oficcejo
441 lines
16 KiB
Python
441 lines
16 KiB
Python
"""
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智能盯盘 - TDX数据获取模块
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使用TDX股票数据API接口获取实时行情和技术指标
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"""
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import logging
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import requests
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import pandas as pd
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from typing import Dict, Optional
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from datetime import datetime, timedelta
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class SmartMonitorTDXDataFetcher:
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"""TDX数据获取器"""
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def __init__(self, base_url: str = "http://192.168.1.222:8181"):
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"""
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初始化TDX数据获取器
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Args:
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base_url: TDX API基础地址
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"""
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self.logger = logging.getLogger(__name__)
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self.base_url = base_url.rstrip('/')
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self.timeout = 10 # 请求超时时间（秒）
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self.logger.info(f"TDX数据源初始化成功，接口地址: {self.base_url}")
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def get_realtime_quote(self, stock_code: str) -> Optional[Dict]:
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"""
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获取实时行情
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Args:
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stock_code: 股票代码（如：600519）
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Returns:
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实时行情数据
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"""
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try:
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url = f"{self.base_url}/api/quote"
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params = {'code': stock_code}
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response = requests.get(url, params=params, timeout=self.timeout)
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result = response.json()
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if result['code'] != 0:
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self.logger.error(f"TDX获取行情失败: {result.get('message')}")
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return None
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data_list = result.get('data', [])
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if not data_list:
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self.logger.warning(f"TDX未返回股票 {stock_code} 的行情数据")
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return None
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# 获取第一条数据
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quote_data = data_list[0]
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k_data = quote_data.get('K', {})
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# 价格单位转换：厘 -> 元（1元 = 1000厘）
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current_price = k_data.get('Close', 0) / 1000
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pre_close = k_data.get('Last', 0) / 1000
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open_price = k_data.get('Open', 0) / 1000
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high_price = k_data.get('High', 0) / 1000
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low_price = k_data.get('Low', 0) / 1000
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# 成交量单位：手（已是手，无需转换）
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volume = quote_data.get('TotalHand', 0)
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# 成交额单位转换：厘 -> 元
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amount = quote_data.get('Amount', 0) / 1000
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# 计算涨跌幅
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change_amount = current_price - pre_close
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change_pct = (change_amount / pre_close * 100) if pre_close > 0 else 0
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# 计算换手率（需要流通股本，TDX不提供，暂时设为0）
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turnover_rate = 0.0
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# 计算量比（现量/均量，这里用总手数/平均手数估算）
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vol_ma5 = volume / 1.2 # 简化估算
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volume_ratio = volume / vol_ma5 if vol_ma5 > 0 else 1.0
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# 获取股票名称（需要调用搜索接口）
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stock_name = self._get_stock_name(stock_code)
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self.logger.info(f"✅ TDX成功获取 {stock_code} ({stock_name}) 实时行情")
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return {
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'code': stock_code,
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'name': stock_name,
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'current_price': current_price,
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'change_pct': change_pct,
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'change_amount': change_amount,
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'volume': volume, # 手
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'amount': amount, # 元
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'high': high_price,
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'low': low_price,
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'open': open_price,
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'pre_close': pre_close,
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'turnover_rate': turnover_rate,
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'volume_ratio': volume_ratio,
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'update_time': datetime.fromtimestamp(int(quote_data.get('ServerTime', 0))).strftime('%Y-%m-%d %H:%M:%S'),
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'data_source': 'tdx'
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}
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except requests.exceptions.Timeout:
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self.logger.error(f"TDX请求超时 {stock_code}")
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return None
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except requests.exceptions.ConnectionError:
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self.logger.error(f"TDX连接失败，请检查接口地址: {self.base_url}")
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return None
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except Exception as e:
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self.logger.error(f"TDX获取行情失败 {stock_code}: {type(e).__name__}: {str(e)}")
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return None
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def _get_stock_name(self, stock_code: str) -> str:
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"""
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获取股票名称
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Args:
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stock_code: 股票代码
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Returns:
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股票名称
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"""
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try:
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url = f"{self.base_url}/api/search"
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params = {'keyword': stock_code}
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response = requests.get(url, params=params, timeout=self.timeout)
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result = response.json()
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if result['code'] == 0:
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data_list = result.get('data', [])
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for item in data_list:
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if item.get('code') == stock_code:
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return item.get('name', 'N/A')
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return 'N/A'
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except Exception as e:
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self.logger.warning(f"获取股票名称失败 {stock_code}: {e}")
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return 'N/A'
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def get_kline_data(self, stock_code: str, kline_type: str = 'day', limit: int = 200) -> Optional[pd.DataFrame]:
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"""
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获取K线数据
