Chan/web/services/runtime.py

from __future__ import annotations

import sys
import os
from collections import OrderedDict
import json
import logging
import time
import traceback
import io
import base64
from concurrent.futures import ThreadPoolExecutor, as_completed
from datetime import datetime, timedelta

import ccxt
import numpy as np
import pandas as pd
import requests
import talib.abstract as ta
from pytz import timezone

_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
if _ROOT not in sys.path:
    sys.path.append(_ROOT)

from chanlun import ChanLun, TF_DF
from chanlun.core.ChanEnum import Chan_BI_DIR, Chan_SEG_DIR, Chan_KLC_FX, Chan_FX_TYPE, Chan_MACDSEG_DIR, Chan_MACDHISTSET_DIR
from chanlun.indicators.ChanMACD import ChanMACD
from chanlun.analysis.ChanZone import StructureZoneConfig, analyze_structure_zones_from_serialized

from config import (
    DATA_SERVICE_URL,
    MACD_FAST,
    MACD_SLOW,
    MACD_SIGNAL,
    ccxt_proxies,
)
from services.cn_stock import ChinaStockData

logger = logging.getLogger(__name__)

class TRADE_POINT_TYPE:
	BUY1 = 1   # 一类买点
	BUY2 = 2   # 二类买点
	BUY3 = 3   # 三类买点
	SELL1 = -1  # 一类卖点
	SELL2 = -2  # 二类卖点
	SELL3 = -3  # 三类卖点



# mutable runtime state
macd_fast_period = MACD_FAST
macd_slow_period = MACD_SLOW
macd_signal_period = MACD_SIGNAL

_proxies = ccxt_proxies()
_exchange_kwargs = {"enableRateLimit": True}
if _proxies:
    _exchange_kwargs["proxies"] = _proxies
exchange = ccxt.binance(_exchange_kwargs)

china_stock = ChinaStockData()
_zone_cache = {}

DEFAULT_TIMEFRAME_LABELS = OrderedDict([
    ("1m", "1分钟"),
    ("3m", "3分钟"),
    ("5m", "5分钟"),
    ("15m", "15分钟"),
    ("30m", "30分钟"),
    ("1h", "1小时"),
    ("2h", "2小时"),
    ("4h", "4小时"),
    ("6h", "6小时"),
    ("8h", "8小时"),
    ("12h", "12小时"),
    ("1d", "日线"),
    ("3d", "3日线"),
    ("1w", "周线"),
    ("1M", "月线"),
])

DEFAULT_SYMBOLS = [
    'SOL/USDT:USDT', 'BTC/USDT:USDT', 'ETH/USDT:USDT', 'BNB/USDT:USDT', 'XRP/USDT:USDT', 'WIF/USDT:USDT',
    'ADA/USDT:USDT', 'DOGE/USDT:USDT', 'AVAX/USDT:USDT', 'DOT/USDT:USDT', 'MATIC/USDT:USDT'
]

TIMEFRAMES = DEFAULT_TIMEFRAME_LABELS.copy()
SYMBOLS = DEFAULT_SYMBOLS.copy()
DATA_SERVICE_AVAILABLE = False
SERVICE_METADATA_LAST_REFRESH = 0


def _zone_cache_ttl(tf_name: str) -> int:
	"""根据时间周期返回缓存过期时间（秒）"""
	minutes = timeframe_to_minutes(tf_name) or 5
	if minutes <= 5:
		return 120      # 5m及以下: 2分钟
	elif minutes <= 15:
		return 300      # 15m: 5分钟
	elif minutes <= 60:
		return 600      # 1h: 10分钟
	else:
		return 1800     # 4h+: 30分钟


def timeframe_to_minutes(tf: str):
	"""将时间周期转换为分钟数，用于排序。"""
	if not tf:
		return None
	unit = tf[-1]
	try:
		value = int(tf[:-1])
	except (ValueError, TypeError):
		return None
	multiplier = {
		'm': 1,
		'h': 60,
		'd': 1440,
		'w': 10080,
		'M': 43200,  # 30天近似
	}.get(unit)
	if multiplier is None:
		return None
	return value * multiplier


def format_timeframe_label(tf: str) -> str:
	"""将时间周期转换为可读标签。"""
	if not tf:
		return tf
	unit = tf[-1]
	try:
		value = int(tf[:-1])
	except (ValueError, TypeError):
		return tf
	if unit == 'm':
		return f"{value}分钟"
	if unit == 'h':
		return f"{value}小时"
	if unit == 'd':
		return "日线" if value == 1 else f"{value}日线"
	if unit == 'w':
		return "周线" if value == 1 else f"{value}周线"
	if unit == 'M':
		return "月线" if value == 1 else f"{value}月线"
	return tf


def build_timeframe_labels(timeframes):
	ordered = sorted(
		timeframes,
		key=lambda tf: timeframe_to_minutes(tf) if timeframe_to_minutes(tf) is not None else float('inf'),
	)
	labels = OrderedDict()
	for tf in ordered:
		labels[tf] = format_timeframe_label(tf)
	return labels


def compute_timeframe_defaults(labels_ordered):
	"""
	根据已排序的「周期 → 中文标签」映射，计算主 / 次 / 次次周期默认值。
	labels_ordered: OrderedDict 或按插入顺序排列的 dict。
	"""
	if not labels_ordered:
		labels_ordered = DEFAULT_TIMEFRAME_LABELS.copy()
	timeframe_keys = list(labels_ordered.keys())
	preferred_main = next((tf for tf in ['5m', '15m', '1h'] if tf in labels_ordered), None)
	default_main = preferred_main or (timeframe_keys[0] if timeframe_keys else '1m')
	if default_main not in labels_ordered and timeframe_keys:
		default_main = timeframe_keys[0]

	if timeframe_keys:
		try:
			idx = timeframe_keys.index(default_main)
			default_element = timeframe_keys[idx - 1] if idx > 0 else timeframe_keys[0]
		except ValueError:
			default_element = timeframe_keys[0]
	else:
		default_element = default_main

