1407 lines
60 KiB
Python
1407 lines
60 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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# 导入chan.py项目的核心模块
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from Chan import CChan
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from ChanConfig import CChanConfig
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from Common.CEnum import AUTYPE, DATA_SRC, KL_TYPE, BI_DIR, SEG_DIR, FX_TYPE
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from Common.CTime import CTime
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from DataAPI.CommonStockAPI import CCommonStockApi
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from KLine.KLine_Unit import CKLine_Unit
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# 从原有的cn_stock_data导入A股数据获取
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try:
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from cn_stock_data import ChinaStockData
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china_stock = ChinaStockData()
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except ImportError:
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print("警告: 无法导入cn_stock_data，A股功能将不可用")
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china_stock = None
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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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# 时间周期映射
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TIMEFRAMES = {
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'1m': '1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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'4h': '4小时',
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'1d': '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() if china_stock else []
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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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print(f"未知的交易对类型: {symbol}")
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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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print(f"无效的起始时间: {start_time}")
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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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print(f"无效的结束时间: {end_time}")
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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 = 500 # 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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# 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 = 50 # 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) > 10000: # 防止数据量过大，设置一个合理的上限
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df = df.tail(10000).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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print(f"获取加密货币数据错误: {e}")
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traceback.print_exc()
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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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print(f"获取A股数据错误: {e}")
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traceback.print_exc()
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return None
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def add_indicators(df):
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fast = 8
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slow = 16
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period = 6
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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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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=20, nbdevup=2.0, nbdevdn=2.0, 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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# 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['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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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=10, 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, timeframe='1d'):
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"""使用chan.py项目进行缠论分析"""
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try:
