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>

web/api/analyze.py

@@ -0,0 +1,660 @@
+"""分析 API。"""
+from flask import Blueprint, jsonify, request
+from services.runtime import *  # noqa: F403
+from services import runtime as R
+
+bp = Blueprint("analyze", __name__)
+
+@bp.route('/api/analyze')
+def analyze():
+	"""分析接口"""
+	symbol = request.args.get('symbol', 'SOL/USDT:USDT')
+	timeframe = request.args.get('timeframe', '5m')
+	
+	# 验证交易对不为空
+	if not symbol or symbol.strip() == '':
+		return jsonify({'error': '交易对不能为空'})
+	
+	# 获取时间范围参数
+	start_time = request.args.get('start_time')
+	end_time = request.args.get('end_time')
+	
+	# 获取客户端请求的时区
+	client_timezone = request.args.get('timezone', 'Asia/Shanghai')
+	
+	# 获取分形元素时间周期与次次周期
+	element_timeframe = request.args.get('element_timeframe')
+	sub_sub_timeframe = request.args.get('sub_sub_timeframe')
+	
+	# 获取是否只需要分形元素数据的参数
+	elements_only_param = request.args.get('elements_only')
+	elements_only = elements_only_param == 'true'
+	
+	# 验证小周期是否小于主周期
+	if element_timeframe and not is_smaller_or_equal_timeframe(element_timeframe, timeframe):
+		return jsonify({'error': '分形元素时间周期必须小于或等于主图表时间周期'})
+	# 验证次次周期是否小于等于次周期
+	if sub_sub_timeframe and element_timeframe and not is_smaller_or_equal_timeframe(sub_sub_timeframe, element_timeframe):
+		return jsonify({'error': '次次周期必须小于或等于次周期'})
+	
+	# 获取数据
+	df = get_kl_data(symbol, timeframe, start_time=start_time, end_time=end_time)
+	if df is None:
+		return jsonify({'error': '获取数据失败'})
+	
+	if len(df) == 0:
+		return jsonify({'error': '所选时间范围内没有数据'})
+	
+	# 使用客户端指定的时区
+	client_tz = timezone(client_timezone)
+	
+	# 如果只需要分形元素数据而不需要主周期数据，则初始化一个空结果
+	result = {
+		'timezone': client_timezone
+	}
+	
+	# 如果不是只需要分形元素数据，则添加主周期数据
+	if not elements_only:
+		# 添加技术指标（包括布林带）
+		df = add_indicators(df)
+		
+		# 进行缠论分析
+		analysis_result = analyze_chan(df, symbol, timeframe)
+		
+		# 计算MACD
+		macd_data = calculate_macd(df)
+		
+		# 基于已有 KLC 列表生成趋势标记（不做额外计算）
+		klc_trend = []
+		try:
+			for klc in analysis_result.get('klc_list', []):
+				trend_val = getattr(klc, 'trend', None)
+				t_obj = getattr(klc, 'end_time', None) or getattr(klc, 'start_time', None)
+				if trend_val is None or t_obj is None:
+					continue
+				# 统一成字符串：UP/DOWN/FLAT/UNKNOWN
+				trend_name = str(trend_val)
+				if '.' in trend_name:
+					trend_name = trend_name.split('.')[-1]
+				time_str = format_time_safely(t_obj, client_tz)
+				if time_str:
+					klc_trend.append({'time': time_str, 'trend': trend_name})
+		except Exception:
+			klc_trend = []
+
+		# 添加主周期分析结果到返回数据
+		result.update({
+			'kline_data': clean_dataframe_for_json(df).to_dict('records'),
+			'klc_list': [{
+				'date': klc.end_time if isinstance(klc.end_time, str) else klc.end_time.astimezone(client_tz).isoformat(),
+				'open': float(klc.open),
+				'high': float(klc.high),
+				'low': float(klc.low),
+				'close': float(klc.close),
+				'volume': float(klc.volume) if hasattr(klc, 'volume') else 0,
+				'direction': str(klc.dir).replace('Chan_KLINE_DIR.', ''),
+				'fx_type': str(klc.fx).replace('Chan_FX_TYPE.', ''),
+				'klc_fx_type': str(klc.klc_fx_type).replace('Chan_KLC_FX.', ''),
+				'trend': str(klc.trend).replace('Chan_PRICE_TREND.', '')
+			} for klc in analysis_result['klc_list'] if hasattr(klc, 'end_time') and klc.end_time],
+			'bi_list': [{
+				'start_time': bi.start_klc.end_time if isinstance(bi.start_klc.end_time, str) else bi.start_klc.end_time.astimezone(client_tz).isoformat(),
+				'end_time': (bi.end_klc.end_time if isinstance(bi.end_klc.end_time, str) else bi.end_klc.end_time.astimezone(client_tz).isoformat()) if bi.end_klc else None,
+				'sure_time': format_time_safely(bi.sure_time, client_tz) if bi.sure_time else None,
+				'start_price': bi.start_klc.low if convert_direction(bi.dir) == 1 else bi.start_klc.high,
+				'end_price': bi.end_klc.high if convert_direction(bi.dir) == 1 else bi.end_klc.low if bi.end_klc else None,
+				'direction': convert_direction(bi.dir),
+				'macd_div': float(bi.macd_div) if hasattr(bi, 'macd_div') else 0
+			} for bi in analysis_result['bi_list'] if bi.is_sure],
+			# 添加未完成笔列表
+			'uncompleted_bi_list': [{
+				'start_time': bi.start_klc.end_time if isinstance(bi.start_klc.end_time, str) else bi.start_klc.end_time.astimezone(client_tz).isoformat(),
+				'end_time': bi.end_time,  # 未完成笔没有结束时间
+				'sure_time': format_time_safely(bi.sure_time, client_tz) if bi.sure_time else None,
+				'start_price': bi.start_klc.low if convert_direction(bi.dir) == 1 else bi.start_klc.high,
+				'end_price':  bi.end_klc.low if convert_direction(bi.dir) == 1 else bi.end_klc.high,  # 未完成笔没有结束价格
+				'direction': convert_direction(bi.dir),
+				'macd_div': float(bi.macd_div) if hasattr(bi, 'macd_div') else 0
+			} for bi in analysis_result['bi_list'] if not bi.is_sure],
+			'seg_list': [{
+				'start_time': seg.start_bi.start_klc.end_time if isinstance(seg.start_bi.start_klc.end_time, str) else seg.start_bi.start_klc.end_time.astimezone(client_tz).isoformat(),
+				'end_time': (seg.end_bi.end_klc.end_time if isinstance(seg.end_bi.end_klc.end_time, str) else seg.end_bi.end_klc.end_time.astimezone(client_tz).isoformat()) if seg.end_bi else None,