	if timeframe_keys:
		try:
			idx_el = timeframe_keys.index(default_element)
			default_sub_sub = timeframe_keys[idx_el - 1] if idx_el > 0 else timeframe_keys[0]
		except ValueError:
			default_sub_sub = timeframe_keys[0]
	else:
		default_sub_sub = default_element

	return default_main, default_element, default_sub_sub, timeframe_keys


def _parse_time_input(value):
	if value in (None, '', 0):
		return None
	try:
		return int(float(value))
	except (ValueError, TypeError):
		return None


def refresh_data_service_metadata(force=False):
	"""刷新数据服务提供的交易对与周期元信息。"""
	global DATA_SERVICE_AVAILABLE, TIMEFRAMES, SYMBOLS, SERVICE_METADATA_LAST_REFRESH
	now = time.time()
	if not force and DATA_SERVICE_AVAILABLE and now - SERVICE_METADATA_LAST_REFRESH < 60:
		return True
	try:
		resp = requests.get(f"{DATA_SERVICE_URL}/health", timeout=5)
		resp.raise_for_status()
		payload = resp.json()
		service_symbols = payload.get("symbols") or payload.get("symbol_list") or []
		base_timeframes = payload.get("timeframes") or payload.get("base_timeframes") or []
		derived = payload.get("derived_timeframes") or []
		service_timeframes = list(base_timeframes)
		for tf in derived:
			if tf not in service_timeframes:
				service_timeframes.append(tf)
		if service_symbols:
			SYMBOLS[:] = service_symbols
		if service_timeframes:
			TIMEFRAMES.clear()
			TIMEFRAMES.update(build_timeframe_labels(service_timeframes))
		DATA_SERVICE_AVAILABLE = True
		SERVICE_METADATA_LAST_REFRESH = now
		return True
	except Exception as exc:
		logger.warning("无法加载数据服务元信息: %s", exc)
		if not DATA_SERVICE_AVAILABLE:
			TIMEFRAMES.clear()
			TIMEFRAMES.update(DEFAULT_TIMEFRAME_LABELS)
			SYMBOLS[:] = DEFAULT_SYMBOLS
		DATA_SERVICE_AVAILABLE = False
		return False


def _fetch_kl_from_datasvc(symbol, timeframe, start_ms=None, end_ms=None, limit=None):
	params = {"symbol": symbol, "tf": timeframe}
	if start_ms is not None:
		params["start"] = int(start_ms)
	if end_ms is not None:
		params["end"] = int(end_ms)
	if limit is not None:
		params["limit"] = limit
	resp = requests.get(f"{DATA_SERVICE_URL}/api/candles", params=params, timeout=10)
	resp.raise_for_status()
	data = resp.json()
	if not data:
		return None
	df = pd.DataFrame(data)
	if df.empty or "timestamp" not in df.columns:
		return None
	numeric_cols = ["open", "high", "low", "close", "volume"]
	df["timestamp"] = pd.to_numeric(df["timestamp"], errors="coerce")
	df = df.dropna(subset=["timestamp"])
	df["timestamp"] = df["timestamp"].astype("int64")
	for col in numeric_cols:
		if col in df.columns:
			df[col] = pd.to_numeric(df[col], errors="coerce")
	df = df.dropna(subset=numeric_cols)
	df = df.sort_values("timestamp")
	if limit and len(df) > limit:
		df = df.tail(limit)
	df = df.reset_index(drop=True)
	df["date"] = pd.to_datetime(df["timestamp"], unit='ms', utc=True).dt.tz_convert('Asia/Shanghai')
	return df


# 模块加载时尝试预取一次元信息，但失败不阻塞后续流程
refresh_data_service_metadata(force=True)

# A股热门股票
# 模板中 A 股下拉仅放默认一项；用户切换到「A股」时由前端请求 /api/a_stocks 填充全市场（约 5500+）
A_STOCK_SYMBOLS = [{'symbol': '000001', 'name': '平安银行'}]

def detect_symbol_type(symbol):
	"""检测交易对类型：crypto 或 a_stock"""
	if '/' in symbol and 'USDT' in symbol:
		return 'crypto'
	elif len(symbol) == 6 and symbol.isdigit():
		return 'a_stock'
	else:
		return 'unknown'

def get_kl_data(symbol, timeframe, limit=100000, start_time=None, end_time=None):
	"""获取K线数据，支持加密货币和A股"""
	symbol_type = detect_symbol_type(symbol)
	
	if symbol_type == 'crypto':
		return get_crypto_kl_data(symbol, timeframe, limit, start_time, end_time)
	elif symbol_type == 'a_stock':
		return get_a_stock_kl_data(symbol, timeframe, limit, start_time, end_time)
	else:
		return None

def _get_crypto_kl_data_via_ccxt(symbol, timeframe, limit=100000, start_time=None, end_time=None):
	"""获取加密货币K线数据，支持分页加载确保获取指定时间范围内的所有数据"""
	try:
		# 初始化参数
		since = None
		if start_time:
			try:
				since = int(start_time)
			except ValueError:
				pass
		
		# 结束时间处理
		until = None
		if end_time:
			try:
				until = int(end_time)
			except ValueError:
				pass
		
		# 根据时间周期调整每次请求的数据量
		batch_size = 1000  # 默认批次大小
		if timeframe in ['1m', '3m', '5m']:
			batch_size = 1000  # 分钟级数据减少批次大小
		elif timeframe in ['15m', '30m', '1h']:
			batch_size = 1000
		else:
			batch_size = 1500  # 日线及以上可以获取更多
		batch_size = 1500  # 默认批次大小
		# 初始化存储所有K线数据的列表
		all_ohlcv = []
		