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# 转换时间周期映射 - 根据用户选择的实际时间周期进行映射
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timeframe_map = {
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'1m': KL_TYPE.K_1M,
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'3m': KL_TYPE.K_3M,
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'5m': KL_TYPE.K_5M,
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'15m': KL_TYPE.K_15M,
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'30m': KL_TYPE.K_30M,
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'1h': KL_TYPE.K_60M,
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'2h': KL_TYPE.K_60M, # 2小时用1小时替代
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'4h': KL_TYPE.K_4H,
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'6h': KL_TYPE.K_6H,
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'8h': KL_TYPE.K_8H,
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'12h': KL_TYPE.K_12H,
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'1d': KL_TYPE.K_DAY,
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'3d': KL_TYPE.K_3DAY,
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'1w': KL_TYPE.K_WEEK,
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'1M': KL_TYPE.K_MON,
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}
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# 根据传入的timeframe选择对应的级别
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kl_type = timeframe_map.get(timeframe, KL_TYPE.K_DAY)
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# 创建配置 - 针对不同时间周期优化买卖点识别
|
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config = CChanConfig({
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"bi_strict": True,
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"trigger_step": False,
|
||
"skip_step": 0,
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||
"divergence_rate": float("inf"),
|
||
"bsp2_follow_1": False,
|
||
"bsp3_follow_1": False,
|
||
"min_zs_cnt": 0,
|
||
"bs1_peak": False,
|
||
"macd_algo": "peak",
|
||
"bs_type": '1,2,3a,1p,2s,3b',
|
||
"print_warning": True, # 开启警告以便调试
|
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"zs_algo": "normal", # 使用标准中枢算法提高稳定性 over_seg, normal, auto
|
||
"bi_algo": "normal",
|
||
})
|
||
# 将数据设置到WebDataAPI
|
||
from DataAPI.WebDataAPI import WebDataAPI
|
||
WebDataAPI.set_data("WEB_DATA", df)
|
||
|
||
# 创建CChan实例，使用自定义数据源
|
||
chan = CChan(
|
||
code="WEB_DATA",
|
||
begin_time=None,
|
||
end_time=None,
|
||
data_src="custom:WebDataAPI.WebDataAPI",
|
||
lv_list=[kl_type],
|
||
config=config,
|
||
autype=AUTYPE.QFQ,
|
||
)
|
||
|
||
# 获取分析结果
|
||
kline_list = chan[kl_type]
|
||
|
||
# 提取笔列表
|
||
bi_list = []
|
||
if hasattr(kline_list, 'bi_list') and kline_list.bi_list:
|
||
bi_list = kline_list.bi_list
|
||
|
||
# 提取线段列表
|
||
seg_list = []
|
||
if hasattr(kline_list, 'seg_list') and kline_list.seg_list:
|
||
seg_list = kline_list.seg_list
|
||
|
||
# 提取中枢列表
|
||
zs_list = []
|
||
if hasattr(kline_list, 'zs_list') and kline_list.zs_list:
|
||
zs_list = kline_list.zs_list
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||
|
||
# 直接从KLine_List获取买卖点
|
||
buy_sell_points = []
|
||
try:
|
||
if hasattr(kline_list, 'bs_point_lst') and kline_list.bs_point_lst:
|
||
bsp_list = sorted(kline_list.bs_point_lst.lst, key=lambda x: x.klu.time)
|
||
for i, bsp in enumerate(bsp_list):
|
||
# 根据买卖点类型选择正确的价格
|
||
if bsp.is_buy:
|
||
# 买点使用低点价格
|
||
price = bsp.klu.low
|
||
else:
|
||
# 卖点使用高点价格
|
||
price = bsp.klu.high
|
||
if bsp.type2str().__contains__("1"):
|
||
buy_sell_points.append({
|
||
'type': 1 if bsp.is_buy else -1, # 简化买卖点类型
|
||
'time': bsp.klu.time, # 保持CTime对象，后续统一格式化
|
||
'price': price,
|
||
'desc': f"{bsp.type2str()}"
|
||
})
|
||
else:
|
||
buy_sell_points = []
|
||
except Exception as e:
|
||
print(f"[{timeframe}] 获取买卖点失败: {e}")
|
||
buy_sell_points = []
|
||
# 提取分型信息
|
||
klc_fx_info = []
|
||
klu_fx_info = []
|
||
# 从K线列表中提取分型信息
|
||
if hasattr(kline_list, 'lst'):
|
||
for klc in kline_list.lst:
|
||
if hasattr(klc, 'fx') and klc.fx != FX_TYPE.UNKNOWN:
|
||
klc_fx_info.append({
|
||
'time': klc.time_end,
|
||
'price': klc.low if klc.fx == FX_TYPE.BOTTOM else klc.high,
|
||
'fx_type': str(klc.fx).replace("FX_TYPE.", ""),
|
||
'is_bottom': klc.fx == FX_TYPE.BOTTOM,
|
||
'fx_strength': 1, # 基础分型强度
|
||
'fx_strength_level': "中",
|
||
