg = ChanSEG(last_down_sbi.start_bi, len(seg_list), Chan_SEG_DIR.DOWN)
+											seg = ChanSEG(last_down_sbi.start_bi, len(seg_list), Chan_SEG_DIR.DOWN, bi)
 											seg_list.append(seg)
 											last_seg.set_next(seg)
 											seg.set_pre(last_seg)
@@ -465,7 +697,7 @@ class TF_DF():
 									if last_up_sbi.has_fx_gap:
 										look_for_top = True
 										last_seg.pre_set_end_bi(bi_list[last_up_sbi.start_bi.index - 1])
-										seg = ChanSEG(last_up_sbi.start_bi, len(seg_list), Chan_SEG_DIR.UP)
+										seg = ChanSEG(last_up_sbi.start_bi, len(seg_list), Chan_SEG_DIR.UP, bi)
 										seg_list.append(seg)
 										last_seg.set_next(seg)
 										seg.set_pre(last_seg)
@@ -491,7 +723,7 @@ class TF_DF():
 											#print(bi.start_time, look_for_top, "DOWN 3")
 										else:
 											last_seg.set_end_bi(bi_list[last_up_sbi.start_bi.index - 1], bi)
-											seg = ChanSEG(last_up_sbi.start_bi, len(seg_list), Chan_SEG_DIR.UP)
+											seg = ChanSEG(last_up_sbi.start_bi, len(seg_list), Chan_SEG_DIR.UP, bi)
 											#print(last_up_sbi.start_bi.start_time)
 											last_seg.set_next(seg)
 											seg.set_pre(last_seg)
@@ -539,14 +771,14 @@ class TF_DF():
 			else:
 				if bi.check_overlap():
 					if bi.dir == Chan_BI_DIR.UP:
-						seg = ChanSEG(bi, len(seg_list), Chan_SEG_DIR.UP)
+						seg = ChanSEG(bi, len(seg_list), Chan_SEG_DIR.UP, bi)
 						last_up_bi = bi
 						last_up_sbi = ChanSBI(bi, len(up_sbi_list), bi.dir)
 						seg_list.append(seg)
 						last_seg = seg
 						#print(bi.start_time, 'Create first UP SEG')
 					else:
-						seg = ChanSEG(bi, len(seg_list), Chan_SEG_DIR.DOWN)
+						seg = ChanSEG(bi, len(seg_list), Chan_SEG_DIR.DOWN, bi)
 						last_down_bi = bi
 						last_down_sbi = ChanSBI(bi, len(down_sbi_list), bi.dir)
 						seg_list.append(seg)
@@ -573,7 +805,7 @@ class TF_DF():
 							# The confirmed
 							print("Last UP seg is broken, create a new seg. 1")
 							seg.pre_set_end_bi(bi_list[i])
-							seg = ChanSEG(bi_list[i+1], len(seg_list), Chan_SEG_DIR.DOWN)
+							seg = ChanSEG(bi_list[i+1], len(seg_list), Chan_SEG_DIR.DOWN, bi)
 							seg_list.append(seg)
 							last_seg = seg_list[-2]
 							if len(last_seg.bi_list) > 3:
@@ -585,7 +817,7 @@ class TF_DF():
 						if bi_list[i].low < last_seg_peak:
 							print("Last DOWN seg is broken, create a new seg. 1")
 							seg.pre_set_end_bi(bi_list[i])
-							seg = ChanSEG(bi_list[i+1], len(seg_list), Chan_SEG_DIR.UP)
+							seg = ChanSEG(bi_list[i+1], len(seg_list), Chan_SEG_DIR.UP, bi)
 							seg_list.append(seg)
 							last_seg = seg_list[-2]
 							if len(last_seg.bi_list) > 3:
@@ -603,12 +835,13 @@ class TF_DF():
 								if bi_list[i].high > last_seg_peak:
 									print("Last seg is broken, create a new seg. 2")
 									last_seg.pre_set_end_bi(bi_list[i-1])
-									seg = ChanSEG(bi_list[i], len(seg_list), Chan_SEG_DIR.UP)
+									seg = ChanSEG(bi_list[i], len(seg_list), Chan_SEG_DIR.UP, bi)
 									seg_list.append(seg)
 									last_seg = seg
 									last_seg_bi = bi_list[i]
 									break
 		"""
+		self.cal_bi_zs(seg_list)
 		return seg_list
 
 	def cal_bi_list(self, klc_list):
@@ -617,7 +850,14 @@ class TF_DF():
 		last_bottom = None
 		for klc in klc_list:
 			fx = self.check_fx(klc)
-			
+			if fx == Chan_FX_TYPE.TOP:
+				if last_bottom:
+					if self.check_top_fx(last_bottom, klc) == False:
+						fx = Chan_FX_TYPE.UNKNOWN
+			if fx == Chan_FX_TYPE.BOTTOM:
+				if last_top:
+					if self.check_bottom_fx(last_top, klc) == False:
+						fx = Chan_FX_TYPE.UNKNOWN
 			# Do nothing
 			if fx == Chan_FX_TYPE.UNKNOWN:
 				continue
@@ -665,6 +905,7 @@ class TF_DF():
 									#klc.set_state("20")
 									bi_list[-1].add_klc(klc)
 									klc.set_bi(bi_list[-1])
+									#klc.cal_invisible()
 									#klc.set_klc_fx_type(Chan_KLC_FX.TOP3)
 									#print(klc.start_time, klc.fx, "二类卖点Sell 1")
 								else:
@@ -789,6 +1030,7 @@ class TF_DF():
 									#klc.set_state("-20")
 									bi_list[-1].add_klc(klc)
 									klc.set_bi(bi_list[-1])
+									#klc.cal_invisible()
 									#klc.set_klc_fx_type(Chan_KLC_FX.BOTTOM3)
 									#print(last_bottom.start_time, last_bottom.end_time, "--------------------------------1")
 									#print(klc.start_time, klc.fx, "二类买点Buy 1")
@@ -917,11 +1159,25 @@ class TF_DF():
 		#for index in range(0, 10):
 			#print(bi_list[index].start_time, bi_list[index].start_klc.start_time, bi_list[index].dir)
 		return bi_list
+	def check_top_fx(self, last_bottom, klc):
+		if last_bottom.high > klc.pre.low or last_bottom.high > klc.next.low:
+			return False
+		return True
+	
+	def check_bottom_fx(self, last_top, klc):
+		if last_top.low < klc.pre.high or last_top.low < klc.next.high:
+			return False
+		return True
 
-	def get_decimal(self, value):
+	def cal_bi_zs(self, seg_list):
-		return Decimal("{:.2f}".format(value))
+		bi_zs_list = []
+		for seg in seg_list:
+			zs_list = seg.cal_bi_zs()
+			if len(zs_list) > 0:
+				bi_zs_list.append(zs_list)
+		return bi_zs_list
 
-	def cal_zs_list(self, bi_list, seg_list):
+	def get_zs_list(self, bi_list, seg_list):
 		zs_list = []
 		bsp_list = []
 		if len(seg_list) > 3:
@@ -940,7 +1196,6 @@ class TF_DF():
 							zd = max(seg.low, seg.next.low, seg.next.next.low)
 							gg = max(seg.high, seg.next.high, seg.next.next.high)
 							dd = min(seg.low, seg.next.low, seg.next.next.low)
-							ddir = Chan_ZS_DIR.UP
 							ddir = None
 							if last_zs:
 								if zg < last_zs.zd:
@@ -981,7 +1236,7 @@ class TF_DF():
 							# SEG is not in ZS
 							if seg.is_sure:
 								if ((seg.low > last_zs.zg and seg.high > last_zs.zg) or (seg.high < last_zs.zd and seg.low < last_zs.zd)):
-									last_zs.set_end_klc(last_zs.last_bi_in.end_klc, seg.sure_time, bi_out_count, seg)
+									last_zs.set_end_klc(seg.pre.end_bi.end_klc, seg.sure_time, bi_out_count, seg.pre)
 									bi_out_count = 0
 									#print(seg.start_bi.start_klc.start_time)
 									first_bi_out = None
@@ -1095,4 +1350,72 @@ class TF_DF():
 													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)
 													bsp_list.append(bsp)
 		#self.print_zs(zs_list)
-		return zs_list
+		return zs_list
+
+	def get_klu_list(self, dataframe):
+		klu_list = self.get_kl_data(dataframe)
+		return self.cal_klu_pattern(klu_list)
+	def cal_klu_pattern(self, klu_list):
+		"""
+		计算裸K的pattern - 只识别反转形态
+		"""
+		if not klu_list or len(klu_list) < 3:
+			return klu_list
+			
+		for i, klu in enumerate(klu_list):
+			# 单根K线反转模式识别
+			self._detect_single_reversal_pattern(klu)
+			if klu.pattern != Chan_KLU_PATTERN.UNKNOWN:
+				print(klu.time, klu.pattern)
+		return klu_list
+	
+	def _detect_single_reversal_pattern(self, klu):
+		"""检测单根K线反转模式"""
+		body = abs(klu.close - klu.open)
+		upper_shadow = klu.high - max(klu.close, klu.open)
+		lower_shadow = min(klu.close, klu.open) - klu.low
+		total_range = klu.high - klu.low
+		
+		# 避免除零
+		if total_range == 0:
+			return
+		