		# 初始化当前查询的开始时间
		current_since = since
		
		# 添加请求计数和最大限制
		request_count = 0
		max_requests = 300  # 最大请求次数，防止无限循环
		
		# 分页加载数据
		while request_count < max_requests:
			request_count += 1
			
			try:
				# 获取当前页的数据
				ohlcv = exchange.fetch_ohlcv(symbol, timeframe, since=current_since, limit=batch_size)
				
				# 如果没有获取到数据，结束循环
				if not ohlcv or len(ohlcv) == 0:
					break
					
				# 将获取到的数据添加到总列表中
				all_ohlcv.extend(ohlcv)
				
				# 获取最后一条数据的时间戳
				last_timestamp = ohlcv[-1][0]
				
				# 如果已达到结束时间，结束循环
				if until and last_timestamp >= until:
					break
					
				# 如果获取的数据条数小于限制数，说明已经获取完所有数据
				if len(ohlcv) < batch_size:
					break
					
				# 更新下一页的开始时间（加1毫秒避免重复）
				current_since = last_timestamp + 1
				
			except Exception as e:
				# 如果单个批次失败，继续尝试下一个批次
				if current_since:
					# 尝试增加时间跳过可能的问题时间点
					current_since += 60000  # 跳过1分钟
				else:
					break
			
			# 防止API请求过于频繁
			time.sleep(0.3)  # 减少到0.3秒提高效率
		
		# 数据为空的情况
		if not all_ohlcv or len(all_ohlcv) == 0:
			return None
		
		# 转换为DataFrame
		df = pd.DataFrame(all_ohlcv, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])
		df['date'] = pd.to_datetime(df['timestamp'], unit='ms').dt.tz_localize('UTC').dt.tz_convert('Asia/Shanghai')
		
		# 在客户端进行结束时间过滤
		if until:
			df = df[df['timestamp'] <= until]
			
		# 去除重复数据
		df = df.drop_duplicates(subset=['timestamp'])
		
		# 按时间排序
		df = df.sort_values('timestamp')
		
		# 限制数据条数的逻辑 - 优先考虑时间范围
		if start_time and end_time:
			# 如果指定了明确的时间范围，返回该时间范围内的所有数据
			if len(df) > 100000:  # 防止数据量过大，设置一个合理的上限
				df = df.tail(100000).reset_index(drop=True)
		elif limit and len(df) > limit:
			# 如果没有指定明确时间范围，使用默认的limit限制
			df = df.tail(limit).reset_index(drop=True)
		
		# 如果过滤后没有数据，返回None
		if len(df) == 0:
			return None
		return df
		
	except Exception as e:
		return None


def get_crypto_kl_data(symbol, timeframe, limit=100000, start_time=None, end_time=None):
	"""优先通过本地数据服务获取加密货币K线，失败时回退至交易所API。"""
	start_ms = _parse_time_input(start_time)
	end_ms = _parse_time_input(end_time)

	refresh_data_service_metadata()
	if DATA_SERVICE_AVAILABLE:
		try:
			df = _fetch_kl_from_datasvc(
				symbol=symbol,
				timeframe=timeframe,
				start_ms=start_ms,
				end_ms=end_ms,
				limit=limit,
			)
			if df is not None and not df.empty:
				return df
		except Exception as exc:
			logger.warning("数据服务请求失败，准备回退至交易所 API：%s", exc)

	return _get_crypto_kl_data_via_ccxt(symbol, timeframe, limit, start_time, end_time)


def get_a_stock_kl_data(symbol, timeframe, limit=100000, start_time=None, end_time=None):
	"""获取A股K线数据"""
	try:
		# 处理时间戳参数转换为日期字符串
		start_date = None
		end_date = None
		
		if start_time:
			try:
				# 尝试解析时间戳（毫秒）
				start_timestamp = int(start_time)
				start_date = datetime.fromtimestamp(start_timestamp / 1000).strftime('%Y-%m-%d')
			except (ValueError, TypeError):
				# 如果不是时间戳，尝试解析datetime-local格式 (YYYY-MM-DDTHH:MM)
				try:
					if 'T' in str(start_time):
						# datetime-local格式：2025-05-19T06:07
						start_date = str(start_time).split('T')[0]  # 只取日期部分
					else:
						start_date = str(start_time)
				except:
					start_date = start_time
		
		if end_time:
			try:
				# 尝试解析时间戳（毫秒）
				end_timestamp = int(end_time)
				end_date = datetime.fromtimestamp(end_timestamp / 1000).strftime('%Y-%m-%d')
			except (ValueError, TypeError):
				# 如果不是时间戳，尝试解析datetime-local格式
				try:
					if 'T' in str(end_time):
						# datetime-local格式：2025-05-26T06:07
						end_date = str(end_time).split('T')[0]  # 只取日期部分
					else:
						end_date = str(end_time)
				except:
					end_date = end_time
		
		# 如果用户指定了时间范围，优先获取该范围内的所有数据
		actual_limit = limit
		if start_date and end_date:
			actual_limit = None  # 不限制数据条数，获取完整时间范围数据
		
		# 调用A股数据获取器
		df = china_stock.get_kl_data(symbol, timeframe, start_date, end_date, actual_limit)
		
		if df is None:
			return None
		return df
		
	except Exception as e:
		return None

def add_indicators(df):
	global macd_fast_period, macd_slow_period, macd_signal_period
	macd = ta.MACD(df, fastperiod=macd_fast_period, slowperiod=macd_slow_period, signalperiod=macd_signal_period)