'is_strong_fx': False
|
||
})
|
||
|
||
# 提取KLU分型信息
|
||
if hasattr(klc, 'lst'):
|
||
for klu in klc.lst:
|
||
if hasattr(klu, 'fx') and klu.fx != FX_TYPE.UNKNOWN:
|
||
klu_fx_info.append({
|
||
'time': klu.time,
|
||
'price': klu.low if klu.fx == FX_TYPE.BOTTOM else klu.high,
|
||
'fx_type': str(klu.fx).replace("FX_TYPE.", ""),
|
||
'is_bottom': klu.fx == FX_TYPE.BOTTOM,
|
||
'fx_strength': 1,
|
||
'fx_strength_level': "中",
|
||
'is_strong_fx': False,
|
||
'fx_confirmed': True
|
||
})
|
||
|
||
# 只在数据量较少时打印分析结果摘要，避免在回放时产生过多日志
|
||
if len(df) <= 50:
|
||
print(f"chan.py分析完成: 笔{len(bi_list)}个, 线段{len(seg_list)}个, 中枢{len(zs_list)}个, 买卖点{len(buy_sell_points)}个")
|
||
|
||
|
||
return {
|
||
'klc_list': kline_list.lst if hasattr(kline_list, 'lst') else [],
|
||
'klu_list': [], # KLU数据在klc中
|
||
'bi_list': bi_list,
|
||
'seg_list': seg_list,
|
||
'zs_list': zs_list,
|
||
'trade_points': buy_sell_points,
|
||
'klc_fx_info': klc_fx_info,
|
||
'klu_fx_info': klu_fx_info
|
||
}
|
||
|
||
except Exception as e:
|
||
print(f"chan.py分析出错: {e}")
|
||
traceback.print_exc()
|
||
# 返回空结果
|
||
return {
|
||
'klc_list': [],
|
||
'klu_list': [],
|
||
'bi_list': [],
|
||
'seg_list': [],
|
||
'zs_list': [],
|
||
'trade_points': [],
|
||
'klc_fx_info': [],
|
||
'klu_fx_info': []
|
||
}
|
||
|
||
|
||
# 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 == BI_DIR.UP or direction == SEG_DIR.UP:
|
||
return 1
|
||
elif direction == BI_DIR.DOWN or direction == 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、datetime和CTime对象"""
|
||
if time_obj is None:
|
||
return None
|
||
|
||
# 如果是CTime对象，转换为字符串
|
||
if hasattr(time_obj, 'ts'): # CTime对象有ts属性
|
||
from datetime import datetime
|
||
dt = datetime.fromtimestamp(time_obj.ts)
|
||
return dt.astimezone(client_tz).isoformat()
|
||
|
||
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
|
||
try:
|
||
return time_obj.astimezone(client_tz).isoformat()
|
||
except:
|
||
return str(time_obj)
|
||
|
||
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
|
||
|
||
@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() == '':
|
||
print(f"错误: 空交易对")
|
||
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'
|
||
|
||
print(f"API请求参数: symbol={symbol}, timeframe={timeframe}, element_timeframe={element_timeframe}")
|
||
print(f"时间范围: start_time={start_time}, end_time={end_time}")
|
||
print(f"elements_only参数: 原始值={elements_only_param}, 处理后={elements_only}")
|
||
|
||
# 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):
|
||
print(f"错误: 元素周期 {element_timeframe} 大于主周期 {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:
|
||
print(f"错误: 获取数据失败 - symbol={symbol}, timeframe={timeframe}")
|
||
return jsonify({'error': '获取数据失败'})
|
||
|
||
if len(df) == 0:
|
||
print(f"错误: 所选时间范围内没有数据 - symbol={symbol}, timeframe={timeframe}")
|
||
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:
|
||
print(f"处理主周期数据 (elements_only={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, timeframe)
|
||
|
||
# 计算MACD
|
||
macd_data = calculate_macd(df)
|
||
|
||
# 计算笔的MACD背离值（用于显示）
|
||
bi_macd_divs = calculate_bi_macd_divergence(analysis_result['bi_list'])
|
||
|
||
# 直接使用小周期相同的方式获取买卖点
|
||
all_trade_points = analysis_result['trade_points']
|
||
print(f"主周期买卖点：共{len(all_trade_points)}个")
|
||
|
||
# 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'),
|
||
'bi_list': [{
|
||
'start_time': format_time_safely(bi.begin_klc.time_end, client_tz),
|
||
'end_time': format_time_safely(bi.end_klc.time_end, client_tz) if bi.end_klc else None,
|
||
'start_price': bi.begin_klc.low if convert_direction(bi.dir) == 1 else bi.begin_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_divs.get(i, 0))
|
||
} for i, bi in enumerate(analysis_result['bi_list']) if bi.end_klc],
|
||
'seg_list': [{
|
||
'start_time': format_time_safely(seg.start_bi.begin_klc.time_end, client_tz),
|
||
'end_time': format_time_safely(seg.end_bi.end_klc.time_end, client_tz) if seg.end_bi else None,
|
||
'start_price': seg.start_bi.begin_klc.low if convert_direction(seg.dir) == 1 else seg.start_bi.begin_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.end_bi],
|
||
'zs_list': [{
|
||
'start_time': format_time_safely(zs.begin.time, client_tz),
|
||
'end_time': format_time_safely(zs.end.time, client_tz) if zs.end else None,
|
||
'zg': zs.high,
|
||
'zd': zs.low,
|
||
'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],
|
||
# 添加未完成中枢列表
|
||