+		body_ratio = body / total_range
+		upper_ratio = upper_shadow / total_range
+		lower_ratio = lower_shadow / total_range
+		
+		# 锤子线/上吊线 - 反转信号
+		if lower_ratio / body_ratio >= 2:
+			# 锤子线：底部反转，需要前面一段
+			if klu.close > klu.open and klu.pre and klu.pre.close < klu.pre.open:
+				klu.set_pattern(Chan_KLU_PATTERN.HAMMER)  # 底部反转
+			# 上吊线：顶部反转，需要前一根是上涨趋势
+			elif klu.close < klu.open and klu.pre and klu.pre.close > klu.pre.open:
+				klu.set_pattern(Chan_KLU_PATTERN.HANGING_MAN)  # 顶部反转
+		
+		# 倒锤子线/射击之星 - 反转信号
+		elif upper_ratio / body_ratio >= 2:
+			# 倒锤子线：底部反转，需要前一根是下跌趋势
+			if klu.close > klu.open and klu.pre and klu.pre.close < klu.pre.open:
+				klu.set_pattern(Chan_KLU_PATTERN.INVERTED_HAMMER)  # 底部反转
+			# 射击之星：顶部反转，需要前一根是上涨趋势
+			elif klu.close < klu.open and klu.pre and klu.pre.close > klu.pre.open:
+				klu.set_pattern(Chan_KLU_PATTERN.SHOOTING_STAR)  # 顶部反转
+		
+		# 十字星 - 反转信号
+		elif body_ratio <= 0.1:
+			if upper_ratio > 0.4 and lower_ratio > 0.4:
+				klu.set_pattern(Chan_KLU_PATTERN.LONG_LEGGED_DOJI)  # 强烈反转信号
+			elif upper_ratio > 0.4 and lower_ratio <= 0.1:
+				# 墓碑十字星：顶部反转，需要前一根是上涨趋势
+				if klu.pre and klu.pre.close > klu.pre.open:
+					klu.set_pattern(Chan_KLU_PATTERN.GRAVESTONE_DOJI)  # 顶部反转
+			elif lower_ratio > 0.4 and upper_ratio <= 0.1:
+				# 蜻蜓十字星：底部反转，需要前一根是下跌趋势
+				if klu.pre and klu.pre.close < klu.pre.open:
+					klu.set_pattern(Chan_KLU_PATTERN.DRAGONFLY_DOJI)  # 底部反转
+			else:
+				klu.set_pattern(Chan_KLU_PATTERN.DOJI)  # 一般反转信号
+	
+	def get_decimal(self, value):
+		return Decimal("{:.2f}".format(value))

algorithm_comparison_test.py

@@ -1,223 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-KLU与KLC分型强度算法一致性测试
-验证两种算法在相同数据下是否产生一致的结果
-"""
-
-from ChanKLU import ChanKLU
-from ChanKLC import ChanKLC
-from ChanEnum import Chan_FX_TYPE, Chan_KLINE_DIR
-import pandas as pd
-from datetime import datetime, timedelta
-
-def create_test_data():
-    """创建测试用的K线数据"""
-    test_cases = [
-        # 测试用例1：标准顶分型
-        {
-            'name': '标准顶分型',
-            'data': [
-                {'open': 100, 'high': 102, 'low': 99, 'close': 101, 'volume': 1000},   # K1
-                {'open': 101, 'high': 105, 'low': 100, 'close': 103, 'volume': 1500}, # K2 (顶分型中心)
-                {'open': 103, 'high': 104, 'low': 98, 'close': 99, 'volume': 1200},   # K3
-            ]
-        },
-        # 测试用例2：标准底分型
-        {
-            'name': '标准底分型',
-            'data': [
-                {'open': 100, 'high': 102, 'low': 99, 'close': 101, 'volume': 1000},   # K1
-                {'open': 101, 'high': 103, 'low': 95, 'close': 97, 'volume': 1500},   # K2 (底分型中心)
-                {'open': 97, 'high': 104, 'low': 96, 'close': 102, 'volume': 1200},    # K3
-            ]
-        },
-        # 测试用例3：强势顶分型（放量+下影线）
-        {
-            'name': '强势顶分型',
-            'data': [
-                {'open': 100, 'high': 102, 'low': 99, 'close': 101, 'volume': 1000},   # K1
-                {'open': 101, 'high': 108, 'low': 100, 'close': 102, 'volume': 2500}, # K2 (强顶分型)
-                {'open': 102, 'high': 103, 'low': 95, 'close': 96, 'volume': 1800},   # K3 (大阴线确认)
-            ]
-        }
-    ]
-    return test_cases
-
-def setup_klu_chain(data_list):
-    """设置KLU链"""
-    klus = []
-    base_time = datetime.now()
-    
-    for i, data in enumerate(data_list):
-        time_str = (base_time + timedelta(minutes=i)).strftime("%Y-%m-%d %H:%M:%S")
-        klu = ChanKLU(time_str, data['open'], data['high'], data['low'], data['close'], data['volume'])