	df['macd'] = macd['macd']
	df['macdsignal'] = macd['macdsignal']
	df['macdhist'] = macd['macdhist']
	df['ma5'] = (ta.MA(df, timeperiod=5)).fillna(0)
	df['ma10'] = (ta.MA(df, timeperiod=10)).fillna(0)
	df['ma30'] = (ta.EMA(df, timeperiod=30)).fillna(0)
	df['ma250'] = (ta.MA(df, timeperiod=250)).fillna(0)
	# 新增 EMA 指标
	df['ema5'] = (ta.EMA(df, timeperiod=5)).fillna(0)
	df['ema10'] = (ta.EMA(df, timeperiod=10)).fillna(0)
	df['ema24'] = (ta.EMA(df, timeperiod=24)).fillna(0)
	df['ema52'] = (ta.EMA(df, timeperiod=52)).fillna(0)
	df['ema26'] = (ta.EMA(df, timeperiod=26)).fillna(0)
	df['ema13'] = (ta.EMA(df, timeperiod=13)).fillna(0)
	df['ema7'] = (ta.EMA(df, timeperiod=7)).fillna(0)
	df['ema104'] = (ta.EMA(df, timeperiod=104)).fillna(0)
	df['ema156'] = (ta.EMA(df, timeperiod=156)).fillna(0)
	df['ema208'] = (ta.EMA(df, timeperiod=208)).fillna(0)
	# 常用SMA 24/52
	try:
		df['sma24'] = (ta.SMA(df, timeperiod=24)).fillna(0)
		df['sma52'] = (ta.SMA(df, timeperiod=52)).fillna(0)
	except Exception:
		df['sma24'] = 0
		df['sma52'] = 0
	df['rsi'] = ta.RSI(df, timeperiod=14)
	
	# 计算布林带 (当前周期 - 20周期，2标准差)
	bb = ta.BBANDS(df, timeperiod=365, nbdevup=3.0, nbdevdn=3.0, matype=0)
	df['bb_upper'] = bb['upperband'].fillna(0)
	df['bb_middle'] = bb['middleband'].fillna(0)
	df['bb_lower'] = bb['lowerband'].fillna(0)
	bb30 = ta.BBANDS(df, timeperiod=41, nbdevup=2.3, nbdevdn=2.3, matype=0)
	#bb30 = ta.BBANDS(df, timeperiod=20, nbdevup=2.0, nbdevdn=2.0, matype=0)
	df['bbup30'] = bb30['upperband'].fillna(0)
	df['bblow30'] = bb30['lowerband'].fillna(0)
	bb302 = ta.BBANDS(df, timeperiod=41, nbdevup=2.0, nbdevdn=2.0, matype=0)
	#bb302 = ta.BBANDS(df, timeperiod=20, nbdevup=2.0, nbdevdn=2.0, matype=0)
	df['bbup302'] = bb302['upperband'].fillna(0)
	df['bblow302'] = bb302['lowerband'].fillna(0)
	# 计算次周期布林带 (14周期，2标准差)
	bb_element = ta.BBANDS(df, timeperiod=14, nbdevup=2.0, nbdevdn=2.0, matype=0)
	df['element_bb_upper'] = bb_element['upperband'].fillna(0)
	df['element_bb_middle'] = bb_element['middleband'].fillna(0)
	df['element_bb_lower'] = bb_element['lowerband'].fillna(0)
	
	df['macd'] = df['macd'].fillna(0)
	df['macdsignal'] = df['macdsignal'].fillna(0)
	df['macdhist'] = df['macdhist'].fillna(0)
	df['ma5'] = df['ma5'].fillna(0)
	df['ma10'] = df['ma10'].fillna(0)
	df['ma30'] = df['ma30'].fillna(0)
	df['ma250'] = df['ma250'].fillna(0)
	df['ema5'] = df['ema5'].fillna(0)
	df['ema10'] = df['ema10'].fillna(0)
	df['ema24'] = df['ema24'].fillna(0)
	df['ema52'] = df['ema52'].fillna(0)
	df['sma24'] = df['sma24'].fillna(0)
	df['sma52'] = df['sma52'].fillna(0)
	df['rsi'] = df['rsi'].fillna(0)
	df['avg_volume'] = df['volume'].rolling(10).mean()
	# 计算量比，避免产生Infinity值
	df['volume_ratio'] = df['volume'] / df['avg_volume']
	# 填充缺失值（前N根K线）
	df['volume_ratio'] = df['volume_ratio'].fillna(1.0)
	df['avg_volume'] = df['avg_volume'].fillna(0)
	
	# 处理Infinity和-Infinity值
	df['volume_ratio'] = df['volume_ratio'].replace([float('inf'), float('-inf')], 1.0)
	
	# 计算ATR (Average True Range) - 14周期
	df['atr'] = ta.ATR(df, timeperiod=14)
	df['atr'] = df['atr'].fillna(0)
	bb2633 = ta.BBANDS(df, timeperiod=26, nbdevup=3.0, nbdevdn=3.0, matype=0)
	bbp2633 = (df['close'] - bb2633['lowerband']) / (bb2633['upperband'] - bb2633['lowerband'])
	df['bb2633upper'] = bb2633['upperband'].fillna(0)
	df['bb2633lower'] = bb2633['lowerband'].fillna(0)
	df['bbp2633'] = bbp2633.fillna(0)
	df['bb2633middle'] = bb2633['middleband'].fillna(0)
	return df

def calculate_macd(df):
	"""计算MACD指标"""
	global macd_fast_period, macd_slow_period, macd_signal_period
	exp1 = df['close'].ewm(span=macd_fast_period, adjust=False).mean()
	exp2 = df['close'].ewm(span=macd_slow_period, adjust=False).mean()
	macd = exp1 - exp2
	signal = macd.ewm(span=macd_signal_period, adjust=False).mean()
	histogram = macd - signal
	
	return {
		'macd': macd.tolist(),
		'signal': signal.tolist(),
		'histogram': histogram.tolist()
	}

def analyze_chan(df, symbol=None, timeframe=None):
	"""进行缠论分析"""
	chan = TF_DF()
	
	# 初始化多时间周期数据以获取EMA52
	ema52_dict = None
	# 获取分析结果
	klu_list = chan.get_kl_data(df)
	klc_list = chan.get_klc_list(klu_list)
	bi_list = chan.cal_bi_list(klc_list)
	#for index in range(0, 10):
		#print(bi_list[index].start_time, bi_list[index].start_klc.end_time, bi_list[index].dir)
	seg_list = chan.get_seg_list(bi_list)
	zs_list = chan.calculate_seg_zs(seg_list)
	# 计算笔中枢（BI中枢）并拍平成列表