'uncompleted_zs_list': [{
|
||
'start_time': format_time_safely(zs.begin.time, client_tz),
|
||
'end_time': None, # 未完成中枢没有结束时间
|
||
'zg': zs.high,
|
||
'zd': zs.low,
|
||
'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],
|
||
'trade_points': [{
|
||
'type': point['type'],
|
||
'time': format_time_safely(point['time'], client_tz),
|
||
'price': point['price'],
|
||
'desc': point['desc']
|
||
} for point in all_trade_points],
|
||
'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']],
|
||
'klu_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
|
||
'fx_confirmed': bool(point['fx_confirmed']) # 分型是否确认
|
||
} for point in analysis_result['klu_fx_info']]
|
||
})
|
||
else:
|
||
print(f"只请求元素数据，跳过主周期数据处理 (elements_only={elements_only})")
|
||
|
||
# 如果有指定分形元素时间周期，获取小周期数据
|
||
if element_timeframe:
|
||
print(f"处理元素周期数据: {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, element_timeframe)
|
||
|
||
# 计算小周期MACD数据
|
||
element_macd_data = calculate_macd(element_df)
|
||
|
||
# 计算小周期笔的MACD背离值（用于显示）
|
||
element_bi_macd_divs = calculate_bi_macd_divergence(element_analysis['bi_list'])
|
||
|
||
# 直接使用小周期Chan.py的买卖点，不再添加自定义买卖点
|
||
element_all_trade_points = element_analysis['trade_points']
|
||
print(f"使用小周期Chan.py内置买卖点：共{len(element_all_trade_points)}个")
|
||
|
||
# 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()
|
||
}
|
||
|
||
result['element_bi_list'] = [{
|
||
'start_time': format_time_safely(bi.begin_klc.time_end, client_tz),
|
||
'end_time': format_time_safely(bi.end_klc.time_end, client_tz) if bi.end_klc else None,
|
||
'start_price': bi.begin_klc.low if convert_direction(bi.dir) == 1 else bi.begin_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(element_bi_macd_divs.get(i, 0))
|
||
} for i, bi in enumerate(element_analysis['bi_list']) if 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')
|
||
|
||
result['element_seg_list'] = [{
|
||
'start_time': format_time_safely(seg.start_bi.begin_klc.time_end, client_tz),
|
||
'end_time': format_time_safely(seg.end_bi.end_klc.time_end, client_tz) if seg.end_bi else None,
|
||
'start_price': seg.start_bi.begin_klc.low if convert_direction(seg.dir) == 1 else seg.start_bi.begin_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.end_bi]
|
||
|
||
result['element_zs_list'] = [{
|
||
'start_time': format_time_safely(zs.begin.time, client_tz),
|
||
'end_time': format_time_safely(zs.end.time, client_tz) if zs.end else None,
|
||
'zg': zs.high,
|
||
'zd': zs.low,
|
||
'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]
|
||
|
||
result['element_uncompleted_zs_list'] = [{
|
||
'start_time': format_time_safely(zs.begin.time, client_tz),
|
||
'end_time': None, # 未完成中枢没有结束时间
|
||
'zg': zs.high,
|
||
'zd': zs.low,
|
||
'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]
|
||
|
||
result['element_trade_points'] = [{
|
||
'type': point['type'],
|
||
'time': format_time_safely(point['time'], client_tz),
|
||
'price': point['price'],
|
||
'desc': point['desc']
|
||
} for point in element_all_trade_points]
|
||
|
||
# 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']]
|
||
|
||
result['element_klu_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
|
||
'fx_confirmed': bool(point['fx_confirmed']) # 分型是否确认
|
||
} for point in element_analysis['klu_fx_info']]
|
||
|
||
print(f"小周期分析完成: {element_timeframe}, 笔数量: {len(result['element_bi_list'])}, {'仅元素数据' if elements_only else '包含主周期数据'}")
|
||
else:
|
||
print(f"无法获取小周期数据: {element_timeframe}")
|
||
|
||
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)})
|
||
|
||
@app.route('/api/filter_stocks', methods=['POST'])
|
||
def filter_stocks():
|
||
"""筛选满足条件的A股股票"""
|
||
try:
|
||
data = request.get_json()
|
||
start_time = data.get('start_time')
|
||
end_time = data.get('end_time')
|
||
timeframe = data.get('timeframe', '1d')
|
||
fx_strength_threshold = data.get('fx_strength_threshold', 1.0)
|
||
|
||
if not start_time or not end_time:
|
||
return jsonify({'error': '开始时间和结束时间不能为空'})
|
||
|
||
# 获取所有A股股票列表，如果失败则使用热门股票作为备用
|
||
stock_list = []
|
||
data_source = ""
|
||
try:
|
||
print("正在获取完整股票列表...")
|
||
stock_list = china_stock.get_stock_list()
|
||
if stock_list and len(stock_list) > 0:
|
||
print(f"成功获取完整股票列表: {len(stock_list)} 只股票")
|
||
data_source = "完整股票列表"
|
||
else:
|
||
raise Exception("获取到的股票列表为空")
|
||
except Exception as e:
|
||
print(f"获取完整股票列表失败: {e}")
|
||
print("使用热门股票列表作为备用...")