+				'sure_time': format_time_safely(seg.sure_time, client_tz) if seg.sure_time else None,
+				'start_price': seg.start_bi.start_klc.low if convert_direction(seg.dir) == 1 else seg.start_bi.start_klc.high,
+				'end_price': seg.end_bi.end_klc.high if convert_direction(seg.dir) == 1 else seg.end_bi.end_klc.low if seg.end_bi else None,
+				'direction': convert_direction(seg.dir)
+			} for seg in analysis_result['seg_list'] if seg.is_sure],
+			# 添加未完成线段列表
+			'uncompleted_seg_list': get_uncompleted_seg_list(analysis_result['seg_list'], client_tz),
+			'zs_list': [{
+				'start_time': zs.start_klc.end_time if isinstance(zs.start_klc.end_time, str) else zs.start_klc.end_time.astimezone(client_tz).isoformat(),
+				'end_time': (zs.end_klc.end_time if isinstance(zs.end_klc.end_time, str) else zs.end_klc.end_time.astimezone(client_tz).isoformat()) if zs.end_klc else None,
+				'zg': zs.zg,
+				'zd': zs.zd,
+				'gg': zs.gg,
+				'dd': zs.dd,
+				'is_sure': zs.is_sure  # 添加中枢是否完成的标志
+			} for zs in analysis_result['zs_list'] if zs.is_sure],
+			# 添加主周期BI中枢列表（已完成）
+			'bi_zs_list': [{
+				'start_time': (
+					(zs.start_klc.end_time if isinstance(zs.start_klc.end_time, str) else zs.start_klc.end_time.astimezone(client_tz).isoformat())
+					if getattr(zs.start_klc, 'end_time', None) else
+					(zs.start_klc.start_time if isinstance(zs.start_klc.start_time, str) else zs.start_klc.start_time.astimezone(client_tz).isoformat())
+				),
+				'end_time': (zs.end_time if isinstance(zs.end_time, str) else zs.end_time.astimezone(client_tz).isoformat()) if getattr(zs, 'end_time', None) else None,
+				'zg': zs.zg,
+				'zd': zs.zd,
+				'gg': zs.gg,
+				'dd': zs.dd,
+				'is_sure': bool(getattr(zs, 'is_sure', False))
+			} for zs in analysis_result.get('bi_zs_list', []) if getattr(zs, 'is_sure', False)],
+			# 添加未完成中枢列表
+			'uncompleted_zs_list': [{
+				'start_time': zs.start_klc.end_time if isinstance(zs.start_klc.end_time, str) else zs.start_klc.end_time.astimezone(client_tz).isoformat(),
+				'end_time': None,  # 未完成中枢没有结束时间
+				'zg': zs.zg,
+				'zd': zs.zd,
+				'gg': zs.gg,
+				'dd': zs.dd,
+				'is_sure': zs.is_sure  # 未完成中枢的is_sure为False
+			} for zs in analysis_result['zs_list'] if not zs.is_sure],
+			# 添加未完成BI中枢列表
+			'uncompleted_bi_zs_list': [{
+				'start_time': (
+					(zs.start_klc.end_time if isinstance(zs.start_klc.end_time, str) else zs.start_klc.end_time.astimezone(client_tz).isoformat())
+					if getattr(zs.start_klc, 'end_time', None) else
+					(zs.start_klc.start_time if isinstance(zs.start_klc.start_time, str) else zs.start_klc.start_time.astimezone(client_tz).isoformat())
+				),
+				'end_time': None,
+				'zg': zs.zg,
+				'zd': zs.zd,
+				'gg': zs.gg,
+				'dd': zs.dd,
+				'is_sure': bool(getattr(zs, 'is_sure', False))
+			} for zs in analysis_result.get('bi_zs_list', []) if not getattr(zs, 'is_sure', False)],
+
+			'macd': macd_data,
+			# 添加布林带数据
+			'bollinger': {
+				'upper': df['bb_upper'].tolist(),
+				'middle': df['bb_middle'].tolist(),
+				'lower': df['bb_lower'].tolist()
+			},
+			'element_bollinger': {
+				'upper': df['element_bb_upper'].tolist(),
+				'middle': df['element_bb_middle'].tolist(),
+				'lower': df['element_bb_lower'].tolist()
+			},
+			# 添加ATR数据
+			'atr': df['atr'].tolist(),
+			# 添加K线分型信息
+			'klc_fx_info': [{
+				'time': format_time_safely(point['time'], client_tz),
+				'start_time': format_time_safely(point['start_time'], client_tz),
+				'end_time': format_time_safely(point['end_time'], client_tz),
+				'price': float(point['price']),
+				'fx_type': point['fx_type'],
+				'is_bottom': bool(point['is_bottom']),
+				'fx_strength': float(point['fx_strength']),           # 分型强度分数
+				'fx_strength_level': str(point['fx_strength_level']), # 分型强度等级
+				'is_strong_fx': bool(point['is_strong_fx']),          # 是否为强分型
+				# 分型框（虚线矩形）用到的高低价
+				'high': float(point['high']) if point.get('high') is not None else None,
+				'low': float(point['low']) if point.get('low') is not None else None
+			} for point in analysis_result['klc_fx_info']],
+			# 添加ChanMACD分析数据
+			'chan_macd': serialize_chan_macd_data(analysis_result.get('chan_macd', {}), client_tz),
+			# 添加多时间周期EMA52数据
+			'ema52_dict': analysis_result.get('ema52_dict', {}),
+			# 直接输出KLC趋势标记（使用已有trend字段）
+			'klc_trend': klc_trend,
+			# 添加主周期买卖点列表
+			# 注意：部分枚举在转为字符串时可能形如 "Chan_BSP_TYPE.BSP1(1)"，
+			# 这里进行健壮的解析，确保前端拿到的始终是 "BSP1" / "BUY" 这种简洁形式，
+			# 以便与前端的 BSP_STYLE 键（如 "BSP1_BUY"）正确匹配。
+			'bsp_list': [{
+				'time': format_time_safely(bsp.end_time, client_tz),
+				'price': float(bsp.klc.low if 'BUY' in str(bsp.dir) else bsp.klc.high),
+			# -- 规范化 type 名称，例如：
+			#   "Chan_BSP_TYPE.BSP1"        -> "BSP1"
+			#   "Chan_BSP_TYPE.BSP1(1)"     -> "BSP1"
+			#   "BSP1"                      -> "BSP1"
+				'type': (
+					lambda raw: (
+						(raw.split('.')[-1] if '.' in raw else raw).split('(')[0]
+					)
+				)(str(bsp.type)),
+			# -- 规范化 dir 名称，例如：
+			#   "Chan_BSP_DIR.BUY"          -> "BUY"
+			#   "Chan_BSP_DIR.BUY(1)"       -> "BUY"
+			#   "BUY"                       -> "BUY"
+				'dir': (
+					lambda raw: (
+						(raw.split('.')[-1] if '.' in raw else raw).split('(')[0]
+					)
+				)(str(bsp.dir)),
+				'is_sure': bool(bsp.is_sure),