	#bi_zs_list = chan.cal_bi_zs_list_pure(bi_list)
	bi_zs_list = chan.cal_bi_zs(seg_list)
	bsp_list = []
	if len(bi_zs_list) > 0:
		bsp_list = chan.find_all_bsp(bi_list, bi_zs_list)
	#bsp_state_list = chan.get_bsp_state(df)
	#for bsp in bsp_list:
		#print(bsp.end_time, bsp.type, bsp.dir)
	# 添加买卖点识别
	for bi in bi_list:
		bi.cal_macdhist()
	for bi in bi_list:
		bi.cal_macd_div()
		#print(bi.start_time, bi.macd_hist, bi.macd_div)
	
	# 添加ChanMACD分析
	chan_macd = None
	chan_macd_data = {}
	try:
		
		if klu_list and len(klu_list) > 0:
			print(f"获取到KLU列表，长度: {len(klu_list)}")
			chan_macd = ChanMACD(klu_list)
			chan_macd_data = {
				'seg_list': chan_macd.seg_list,
				'unittf_list': chan_macd.unittf_list,
				'histset_list': chan_macd.histset_list,
				'klu_list': chan_macd.klu_list,
				'high_position_list': chan_macd.high_position_list,
				'high_empty_list': chan_macd.high_empty_list,
				'low_position_list': getattr(chan_macd, 'low_position_list', []),
				'low_empty_list': getattr(chan_macd, 'low_empty_list', []),
				'return_zero_list': chan_macd.return_zero_list,
				'cross0_up_list': chan_macd.cross0_up_list,
				'cross0_down_list': chan_macd.cross0_down_list
			}
			print(f"ChanMACD分析完成: seg={len(chan_macd.seg_list)}, unittf={len(chan_macd.unittf_list)}, histset={len(chan_macd.histset_list)}")
		else:
			print("未能获取KLU列表或列表为空")
			chan_macd_data = {
				'seg_list': [],
				'unittf_list': [],
				'histset_list': [],
				'high_position_list': [],
				'high_empty_list': [],
				'return_zero_list': [],
				'cross0_up_list': [],
				'cross0_down_list': []
			}
	except Exception as e:
		print(f"ChanMACD分析出错: {e}")
		import traceback
		traceback.print_exc()
		chan_macd_data = {
			'seg_list': [],
			'unittf_list': [],
			'histset_list': [],
			'high_position_list': [],
			'high_empty_list': [],
			'low_position_list': [],
			'low_empty_list': [],
			'return_zero_list': [],
			'cross0_up_list': [],
			'cross0_down_list': []
		}
	
	# 提取K线分型信息
	klc_fx_info = []
	for klc in klc_list:
		if hasattr(klc, 'klc_fx_type') and klc.klc_fx_type != Chan_KLC_FX.UNKNOWN:
			try:
				# 计算分型强度
				fx_strength = 0
				fx_strength_level = ""
				is_strong_fx = False
				
				# 统一使用cal_fx_strength函数
				if hasattr(klc, 'cal_fx_strength'):
					fx_strength = klc.cal_fx_strength(5)
				
				# 尝试获取分型强度等级
				if hasattr(klc, 'get_fx_strength_level'):
					fx_strength_level = klc.get_fx_strength_level()
				
				# 尝试判断是否为强分型
				if hasattr(klc, 'is_strong_fx'):
					is_strong_fx = klc.is_strong_fx()
				
				# 如果分型强度小于1，设为0
				if fx_strength < 1:
					fx_strength = 0

				# KLC 分型框（起止时间+高低价）：
				# 仅使用 cal_fx_box 通过 display 条件后生成的 klc.fx_box。
				# 若无 fx_box，则前端不应绘制分型框。
				fx_box = getattr(klc, 'fx_box', None)
				box_start_time = getattr(fx_box, 'start_time', None) if fx_box else None
				box_end_time = getattr(fx_box, 'end_time', None) if fx_box else None
				box_high = getattr(fx_box, 'high', None) if fx_box else None
				box_low = getattr(fx_box, 'low', None) if fx_box else None

				if klc.bb_out:
					klc_fx_info.append({
						'time': klc.end_time,
						'price': klc.low if klc.fx == Chan_FX_TYPE.BOTTOM else klc.high,
						'fx_type': str(klc.klc_fx_type).replace("Chan_KLC_FX.", ""),
						'is_bottom': klc.fx == Chan_FX_TYPE.BOTTOM,
						'fx_strength': fx_strength,           # 分型强度分数 (0-100)
						'fx_strength_level': fx_strength_level,  # 分型强度等级 (极强/强/中等/弱/极弱)
						'is_strong_fx': is_strong_fx,        # 是否为强分型

						# 虚线分型框信息（给前端画框用）
						'start_time': box_start_time,
						'end_time': box_end_time,
						'high': float(box_high) if box_high is not None else None,
						'low': float(box_low) if box_low is not None else None,
					})
			except Exception as e:
				# 如果出错，仍然添加基本信息，但分型强度为0
				fx_box = getattr(klc, 'fx_box', None)
				box_start_time = getattr(fx_box, 'start_time', None) if fx_box else None
				box_end_time = getattr(fx_box, 'end_time', None) if fx_box else None
				box_high = getattr(fx_box, 'high', None) if fx_box else None
				box_low = getattr(fx_box, 'low', None) if fx_box else None

				klc_fx_info.append({
					'time': klc.end_time,
					'price': klc.low if klc.fx == Chan_FX_TYPE.BOTTOM else klc.high,
					'fx_type': str(klc.klc_fx_type).replace("Chan_KLC_FX.", ""),
					'is_bottom': klc.fx == Chan_FX_TYPE.BOTTOM,
					'fx_strength': 0,
					'fx_strength_level': "",
					'is_strong_fx': False,