-        klu.set_idx(i)
-        
-        # 设置基础技术指标
-        indicators = {
-            'ma5': data['close'] + (i-1) * 0.1,
-            'ma10': data['close'] + (i-1) * 0.05,
-            'rsi': 50 + (i % 3 - 1) * 15,
-            'macd': (i % 3 - 1) * 0.01,
-            'macdhist': (i % 2) * 0.005,
-            'volume_ratio': 1.0 + (i % 2) * 0.3
-        }
-        klu.set_indicators(indicators)
-        klus.append(klu)
-    
-    # 建立前后关系
-    for i in range(len(klus)):
-        if i > 0:
-            klus[i].set_pre(klus[i-1])
-        if i < len(klus) - 1:
-            klus[i].set_next(klus[i+1])
-    
-    return klus
-
-def setup_klc_chain(data_list):
-    """设置KLC链（基于KLU）"""
-    klus = setup_klu_chain(data_list)
-    klcs = []
-    
-    # 为简化测试，假设每个KLU对应一个KLC（无包含关系处理）
-    for i, klu in enumerate(klus):
-        klc = ChanKLC(klu, i, Chan_KLINE_DIR.UP)
-        klc.set_end_klu(klu)
-        klcs.append(klc)
-    
-    # 建立前后关系
-    for i in range(len(klcs)):
-        if i > 0:
-            klcs[i].set_pre(klcs[i-1])
-        if i < len(klcs) - 1:
-            klcs[i].set_next(klcs[i+1])
-    
-    # 设置分型类型
-    if len(klcs) >= 3:
-        middle_klc = klcs[1]
-        if (middle_klc.high > klcs[0].high and middle_klc.high > klcs[2].high):
-            middle_klc.set_fx(Chan_FX_TYPE.TOP)
-        elif (middle_klc.low < klcs[0].low and middle_klc.low < klcs[2].low):
-            middle_klc.set_fx(Chan_FX_TYPE.BOTTOM)
-    
-    return klcs
-
-def compare_algorithms(test_cases):
-    """对比KLU和KLC算法"""
-    
-    print("=" * 80)
-    print("KLU与KLC分型强度算法一致性测试")
-    print("=" * 80)
-    
-    for case in test_cases:
-        print(f"\n🔍 测试用例: {case['name']}")
-        print("-" * 50)
-        
-        # 准备数据
-        klus = setup_klu_chain(case['data'])
-        klcs = setup_klc_chain(case['data'])
-        
-        if len(klus) >= 3 and len(klcs) >= 3:
-            middle_klu = klus[1]
-            middle_klc = klcs[1]
-            
-            # KLU分析
-            middle_klu.update_realtime_analysis()
-            klu_fx_type = middle_klu.fx_type
-            klu_strength = middle_klu.fx_strength
-            klu_confirmed = middle_klu.fx_confirmed
-            
-            # KLC分析 
-            klc_fx_type = middle_klc.fx
-            klc_strength_raw = middle_klc.cal_fx_strength()  # -3到3
-            klc_strength_converted = int((klc_strength_raw + 3) * 100 / 6)  # 转换为0-100
-            
-            # 输出对比结果
-            print(f"K线数据: {case['data'][1]}")
-            print(f"\nKLU算法结果:")
-            print(f"  分型类型: {klu_fx_type}")
-            print(f"  分型强度: {klu_strength}")
-            print(f"  是否确认: {klu_confirmed}")
-            
-            print(f"\nKLC算法结果:")
-            print(f"  分型类型: {klc_fx_type}")
-            print(f"  分型强度(原始): {klc_strength_raw}")
-            print(f"  分型强度(转换): {klc_strength_converted}")
-            
-            # 一致性检查
-            type_consistent = (klu_fx_type == klc_fx_type)
-            strength_diff = abs(klu_strength - klc_strength_converted)
-            strength_consistent = strength_diff <= 10  # 允许10分以内的差异
-            
-            print(f"\n一致性检查:")
-            print(f"  分型类型一致: {'✅' if type_consistent else '❌'}")
-            print(f"  强度差异: {strength_diff}分 {'✅' if strength_consistent else '❌'}")
-            
-            if not type_consistent or not strength_consistent:
-                print(f"  ⚠️  算法结果不一致!")