+				'sure_time': format_time_safely(bsp.sure_time, client_tz) if bsp.sure_time else None,
+				'zs_count': int(bsp.zs_count) if hasattr(bsp, 'zs_count') else 0
+			} for bsp in analysis_result.get('bsp_list', [])]
+		})
+		
+	
+	# 如果有指定分形元素时间周期，获取小周期数据
+	if element_timeframe:
+		# 获取小周期数据，使用与主周期相同的时间范围
+		element_df = get_kl_data(symbol, element_timeframe, start_time=start_time, end_time=end_time)
+		
+		if element_df is not None and len(element_df) > 0:
+			# 添加小周期技术指标（包括布林带）
+			element_df = add_indicators(element_df)
+			
+			# 对小周期数据进行缠论分析
+			element_analysis = analyze_chan(element_df, symbol, element_timeframe)
+			
+			# 计算小周期MACD数据
+			element_macd_data = calculate_macd(element_df)
+			
+			# 组装小周期 KLC 趋势（仅提取已有 trend，不做重算）
+			try:
+				element_klc_trend = []
+				for klc in element_analysis.get('klc_list', []):
+					trend_val = getattr(klc, 'trend', None)
+					t_obj = getattr(klc, 'end_time', None) or getattr(klc, 'start_time', None)
+					if trend_val is None or t_obj is None:
+						continue
+					trend_name = str(trend_val)
+					if '.' in trend_name:
+						trend_name = trend_name.split('.')[-1]
+					time_str = format_time_safely(t_obj, client_tz)
+					if time_str:
+						element_klc_trend.append({'time': time_str, 'trend': trend_name})
+			except Exception:
+				element_klc_trend = []
+
+			# 添加小周期分析结果到返回数据
+			result['element_timeframe'] = element_timeframe
+			result['element_macd'] = element_macd_data  # 添加小周期MACD数据
+			
+			# 添加小周期布林带数据
+			result['element_bollinger'] = {
+				'upper': element_df['bb_upper'].tolist(),
+				'middle': element_df['bb_middle'].tolist(),
+				'lower': element_df['bb_lower'].tolist()
+			}
+			result['element_element_bollinger'] = {
+				'upper': element_df['element_bb_upper'].tolist(),
+				'middle': element_df['element_bb_middle'].tolist(),
+				'lower': element_df['element_bb_lower'].tolist()
+			}
+			
+			# 添加小周期ATR数据
+			result['element_atr'] = element_df['atr'].tolist()
+			
+			result['element_bi_list'] = [{
+				'start_time': bi.start_klc.end_time if isinstance(bi.start_klc.end_time, str) else bi.start_klc.end_time.astimezone(client_tz).isoformat(),
+				'end_time': (bi.end_klc.end_time if isinstance(bi.end_klc.end_time, str) else bi.end_klc.end_time.astimezone(client_tz).isoformat()) if bi.end_klc else None,
+				'sure_time': format_time_safely(bi.sure_time, client_tz) if bi.sure_time else None,
+				'start_price': bi.start_klc.low if convert_direction(bi.dir) == 1 else bi.start_klc.high,
+				'end_price': bi.end_klc.high if convert_direction(bi.dir) == 1 else bi.end_klc.low if bi.end_klc else None,
+				'direction': convert_direction(bi.dir),
+				'macd_div': float(bi.macd_div) if hasattr(bi, 'macd_div') else 0
+			} for bi in element_analysis['bi_list'] if bi.is_sure]
+			# 添加次周期未完成笔列表
+			result['element_uncompleted_bi_list'] = [{
+				'start_time': bi.start_klc.end_time if isinstance(bi.start_klc.end_time, str) else bi.start_klc.end_time.astimezone(client_tz).isoformat(),
+				'end_time': bi.end_time,  # 未完成笔没有结束时间
+				'sure_time': format_time_safely(bi.sure_time, client_tz) if bi.sure_time else None,
+				'start_price': bi.start_klc.low if convert_direction(bi.dir) == 1 else bi.start_klc.high,
+				'end_price': bi.end_klc.low if convert_direction(bi.dir) == 1 else bi.end_klc.high,  # 未完成笔没有结束价格
+				'direction': convert_direction(bi.dir),
+				'macd_div': float(bi.macd_div) if hasattr(bi, 'macd_div') else 0
+			} for bi in element_analysis['bi_list'] if not bi.is_sure]
+			
+			# 添加小周期K线数据
+			result['element_kline_data'] = clean_dataframe_for_json(element_df).to_dict('records')
+			
+			# 添加小周期KLC列表
+			result['element_klc_list'] = [{
+				'date': klc.end_time if isinstance(klc.end_time, str) else klc.end_time.astimezone(client_tz).isoformat(),
+				'open': float(klc.open),
+				'high': float(klc.high),
+				'low': float(klc.low),
+				'close': float(klc.close),
+				'volume': float(klc.volume) if hasattr(klc, 'volume') else 0,
+				'direction': str(klc.dir).replace('Chan_KLINE_DIR.', ''),
+				'fx_type': str(klc.fx).replace('Chan_FX_TYPE.', ''),
+				'klc_fx_type': str(klc.klc_fx_type).replace('Chan_KLC_FX.', ''),
+				'trend': str(klc.trend).replace('Chan_PRICE_TREND.', '')
+			} for klc in element_analysis['klc_list'] if hasattr(klc, 'end_time') and klc.end_time]
+			
+			result['element_seg_list'] = [{
+				'start_time': seg.start_bi.start_klc.end_time if isinstance(seg.start_bi.start_klc.end_time, str) else seg.start_bi.start_klc.end_time.astimezone(client_tz).isoformat(),
+				'end_time': (seg.end_bi.end_klc.end_time if isinstance(seg.end_bi.end_klc.end_time, str) else seg.end_bi.end_klc.end_time.astimezone(client_tz).isoformat()) if seg.end_bi else None,
+				'sure_time': format_time_safely(seg.sure_time, client_tz) if seg.sure_time else None,
+				'start_price': seg.start_bi.start_klc.low if convert_direction(seg.dir) == 1 else seg.start_bi.start_klc.high,
+				'end_price': seg.end_bi.end_klc.high if convert_direction(seg.dir) == 1 else seg.end_bi.end_klc.low if seg.end_bi else None,
+				'direction': convert_direction(seg.dir)
+			} for seg in element_analysis['seg_list'] if seg.is_sure]
+			# 添加次周期未完成线段列表