					# 虚线分型框信息（给前端画框用）
					'start_time': box_start_time,
					'end_time': box_end_time,
					'high': float(box_high) if box_high is not None else None,
					'low': float(box_low) if box_low is not None else None,
				})
	
	
	return {
		'klc_list': klc_list,
		'klu_list': klu_list,  # 添加KLU列表
		'bi_list': bi_list,
		'seg_list': seg_list,
		'zs_list': zs_list,
		'bi_zs_list': bi_zs_list,  # 添加BI中枢列表
		'bsp_list': bsp_list,  # 添加买卖点列表
		'klc_fx_info': klc_fx_info,  # KLC分型信息
		'chan_macd': chan_macd_data,   # 添加ChanMACD分析数据
		'ema52_dict': ema52_dict  # 添加多时间周期EMA52数据
	}

# 辅助函数，转换缠论方向枚举为整数
def convert_direction(direction):
	"""转换方向枚举为数字"""
	if direction == Chan_BI_DIR.UP or direction == Chan_SEG_DIR.UP:
		return 1
	elif direction == Chan_BI_DIR.DOWN or direction == Chan_SEG_DIR.DOWN:
		return -1
	else:
		return 0

def format_time_safely(time_obj, client_tz):
	"""安全地格式化时间对象，处理字符串和datetime两种情况"""
	if time_obj is None:
		return None
		
	if isinstance(time_obj, str):
		# 尝试将字符串解析为datetime
		try:
			from dateutil import parser
			time_obj = parser.parse(time_obj)
			return time_obj.astimezone(client_tz).isoformat()
		except:
			return time_obj
	else:
		# 已经是datetime对象
		return time_obj.astimezone(client_tz).isoformat()

def serialize_chan_macd_data(chan_macd_data, client_tz):
	"""序列化ChanMACD数据为JSON可序列化格式"""
	serialized_data = {
		'seg_list': [],
		'unittf_list': [],
		'histset_list': [],
		# 状态标记数据
		'high_position_list': [],
		'high_empty_list': [],
		'low_position_list': [],
		'low_empty_list': [],
		'return_zero_list': [],
		'cross0_up_list': [],
		'cross0_down_list': [],
		# 新增：输出KLU的继续背驰/分离背驰标志
		'klu_list': []
	}
	
	# 序列化seg_list
	for seg in chan_macd_data.get('seg_list', []):
		try:
			seg_data = {
				'start_time': format_time_safely(seg.start_time, client_tz),
				'end_time': format_time_safely(seg.end_time, client_tz) if seg.end_time else None,
				'seg_dir': 'ABOVE' if seg.seg_dir == Chan_MACDSEG_DIR.ABOVE else 'UNDER',
				'klu_count': len(seg.klu_list) if hasattr(seg, 'klu_list') else 0,
				'unittf_count': len(seg.unittf_list) if hasattr(seg, 'unittf_list') else 0,
				'histset_count': len(seg.hist_set) if hasattr(seg, 'hist_set') else 0
			}
			serialized_data['seg_list'].append(seg_data)
		except Exception as e:
			print(f"序列化seg出错: {e}")
			continue
	
	# 序列化unittf_list（兼容新结构与枚举类型）
	for unittf in chan_macd_data.get('unittf_list', []):
		try:
			dir_value = getattr(unittf, 'uinttf_dir', None)
			dir_name = getattr(dir_value, 'name', dir_value if isinstance(dir_value, str) else None)
			start_t = getattr(unittf, 'start_type', None)
			start_type = getattr(start_t, 'name', start_t)
			end_t = getattr(unittf, 'end_type', None)
			end_type = getattr(end_t, 'name', end_t)
			peak_abs = getattr(unittf, 'peak_abs', None)
			if peak_abs is None:
				peak_abs = getattr(unittf, 'peak_hist', None)
			length = getattr(unittf, 'length', None)
			if length is None:
				length = len(unittf.klu_list) if hasattr(unittf, 'klu_list') else None

			unittf_data = {
				'start_time': format_time_safely(getattr(unittf, 'start_time', None), client_tz),
				'end_time': format_time_safely(getattr(unittf, 'end_time', None), client_tz) if getattr(unittf, 'end_time', None) else None,
				'dir': dir_name,  # 'ABOVE' | 'UNDER' | None
				'start_type': start_type,  # e.g. 'START' | 'CROSS0' | 'NEAR0_UP' | 'NEAR0_DOWN'
				'end_type': end_type,
				'invalid': getattr(unittf, 'invalid', False),
				'peak_abs': peak_abs,
				'length': length,
				'klu_count': len(unittf.klu_list) if hasattr(unittf, 'klu_list') else 0,
				'histset_count': len(unittf.histset_list) if hasattr(unittf, 'histset_list') else 0
			}
			serialized_data['unittf_list'].append(unittf_data)
		except Exception as e:
			print(f"序列化unittf出错: {e}")
			continue
	
	# 序列化histset_list
	for histset in chan_macd_data.get('histset_list', []):
		try:
			histset_data = {
				'start_time': format_time_safely(getattr(histset, 'start_time', None), client_tz),
				'end_time': format_time_safely(getattr(histset, 'end_time', None), client_tz),
				'histset_dir': 'ABOVE' if histset.histset_dir == Chan_MACDHISTSET_DIR.ABOVE else 'UNDER',
				'klu_count': len(histset.klu_list) if hasattr(histset, 'klu_list') else 0
			}
			serialized_data['histset_list'].append(histset_data)
		except Exception as e:
			print(f"序列化histset出错: {e}")
			continue
	