|
||
try:
|
||
popular_stocks = china_stock.get_popular_stocks()
|
||
stock_list = [{'symbol': stock['symbol'], 'name': stock['name']} for stock in popular_stocks]
|
||
print(f"使用热门股票列表: {len(stock_list)} 只股票")
|
||
data_source = "热门股票列表"
|
||
except Exception as e2:
|
||
print(f"获取热门股票列表也失败: {e2}")
|
||
# 检查是否是网络连接问题
|
||
if "timeout" in str(e).lower() or "connection" in str(e).lower() or "network" in str(e).lower():
|
||
return jsonify({
|
||
'error': '网络连接超时，无法获取股票数据。请检查网络连接后重试。',
|
||
'error_type': 'network_error',
|
||
'suggestion': '请确保网络连接正常，或稍后重试。'
|
||
})
|
||
else:
|
||
return jsonify({'error': f'无法获取股票列表: {str(e)}'})
|
||
|
||
if not stock_list:
|
||
return jsonify({
|
||
'error': '无法获取股票列表，请检查网络连接后重试',
|
||
'error_type': 'network_error',
|
||
'suggestion': '请确保网络连接正常，或稍后重试。'
|
||
})
|
||
|
||
results = []
|
||
processed_count = 0
|
||
total_count = len(stock_list)
|
||
failed_count = 0
|
||
|
||
print(f"开始筛选股票，总数: {total_count}, 时间范围: {start_time} 到 {end_time}, 周期: {timeframe}")
|
||
|
||
for stock in stock_list:
|
||
try:
|
||
symbol = stock['symbol']
|
||
name = stock['name']
|
||
processed_count += 1
|
||
|
||
# 每处理20只股票打印一次进度
|
||
if processed_count % 20 == 0:
|
||
print(f"已处理 {processed_count}/{total_count} 只股票，成功: {len(results)}, 失败: {failed_count}")
|
||
|
||
# 获取股票K线数据
|
||
df = get_a_stock_kl_data(symbol, timeframe, start_time=start_time, end_time=end_time)
|
||
|
||
if df is None or len(df) < 3:
|
||
failed_count += 1
|
||
# 如果连续失败太多，可能是网络问题
|
||
if failed_count > 10 and len(results) == 0:
|
||
print(f"连续失败 {failed_count} 次，可能是网络问题")
|
||
return jsonify({
|
||
'error': '网络连接不稳定，无法获取股票数据。请检查网络连接后重试。',
|
||
'error_type': 'network_error',
|
||
'processed_count': processed_count,
|
||
'failed_count': failed_count
|
||
})
|
||
continue
|
||
|
||
# 进行缠论分析
|
||
analysis_result = analyze_chan(df, timeframe)
|
||
|
||
if not analysis_result or 'klc_fx_info' not in analysis_result:
|
||
continue
|
||
|
||
klc_fx_info = analysis_result['klc_fx_info']
|
||
|
||
# 检查最近2个KLC是否有满足条件的分型
|
||
recent_klcs = klc_fx_info[-2:] if len(klc_fx_info) >= 2 else klc_fx_info
|
||
|
||
for klc_info in recent_klcs:
|
||
fx_strength = klc_info.get('fx_strength', 0)
|
||
fx_type = klc_info.get('fx_type', 'UNKNOWN')
|
||
|
||
# 检查是否满足条件：分型强度>=阈值 且 分型类型不为UNKNOWN
|
||
if fx_strength >= fx_strength_threshold and fx_type != 'UNKNOWN':
|
||
# 获取当前价格（最新收盘价）
|
||
current_price = df['close'].iloc[-1] if len(df) > 0 else None
|
||
fx_price = klc_info.get('price', 0)
|
||
|
||
# 计算涨跌幅
|
||
change_percent = 0
|
||
if current_price and fx_price and fx_price > 0:
|
||
change_percent = ((current_price - fx_price) / fx_price) * 100
|
||
|
||
# 格式化分型类型显示
|
||
fx_type_display = format_fx_type(fx_type)
|
||
|
||
results.append({
|
||
'symbol': symbol,
|
||
'name': name,
|
||
'fx_time': klc_info.get('time', ''),
|
||
'fx_type': fx_type_display,
|
||
'fx_strength': fx_strength,
|
||
'fx_price': fx_price,
|
||
'current_price': current_price,
|
||
'change_percent': change_percent
|
||
})
|
||
break # 找到一个满足条件的就跳出循环
|
||
|
||
except Exception as e:
|
||
print(f"处理股票 {symbol} 时出错: {str(e)}")
|
||
failed_count += 1
|
||
continue
|
||
|
||
print(f"筛选完成，共找到 {len(results)} 只满足条件的股票")
|
||
|
||
# 按分型强度降序排列
|
||
results.sort(key=lambda x: x['fx_strength'], reverse=True)
|
||
|
||
return jsonify({
|
||
'results': results,
|
||
'total_processed': processed_count,
|
||
'total_found': len(results),
|
||
'failed_count': failed_count,
|
||
'data_source': data_source,
|
||
