refactor: 缠论引擎包化与 Web 分层（ECR-001）

将根目录引擎迁入 chanlun/ 并保留兼容 shim；拆分 TF_DF 与 web 服务；
前端模块化；strategies 改用 chanlun 导入；补充 ESS 文档与 golden 回归。

Co-authored-by: Cursor <cursoragent@cursor.com>

ChanLun.py

@@ -1,188 +1,3 @@
-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 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 ChanKLU import ChanKLU
-from ChanKLC import ChanKLC
-from ChanBI import ChanBI
-from ChanSBI import ChanSBI
-from ChanSEG import ChanSEG
-from ChanZS import ChanZS
-from 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 ChanMACD import ChanMACD
-from TF_DF import TF_DF
-from 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))
+"""兼容 shim — 请优先 from chanlun import ..."""
+from chanlun.pipeline.orchestrator import ChanLun  # noqa: F401
+from chanlun.pipeline.timeframe import TF_DF  # noqa: F401