	# 序列化状态标记数据
	# 序列化高位列表
	for high_pos in chan_macd_data.get('high_position_list', []):
		try:
			high_pos_data = {
				'time': format_time_safely(high_pos['time'], client_tz),
				'end_time': format_time_safely(high_pos.get('end_time'), client_tz) if high_pos.get('end_time') else None,
				'type': high_pos.get('type', 'start'),
				'macd': high_pos.get('macd'),
				'signal': high_pos.get('signal'),
				'macdhist': high_pos.get('macdhist'),
				'end_macd': high_pos.get('end_macd'),
				'end_signal': high_pos.get('end_signal'),
				'end_macdhist': high_pos.get('end_macdhist')
			}
			serialized_data['high_position_list'].append(high_pos_data)
		except Exception as e:
			print(f"序列化high_position出错: {e}")
			continue
	
	# 序列化高位空列表
	for high_empty in chan_macd_data.get('high_empty_list', []):
		try:
			high_empty_data = {
				'time': format_time_safely(high_empty['time'], client_tz),
				'end_time': format_time_safely(high_empty.get('end_time'), client_tz) if high_empty.get('end_time') else None,
				'type': high_empty.get('type', 'start'),
				'macd': high_empty.get('macd'),
				'signal': high_empty.get('signal'),
				'macdhist': high_empty.get('macdhist'),
				'end_macd': high_empty.get('end_macd'),
				'end_signal': high_empty.get('end_signal'),
				'end_macdhist': high_empty.get('end_macdhist')
			}
			serialized_data['high_empty_list'].append(high_empty_data)
		except Exception as e:
			print(f"序列化high_empty出错: {e}")
			continue
	
	# 序列化低位与低位空
	for low_pos in chan_macd_data.get('low_position_list', []):
		try:
			low_pos_data = {
				'time': format_time_safely(low_pos['time'], client_tz),
				'end_time': format_time_safely(low_pos.get('end_time'), client_tz) if low_pos.get('end_time') else None,
				'type': low_pos.get('type', 'start'),
				'macd': low_pos.get('macd'),
				'signal': low_pos.get('signal'),
				'macdhist': low_pos.get('macdhist'),
				'end_macd': low_pos.get('end_macd'),
				'end_signal': low_pos.get('end_signal'),
				'end_macdhist': low_pos.get('end_macdhist')
			}
			serialized_data['low_position_list'].append(low_pos_data)
		except Exception as e:
			print(f"序列化low_position出错: {e}")
			continue

	for low_empty in chan_macd_data.get('low_empty_list', []):
		try:
			low_empty_data = {
				'time': format_time_safely(low_empty['time'], client_tz),
				'end_time': format_time_safely(low_empty.get('end_time'), client_tz) if low_empty.get('end_time') else None,
				'type': low_empty.get('type', 'start'),
				'macd': low_empty.get('macd'),
				'signal': low_empty.get('signal'),
				'macdhist': low_empty.get('macdhist'),
				'end_macd': low_empty.get('end_macd'),
				'end_signal': low_empty.get('end_signal'),
				'end_macdhist': low_empty.get('end_macdhist')
			}
			serialized_data['low_empty_list'].append(low_empty_data)
		except Exception as e:
			print(f"序列化low_empty出错: {e}")
			continue

	# 序列化归零轴列表
	for return_zero in chan_macd_data.get('return_zero_list', []):
		try:
			return_zero_data = {
				'time': format_time_safely(return_zero['time'], client_tz),
				'end_time': format_time_safely(return_zero.get('end_time'), client_tz) if return_zero.get('end_time') else None,
				'type': return_zero.get('type', 'start'),
				'macd': return_zero.get('macd'),
				'signal': return_zero.get('signal'),
				'macdhist': return_zero.get('macdhist'),
				'end_macd': return_zero.get('end_macd'),
				'end_signal': return_zero.get('end_signal'),
				'end_macdhist': return_zero.get('end_macdhist')
			}
			serialized_data['return_zero_list'].append(return_zero_data)
		except Exception as e:
			print(f"序列化return_zero出错: {e}")
			continue
	
	# 序列化穿越零轴列表
	for cross0_up in chan_macd_data.get('cross0_up_list', []):
		try:
			cross0_up_data = {
				'time': format_time_safely(cross0_up['time'], client_tz),
				'type': cross0_up.get('type', 'start'),
				'macd': cross0_up.get('macd'),
				'signal': cross0_up.get('signal'),
				'macdhist': cross0_up.get('macdhist')
			}
			serialized_data['cross0_up_list'].append(cross0_up_data)
		except Exception as e:
			print(f"序列化cross0_up出错: {e}")
			continue
	
	for cross0_down in chan_macd_data.get('cross0_down_list', []):
		try:
			cross0_down_data = {
				'time': format_time_safely(cross0_down['time'], client_tz),
				'type': cross0_down.get('type', 'start'),
				'macd': cross0_down.get('macd'),
				'signal': cross0_down.get('signal'),
				'macdhist': cross0_down.get('macdhist')
			}
			serialized_data['cross0_down_list'].append(cross0_down_data)
		except Exception as e:
			print(f"序列化cross0_down出错: {e}")
			continue

	# 序列化 KLU 列表（仅导出需要的时间与背驰标志）
	for klu in chan_macd_data.get('klu_list', []):
		try:
			serialized_data['klu_list'].append({
				'time': format_time_safely(getattr(klu, 'time', None), client_tz),
				'continue_div': bool(getattr(klu, 'continue_div', False)),
				'separate_div': int(getattr(klu, 'separate_div', 0)) if getattr(klu, 'separate_div', 0) is not None else 0,
				'near0_return': int(getattr(klu, 'near0_return', 0)) if getattr(klu, 'near0_return', 0) is not None else 0
			})
		except Exception as e:
			print(f"序列化klu出错: {e}")
			continue
	
	return serialized_data

def is_smaller_timeframe(tf1, tf2):
	"""判断时间周期tf1是否小于tf2"""
	tf1_value = timeframe_to_minutes(tf1)
	tf2_value = timeframe_to_minutes(tf2)
	if tf1_value is None or tf2_value is None:
		return False
	return tf1_value < tf2_value

def is_smaller_or_equal_timeframe(tf1, tf2):
	"""判断时间周期tf1是否小于等于tf2"""
	tf1_value = timeframe_to_minutes(tf1)
	tf2_value = timeframe_to_minutes(tf2)
	if tf1_value is None or tf2_value is None:
		return False
	return tf1_value <= tf2_value

def clean_dataframe_for_json(df):
	"""清理DataFrame数据用于JSON序列化"""
	# 创建副本避免修改原始数据
	clean_df = df.copy()
	
