refactor: 缠论引擎迁入 chan/ 分层解耦，指标外置

将核心结构、指标与分析拆到 chan/{core,indicators,analysis,pipeline}；根目录保留兼容 shim；strategies 改为从 chan 包导入；买卖点经 bsp_macd 与 MACD 接合。 Co-authored-by: Cursor <cursoragent@cursor.com>
2026-08-03 14:47:13 +08:00
parent 2e905e7238
commit 2c1232555e
90 changed files with 9067 additions and 8535 deletions
@@ -0,0 +1,188 @@
+import warnings
+
+# 抑制 Docker 内 technical.util 的 fillna/ffill/bfill 的 pandas FutureWarning（pandas 2.x 弃用 object 静默 downcast）
+warnings.filterwarnings(
+    "ignore",
+    category=FutureWarning,
+    message=".*Downcasting object dtype arrays on \\.fillna.*",
+)
+
+from datetime import timedelta
+from pandas import DataFrame
+from ..core.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_MACD_STATE, Chan_PRICE_TREND, Chan_KLU_PATTERN
+from ..core.ChanKLU import ChanKLU
+from ..core.ChanKLC import ChanKLC
+from ..core.ChanBI import ChanBI
+from ..core.ChanSBI import ChanSBI
+from ..core.ChanSEG import ChanSEG
+from ..core.ChanZS import ChanZS
+from ..core.ChanBSP import ChanBSP
+import talib.abstract as ta
+import pandas as pd
+from technical.util import resample_to_interval
+from decimal import Decimal
+import numpy as np
+from ..analysis.ChanMACD import ChanMACD
+from .TF_DF import TF_DF
+from ..analysis.ChanZone import StructureZone, StructureZoneConfig, analyze_structure_zones
+
+class ChanLun():
+	def __init__(self):
+		self.time2m = 2
+		self.time3m = 3
+		self.time5m = 5
+		self.time10m = 10
+		self.time20m = 20
+		self.time_m_intervals = [2, 3, 5, 10, 20]
+		self.time_m_symbols = ['2m', '3m', '5m', '10m', '20m']
+		self.time30m = 30
+		self.time45m = 45
+		self.time_m15_intervals = [30, 45]
+		self.time_m15_symbols = ['30m', '45m']
+		self.time2h = 2*60
+		self.time4h = 4*60
+		self.time6h = 6*60
+		self.time8h = 8*60
+		self.time12h = 12*60
+		self.time16h = 16*60
+		self.time_h_intervals = [2*60, 4*60, 6*60, 8*60, 12*60, 16*60]
+		self.time_h_symbols = ['2h', '4h', '6h', '8h', '12h', '16h']
+		self.time2d = 2*24*60
+		self.time3d = 3*24*60
+		self.time_d_intervals = [2*24*60, 3*24*60]
+		self.time_d_symbols	 = ['2d', '3d']
+		self.time1w = 7*24*60
+		self.time2w = 14*24*60
+		self.time_w_intervals = [14*24*60]
+		self.time_w_symbols = ['2w']
+		self.time2M = 2*30*24*60
+		self.time3M = 3*30*24*60
+		self.time6M = 6*30*24*60
+		self.time1y = 12*30*24*60
+		self.time_M_intervals = [2*30*24*60, 3*30*24*60, 6*30*24*60, 12*30*24*60]
+		self.time_M_symbols	 = ['2M', '3M', '6M', '1y']
+		self.time_symbols = ['1m', '2m', '3m', '5m', '10m', '15m', '20m', '30m', '45m','1h', '2h', '4h', '6h', '8h', '12h', '16h', '1d', '2d', '3d']
+		self.tf_df_dict = {}
+		self.ema_symbols = ['5m', '15m', '30m', '45m', '1h', '2h', '4h', '8h', '12h', '1d', '2d', '3d']
+		self.tf_df = TF_DF()
+	def init_data(self, dataframe, intervals, timeframes):
+		for index in range(0, len(intervals)):
+			timeframe = timeframes[index]
+			interval = intervals[index]
+			self.tf_df_dict[timeframe] = TF_DF(dataframe, interval, timeframe)
+	def init_dataframes(self, dataframe_m=None, dataframe_15m=None, dataframe_h=None, dataframe_d=None, dataframe_w=None, dataframe_M=None):
+		self.tf_df_dict = {}
+		if dataframe_m is not None:
+			self.tf_df_dict['1m'] = TF_DF(dataframe_m, 1, '1m')
+			self.init_data(dataframe_m, self.time_m_intervals, self.time_m_symbols)
+		if dataframe_15m is not None:
+			self.tf_df_dict['15m'] = TF_DF(dataframe_15m, 1, '15m')
+			self.init_data(dataframe_15m, self.time_m15_intervals, self.time_m15_symbols)
+		if dataframe_h is not None:
+			self.tf_df_dict['1h'] = TF_DF(dataframe_h, 1, '1h')
+			self.init_data(dataframe_h, self.time_h_intervals, self.time_h_symbols)
+		if dataframe_d is not None:
+			self.tf_df_dict['1d'] = TF_DF(dataframe_d, 1, '1d')
+			self.init_data(dataframe_d, self.time_d_intervals, self.time_d_symbols)
+		if dataframe_w is not None and False:
+			self.tf_df_dict['1w'] = TF_DF(dataframe_w, 1, '1w')