'message': f'使用{data_source}进行筛选，共处理{processed_count}只股票，找到{len(results)}只满足条件的股票'
|
||
})
|
||
|
||
except Exception as e:
|
||
print(f"筛选股票时发生错误: {str(e)}")
|
||
# 检查是否是网络连接问题
|
||
if "timeout" in str(e).lower() or "connection" in str(e).lower() or "network" in str(e).lower():
|
||
return jsonify({
|
||
'error': '网络连接超时，请检查网络连接后重试。',
|
||
'error_type': 'network_error',
|
||
'suggestion': '请确保网络连接正常，或稍后重试。'
|
||
})
|
||
else:
|
||
return jsonify({'error': str(e)})
|
||
|
||
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)
|
||
|
||
def calculate_bi_macd_divergence(bi_list):
|
||
"""计算每个笔的MACD背离值 - 使用CBi内置的Cal_MACD_area方法"""
|
||
bi_macd_divs = {}
|
||
|
||
if len(bi_list) < 4:
|
||
return bi_macd_divs
|
||
|
||
for i in range(3, len(bi_list)):
|
||
current_bi = bi_list[i]
|
||
prev_same_dir_bi = None
|
||
|
||
# 找到同方向的前一个笔
|
||
for j in range(i-2, -1, -1):
|
||
if convert_direction(bi_list[j].dir) == convert_direction(current_bi.dir):
|
||
prev_same_dir_bi = bi_list[j]
|
||
break
|
||
|
||
if not prev_same_dir_bi or not current_bi.end_klc or not prev_same_dir_bi.end_klc:
|
||
continue
|
||
|
||
try:
|
||
# 使用CBi内置的Cal_MACD_area方法计算MACD面积
|
||
current_macd_area = current_bi.Cal_MACD_area()
|
||
prev_macd_area = prev_same_dir_bi.Cal_MACD_area()
|
||
|
||
if current_macd_area <= 0 or prev_macd_area <= 0:
|
||
continue
|
||
|
||
# 计算价格变化
|
||
current_price = current_bi.get_end_val()
|
||
prev_price = prev_same_dir_bi.get_end_val()
|
||
|
||
# 计算背驰度：价格创新高/低但MACD面积没有创新高/低
|
||
divergence_value = 0
|
||
if convert_direction(current_bi.dir) == -1: # 向下笔
|
||
# 底背驰：价格创新低但MACD面积没有创新高（向下笔MACD面积越大表示力度越大）
|
||
if current_price < prev_price and current_macd_area < prev_macd_area:
|
||
divergence_value = (prev_macd_area - current_macd_area) / prev_macd_area
|
||
elif convert_direction(current_bi.dir) == 1: # 向上笔
|
||
# 顶背驰：价格创新高但MACD面积没有创新高
|
||
if current_price > prev_price and current_macd_area < prev_macd_area:
|
||
divergence_value = (prev_macd_area - current_macd_area) / prev_macd_area
|
||
|
||
# 存储背离值（使用笔的索引作为key）
|
||
bi_macd_divs[i] = divergence_value
|
||
|
||
except Exception as e:
|
||
# 如果计算出错，跳过这个笔
|
||
print(f"计算笔{i}的MACD背离时出错: {e}")
|
||
continue
|
||
|
||
return bi_macd_divs
|
||
|
||
@app.route('/api/replay_data')
|
||
def get_replay_data():
|
||
"""生成回放数据接口 - 为每个时间点计算当时可用的数据"""
|
||
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() == '':
|
||
print(f"错误: 空交易对")
|
||
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')
|
||
|
||
print(f"回放数据API请求参数: symbol={symbol}, timeframe={timeframe}, element_timeframe={element_timeframe}")
|
||
print(f"时间范围: start_time={start_time}, end_time={end_time}")
|
||
|
||
# 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):
|
||
print(f"错误: 元素周期 {element_timeframe} 大于主周期 {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'})
|
||
|
||
# 获取完整数据
|
||
df = get_kl_data(symbol, timeframe, start_time=start_time, end_time=end_time)
|
||
if df is None:
|
||
print(f"错误: 获取数据失败 - symbol={symbol}, timeframe={timeframe}")
|
||
return jsonify({'error': '获取数据失败'})
|
||
|
||
if len(df) == 0:
|
||
print(f"错误: 所选时间范围内没有数据 - symbol={symbol}, timeframe={timeframe}")
|
||
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)
|
||
|
||
# 预先获取小周期数据（如果需要的话），避免在循环中重复拉取
|
||
element_df = None
|
||
if element_timeframe:
|
||
element_df = get_kl_data(symbol, element_timeframe, start_time=start_time, end_time=end_time)
|
||
|
||
# 生成回放数据
|
||
replay_data = {}
|
||
|
||
print(f"开始生成回放数据，主周期: {len(df)} 条记录")
|
||
|
||
# 为每个时间点计算当时可用的数据
|
||