+			result['element_uncompleted_seg_list'] = get_uncompleted_seg_list(element_analysis['seg_list'], client_tz)
+			
+			result['element_zs_list'] = [{
+				'start_time': zs.start_klc.end_time if isinstance(zs.start_klc.end_time, str) else zs.start_klc.end_time.astimezone(client_tz).isoformat(),
+				'end_time': (zs.end_klc.end_time if isinstance(zs.end_klc.end_time, str) else zs.end_klc.end_time.astimezone(client_tz).isoformat()) if zs.end_klc else None,
+				'zg': zs.zg,
+				'zd': zs.zd,
+				'gg': zs.gg,
+				'dd': zs.dd,
+				'is_sure': zs.is_sure  # 添加中枢是否完成的标志
+			} for zs in element_analysis['zs_list'] if zs.end_klc]
+			
+			result['element_uncompleted_zs_list'] = [{
+				'start_time': zs.start_klc.end_time if isinstance(zs.start_klc.end_time, str) else zs.start_klc.end_time.astimezone(client_tz).isoformat(),
+				'end_time': None,  # 未完成中枢没有结束时间
+				'zg': zs.zg,
+				'zd': zs.zd,
+				'gg': zs.gg,
+				'dd': zs.dd,
+				'is_sure': zs.is_sure  # 未完成中枢的is_sure为False
+			} for zs in element_analysis['zs_list'] if not zs.is_sure]
+
+			# 添加次周期 BI 中枢（已完成/未完成）
+			result['element_bi_zs_list'] = [{
+				'start_time': (
+					(zs.start_klc.end_time if isinstance(zs.start_klc.end_time, str) else zs.start_klc.end_time.astimezone(client_tz).isoformat())
+					if getattr(zs.start_klc, 'end_time', None) else
+					(zs.start_klc.start_time if isinstance(zs.start_klc.start_time, str) else zs.start_klc.start_time.astimezone(client_tz).isoformat())
+				),
+				'end_time': (zs.end_time if isinstance(zs.end_time, str) else zs.end_time.astimezone(client_tz).isoformat()) if getattr(zs, 'end_time', None) else None,
+				'zg': zs.zg,
+				'zd': zs.zd,
+				'gg': zs.gg,
+				'dd': zs.dd,
+				'is_sure': bool(getattr(zs, 'is_sure', False))
+			} for zs in element_analysis.get('bi_zs_list', []) if getattr(zs, 'is_sure', False)]
+			result['element_uncompleted_bi_zs_list'] = [{
+				'start_time': (
+					(zs.start_klc.end_time if isinstance(zs.start_klc.end_time, str) else zs.start_klc.end_time.astimezone(client_tz).isoformat())
+					if getattr(zs.start_klc, 'end_time', None) else
+					(zs.start_klc.start_time if isinstance(zs.start_klc.start_time, str) else zs.start_klc.start_time.astimezone(client_tz).isoformat())
+				),
+				'end_time': None,
+				'zg': zs.zg,
+				'zd': zs.zd,
+				'gg': zs.gg,
+				'dd': zs.dd,
+				'is_sure': bool(getattr(zs, 'is_sure', False))
+			} for zs in element_analysis.get('bi_zs_list', []) if not getattr(zs, 'is_sure', False)]
+			
+			
+			# 添加小周期分型信息
+			result['element_klc_fx_info'] = [{
+				'time': format_time_safely(point['time'], client_tz),
+				'start_time': format_time_safely(point['start_time'], client_tz),
+				'end_time': format_time_safely(point['end_time'], client_tz),
+				'price': float(point['price']),
+				'fx_type': point['fx_type'],
+				'is_bottom': bool(point['is_bottom']),
+				'fx_strength': float(point['fx_strength']),           # 分型强度分数
+				'fx_strength_level': str(point['fx_strength_level']), # 分型强度等级
+				'is_strong_fx': bool(point['is_strong_fx']),          # 是否为强分型
+				# 分型框（虚线矩形）用到的高低价
+				'high': float(point['high']) if point.get('high') is not None else None,
+				'low': float(point['low']) if point.get('low') is not None else None
+			} for point in element_analysis['klc_fx_info']]
+
+			# 添加次周期ChanMACD分析数据
+			result['element_chan_macd'] = serialize_chan_macd_data(element_analysis.get('chan_macd', {}), client_tz)
+			
+			# 添加小周期 KLC 趋势标记
+			result['element_klc_trend'] = element_klc_trend
+			
+			# 添加次周期买卖点列表
+			result['element_bsp_list'] = [{
+				'time': format_time_safely(bsp.end_time, client_tz),
+				'price': float(bsp.klc.low if str(bsp.dir) == 'Chan_BSP_DIR.BUY' else bsp.klc.high),
+				'type': str(bsp.type).replace('Chan_BSP_TYPE.', ''),
+				'dir': str(bsp.dir).replace('Chan_BSP_DIR.', ''),
+				'is_sure': bool(bsp.is_sure),
+				'sure_time': format_time_safely(bsp.sure_time, client_tz) if bsp.sure_time else None,
+				'zs_count': int(bsp.zs_count) if hasattr(bsp, 'zs_count') else 0
+			} for bsp in element_analysis.get('bsp_list', [])]
+
+			# 次次周期：仅当已指定次周期且次次周期有效时获取
+			if sub_sub_timeframe and is_smaller_or_equal_timeframe(sub_sub_timeframe, element_timeframe):
+				sub_sub_df = get_kl_data(symbol, sub_sub_timeframe, start_time=start_time, end_time=end_time)
+				if sub_sub_df is not None and len(sub_sub_df) > 0:
+					sub_sub_df = add_indicators(sub_sub_df)
+					sub_sub_analysis = analyze_chan(sub_sub_df, symbol, sub_sub_timeframe)
+					result['sub_sub_timeframe'] = sub_sub_timeframe
+					result['sub_sub_kline_data'] = clean_dataframe_for_json(sub_sub_df).to_dict('records')
+					result['sub_sub_atr'] = sub_sub_df['atr'].tolist()
+					result['sub_sub_macd'] = calculate_macd(sub_sub_df)
+					result['sub_sub_bi_list'] = [{
+						'start_time': bi.start_klc.end_time if isinstance(bi.start_klc.end_time, str) else bi.start_klc.end_time.astimezone(client_tz).isoformat(),
+						'end_time': (bi.end_klc.end_time if isinstance(bi.end_klc.end_time, str) else bi.end_klc.end_time.astimezone(client_tz).isoformat()) if bi.end_klc else None,
+						'sure_time': format_time_safely(bi.sure_time, client_tz) if bi.sure_time else None,
+						'start_price': bi.start_klc.low if convert_direction(bi.dir) == 1 else bi.start_klc.high,