+			self.init_data(dataframe_w, self.time_w_intervals, self.time_w_symbols)
+		if dataframe_M is not None and False:
+			self.tf_df_dict['1M'] = TF_DF(dataframe_M, 1, '1M')
+			self.init_data(dataframe_M, self.time_M_intervals, self.time_M_symbols)
+	def get_ema52_dict(self):
+		if len(self.tf_df_dict) > 0:
+			return {key: self.tf_df_dict[key].get_ema52() for key in self.ema_symbols}
+		return None
+	def get_ema24_dict(self):
+		if len(self.tf_df_dict) > 0:
+			return {key: self.tf_df_dict[key].get_ema24() for key in self.ema_symbols}
+		return None
+	def get_current_klc_dict(self):
+		if len(self.tf_df_dict) > 0:
+			return {key: self.tf_df_dict[key].get_current_klc() for key in self.ema_symbols}
+		return None
+	def get_tf_df_by_timeframe(self, timeframe):
+		if timeframe in self.tf_df_dict:
+			return self.tf_df_dict[timeframe]
+		return None
+	def check_price_ema52(self, price):
+		key_list = []
+		if len(self.tf_df_dict) > 0:
+			ema52_dict = self.get_ema52_dict()
+			for key in self.ema_symbols:
+				if ema52_dict[key] is not None:
+					if abs(price - ema52_dict[key]) < 100:
+						key_list.append(key)
+		return key_list
+	def get_ema_bsp(self, long_tf='1h', short_tf='15m'):
+		if long_tf in self.tf_df_dict and short_tf in self.tf_df_dict:
+			long_df = self.tf_df_dict[long_tf]
+			short_df = self.tf_df_dict[short_tf]
+			return long_df.get_ema_bsp(short_df)
+		return None
+
+
+ 
+ 
+ 
+	def get_bsp_state(self, dataframe):
+		return self.tf_df.get_bsp_state(dataframe)
+
+	def get_structure_zones(self, current_price=None, config=None):
+		if config is None:
+			config = StructureZoneConfig()
+		return analyze_structure_zones(
+			self.tf_df_dict,
+			self.ema_symbols,
+			current_price=current_price,
+			config=config,
+		)
+ 	# TF_DF methods ------------------------------------------
+	def get_ema_state(self, dataframe):
+		return self.tf_df.get_ema_state(dataframe)
+	def get_klu_state(self, dataframe):
+		return self.tf_df.get_klu_state(dataframe)
+	def check_fx(self, klc):
+		return self.tf_df.check_fx(klc)
+	def add_indicators1(self, df):
+		return self.tf_df.add_indicators(df)
+	def get_bi_list(self, dataframe):
+		return self.tf_df.get_bi_list(dataframe)
+	def get_kl_data(self, dataframe:DataFrame):
+		return self.tf_df.cal_kl_data(dataframe)
+	def cal_volume_ratio(self, dataframe, window=10):
+		return self.tf_df.cal_volume_ratio(dataframe, window)
+	def calculate_seg_zs(self, bi_list, seg_list):
+		return self.get_seg_zs_list(bi_list, seg_list)
+	def get_seg_list(self, bi_list):
+		return self.tf_df.get_seg_list(bi_list)
+	def cal_trend(self, klc_list):
+		return self.tf_df.cal_trend(klc_list)
+	def check_top_fx(self, last_bottom, klc):
+		return self.tf_df.check_top_fx(last_bottom, klc)
+	def check_bottom_fx(self, last_top, klc):
+		return self.tf_df.check_bottom_fx(last_top, klc)
+	def cal_bi_list(self, klc_list):
+		return self.tf_df.cal_bi_list(klc_list)
+	def find_first_bsp(self, bi_list, bi_zs_list):
+		return self.tf_df.find_first_bsp(bi_list, bi_zs_list)
+	def find_second_bsp(self, bi_list, first_bsp_list):
+		return self.tf_df.find_second_bsp(bi_list, first_bsp_list)
+	def find_all_bsp(self, bi_list, bi_zs_list):
+		return self.tf_df.find_all_bsp(bi_list, bi_zs_list)
+	def get_zs_list(self, bi_list, seg_list):
+		return self.tf_df.get_zs_list(bi_list, seg_list)
+	def cal_bi_zs(self, seg_list):
+		return self.tf_df.cal_bi_zs(seg_list)
+	def cal_bi_zs_list(self, bi_list):
+		#return self.tf_df.cal_bi_zs(bi_list)
+		return self.tf_df.cal_bi_zs_list(bi_list)
+	def get_bi_zs_list(self, bi_list):
+		return self.tf_df.get_bi_zs_list(bi_list)
+	def get_decimal(self, value):
+		return Decimal("{:.2f}".format(value))
+	def get_klc_list(self, klu_list):
+		return self.tf_df.get_klc_list(klu_list)
+	def get_klu_list(self, dataframe):
+		return self.tf_df.cal_klu_pattern(self.get_kl_data(dataframe))