1535 lines
64 KiB
Python
1535 lines
64 KiB
Python
from flask import Flask, render_template, jsonify, request
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import ccxt
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import pandas as pd
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from datetime import datetime, timedelta
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import sys
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import os
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import matplotlib
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matplotlib.use('Agg') # 设置使用非GUI后端，必须在导入pyplot之前设置
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import matplotlib.pyplot as plt
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import io
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import base64
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import time
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import traceback
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from pytz import timezone
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import talib.abstract as ta
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import numpy as np
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# 添加父目录到系统路径
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from ChanLun import ChanLun
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from ChanEnum import Chan_BI_DIR, Chan_SEG_DIR, Chan_KLC_FX, Chan_FX_TYPE, Chan_MACDSEG_DIR, Chan_MACDHISTSET_DIR
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from cn_stock_data import ChinaStockData
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from ChanMACD import ChanMACD
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# 添加买卖点枚举类型
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class TRADE_POINT_TYPE:
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BUY1 = 1 # 一类买点
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BUY2 = 2 # 二类买点
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BUY3 = 3 # 三类买点
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SELL1 = -1 # 一类卖点
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SELL2 = -2 # 二类卖点
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SELL3 = -3 # 三类卖点
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app = Flask(__name__)
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# 初始化交易所
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exchange = ccxt.binance({
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'enableRateLimit': True,
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})
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# 初始化A股数据获取器
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china_stock = ChinaStockData()
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# 时间周期映射
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TIMEFRAMES = {
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'1m': '1分钟',
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'3m': '3分钟',
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'5m': '5分钟',
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'15m': '15分钟',
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'30m': '30分钟',
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'1h': '1小时',
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'2h': '2小时',
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'4h': '4小时',
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'6h': '6小时',
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'8h': '8小时',
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'12h': '12小时',
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'1d': '日线',
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'3d': '3日线',
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'1w': '周线',
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'1M': '月线',
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}
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# 常见交易对
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SYMBOLS = [
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'SOL/USDT:USDT', 'BTC/USDT:USDT', 'ETH/USDT:USDT', 'BNB/USDT:USDT', 'XRP/USDT:USDT',
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'ADA/USDT:USDT', 'DOGE/USDT:USDT', 'AVAX/USDT:USDT', 'DOT/USDT:USDT', 'MATIC/USDT:USDT'
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]
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# A股热门股票
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A_STOCK_SYMBOLS = china_stock.get_popular_stocks()
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def detect_symbol_type(symbol):
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"""检测交易对类型：crypto 或 a_stock"""
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if '/' in symbol and 'USDT' in symbol:
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return 'crypto'
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elif len(symbol) == 6 and symbol.isdigit():
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return 'a_stock'
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else:
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return 'unknown'
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def get_kl_data(symbol, timeframe, limit=1000, start_time=None, end_time=None):
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"""获取K线数据，支持加密货币和A股"""
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symbol_type = detect_symbol_type(symbol)
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if symbol_type == 'crypto':
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return get_crypto_kl_data(symbol, timeframe, limit, start_time, end_time)
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elif symbol_type == 'a_stock':
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return get_a_stock_kl_data(symbol, timeframe, limit, start_time, end_time)
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else:
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return None
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def get_crypto_kl_data(symbol, timeframe, limit=1000, start_time=None, end_time=None):
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"""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"""
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try:
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# 初始化参数
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since = None
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if start_time:
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try:
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since = int(start_time)
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except ValueError:
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pass
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# 结束时间处理