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Args:
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stock_code: 股票代码
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kline_type: K线类型（minute1/minute5/minute15/minute30/hour/day/week/month）
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limit: 返回条数（最多800）
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Returns:
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K线数据DataFrame
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"""
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try:
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url = f"{self.base_url}/api/kline"
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params = {
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'code': stock_code,
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'type': kline_type
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}
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response = requests.get(url, params=params, timeout=self.timeout)
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result = response.json()
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if result['code'] != 0:
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self.logger.error(f"TDX获取K线失败: {result.get('message')}")
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return None
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kline_list = result.get('data', {}).get('List', [])
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if not kline_list:
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self.logger.warning(f"TDX未返回股票 {stock_code} 的K线数据")
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return None
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# 转换为DataFrame
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rows = []
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for item in kline_list:
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rows.append({
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'日期': item.get('Time', '').split('T')[0], # 只取日期部分
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'开盘': item.get('Open', 0) / 1000, # 厘转元
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'收盘': item.get('Close', 0) / 1000,
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'最高': item.get('High', 0) / 1000,
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'最低': item.get('Low', 0) / 1000,
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'成交量': item.get('Volume', 0), # 手
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'成交额': item.get('Amount', 0) / 1000, # 厘转元
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})
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df = pd.DataFrame(rows)
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# TDX返回的数据是倒序（最新的在前），需要反转
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df = df.iloc[::-1].reset_index(drop=True)
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# 只保留最近limit条
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if len(df) > limit:
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df = df.tail(limit).reset_index(drop=True)
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# 转换日期格式
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df['日期'] = pd.to_datetime(df['日期'])
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self.logger.info(f"✅ TDX成功获取 {stock_code} K线数据，共{len(df)}条")
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return df
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except Exception as e:
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self.logger.error(f"TDX获取K线失败 {stock_code}: {type(e).__name__}: {str(e)}")
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return None
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def get_technical_indicators(self, stock_code: str, period: str = 'daily') -> Optional[Dict]:
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"""
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计算技术指标
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Args:
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stock_code: 股票代码
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period: 周期（daily/weekly/monthly）
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Returns:
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技术指标数据
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"""
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try:
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# 映射周期类型
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kline_type_map = {
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'daily': 'day',
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'weekly': 'week',
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'monthly': 'month'
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}
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kline_type = kline_type_map.get(period, 'day')
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# 获取K线数据（需要足够的数据计算指标，至少200条）
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df = self.get_kline_data(stock_code, kline_type=kline_type, limit=200)
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if df is None or df.empty or len(df) < 60:
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self.logger.warning(f"股票 {stock_code} K线数据不足，无法计算技术指标")
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return None
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# 计算技术指标
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return self._calculate_all_indicators(df, stock_code)
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except Exception as e:
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self.logger.error(f"TDX计算技术指标失败 {stock_code}: {e}")
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return None
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def _calculate_all_indicators(self, df: pd.DataFrame, stock_code: str) -> Optional[Dict]:
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"""
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根据历史数据计算所有技术指标
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Args:
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df: 历史数据DataFrame
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stock_code: 股票代码
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Returns:
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技术指标数据
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"""
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try:
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if df.empty or len(df) < 60:
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self.logger.warning(f"股票 {stock_code} 历史数据不足")
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return None
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# 计算均线
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df['ma5'] = df['收盘'].rolling(window=5).mean()
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df['ma20'] = df['收盘'].rolling(window=20).mean()
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df['ma60'] = df['收盘'].rolling(window=60).mean()
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# 计算MACD
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df = self._calculate_macd(df)
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# 计算RSI
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df = self._calculate_rsi(df, periods=[6, 12, 24])
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# 计算KDJ
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df = self._calculate_kdj(df)
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# 计算布林带
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df = self._calculate_bollinger(df)
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# 计算量能均线
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df['vol_ma5'] = df['成交量'].rolling(window=5).mean()
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df['vol_ma10'] = df['成交量'].rolling(window=10).mean()
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# 取最后一行数据
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latest = df.iloc[-1]
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# 判断趋势
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current_price = float(latest['收盘'])
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ma5 = float(latest['ma5'])
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ma20 = float(latest['ma20'])
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ma60 = float(latest['ma60'])
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if current_price > ma5 > ma20 > ma60:
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trend = 'up'
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elif current_price < ma5 < ma20 < ma60:
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trend = 'down'
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else:
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trend = 'sideways'
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# 布林带位置
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boll_upper = float(latest['boll_upper'])
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boll_mid = float(latest['boll_mid'])
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boll_lower = float(latest['boll_lower'])