	# 替换NaN值为None
	clean_df = clean_df.where(pd.notnull(clean_df), None)
	
	return clean_df

# ====== 趋势判定与趋势筛选（币对） ======

def classify_trend_stage(df):
	"""根据 EMA 斜率与多空排列判断趋势方向与阶段
	返回: direction in {"bull","bear","sideways"}, stage in {"early","mid","late"}, strength_score (0-100)
	"""
	if df is None or len(df) < 60:
		return "sideways", "early", 0

	# 使用 EMA5/10/24/52
	closes = df['close'].values
	ema5 = df['ema5'].values if 'ema5' in df else ta.EMA(df, timeperiod=5)
	ema10 = df['ema10'].values if 'ema10' in df else ta.EMA(df, timeperiod=10)
	ema24 = df['ema24'].values if 'ema24' in df else ta.EMA(df, timeperiod=24)
	ema52 = df['ema52'].values if 'ema52' in df else ta.EMA(df, timeperiod=52)

	# 最近N根用于斜率与排列判定
	lookback = min(30, len(df) - 1)
	if lookback <= 5:
		return "sideways", "early", 0

	# 简单斜率: 最近k根的线性变化率近似
	def slope(arr, k=10):
		k = min(k, len(arr) - 1)
		if k < 2:
			return 0.0
		y = arr[-k:]
		x = np.arange(k)
		# 最小二乘拟合斜率
		denom = np.dot(x - x.mean(), x - x.mean())
		if denom == 0:
			return 0.0
		m = np.dot(y - y.mean(), x - x.mean()) / denom
		return float(m)

	k_slope = 12  # 斜率窗口
	s5 = slope(ema5, k_slope)
	s10 = slope(ema10, k_slope)
	s24 = slope(ema24, k_slope)
	s52 = slope(ema52, k_slope)

	# 多空排列
	last5, last10, last24, last52 = ema5[-1], ema10[-1], ema24[-1], ema52[-1]
	bull_stack = last5 > last10 > last24 > last52
	bear_stack = last5 < last10 < last24 < last52

	# 波动性与动量增强: MACD 柱体最近均值
	macdhist = df['macdhist'].values if 'macdhist' in df else calculate_macd(df)['histogram']
	hist_recent = macdhist[-lookback:]
	hist_power = float(np.mean(np.abs(hist_recent))) if len(hist_recent) else 0.0

	# 方向
	if bull_stack and s24 > 0 and s52 > 0:
		direction = "bull"
	elif bear_stack and s24 < 0 and s52 < 0:
		direction = "bear"
	else:
		# 用价格相对 EMA52 辅助
		if closes[-1] > last52 and (s24 + s52) > 0:
			direction = "bull"
		elif closes[-1] < last52 and (s24 + s52) < 0:
			direction = "bear"
		else:
			direction = "sideways"

	# 阶段: 依据（斜率大小、与EMA52距离、MACD柱体扩张/收敛）
	dist52 = float((closes[-1] - last52) / last52) if last52 else 0.0
	slope_score = max(0.0, (abs(s24) + abs(s52)) * 1000.0)  # 归一化
	dist_score = min(50.0, abs(dist52) * 200.0)
	hist_score = min(30.0, hist_power * 10.0)
	strength = float(min(100.0, slope_score + dist_score + hist_score))

	# 简单阶段判定
	if direction == "sideways":
		stage = "early"
		strength = min(strength, 30.0)
	else:
		# 查看最近 hist 是否在扩大或收敛
		if len(hist_recent) >= 6:
			recent_growth = np.mean(np.abs(hist_recent[-3:])) - np.mean(np.abs(hist_recent[-6:-3]))
		else:
			recent_growth = 0.0

		if recent_growth > 0 and abs(dist52) < 0.05:
			stage = "early"
		elif recent_growth > 0 and abs(dist52) >= 0.05:
			stage = "mid"
		else:
			stage = "late"

	return direction, stage, strength


def load_crypto_symbols(limit=200):
	"""加载常见USDT永续合约交易对，返回列表"""
	refresh_data_service_metadata()
	if SYMBOLS:
		return SYMBOLS[:limit]
	try:
		markets = exchange.load_markets()
		symbols = [s for s in markets.keys() if '/USDT' in s and ':USDT' in s]
		return symbols[:limit]
	except Exception:
		return DEFAULT_SYMBOLS[:limit]



def get_uncompleted_seg_list(seg_list, client_tz):
	"""获取未完成线段列表，正确处理倒数第二个和最后一个未完成线段"""
	uncompleted_segs = [seg for seg in seg_list if not seg.is_sure]
	
	if len(uncompleted_segs) == 0:
		return []
	
	result = []
	
	for i, seg in enumerate(uncompleted_segs):
		is_last = (i == len(uncompleted_segs) - 1)  # 是否为最后一个未完成线段
		
		seg_data = {
			'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(),
			'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,
			'direction': convert_direction(seg.dir)
		}
		
		if is_last:
			# 最后一个未完成线段：没有结束时间和价格
			seg_data['end_time'] = None
			seg_data['end_price'] = None
		else:
			# 倒数第二个及之前的未完成线段：使用实际的结束时间和价格
			if seg.end_bi and seg.end_bi.end_klc:
				seg_data['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()
				seg_data['end_price'] = seg.end_bi.end_klc.high if convert_direction(seg.dir) == 1 else seg.end_bi.end_klc.low
			else:
				# 如果没有结束笔，设为None
				seg_data['end_time'] = None
				seg_data['end_price'] = None
		
		result.append(seg_data)
	
	return result