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until = None
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if end_time:
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try:
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until = int(end_time)
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except ValueError:
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pass
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# 根据时间周期调整每次请求的数据量
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batch_size = 1000 # 默认批次大小
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if timeframe in ['1m', '3m', '5m']:
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batch_size = 1000 # 分钟级数据减少批次大小
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elif timeframe in ['15m', '30m', '1h']:
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batch_size = 1000
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else:
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batch_size = 1500 # 日线及以上可以获取更多
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batch_size = 1500 # 默认批次大小
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# 初始化存储所有K线数据的列表
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all_ohlcv = []
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# 初始化当前查询的开始时间
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current_since = since
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# 添加请求计数和最大限制
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request_count = 0
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max_requests = 300 # 最大请求次数，防止无限循环
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# 分页加载数据
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while request_count < max_requests:
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request_count += 1
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try:
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# 获取当前页的数据
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ohlcv = exchange.fetch_ohlcv(symbol, timeframe, since=current_since, limit=batch_size)
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# 如果没有获取到数据，结束循环
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if not ohlcv or len(ohlcv) == 0:
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break
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# 将获取到的数据添加到总列表中
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all_ohlcv.extend(ohlcv)
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# 获取最后一条数据的时间戳
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last_timestamp = ohlcv[-1][0]
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# 如果已达到结束时间，结束循环
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if until and last_timestamp >= until:
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break
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# 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
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if len(ohlcv) < batch_size:
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break
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# 更新下一页的开始时间（加1毫秒避免重复）
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current_since = last_timestamp + 1
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except Exception as e:
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# 如果单个批次失败，继续尝试下一个批次
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if current_since:
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# 尝试增加时间跳过可能的问题时间点
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current_since += 60000 # 跳过1分钟
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else:
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break
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# 防止API请求过于频繁
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time.sleep(0.3) # 减少到0.3秒提高效率
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# 数据为空的情况
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if not all_ohlcv or len(all_ohlcv) == 0:
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return None
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# 转换为DataFrame
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df = pd.DataFrame(all_ohlcv, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])
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df['date'] = pd.to_datetime(df['timestamp'], unit='ms').dt.tz_localize('UTC').dt.tz_convert('Asia/Shanghai')
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# 在客户端进行结束时间过滤
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if until:
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df = df[df['timestamp'] <= until]
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# 去除重复数据
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df = df.drop_duplicates(subset=['timestamp'])
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# 按时间排序
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df = df.sort_values('timestamp')
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# 限制数据条数的逻辑 - 优先考虑时间范围
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if start_time and end_time:
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# 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
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if len(df) > 100000: # 防止数据量过大，设置一个合理的上限
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df = df.tail(100000).reset_index(drop=True)
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elif limit and len(df) > limit:
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# 如果没有指定明确时间范围，使用默认的limit限制
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df = df.tail(limit).reset_index(drop=True)
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# 如果过滤后没有数据，返回None
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if len(df) == 0:
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return None
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return df
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except Exception as e:
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return None
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def get_a_stock_kl_data(symbol, timeframe, limit=1000, start_time=None, end_time=None):
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"""获取A股K线数据"""
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try:
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# 处理时间戳参数转换为日期字符串
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start_date = None
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end_date = None
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if start_time:
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try:
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# 尝试解析时间戳（毫秒）
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start_timestamp = int(start_time)