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if current_price >= boll_upper:
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boll_position = '上轨附近（超买）'
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elif current_price <= boll_lower:
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boll_position = '下轨附近（超卖）'
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elif current_price > boll_mid:
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boll_position = '中轨上方'
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else:
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boll_position = '中轨下方'
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return {
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'ma5': ma5,
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'ma20': ma20,
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'ma60': ma60,
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'trend': trend,
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'macd_dif': float(latest['dif']),
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'macd_dea': float(latest['dea']),
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'macd': float(latest['macd']),
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'rsi6': float(latest['rsi6']),
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'rsi12': float(latest['rsi12']),
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'rsi24': float(latest['rsi24']),
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'kdj_k': float(latest['kdj_k']),
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'kdj_d': float(latest['kdj_d']),
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'kdj_j': float(latest['kdj_j']),
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'boll_upper': boll_upper,
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'boll_mid': boll_mid,
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'boll_lower': boll_lower,
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'boll_position': boll_position,
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'vol_ma5': float(latest['vol_ma5']),
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'volume_ratio': float(latest['成交量']) / float(latest['vol_ma5']) if latest['vol_ma5'] > 0 else 1.0
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}
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except Exception as e:
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self.logger.error(f"计算技术指标失败 {stock_code}: {e}")
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import traceback
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self.logger.debug(traceback.format_exc())
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return None
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def get_comprehensive_data(self, stock_code: str) -> Dict:
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"""
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获取综合数据（实时行情+技术指标）
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Args:
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stock_code: 股票代码
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Returns:
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综合数据
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"""
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result = {}
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# 实时行情
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quote = self.get_realtime_quote(stock_code)
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if quote:
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result.update(quote)
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# 技术指标
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indicators = self.get_technical_indicators(stock_code)
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if indicators:
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result.update(indicators)
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return result
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# ========== 技术指标计算方法 ==========
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def _calculate_macd(self, df: pd.DataFrame,
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fast: int = 12, slow: int = 26, signal: int = 9) -> pd.DataFrame:
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"""计算MACD指标"""
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ema_fast = df['收盘'].ewm(span=fast, adjust=False).mean()
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ema_slow = df['收盘'].ewm(span=slow, adjust=False).mean()
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df['dif'] = ema_fast - ema_slow
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df['dea'] = df['dif'].ewm(span=signal, adjust=False).mean()
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df['macd'] = (df['dif'] - df['dea']) * 2
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return df
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def _calculate_rsi(self, df: pd.DataFrame, periods: list = [6, 12, 24]) -> pd.DataFrame:
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"""计算RSI指标"""
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for period in periods:
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delta = df['收盘'].diff()
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gain = (delta.where(delta > 0, 0)).rolling(window=period).mean()
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loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean()
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rs = gain / loss
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df[f'rsi{period}'] = 100 - (100 / (1 + rs))
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return df
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def _calculate_kdj(self, df: pd.DataFrame, n: int = 9,
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m1: int = 3, m2: int = 3) -> pd.DataFrame:
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"""计算KDJ指标"""
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low_list = df['最低'].rolling(window=n).min()
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high_list = df['最高'].rolling(window=n).max()
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rsv = (df['收盘'] - low_list) / (high_list - low_list) * 100
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df['kdj_k'] = rsv.ewm(com=m1-1, adjust=False).mean()
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df['kdj_d'] = df['kdj_k'].ewm(com=m2-1, adjust=False).mean()
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df['kdj_j'] = 3 * df['kdj_k'] - 2 * df['kdj_d']
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return df
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def _calculate_bollinger(self, df: pd.DataFrame,
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period: int = 20, std_num: int = 2) -> pd.DataFrame:
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"""计算布林带"""
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df['boll_mid'] = df['收盘'].rolling(window=period).mean()
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std = df['收盘'].rolling(window=period).std()
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df['boll_upper'] = df['boll_mid'] + std_num * std
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df['boll_lower'] = df['boll_mid'] - std_num * std
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return df
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if __name__ == '__main__':
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# 测试代码
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logging.basicConfig(level=logging.INFO)
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# 使用默认地址测试
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fetcher = SmartMonitorTDXDataFetcher(base_url="http://192.168.1.222:8181")
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# 测试平安银行(000001)
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print("测试获取平安银行(000001)数据...")
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data = fetcher.get_comprehensive_data('000001')
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if data:
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print("\n实时行情:")
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print(f" 股票名称: {data.get('name')}")
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print(f" 当前价: {data.get('current_price')} 元")
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print(f" 涨跌幅: {data.get('change_pct')}%")
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print(f" 数据源: {data.get('data_source')}")
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print("\n技术指标:")
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print(f" MA5: {data.get('ma5', 0):.2f}")
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print(f" MA20: {data.get('ma20', 0):.2f}")
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print(f" MACD: {data.get('macd', 0):.4f}")
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print(f" RSI(6): {data.get('rsi6', 0):.2f}")
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print(f" 趋势: {data.get('trend')}")
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else:
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print("获取数据失败")
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