-            else:
-                print(f"  ✅ 算法结果一致")
-        else:
-            print("❌ 数据不足，无法进行对比")
-
-def detailed_strength_analysis():
-    """详细的强度分析对比"""
-    print("\n" + "=" * 80)
-    print("详细强度分析对比")
-    print("=" * 80)
-    
-    # 创建一个明确的强分型案例
-    strong_top_data = [
-        {'open': 100, 'high': 101, 'low': 99, 'close': 100, 'volume': 1000},
-        {'open': 100, 'high': 110, 'low': 99, 'close': 102, 'volume': 3000},  # 强顶分型
-        {'open': 102, 'high': 103, 'low': 92, 'close': 93, 'volume': 2000},   # 强确认
-        {'open': 93, 'high': 94, 'low': 90, 'close': 91, 'volume': 1500},     # 继续下跌
-        {'open': 91, 'high': 92, 'low': 88, 'close': 89, 'volume': 1200},     # 进一步确认
-    ]
-    
-    klus = setup_klu_chain(strong_top_data)
-    
-    if len(klus) >= 5:
-        target_klu = klus[1]  # 目标分型K线
-        
-        print(f"分析目标: 第2根K线 (索引1)")
-        print(f"K线数据: {strong_top_data[1]}")
-        
-        # 更新分析
-        target_klu.update_realtime_analysis()
-        
-        print(f"\n分型检测结果:")
-        print(f"  分型类型: {target_klu.fx_type}")
-        print(f"  分型确认: {target_klu.fx_confirmed}")
-        print(f"  最终强度: {target_klu.fx_strength}")
-        
-        # 显示中间计算过程（需要重新调用以获取详细信息）
-        if target_klu.fx_confirmed:
-            print(f"\n强度计算过程:")
-            is_bi_end = target_klu._check_if_bi_ending_fx()
-            post_confirmation = target_klu._check_post_fx_confirmation()
-            fx_quality = target_klu._check_fx_quality()
-            
-            print(f"  笔终结判断: {is_bi_end}")
-            print(f"  后续确认: {post_confirmation}")
-            print(f"  分型质量: {fx_quality}")
-            
-            raw_score = is_bi_end + post_confirmation + fx_quality
-            final_raw = max(-3, min(3, raw_score))
-            converted_score = int((final_raw + 3) * 100 / 6)
-            
-            print(f"  原始总分: {raw_score} -> {final_raw}")
-            print(f"  转换分数: {converted_score}")
-
-if __name__ == "__main__":
-    # 运行测试
-    test_cases = create_test_data()
-    compare_algorithms(test_cases)
-    
-    # 详细分析
-    detailed_strength_analysis()
-    
-    print("\n" + "=" * 80)
-    print("测试完成！")
-    print("=" * 80) 

chanlun_trading.log

@@ -1,39 +0,0 @@
-2025-04-18 20:25:03,767 - INFO - Fetched 500 K-lines for BTC/USDT (5m)
-2025-04-18 20:25:06,074 - INFO - Fetched 200 K-lines for BTC/USDT (30m)
-2025-04-18 20:25:06,104 - INFO - Merged K-lines: 500 -> 404
-2025-04-18 20:25:06,105 - INFO - Detected 49 top fractals and 48 bottom fractals
-2025-04-18 20:25:06,111 - INFO - Detected 80 strokes
-2025-04-18 20:25:06,111 - INFO - Detected 22 segments
-2025-04-18 20:25:06,111 - INFO - Detected 0 pivots
-2025-04-18 20:25:06,112 - INFO - Detected 27 top fractals and 29 bottom fractals
-2025-04-18 20:25:06,115 - INFO - Detected 45 strokes
-2025-04-18 20:25:06,115 - INFO - 30m trend: down
-2025-04-18 20:25:06,119 - INFO - Detected 4 buy signals and 0 sell signals
-2025-04-18 20:25:06,233 - ERROR - Chart plotting failed: x and y must have same first dimension, but have shapes (404,) and (2,)
-2025-04-18 20:25:06,233 - ERROR - Main function failed: x and y must have same first dimension, but have shapes (404,) and (2,)
-2025-04-18 20:27:05,455 - INFO - Fetched 500 K-lines for BTC/USDT (5m)
-2025-04-18 20:27:08,491 - INFO - Fetched 200 K-lines for BTC/USDT (30m)
-2025-04-18 20:27:08,521 - INFO - Merged K-lines: 500 -> 404
-2025-04-18 20:27:08,522 - INFO - Detected 49 top fractals and 48 bottom fractals