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start_date = datetime.fromtimestamp(start_timestamp / 1000).strftime('%Y-%m-%d')
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except (ValueError, TypeError):
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# 如果不是时间戳，尝试解析datetime-local格式 (YYYY-MM-DDTHH:MM)
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try:
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if 'T' in str(start_time):
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# datetime-local格式：2025-05-19T06:07
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start_date = str(start_time).split('T')[0] # 只取日期部分
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else:
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start_date = str(start_time)
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except:
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start_date = start_time
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if end_time:
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try:
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# 尝试解析时间戳（毫秒）
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end_timestamp = int(end_time)
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end_date = datetime.fromtimestamp(end_timestamp / 1000).strftime('%Y-%m-%d')
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except (ValueError, TypeError):
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# 如果不是时间戳，尝试解析datetime-local格式
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try:
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if 'T' in str(end_time):
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# datetime-local格式：2025-05-26T06:07
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end_date = str(end_time).split('T')[0] # 只取日期部分
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else:
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end_date = str(end_time)
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except:
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end_date = end_time
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# 如果用户指定了时间范围，优先获取该范围内的所有数据
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actual_limit = limit
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if start_date and end_date:
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actual_limit = None # 不限制数据条数，获取完整时间范围数据
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# 调用A股数据获取器
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df = china_stock.get_kl_data(symbol, timeframe, start_date, end_date, actual_limit)
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if df is None:
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return None
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return df
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except Exception as e:
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return None
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def add_indicators(df):
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fast = 12*1
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slow = 26*1
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period = 9*1
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macd = ta.MACD(df, fastperiod=fast, slowperiod=slow, signalperiod=period)
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df['macd'] = macd['macd']
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df['macdsignal'] = macd['macdsignal']
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df['macdhist'] = macd['macdhist']
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df['ma5'] = (ta.MA(df, timeperiod=5)).fillna(0)
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df['ma10'] = (ta.MA(df, timeperiod=10)).fillna(0)
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df['ma30'] = (ta.EMA(df, timeperiod=30)).fillna(0)
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df['ma250'] = (ta.MA(df, timeperiod=250)).fillna(0)
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# 新增 EMA 指标
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df['ema5'] = (ta.EMA(df, timeperiod=5)).fillna(0)
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df['ema10'] = (ta.EMA(df, timeperiod=10)).fillna(0)
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df['ema24'] = (ta.EMA(df, timeperiod=24)).fillna(0)
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df['ema52'] = (ta.EMA(df, timeperiod=52)).fillna(0)
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# 常用SMA 24/52
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try:
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df['sma24'] = (ta.SMA(df, timeperiod=24)).fillna(0)
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df['sma52'] = (ta.SMA(df, timeperiod=52)).fillna(0)
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except Exception:
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df['sma24'] = 0
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df['sma52'] = 0
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df['rsi'] = ta.RSI(df, timeperiod=14)
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# 计算布林带 (当前周期 - 20周期，2标准差)
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bb = ta.BBANDS(df, timeperiod=365, nbdevup=3.0, nbdevdn=3.0, matype=0)
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df['bb_upper'] = bb['upperband'].fillna(0)
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df['bb_middle'] = bb['middleband'].fillna(0)
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df['bb_lower'] = bb['lowerband'].fillna(0)
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bb30 = ta.BBANDS(df, timeperiod=41, nbdevup=2.3, nbdevdn=2.3, matype=0)
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#bb30 = ta.BBANDS(df, timeperiod=20, nbdevup=2.0, nbdevdn=2.0, matype=0)
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df['bbup30'] = bb30['upperband'].fillna(0)
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df['bblow30'] = bb30['lowerband'].fillna(0)
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bb302 = ta.BBANDS(df, timeperiod=41, nbdevup=2.0, nbdevdn=2.0, matype=0)
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#bb302 = ta.BBANDS(df, timeperiod=20, nbdevup=2.0, nbdevdn=2.0, matype=0)
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df['bbup302'] = bb302['upperband'].fillna(0)
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df['bblow302'] = bb302['lowerband'].fillna(0)
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# 计算次周期布林带 (14周期，2标准差)
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bb_element = ta.BBANDS(df, timeperiod=14, nbdevup=2.0, nbdevdn=2.0, matype=0)
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df['element_bb_upper'] = bb_element['upperband'].fillna(0)