+						'end_price': bi.end_klc.high if convert_direction(bi.dir) == 1 else bi.end_klc.low if bi.end_klc else None,
+						'direction': convert_direction(bi.dir),
+						'macd_div': float(bi.macd_div) if hasattr(bi, 'macd_div') else 0
+					} for bi in sub_sub_analysis['bi_list'] if bi.is_sure]
+					result['sub_sub_uncompleted_bi_list'] = [{
+						'start_time': bi.start_klc.end_time if isinstance(bi.start_klc.end_time, str) else bi.start_klc.end_time.astimezone(client_tz).isoformat(),
+						'end_time': bi.end_time,
+						'sure_time': format_time_safely(bi.sure_time, client_tz) if bi.sure_time else None,
+						'start_price': bi.start_klc.low if convert_direction(bi.dir) == 1 else bi.start_klc.high,
+						'end_price': bi.end_klc.low if convert_direction(bi.dir) == 1 else bi.end_klc.high,
+						'direction': convert_direction(bi.dir),
+						'macd_div': float(bi.macd_div) if hasattr(bi, 'macd_div') else 0
+					} for bi in sub_sub_analysis['bi_list'] if not bi.is_sure]
+					# 次次周期 KLC 列表
+					result['sub_sub_klc_list'] = [{
+						'date': klc.end_time if isinstance(klc.end_time, str) else klc.end_time.astimezone(client_tz).isoformat(),
+						'open': float(klc.open),
+						'high': float(klc.high),
+						'low': float(klc.low),
+						'close': float(klc.close),
+						'volume': float(klc.volume) if hasattr(klc, 'volume') else 0,
+						'direction': str(klc.dir).replace('Chan_KLINE_DIR.', ''),
+						'fx_type': str(klc.fx).replace('Chan_FX_TYPE.', ''),
+						'klc_fx_type': str(klc.klc_fx_type).replace('Chan_KLC_FX.', ''),
+						'trend': str(klc.trend).replace('Chan_PRICE_TREND.', '')
+					} for klc in sub_sub_analysis.get('klc_list', []) if hasattr(klc, 'end_time') and klc.end_time]
+
+					result['sub_sub_seg_list'] = [{
+						'start_time': seg.start_bi.start_klc.end_time if isinstance(seg.start_bi.start_klc.end_time, str) else seg.start_bi.start_klc.end_time.astimezone(client_tz).isoformat(),
+						'end_time': (seg.end_bi.end_klc.end_time if isinstance(seg.end_bi.end_klc.end_time, str) else seg.end_bi.end_klc.end_time.astimezone(client_tz).isoformat()) if seg.end_bi else None,
+						'sure_time': format_time_safely(seg.sure_time, client_tz) if seg.sure_time else None,
+						'start_price': seg.start_bi.start_klc.low if convert_direction(seg.dir) == 1 else seg.start_bi.start_klc.high,
+						'end_price': seg.end_bi.end_klc.high if convert_direction(seg.dir) == 1 else seg.end_bi.end_klc.low if seg.end_bi else None,
+						'direction': convert_direction(seg.dir)
+					} for seg in sub_sub_analysis['seg_list'] if seg.is_sure]
+					result['sub_sub_uncompleted_seg_list'] = get_uncompleted_seg_list(sub_sub_analysis['seg_list'], client_tz)
+					result['sub_sub_zs_list'] = [{
+						'start_time': zs.start_klc.end_time if isinstance(zs.start_klc.end_time, str) else zs.start_klc.end_time.astimezone(client_tz).isoformat(),
+						'end_time': (zs.end_klc.end_time if isinstance(zs.end_klc.end_time, str) else zs.end_klc.end_time.astimezone(client_tz).isoformat()) if zs.end_klc else None,
+						'zg': zs.zg, 'zd': zs.zd, 'gg': zs.gg, 'dd': zs.dd, 'is_sure': zs.is_sure
+					} for zs in sub_sub_analysis['zs_list'] if zs.end_klc]
+					result['sub_sub_uncompleted_zs_list'] = [{
+						'start_time': zs.start_klc.end_time if isinstance(zs.start_klc.end_time, str) else zs.start_klc.end_time.astimezone(client_tz).isoformat(),
+						'end_time': None, 'zg': zs.zg, 'zd': zs.zd, 'gg': zs.gg, 'dd': zs.dd, 'is_sure': zs.is_sure
+					} for zs in sub_sub_analysis['zs_list'] if not zs.is_sure]
+					result['sub_sub_bi_zs_list'] = [{
+						'start_time': (
+							(zs.start_klc.end_time if isinstance(zs.start_klc.end_time, str) else zs.start_klc.end_time.astimezone(client_tz).isoformat())
+							if getattr(zs.start_klc, 'end_time', None) else
+							(zs.start_klc.start_time if isinstance(zs.start_klc.start_time, str) else zs.start_klc.start_time.astimezone(client_tz).isoformat())
+						),
+						'end_time': (zs.end_time if isinstance(zs.end_time, str) else zs.end_time.astimezone(client_tz).isoformat()) if getattr(zs, 'end_time', None) else None,
+						'zg': zs.zg, 'zd': zs.zd, 'gg': zs.gg, 'dd': zs.dd, 'is_sure': bool(getattr(zs, 'is_sure', False))
+					} for zs in sub_sub_analysis.get('bi_zs_list', []) if getattr(zs, 'is_sure', False)]
+					result['sub_sub_uncompleted_bi_zs_list'] = [{
+						'start_time': (
+							(zs.start_klc.end_time if isinstance(zs.start_klc.end_time, str) else zs.start_klc.end_time.astimezone(client_tz).isoformat())
+							if getattr(zs.start_klc, 'end_time', None) else
+							(zs.start_klc.start_time if isinstance(zs.start_klc.start_time, str) else zs.start_klc.start_time.astimezone(client_tz).isoformat())
+						),
+						'end_time': None, 'zg': zs.zg, 'zd': zs.zd, 'gg': zs.gg, 'dd': zs.dd, 'is_sure': bool(getattr(zs, 'is_sure', False))
+					} for zs in sub_sub_analysis.get('bi_zs_list', []) if not getattr(zs, 'is_sure', False)]
+					result['sub_sub_klc_fx_info'] = [{
+						'time': format_time_safely(point['time'], client_tz),
+						'start_time': format_time_safely(point['start_time'], client_tz),
+						'end_time': format_time_safely(point['end_time'], client_tz),
+						'price': float(point['price']),