for i in range(len(df)):
|
||
# 获取到当前时间点的数据子集
|
||
current_df = df.iloc[:i+1].copy()
|
||
|
||
# 添加技术指标（包括布林带）
|
||
current_df = add_indicators(current_df)
|
||
|
||
# 进行缠论分析
|
||
analysis_result = analyze_chan(current_df, timeframe)
|
||
|
||
# 计算MACD
|
||
macd_data = calculate_macd(current_df)
|
||
|
||
# 计算笔的MACD背离值
|
||
bi_macd_divs = calculate_bi_macd_divergence(analysis_result['bi_list'])
|
||
|
||
# 获取买卖点
|
||
all_trade_points = analysis_result['trade_points']
|
||
|
||
# 构建当前时间点的数据
|
||
current_data = {
|
||
'kline_data': clean_dataframe_for_json(current_df).to_dict('records'),
|
||
'bi_list': [{
|
||
'start_time': format_time_safely(bi.begin_klc.time_end, client_tz),
|
||
'end_time': format_time_safely(bi.end_klc.time_end, client_tz) if bi.end_klc else None,
|
||
'start_price': bi.begin_klc.low if convert_direction(bi.dir) == 1 else bi.begin_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_divs.get(idx, 0))
|
||
} for idx, bi in enumerate(analysis_result['bi_list']) if bi.end_klc],
|
||
'seg_list': [{
|
||
'start_time': format_time_safely(seg.start_bi.begin_klc.time_end, client_tz),
|
||
'end_time': format_time_safely(seg.end_bi.end_klc.time_end, client_tz) if seg.end_bi else None,
|
||
'start_price': seg.start_bi.begin_klc.low if convert_direction(seg.dir) == 1 else seg.start_bi.begin_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.end_bi],
|
||
'zs_list': [{
|
||
'start_time': format_time_safely(zs.begin.time, client_tz),
|
||
'end_time': format_time_safely(zs.end.time, client_tz) if zs.end else None,
|
||
'zg': zs.high,
|
||
'zd': zs.low,
|
||
'is_sure': zs.is_sure
|
||
} for zs in analysis_result['zs_list'] if zs.end],
|
||
'uncompleted_zs_list': [{
|
||
'start_time': format_time_safely(zs.begin.time, client_tz),
|
||
'end_time': None,
|
||
'zg': zs.high,
|
||
'zd': zs.low,
|
||
'is_sure': zs.is_sure
|
||
} for zs in analysis_result['zs_list'] if not zs.is_sure],
|
||
'trade_points': [{
|
||
'type': point['type'],
|
||
'time': format_time_safely(point['time'], client_tz),
|
||
'price': point['price'],
|
||
'desc': point['desc']
|
||
} for point in all_trade_points],
|
||
'macd': macd_data,
|
||
'bollinger': {
|
||
'upper': current_df['bb_upper'].tolist(),
|
||
'middle': current_df['bb_middle'].tolist(),
|
||
'lower': current_df['bb_lower'].tolist()
|
||
},
|
||
'element_bollinger': {
|
||
'upper': current_df['element_bb_upper'].tolist(),
|
||
'middle': current_df['element_bb_middle'].tolist(),
|
||
'lower': current_df['element_bb_lower'].tolist()
|
||
},
|
||
'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']),
|
||
'fx_strength_level': str(point['fx_strength_level']),
|
||
'is_strong_fx': bool(point['is_strong_fx'])
|
||
} for point in analysis_result['klc_fx_info']],
|
||
'klu_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']),
|
||
'fx_strength_level': str(point['fx_strength_level']),
|
||
'is_strong_fx': bool(point['is_strong_fx']),
|
||
'fx_confirmed': bool(point['fx_confirmed'])
|
||
} for point in analysis_result['klu_fx_info']]
|
||
}
|
||
|
||
# 如果有小周期数据，也要计算小周期的历史数据
|
||
if element_timeframe and element_df is not None and len(element_df) > 0:
|
||
# 找到对应当前主周期时间点的小周期数据截止位置
|
||
current_time = current_df['date'].iloc[-1]
|
||
element_subset = element_df[element_df['date'] <= current_time]
|
||
|
||
if len(element_subset) > 0:
|
||
# 添加小周期技术指标
|
||
element_subset = add_indicators(element_subset)
|
||
|
||
# 对小周期数据进行缠论分析
|
||
element_analysis = analyze_chan(element_subset, element_timeframe)
|
||
|
||
# 计算小周期MACD数据
|