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df['element_bb_middle'] = bb_element['middleband'].fillna(0)
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df['element_bb_lower'] = bb_element['lowerband'].fillna(0)
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df['macd'] = df['macd'].fillna(0)
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df['macdsignal'] = df['macdsignal'].fillna(0)
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df['macdhist'] = df['macdhist'].fillna(0)
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df['ma5'] = df['ma5'].fillna(0)
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df['ma10'] = df['ma10'].fillna(0)
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df['ma30'] = df['ma30'].fillna(0)
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df['ma250'] = df['ma250'].fillna(0)
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df['ema5'] = df['ema5'].fillna(0)
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df['ema10'] = df['ema10'].fillna(0)
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df['ema24'] = df['ema24'].fillna(0)
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df['ema52'] = df['ema52'].fillna(0)
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df['sma24'] = df['sma24'].fillna(0)
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df['sma52'] = df['sma52'].fillna(0)
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df['rsi'] = df['rsi'].fillna(0)
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df['avg_volume'] = df['volume'].rolling(10).mean()
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# 计算量比，避免产生Infinity值
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df['volume_ratio'] = df['volume'] / df['avg_volume']
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# 填充缺失值（前N根K线）
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df['volume_ratio'] = df['volume_ratio'].fillna(1.0)
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df['avg_volume'] = df['avg_volume'].fillna(0)
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# 处理Infinity和-Infinity值
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df['volume_ratio'] = df['volume_ratio'].replace([float('inf'), float('-inf')], 1.0)
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# 计算ATR (Average True Range) - 14周期
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df['atr'] = ta.ATR(df, timeperiod=14)
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df['atr'] = df['atr'].fillna(0)
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return df
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def calculate_macd(df):
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"""计算MACD指标"""
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exp1 = df['close'].ewm(span=12, adjust=False).mean()
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exp2 = df['close'].ewm(span=26, adjust=False).mean()
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macd = exp1 - exp2
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signal = macd.ewm(span=9, adjust=False).mean()
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histogram = macd - signal
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return {
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'macd': macd.tolist(),
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'signal': signal.tolist(),
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'histogram': histogram.tolist()
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}
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def analyze_chan(df, symbol=None, timeframe=None):
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"""进行缠论分析"""
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chan = ChanLun()
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# 初始化多时间周期数据以获取EMA52
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ema52_dict = None
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# 获取分析结果
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klu_list = chan.get_kl_data(df)
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klc_list = chan.get_klc_list(df)
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bi_list = chan.cal_bi_list(klc_list)
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#for index in range(0, 10):
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#print(bi_list[index].start_time, bi_list[index].start_klc.end_time, bi_list[index].dir)
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seg_list = chan.get_seg_list(bi_list)
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zs_list = chan.calculate_zs(bi_list, seg_list)
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# 计算笔中枢（BI中枢）并拍平成列表
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try:
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bi_zs_nested = chan.cal_bi_zs(seg_list)
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bi_zs_list = [zs for group in bi_zs_nested for zs in (group or [])] if bi_zs_nested else []
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except Exception:
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bi_zs_list = []
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# 添加买卖点识别
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for bi in bi_list:
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bi.cal_macdhist()
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for bi in bi_list:
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bi.cal_macd_div()
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#print(bi.start_time, bi.macd_hist, bi.macd_div)
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# 添加ChanMACD分析
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chan_macd = None
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chan_macd_data = {}
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try:
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if klu_list and len(klu_list) > 0:
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print(f"获取到KLU列表，长度: {len(klu_list)}")
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chan_macd = ChanMACD(klu_list)
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chan_macd_data = {
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'seg_list': chan_macd.seg_list,
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'unittf_list': chan_macd.unittf_list,
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'histset_list': chan_macd.histset_list,
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'klu_list': chan_macd.klu_list,
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'high_position_list': chan_macd.high_position_list,
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'high_empty_list': chan_macd.high_empty_list,
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'low_position_list': getattr(chan_macd, 'low_position_list', []),