-2025-04-18 20:27:08,528 - INFO - Detected 80 strokes
-2025-04-18 20:27:08,528 - INFO - Detected 22 segments
-2025-04-18 20:27:08,528 - INFO - Detected 0 pivots
-2025-04-18 20:27:08,529 - INFO - Detected 27 top fractals and 29 bottom fractals
-2025-04-18 20:27:08,532 - INFO - Detected 45 strokes
-2025-04-18 20:27:08,532 - INFO - 30m trend: down
-2025-04-18 20:27:08,537 - INFO - Detected 4 buy signals and 0 sell signals
-2025-04-18 20:27:08,647 - ERROR - Chart plotting failed: x and y must have same first dimension, but have shapes (404,) and (2,)
-2025-04-18 20:27:08,647 - ERROR - Main function failed: x and y must have same first dimension, but have shapes (404,) and (2,)
-2025-04-18 20:28:51,970 - INFO - Fetched 500 K-lines for BTC/USDT (5m)
-2025-04-18 20:28:53,861 - INFO - Fetched 200 K-lines for BTC/USDT (30m)
-2025-04-18 20:28:53,897 - INFO - Merged K-lines: 500 -> 404
-2025-04-18 20:28:53,898 - INFO - Detected 49 top fractals and 48 bottom fractals
-2025-04-18 20:28:53,904 - INFO - Detected 80 strokes
-2025-04-18 20:28:53,904 - INFO - Detected 22 segments
-2025-04-18 20:28:53,904 - INFO - Detected 0 pivots
-2025-04-18 20:28:53,905 - INFO - Detected 27 top fractals and 29 bottom fractals
-2025-04-18 20:28:53,908 - INFO - Detected 45 strokes
-2025-04-18 20:28:53,908 - INFO - 30m trend: down
-2025-04-18 20:28:53,913 - INFO - Detected 4 buy signals and 0 sell signals
-2025-04-18 20:28:53,913 - ERROR - Chart plotting failed: Wrong type for data, in make_addplot()
-2025-04-18 20:28:53,913 - ERROR - Main function failed: Wrong type for data, in make_addplot()

config/EMA_Pattern.json

@@ -0,0 +1,83 @@
+{
+    "$schema": "https://schema.freqtrade.io/schema.json",
+    "max_open_trades": 1,
+    "stake_currency": "USDT",
+    "stake_amount": "unlimited",
+    "tradable_balance_ratio": 0.99,
+    "fiat_display_currency": "USD",
+    "dry_run": true,
+    "db_url": "sqlite:///tradesv3.ema_pattern.sqlite",
+    "dry_run_wallet": 1000,
+    "cancel_open_orders_on_exit": true,
+    "trading_mode": "futures",
+    "margin_mode": "isolated",
+    "can_short" : true,
+    "timeframe" : "15m",
+    "process_only_new_candles" : false,
+    "unfilledtimeout": {
+        "entry": 1,
+        "exit": 1,
+        "exit_timeout_count": 5,
+        "unit": "minutes"
+    },
+    "entry_pricing": {
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1,
+        "price_last_balance": 0.0,
+        "check_depth_of_market": {
+            "enabled": false,
+            "bids_to_ask_delta": 1
+        }
+    },
+    "exit_pricing":{
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1
+    },
+    "exchange": {
+        "name": "binance",
+        "key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
+        "secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
+        "ccxt_config": {},
+        "ccxt_async_config": {},
+        "pair_whitelist": [
+            "BTC/USDT:USDT"
+        ],
+        "pair_blacklist": [
+            "BNB/.*"
+        ]
+    },
+    "pairlists": [
+        {
+            "method": "StaticPairList",
+            "number_assets": 1,
+            "sort_key": "quoteVolume",
+            "min_value": 0,
+            "refresh_period": 1800
+        }
+    ],
+    "telegram": {
+        "enabled": false,
+        "token": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y",
+        "chat_id": "580807463"
+    },
+    "api_server": {
+        "enabled": true,
+        "listen_ip_address": "0.0.0.0",
+        "listen_port": 8888,