||
element_macd_data = calculate_macd(element_subset)
|
||
|
||
# 计算小周期笔的MACD背离值
|
||
element_bi_macd_divs = calculate_bi_macd_divergence(element_analysis['bi_list'])
|
||
|
||
# 获取小周期买卖点
|
||
element_all_trade_points = element_analysis['trade_points']
|
||
|
||
# 添加小周期数据到当前时间点
|
||
current_data.update({
|
||
'element_timeframe': element_timeframe,
|
||
'element_macd': element_macd_data,
|
||
'element_bollinger': {
|
||
'upper': element_subset['bb_upper'].tolist(),
|
||
'middle': element_subset['bb_middle'].tolist(),
|
||
'lower': element_subset['bb_lower'].tolist()
|
||
},
|
||
'element_element_bollinger': {
|
||
'upper': element_subset['element_bb_upper'].tolist(),
|
||
'middle': element_subset['element_bb_middle'].tolist(),
|
||
'lower': element_subset['element_bb_lower'].tolist()
|
||
},
|
||
'element_bi_list': [{
|
||
'start_time': format_time_safely(bi.begin_klc.time_end, client_tz),
|
||
'end_time': format_time_safely(bi.end_klc.time_end, client_tz) if bi.end_klc else None,
|
||
'start_price': bi.begin_klc.low if convert_direction(bi.dir) == 1 else bi.begin_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(element_bi_macd_divs.get(idx, 0))
|
||
} for idx, bi in enumerate(element_analysis['bi_list']) if bi.end_klc],
|
||
'element_kline_data': clean_dataframe_for_json(element_subset).to_dict('records'),
|
||
'element_seg_list': [{
|
||
'start_time': format_time_safely(seg.start_bi.begin_klc.time_end, client_tz),
|
||
'end_time': format_time_safely(seg.end_bi.end_klc.time_end, client_tz) if seg.end_bi else None,
|
||
'start_price': seg.start_bi.begin_klc.low if convert_direction(seg.dir) == 1 else seg.start_bi.begin_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.end_bi],
|
||
'element_zs_list': [{
|
||
'start_time': format_time_safely(zs.begin.time, client_tz),
|
||
'end_time': format_time_safely(zs.end.time, client_tz) if zs.end else None,
|
||
'zg': zs.high,
|
||
'zd': zs.low,
|
||
'is_sure': zs.is_sure
|
||
} for zs in element_analysis['zs_list'] if zs.end],
|
||
'element_uncompleted_zs_list': [{
|
||
'start_time': format_time_safely(zs.begin.time, client_tz),
|
||
'end_time': None,
|
||
'zg': zs.high,
|
||
'zd': zs.low,
|
||
'is_sure': zs.is_sure
|
||
} for zs in element_analysis['zs_list'] if not zs.is_sure],
|
||
'element_trade_points': [{
|
||
'type': point['type'],
|
||
'time': format_time_safely(point['time'], client_tz),
|
||
'price': point['price'],
|
||
'desc': point['desc']
|
||
} for point in element_all_trade_points],
|
||
'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']),
|
||
'fx_strength_level': str(point['fx_strength_level']),
|
||
'is_strong_fx': bool(point['is_strong_fx'])
|
||
} for point in element_analysis['klc_fx_info']],
|
||
'element_klu_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']),
|
||
'fx_strength_level': str(point['fx_strength_level']),
|
||
'is_strong_fx': bool(point['is_strong_fx']),
|
||
'fx_confirmed': bool(point['fx_confirmed'])
|
||
} for point in element_analysis['klu_fx_info']]
|
||
})
|
||
|
||
replay_data[i] = current_data
|
||
|
||
# 打印进度（减少频率）
|
||
if (i + 1) % 100 == 0 or i == len(df) - 1:
|
||
percent = int((i + 1) / len(df) * 100)
|
||
print(f"回放数据生成进度: {percent}% ({i + 1}/{len(df)})")
|
||
|
||
result = {
|
||
'replay_data': replay_data,
|
||
'total_length': len(df),
|
||
'timezone': client_timezone
|
||
}
|
||
|
||
print(f"回放数据生成完成: {len(df)} 条记录")
|
||
return jsonify(result)
|
||
|
||
if __name__ == '__main__':
|
||
app.run(debug=True, host='0.0.0.0', port=8120) |