+						'fx_type': point['fx_type'],
+						'is_bottom': bool(point['is_bottom']),
+						'fx_strength': float(point['fx_strength']),
+						'fx_strength_level': str(point['fx_strength_level']),
+						'is_strong_fx': bool(point['is_strong_fx']),
+						# 分型框（虚线矩形）用到的高低价
+						'high': float(point['high']) if point.get('high') is not None else None,
+						'low': float(point['low']) if point.get('low') is not None else None
+					} for point in sub_sub_analysis['klc_fx_info']]
+					result['sub_sub_bsp_list'] = [{
+						'time': format_time_safely(bsp.end_time, client_tz),
+						'price': float(bsp.klc.low if str(bsp.dir) == 'Chan_BSP_DIR.BUY' else bsp.klc.high),
+						'type': str(bsp.type).replace('Chan_BSP_TYPE.', ''),
+						'dir': str(bsp.dir).replace('Chan_BSP_DIR.', ''),
+						'is_sure': bool(bsp.is_sure),
+						'sure_time': format_time_safely(bsp.sure_time, client_tz) if bsp.sure_time else None,
+						'zs_count': int(bsp.zs_count) if hasattr(bsp, 'zs_count') else 0
+					} for bsp in sub_sub_analysis.get('bsp_list', [])]
+					result['sub_sub_chan_macd'] = serialize_chan_macd_data(sub_sub_analysis.get('chan_macd', {}), client_tz)
+					try:
+						sub_sub_klc_trend = []
+						for klc in sub_sub_analysis.get('klc_list', []):
+							trend_val = getattr(klc, 'trend', None)
+							t_obj = getattr(klc, 'end_time', None) or getattr(klc, 'start_time', None)
+							if trend_val is None or t_obj is None:
+								continue
+							trend_name = str(trend_val)
+							if '.' in trend_name:
+								trend_name = trend_name.split('.')[-1]
+							time_str = format_time_safely(t_obj, client_tz)
+							if time_str:
+								sub_sub_klc_trend.append({'time': time_str, 'trend': trend_name})
+						result['sub_sub_klc_trend'] = sub_sub_klc_trend
+					except Exception:
+						result['sub_sub_klc_trend'] = []
+
+			pass
+	
+	# 结构价值区分析（Structure Zone）—— 按需拉取：仅当 include_structure_zones 为真时执行多周期拉取（默认跳过以减轻负载）
+	include_zones_param = request.args.get('include_structure_zones', '')
+	include_structure_zones = str(include_zones_param).lower() in ('1', 'true', 'yes')
+	if include_structure_zones:
+		zone_timeframes_str = request.args.get('zone_timeframes', '')
+		zone_kl_lines = int(request.args.get('zone_kl_lines', 1000))
+		try:
+			zone_config = StructureZoneConfig(kl_lines_per_tf=zone_kl_lines)
+			if zone_timeframes_str:
+				zone_config.zone_timeframes = [t.strip() for t in zone_timeframes_str.split(',') if t.strip()]
+			analyses = {}
+			ema52_dict = {}
+			latest_close = 0.0
+			now = time.time()
+
+			def _fetch_single_tf_zone(tf_name):
+				"""单个时间周期的结构区数据拉取（线程安全）"""
+				cache_key = f"{symbol}:{tf_name}:{zone_kl_lines}"
+				cached = _zone_cache.get(cache_key)
+				if cached and cached['expires'] > now:
+					print(f"  结构区缓存命中: {tf_name}")
+					return {
+						'tf_name': tf_name,
+						'analyses': cached['analyses'],
+						'ema52': cached['ema52'],
+						'close': cached.get('close', 0.0),
+						'cached': True,
+					}
+
+				try:
+					tf_df = get_kl_data(symbol, tf_name, limit=zone_kl_lines)
+					if tf_df is None or len(tf_df) == 0:
+						return None
+					tf_df = add_indicators(tf_df)
+					tf_analysis = analyze_chan(tf_df, symbol, tf_name)
+					zs_serialized = [{
+						'start_time': (zs.start_klc.end_time if isinstance(zs.start_klc.end_time, str) else zs.start_klc.end_time.astimezone(client_tz).isoformat()) if zs.start_klc else None,
+						'end_time': (zs.end_klc.end_time if isinstance(zs.end_klc.end_time, str) else zs.end_klc.end_time.astimezone(client_tz).isoformat()) if zs.end_klc else None,
+						'zg': zs.zg, 'zd': zs.zd, 'gg': zs.gg, 'dd': zs.dd,
+						'is_sure': zs.is_sure
+					} for zs in tf_analysis.get('zs_list', []) if zs.is_sure]
+					bi_zs_serialized = [{
+						'start_time': ((zs.start_klc.end_time if isinstance(zs.start_klc.end_time, str) else zs.start_klc.end_time.astimezone(client_tz).isoformat()) if getattr(zs.start_klc, 'end_time', None) else (zs.start_klc.start_time if isinstance(zs.start_klc.start_time, str) else zs.start_klc.start_time.astimezone(client_tz).isoformat())),
+						'end_time': (zs.end_time if isinstance(zs.end_time, str) else zs.end_time.astimezone(client_tz).isoformat()) if getattr(zs, 'end_time', None) else None,
+						'zg': zs.zg, 'zd': zs.zd, 'gg': zs.gg, 'dd': zs.dd,
+						'is_sure': bool(getattr(zs, 'is_sure', False))
+					} for zs in tf_analysis.get('bi_zs_list', []) if getattr(zs, 'is_sure', False)]
+					last_ema = tf_df['ema52'].iloc[-1] if 'ema52' in tf_df.columns else 0
+					ema_val = float(last_ema) if last_ema and last_ema > 0 else None
+					last_close = float(tf_df['close'].iloc[-1])
+					tf_result = {
+						'tf_name': tf_name,
+						'analyses': {'zs_list': zs_serialized, 'bi_zs_list': bi_zs_serialized},
+						'ema52': ema_val,
+						'close': last_close,
+						'cached': False,
+					}
+					# 写入缓存
+					_zone_cache[cache_key] = {
+						'analyses': tf_result['analyses'],
+						'ema52': ema_val,
+						'close': last_close,
+						'expires': now + _zone_cache_ttl(tf_name),
+					}
+					print(f"  结构区数据: {tf_name} -> zs={len(zs_serialized)}, bi_zs={len(bi_zs_serialized)}, ema52={ema_val}")
+					return tf_result
+				except Exception as e:
+					print(f"  结构区 {tf_name} 拉取失败: {e}")
+					return None
+
+			with ThreadPoolExecutor(max_workers=len(zone_config.zone_timeframes)) as executor:
+				futures = {executor.submit(_fetch_single_tf_zone, tf): tf for tf in zone_config.zone_timeframes}
+				for future in as_completed(futures):
+					tf_result = future.result()
+					if tf_result is None:
+						continue
+					tf_name = tf_result['tf_name']
+					analyses[tf_name] = tf_result['analyses']
+					ema52_dict[tf_name] = tf_result['ema52']
+					if tf_result['close'] and (not latest_close or latest_close == 0.0):
+						latest_close = tf_result['close']
+
+			structure_zones = analyze_structure_zones_from_serialized(
+				analyses, ema52_dict, latest_close, config=zone_config
+			)
+			result['structure_zones'] = [{
+				'id': z.id,
+				'lower': z.lower,
+				'upper': z.upper,
+				'center': z.center,
+				'width_pct': z.width_pct,
+				'zone_type': z.zone_type,
+				'timeframes': z.timeframes,
+				'structure_types': z.structure_types,
+				'boundary_types': z.boundary_types,
+				'overlap_count': z.overlap_count,
+				'touch_count': z.touch_count,
+				'recency_score': z.recency_score,
+				'ema52_distance_pct': z.ema52_distance_pct,
+				'ema52_aligned': z.ema52_aligned,
+				'strength_score': z.strength_score,
+				'confidence': z.confidence,
+				'first_seen': z.first_seen,
+				'last_seen': z.last_seen,
+				'metadata': z.metadata,
+			} for z in structure_zones]
+		except Exception as e:
+			print(f"StructureZone 分析出错: {e}")
+			import traceback
+			traceback.print_exc()
+			result['structure_zones'] = []
+	else:
+		result['structure_zones'] = []
+
+	return jsonify(result)
+

web/api/pages.py

@@ -0,0 +1,73 @@
+"""页面路由。"""
+from flask import Blueprint, render_template, send_from_directory
+from services.runtime import *  # noqa: F403
+from services import runtime as R
+
+bp = Blueprint("pages", __name__)
+
+@bp.route('/chan_tv')
+def chan_tv():
+	"""缠论 TradingView 高级图表页面"""
+	return render_template('chan_tv.html')
+
+@bp.route('/charting_library/<path:filename>')
+def serve_charting_library(filename):
+	"""提供 TradingView Charting Library 静态文件"""
+	return send_from_directory('charting_library', filename)
+
+@bp.route('/')
+def index():
+	"""主页"""
+	refresh_data_service_metadata()
+	tf_map = TIMEFRAMES if TIMEFRAMES else DEFAULT_TIMEFRAME_LABELS.copy()
+	default_main, default_element, default_sub_sub, timeframe_keys = compute_timeframe_defaults(OrderedDict(tf_map))
+	symbols = SYMBOLS if SYMBOLS else DEFAULT_SYMBOLS
+
+	default_symbol = 'BTC/USDT:USDT' if 'BTC/USDT:USDT' in symbols else (symbols[0] if symbols else '')
+
+	return render_template(
+		'index.html',
+		timeframes=tf_map,
+		symbols=symbols,
+		a_stock_symbols=A_STOCK_SYMBOLS,
+		default_main_timeframe=default_main,
+		default_element_timeframe=default_element,
+		default_sub_sub_timeframe=default_sub_sub,
+		default_symbol=default_symbol,
+		timeframe_keys_json=json.dumps(timeframe_keys),
+		data_service_available=DATA_SERVICE_AVAILABLE,
+	)
+
+
+@bp.route('/api/chart_metadata')
+def api_chart_metadata():
+	"""
+	按数据源返回图表用 K 线周期（中文标签）及主/次/次次默认周期。
+	crypto：强制刷新 DATA_SERVICE_URL /health 元信息；
+	a_stock：读取 ASHARE_DP_URL 的 /api/v1/klines/available-freqs，不修改全局加密货币 TIMEFRAMES。
+	"""
+	source = (request.args.get('source') or 'crypto').strip().lower()
+	if source not in ('crypto', 'a_stock'):
+		source = 'crypto'
+	try:
+		if source == 'a_stock':
+			raw = china_stock.get_available_kline_freqs()
+			labels_od = build_timeframe_labels(raw)
+		else:
+			refresh_data_service_metadata(force=True)
+			labels_od = OrderedDict(TIMEFRAMES if TIMEFRAMES else DEFAULT_TIMEFRAME_LABELS.copy())
+
+		default_main, default_element, default_sub_sub, keys = compute_timeframe_defaults(labels_od)
+		return jsonify({
+			'source': source,
+			'timeframes': {k: v for k, v in labels_od.items()},
+			'timeframe_keys': keys,
+			'default_main': default_main,
+			'default_element': default_element,
+			'default_sub_sub': default_sub_sub,
+		})
+	except Exception as exc:
+		logger.exception('chart_metadata 失败: %s', exc)
+		return jsonify({'error': str(exc)}), 500
+
+

web/api/symbols.py

@@ -0,0 +1,101 @@
+"""交易对 / A股 / MACD 配置 API。"""
+from flask import Blueprint, jsonify, request
+from services.runtime import *  # noqa: F403
+
+bp = Blueprint("symbols", __name__)
+
+@bp.route('/api/symbols')
+def get_symbols():
+	"""获取可用交易对"""
+	refresh_data_service_metadata()
+	if SYMBOLS:
+		return jsonify(SYMBOLS)
+	try:
+		markets = exchange.load_markets()
+		# 合约交易对通常是以USDT结尾的永续合约
+		symbols = [symbol for symbol in markets.keys() if '/USDT' in symbol and ':USDT' in symbol]
+		return jsonify(symbols)
+	except Exception as e:
+		return jsonify(DEFAULT_SYMBOLS)
+
+@bp.route('/api/a_stocks')
+def get_a_stocks():
+	"""获取A股股票列表"""
+	try:
+		stock_list = china_stock.get_stock_list()
+		return jsonify(stock_list)
+	except Exception as e:
+		return jsonify({'error': str(e)})
+
+@bp.route('/api/popular_a_stocks')  
+def get_popular_a_stocks():
+	"""获取热门A股股票"""
+	try:
+		return jsonify(china_stock.get_popular_stocks())
+	except Exception as e:
+		return jsonify({'error': str(e)})
+
+@bp.route('/api/sectors')
+def get_sectors():
+	"""获取所有行业分类"""
+	try:
+		sectors = china_stock.get_all_sectors()
+		return jsonify(sectors)
+	except Exception as e:
+		return jsonify({'error': str(e)})
+
+@bp.route('/api/stocks_by_sector')
+def get_stocks_by_sector():
+	"""根据行业获取股票"""
+	try:
+		sector = request.args.get('sector')
+		if sector:
+			stocks = china_stock.get_stock_by_sector(sector)
+			return jsonify(stocks)
+		else:
+			# 返回所有行业的股票分组
+			all_sectors = china_stock.get_stock_by_sector()
+			return jsonify(all_sectors)
+	except Exception as e:
+		return jsonify({'error': str(e)})
+
+@bp.route('/api/search_stock')
+def search_stock():
+	"""搜索股票 - 增强版"""
+	try:
+		keyword = request.args.get('keyword', '')
+		if not keyword:
+			return jsonify({'error': '搜索关键词不能为空'})
+		
+		results = china_stock.search_stock(keyword)
+		return jsonify(results)
+	except Exception as e:
+		return jsonify({'error': str(e)})
+
+
+@bp.route('/api/macd_config', methods=['GET', 'POST'])
+def macd_config():
+	"""获取或设置MACD参数"""
+	global macd_fast_period, macd_slow_period, macd_signal_period
+	if request.method == 'GET':
+		return jsonify({
+			'fast': macd_fast_period,
+			'slow': macd_slow_period,
+			'signal': macd_signal_period
+		})
+	else:
+		data = request.get_json(silent=True) or {}
+		fast = data.get('fast')
+		slow = data.get('slow')
+		signal = data.get('signal')
+		if fast is not None:
+			macd_fast_period = int(fast)
+		if slow is not None:
+			macd_slow_period = int(slow)
+		if signal is not None:
+			macd_signal_period = int(signal)
+		return jsonify({
+			'fast': macd_fast_period,
+			'slow': macd_slow_period,
+			'signal': macd_signal_period
+		})

web/api/trend.py

@@ -0,0 +1,126 @@
+"""趋势相关 API。"""
+from flask import Blueprint, jsonify, request
+from services.runtime import *  # noqa: F403
+from services import runtime as R
+
+bp = Blueprint("trend", __name__)
+
+@bp.route('/api/trend_filter', methods=['GET'])
+def trend_filter():
+	"""趋势筛选接口（币对）
+	参数:
+	  timeframe: K线周期
+	  start_time, end_time: 毫秒时间戳，可选
+	  direction: bull/bear/sideways 可选
+	  stage: early/mid/late 可选
+	  min_strength: 0-100 可选
+	  symbols: 逗号分隔列表，可选；不传则自动加载部分USDT币对
+	返回符合条件的币对与简要统计
+	"""
+	timeframe = request.args.get('timeframe', '1h')
+	start_time = request.args.get('start_time')
+	end_time = request.args.get('end_time')
+	want_direction = request.args.get('direction')  # 可为 None
+	want_stage = request.args.get('stage')  # 可为 None
+	try:
+		min_strength = float(request.args.get('min_strength', '0'))
+	except ValueError:
+		min_strength = 0.0
+
+	symbols_param = request.args.get('symbols')
+	if symbols_param:
+		symbols_list = [s.strip() for s in symbols_param.split(',') if s.strip()]
+	else:
+		symbols_list = load_crypto_symbols(limit=150)
+
+	results = []
+	for sym in symbols_list:
+		try:
+			df = get_crypto_kl_data(sym, timeframe, start_time=start_time, end_time=end_time)
+			if df is None or len(df) < 60:
+				continue
+			df = add_indicators(df)
+			direction, stage, strength = classify_trend_stage(df)
+
+			if want_direction and direction != want_direction:
+				continue
+			if want_stage and stage != want_stage:
+				continue
+			if strength < min_strength:
+				continue
+
+			last_row = df.iloc[-1]
+			results.append({
+				'symbol': sym,
+				'time': int(last_row['timestamp']),
+				'close': float(last_row['close']),
+				'direction': direction,
+				'stage': stage,
+				'strength': float(round(strength, 2)),
+				'ema5': float(last_row['ema5']),
+				'ema10': float(last_row['ema10']),
+				'ema24': float(last_row['ema24']),
+				'ema52': float(last_row['ema52'])
+			})
+		except Exception:
+			continue
+
+	# 按强度降序
+	results.sort(key=lambda x: x['strength'], reverse=True)
+	return jsonify({
+		'count': len(results),
+		'results': results
+	})
+
+
+@bp.route('/api/trend_detail', methods=['GET'])
+def trend_detail():
+	"""返回单个币对的K线与EMA、用于前端绘制趋势线
+	参数: symbol, timeframe, start_time, end_time
+	"""
+	symbol = request.args.get('symbol')
+	timeframe = request.args.get('timeframe', '1h')
+	start_time = request.args.get('start_time')
+	end_time = request.args.get('end_time')
+	timezone_name = request.args.get('timezone', 'Asia/Shanghai')
+
+	if not symbol:
+		return jsonify({'error': 'symbol不能为空'})
+
+	df = get_crypto_kl_data(symbol, timeframe, start_time=start_time, end_time=end_time)
+	if df is None or len(df) == 0:
+		return jsonify({'error': '获取数据失败'})
+
+	df = add_indicators(df)
+	direction, stage, strength = classify_trend_stage(df)
+
+	# 简单趋势线: 用最近N根收盘价做线性拟合
+	N = min(80, len(df))
+	sub = df.tail(N)
+	y = sub['close'].values
+	x = np.arange(len(y))
+	denom = np.dot(x - x.mean(), x - x.mean())
+	if denom != 0:
+		m = float(np.dot(y - y.mean(), x - x.mean()) / denom)
+		b = float(y.mean() - m * x.mean())
+	else:
+		m, b = 0.0, float(y[-1])
+
+	client_tz = timezone(timezone_name)
+
+	return jsonify({
+		'symbol': symbol,
+		'timeframe': timeframe,
+		'timezone': timezone_name,
+		'direction': direction,
+		'stage': stage,
+		'strength': float(round(strength, 2)),
+		'kline_data': clean_dataframe_for_json(df)[['timestamp','open','high','low','close','volume','ema5','ema10','ema24','ema52']].to_dict('records'),
+		'trend_line': {
+			'offset': int(df.index[-N]),
+			'slope': m,
+			'intercept': b,
+			'length': int(N)
+		}
+	})
+