+        "verbosity": "error",
+        "enable_openapi": false,
+        "jwt_secret_key": "14d3510740e2c39a973a8895f1aa2704d98d08b86170260085709fa5ea48251d",
+        "ws_token": "dtKKDnafBrX4icq_ZCw7acJTahTK4h_yvg",
+        "CORS_origins": [],
+        "username": "freqtrader",
+        "password": "FreqTrade007"
+    },
+    "bot_name": "freqtrade",
+    "initial_state": "running",
+    "force_entry_enable": false,
+    "internals": {
+        "process_throttle_secs": 2
+    }
+}

strategies/BB9033.py

@@ -22,7 +22,7 @@ logger = logging.getLogger(__name__)
 # freqtrade plot-dataframe --strategy BB9033 --datadir user_data/data/binance -c ./user_data/ChanLun_SOL_30.json --timerange=20250309-
 
 # freqtrade trade -c ./user_data/Chan/config/ChanLun_BTC_30.json --strategy BB9033 --strategy-path ./user_data/Chan/strategies
-# freqtrade backtesting -c ./user_data/Chan/config/ChanLun_BTC_30.json --strategy BB9033 --strategy-path ./user_data/Chan/strategies --timerange=20250623-
+# freqtrade backtesting -c ./user_data/Chan/config/ChanLun_BTC_30.json --strategy BB9033 --strategy-path ./user_data/Chan/strategies --timerange=20251023-
 # freqtrade download-data -c ./user_data/Chan/config/ChanLun_BTC_30.json -t 1m --pairs BTC/USDT:USDT --timerange=20250501-
 # freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --spaces roi stoploss --strategy BB9033 --strategy-path ./user_data/Chan/strategies -c ./user_data/Chan/config/ChanLun_BTC_30.json -e 200 --timerange=20250201-20250401
 

strategies/EMA_Pattern.py

@@ -0,0 +1,115 @@
+# --- Do not remove these libs ---
+from statistics import median
+from freqtrade.strategy import IStrategy
+from technical.util import resample_to_interval, resampled_merge
+from pandas import DataFrame
+import talib.abstract as ta
+from technical import qtpylib
+
+### Now you can use logger.info('asfd') to log
+# freqtrade plot-dataframe --strategy EMA_Pattern --datadir user_data/data/binance -c ./user_data/Chan/EMA_Pattern.json --timerange=20250309-
+# freqtrade backtesting -c ./user_data/Chan/config/EMA_Pattern.json --strategy EMA_Pattern --strategy-path ./user_data/Chan/strategies --timerange=20251030-
+# freqtrade download-data -c ./user_data/Chan/config/EMA_Pattern.json -t 1m 3m 5m 15m 30m 1h  --pairs BTC/USDT:USDT --timerange=20250405-
+# freqtrade download-data -c ./user_data/Chan/config/EMA_Pattern.json -t 1m 1h 1d 1M --pairs BTC/USDT --timerange=20170101-
+# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --spaces roi --strategy EMA_Pattern --strategy-path ./user_data/Chan/strategies -c ./user_data/Chan/config/EMA_Pattern.json -e 200 --timerange=20250201-20250901
+# freqtrade edge -c ./user_data/Chan/config/EMA_Pattern.json --strategy EMA_Pattern --strategy-path ./user_data/Chan/strategies --timerange 20250721-20250901
+# freqtrade plot-dataframe -c ./user_data/Chan/config/EMA_Pattern.json --strategy EMA_Pattern --strategy-path ./user_data/Chan/strategies --timerange 20250721-20250901
+
+class EMA_Pattern(IStrategy):
+	time1h = 1440
+	can_short: bool = True
+	timeframe: str = "1m"
+	process_only_new_candles: bool = False
+
+	# ROI 与止损可根据需要在配置中覆盖
+	minimal_roi = {
+		"60": 0.005,
+		"30": 0.01,
+		"0": 0.02,
+	}
+	stoploss: float = -0.30
+	# 需要的历史K线数量（包含EMA等指标预热）
+	startup_candle_count: int = 200
+
+	def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+		if dataframe is None or dataframe.empty:
+			return dataframe
+		dataframe = self.add_indicators(dataframe)
+		return dataframe
+	def add_indicators(self, dataframe: DataFrame) -> DataFrame:
+		macd = ta.MACD(dataframe, timeperiod=12, fastperiod=12, slowperiod=26, signalperiod=9)
+		dataframe['macd'] = macd['macd']
+		dataframe['macdsignal'] = macd['macdsignal']
+		dataframe['macdhist'] = macd['macdhist']
+		dataframe['ema6'] = ta.EMA(dataframe, timeperiod=6)
+		dataframe['ema12'] = ta.EMA(dataframe, timeperiod=12)
+		dataframe['ema24'] = ta.EMA(dataframe, timeperiod=24)
+		dataframe['ema52'] = ta.EMA(dataframe, timeperiod=52)
+		dataframe['adx'] = ta.ADX(dataframe, timeperiod=14)
+		dataframe['strong_trend'] = dataframe['adx'] > 25
+		dataframe['UP_Pattern'] = (dataframe['ema6'] > dataframe['ema12']) & (dataframe['ema12'] > dataframe['ema24']) & (dataframe['ema24'] > dataframe['ema52'])
+		dataframe['DOWN_Pattern'] = (dataframe['ema6'] < dataframe['ema12']) & (dataframe['ema12'] < dataframe['ema24']) & (dataframe['ema24'] < dataframe['ema52'])
+		dataframe['UP_Confirm'] = (dataframe['ema6'] > dataframe['ema6'].shift(1)) & (dataframe['ema12'] > dataframe['ema12'].shift(1)) & (dataframe['ema24'] > dataframe['ema24'].shift(1)) & (dataframe['ema52'] > dataframe['ema52'].shift(1))
+		dataframe['DOWN_Confirm'] = (dataframe['ema6'] < dataframe['ema6'].shift(1)) & (dataframe['ema12'] < dataframe['ema12'].shift(1)) & (dataframe['ema24'] < dataframe['ema24'].shift(1)) & (dataframe['ema52'] < dataframe['ema52'].shift(1))
+		dataframe['EMA52_Cross_EMA24_UP'] = (dataframe['ema52'] < dataframe['ema24']) & (dataframe['ema52'].shift(1) > dataframe['ema24'].shift(1))
+		dataframe['EMA52_Cross_EMA24_DOWN'] = (dataframe['ema52'] > dataframe['ema24']) & (dataframe['ema52'].shift(1) < dataframe['ema24'].shift(1))
+		dataframe['Price_Above_EMA52'] = (dataframe['close'] > dataframe['ema52'])
+		dataframe['Price_Below_EMA52'] = (dataframe['close'] < dataframe['ema52'])
+		dataframe['MACD_Above_Zero'] = (dataframe['macd'] > 0) & (dataframe['macdsignal'] > 0)
+		dataframe['MACD_Below_Zero'] = (dataframe['macd'] < 0) & (dataframe['macdsignal'] < 0)
+		dataframe['BUY_END'] = dataframe['close'] < dataframe['ema52']
+		dataframe['SELL_END'] = dataframe['close'] > dataframe['ema52']
+		return dataframe
+	def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+		if dataframe is None or dataframe.empty:
+			return dataframe
+		dataframe.loc[
+			(
+				(dataframe['UP_Pattern']) &
+				(dataframe['UP_Confirm']) &
+				(dataframe['Price_Above_EMA52']) &
+				(dataframe['MACD_Above_Zero']) &
+				(dataframe['EMA52_Cross_EMA24_UP']) &
+				(dataframe['strong_trend'])
+			),
+			["enter_long", "enter_tag"],
+		] = (1, "ema_up_trend")
+
+		dataframe.loc[
+			(
+				(dataframe['DOWN_Pattern']) &
+				(dataframe['DOWN_Confirm']) &
+				(dataframe['Price_Below_EMA52']) &
+				(dataframe['MACD_Below_Zero']) &
+				(dataframe['EMA52_Cross_EMA24_DOWN']) &
+				(dataframe['strong_trend'])
+			),
+			["enter_short", "enter_tag"],
+		] = (1, "ema_down_trend")
+
+		return dataframe
+
+	def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+		if dataframe is None or dataframe.empty:
+			return dataframe
+		dataframe.loc[
+			(
+				(dataframe['BUY_END']) |
+				(dataframe['DOWN_Pattern']) |
+				(dataframe['EMA52_Cross_EMA24_DOWN'])
+			),
+			["exit_long", "exit_tag"],
+		] = (1, "ema_long_exit")
+
+		dataframe.loc[
+			(
+				(dataframe['SELL_END']) |
+				(dataframe['UP_Pattern']) |
+				(dataframe['EMA52_Cross_EMA24_UP'])
+			),
+			["exit_short", "exit_tag"],
+		] = (1, "ema_short_exit")
+
+		return dataframe
+	def get_ticker_indicator(self):
+		return int(self.timeframe[:-1])