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'low_empty_list': getattr(chan_macd, 'low_empty_list', []),
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'return_zero_list': chan_macd.return_zero_list,
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'cross0_up_list': chan_macd.cross0_up_list,
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'cross0_down_list': chan_macd.cross0_down_list
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}
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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)}")
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else:
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print("未能获取KLU列表或列表为空")
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chan_macd_data = {
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'seg_list': [],
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'unittf_list': [],
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'histset_list': [],
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'high_position_list': [],
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'high_empty_list': [],
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'return_zero_list': [],
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'cross0_up_list': [],
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'cross0_down_list': []
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}
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except Exception as e:
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print(f"ChanMACD分析出错: {e}")
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|
import traceback
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|
traceback.print_exc()
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chan_macd_data = {
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'seg_list': [],
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'unittf_list': [],
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'histset_list': [],
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'high_position_list': [],
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'high_empty_list': [],
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'low_position_list': [],
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'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
|
|
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
|
|
})
|
|
except Exception as e:
|
|
# 如果出错，仍然添加基本信息，但分型强度为0
|
|
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
|
|
})
|
|
|
|
|
|
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
|
|
'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"""
|
|
# 定义时间周期的分钟数映射
|
|
tf_values = {
|
|
'1m': 1,
|
|
'3m': 3,
|
|
'5m': 5,
|
|
'15m': 15,
|
|
'30m': 30,
|
|
'1h': 60,
|
|
'2h': 120,
|
|
'4h': 240,
|
|
'6h': 360,
|
|
'8h': 480,
|
|
'12h': 720,
|
|
'1d': 1440,
|
|
'3d': 4320,
|
|
'1w': 10080,
|
|
'1M': 43200
|
|
}
|
|
|
|
# 获取时间周期对应的分钟数
|
|
tf1_value = tf_values.get(tf1)
|
|
tf2_value = tf_values.get(tf2)
|
|
|
|
# 如果某个时间周期不在映射中，返回False
|
|
if tf1_value is None or tf2_value is None:
|
|
return False
|
|
|
|
# 返回tf1是否小于tf2
|
|
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"""
|
|
# 定义时间周期的分钟数映射
|
|
tf_values = {
|
|
'1m': 1,
|
|
'3m': 3,
|
|
'5m': 5,
|
|
'15m': 15,
|
|
'30m': 30,
|
|
'1h': 60,
|
|
'2h': 120,
|
|
'4h': 240,
|
|
'6h': 360,
|
|
'8h': 480,
|
|
'12h': 720,
|
|
'1d': 1440,
|
|
'3d': 4320,
|
|
'1w': 10080,
|
|
'1M': 43200
|
|
}
|
|
|
|
# 获取时间周期对应的分钟数
|
|
tf1_value = tf_values.get(tf1)
|
|
tf2_value = tf_values.get(tf2)
|
|
|
|
# 如果某个时间周期不在映射中，返回False
|
|
if tf1_value is None or tf2_value is None:
|
|
return False
|
|
|
|
# 返回tf1是否小于等于tf2
|
|
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"""
|
|
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 SYMBOLS
|
|
|
|
|
|
@app.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
|
|
})
|
|
|
|
|
|
@app.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)
|
|
}
|
|
})
|
|
|
|
@app.route('/')
|
|
def index():
|
|
"""主页"""
|
|
return render_template('index.html',
|
|
timeframes=TIMEFRAMES,
|
|
symbols=SYMBOLS,
|
|
a_stock_symbols=A_STOCK_SYMBOLS)
|
|
|
|
@app.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
|
|
element_timeframe = request.args.get('element_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'
|
|
|
|
# 获取是否需要回放数据的参数
|
|
need_replay_data = request.args.get('need_replay_data', 'false').lower() == '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'})
|
|
|
|
# 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)
|
|
|
|
# 如果需要回放数据，生成逐步计算的回放数据
|
|
if need_replay_data:
|
|
replay_data = generate_replay_data(df, client_tz, symbol, element_timeframe, start_time, end_time)
|
|
else:
|
|
replay_data = None
|
|
|
|
# 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.end_klc],
|
|
# 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': None, # 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': None, # 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.end_klc],
|
|
'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.end_klc],
|
|
# 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),
|
|
'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
|
|
} 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
|
|
})
|
|
|
|
# 如果生成了回放数据，添加到返回结果中
|
|
if replay_data is not None:
|
|
result['replay_data'] = replay_data
|
|
|
|
# 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.end_klc]
|
|
# 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': None, # 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': None, # 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.end_klc]
|
|
|
|
# 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),
|
|
'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
|
|
} 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
|
|
|
|
pass
|
|
|
|
return jsonify(result)
|
|
|
|
@app.route('/api/symbols')
|
|
def get_symbols():
|
|
"""获取可用交易对"""
|
|
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({'error': str(e)})
|
|
|
|
@app.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)})
|
|
|
|
@app.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)})
|
|
|
|
@app.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)})
|
|
|
|
@app.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)})
|
|
|
|
@app.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)})
|
|
|
|
|
|
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
|
|
|
|
def format_fx_type(fx_type):
|
|
"""格式化分型类型显示"""
|
|
fx_type_map = {
|
|
'TOP1': '顶分型1',
|
|
'TOP2': '顶分型2',
|
|
'TOP3': '顶分型3',
|
|
'BOTTOM1': '底分型1',
|
|
'BOTTOM2': '底分型2',
|
|
'BOTTOM3': '底分型3',
|
|
'TOP': '顶分型',
|
|
'BOTTOM': '底分型'
|
|
}
|
|
return fx_type_map.get(fx_type, fx_type)
|
|
|
|
if __name__ == '__main__':
|
|
app.run(debug=True, host='0.0.0.0', port=8128) |