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增加TDX数据源，增加交易时段盯盘设置

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oficcejo
2025-11-04 22:25:42 +08:00
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"""
智能盯盘 - TDX数据获取模块
使用TDX股票数据API接口获取实时行情和技术指标
"""
import logging
import requests
import pandas as pd
from typing import Dict, Optional
from datetime import datetime, timedelta
class SmartMonitorTDXDataFetcher:
"""TDX数据获取器"""
def __init__(self, base_url: str = "http://192.168.1.222:8181"):
"""
初始化TDX数据获取器
Args:
base_url: TDX API基础地址
"""
self.logger = logging.getLogger(__name__)
self.base_url = base_url.rstrip('/')
self.timeout = 10 # 请求超时时间（秒）
self.logger.info(f"TDX数据源初始化成功，接口地址: {self.base_url}")
def get_realtime_quote(self, stock_code: str) -> Optional[Dict]:
"""
获取实时行情
Args:
stock_code: 股票代码（如：600519）
Returns:
实时行情数据
"""
try:
url = f"{self.base_url}/api/quote"
params = {'code': stock_code}
response = requests.get(url, params=params, timeout=self.timeout)
result = response.json()
if result['code'] != 0:
self.logger.error(f"TDX获取行情失败: {result.get('message')}")
return None
data_list = result.get('data', [])
if not data_list:
self.logger.warning(f"TDX未返回股票 {stock_code} 的行情数据")
return None
# 获取第一条数据
quote_data = data_list[0]
k_data = quote_data.get('K', {})
# 价格单位转换：厘 -> 元（1元 = 1000厘）
current_price = k_data.get('Close', 0) / 1000
pre_close = k_data.get('Last', 0) / 1000
open_price = k_data.get('Open', 0) / 1000
high_price = k_data.get('High', 0) / 1000
low_price = k_data.get('Low', 0) / 1000
# 成交量单位：手（已是手，无需转换）
volume = quote_data.get('TotalHand', 0)
# 成交额单位转换：厘 -> 元
amount = quote_data.get('Amount', 0) / 1000
# 计算涨跌幅
change_amount = current_price - pre_close
change_pct = (change_amount / pre_close * 100) if pre_close > 0 else 0
# 计算换手率（需要流通股本，TDX不提供，暂时设为0）
turnover_rate = 0.0
# 计算量比（现量/均量，这里用总手数/平均手数估算）
vol_ma5 = volume / 1.2 # 简化估算
volume_ratio = volume / vol_ma5 if vol_ma5 > 0 else 1.0
# 获取股票名称（需要调用搜索接口）
stock_name = self._get_stock_name(stock_code)
self.logger.info(f"✅ TDX成功获取 {stock_code} ({stock_name}) 实时行情")
return {
'code': stock_code,
'name': stock_name,
'current_price': current_price,
'change_pct': change_pct,
'change_amount': change_amount,
'volume': volume, # 手
'amount': amount, # 元
'high': high_price,
'low': low_price,
'open': open_price,
'pre_close': pre_close,
'turnover_rate': turnover_rate,
'volume_ratio': volume_ratio,
'update_time': datetime.fromtimestamp(int(quote_data.get('ServerTime', 0))).strftime('%Y-%m-%d %H:%M:%S'),
'data_source': 'tdx'
}
except requests.exceptions.Timeout:
self.logger.error(f"TDX请求超时 {stock_code}")
return None
except requests.exceptions.ConnectionError:
self.logger.error(f"TDX连接失败，请检查接口地址: {self.base_url}")
return None
except Exception as e:
self.logger.error(f"TDX获取行情失败 {stock_code}: {type(e).__name__}: {str(e)}")
return None
def _get_stock_name(self, stock_code: str) -> str:
"""
获取股票名称
Args:
stock_code: 股票代码
Returns:
股票名称
"""
try:
url = f"{self.base_url}/api/search"
params = {'keyword': stock_code}
response = requests.get(url, params=params, timeout=self.timeout)
result = response.json()
if result['code'] == 0:
data_list = result.get('data', [])
for item in data_list:
if item.get('code') == stock_code:
return item.get('name', 'N/A')
return 'N/A'
except Exception as e:
self.logger.warning(f"获取股票名称失败 {stock_code}: {e}")
return 'N/A'
def get_kline_data(self, stock_code: str, kline_type: str = 'day', limit: int = 200) -> Optional[pd.DataFrame]:
"""
获取K线数据
Args:
stock_code: 股票代码
kline_type: K线类型（minute1/minute5/minute15/minute30/hour/day/week/month）
limit: 返回条数（最多800）
Returns:
K线数据DataFrame
"""
try:
url = f"{self.base_url}/api/kline"
params = {
'code': stock_code,
'type': kline_type
}
response = requests.get(url, params=params, timeout=self.timeout)
result = response.json()
if result['code'] != 0:
self.logger.error(f"TDX获取K线失败: {result.get('message')}")
return None
kline_list = result.get('data', {}).get('List', [])
if not kline_list:
self.logger.warning(f"TDX未返回股票 {stock_code} 的K线数据")
return None
# 转换为DataFrame
rows = []
for item in kline_list:
rows.append({
'日期': item.get('Time', '').split('T')[0], # 只取日期部分
'开盘': item.get('Open', 0) / 1000, # 厘转元
'收盘': item.get('Close', 0) / 1000,
'最高': item.get('High', 0) / 1000,
'最低': item.get('Low', 0) / 1000,
'成交量': item.get('Volume', 0), # 手
'成交额': item.get('Amount', 0) / 1000, # 厘转元
})
df = pd.DataFrame(rows)
# TDX返回的数据是倒序（最新的在前），需要反转
df = df.iloc[::-1].reset_index(drop=True)
# 只保留最近limit条
if len(df) > limit:
df = df.tail(limit).reset_index(drop=True)
# 转换日期格式
df['日期'] = pd.to_datetime(df['日期'])
self.logger.info(f"✅ TDX成功获取 {stock_code} K线数据，共{len(df)}条")
return df
except Exception as e:
self.logger.error(f"TDX获取K线失败 {stock_code}: {type(e).__name__}: {str(e)}")
return None
def get_technical_indicators(self, stock_code: str, period: str = 'daily') -> Optional[Dict]:
"""
计算技术指标
Args:
stock_code: 股票代码
period: 周期（daily/weekly/monthly）
Returns:
技术指标数据
"""
try:
# 映射周期类型
kline_type_map = {
'daily': 'day',
'weekly': 'week',
'monthly': 'month'
}
kline_type = kline_type_map.get(period, 'day')
# 获取K线数据（需要足够的数据计算指标，至少200条）
df = self.get_kline_data(stock_code, kline_type=kline_type, limit=200)
if df is None or df.empty or len(df) < 60:
self.logger.warning(f"股票 {stock_code} K线数据不足，无法计算技术指标")
return None
# 计算技术指标
return self._calculate_all_indicators(df, stock_code)
except Exception as e:
self.logger.error(f"TDX计算技术指标失败 {stock_code}: {e}")
return None
def _calculate_all_indicators(self, df: pd.DataFrame, stock_code: str) -> Optional[Dict]:
"""
根据历史数据计算所有技术指标
Args:
df: 历史数据DataFrame
stock_code: 股票代码
Returns:
技术指标数据
"""
try:
if df.empty or len(df) < 60:
self.logger.warning(f"股票 {stock_code} 历史数据不足")
return None
# 计算均线
df['ma5'] = df['收盘'].rolling(window=5).mean()
df['ma20'] = df['收盘'].rolling(window=20).mean()
df['ma60'] = df['收盘'].rolling(window=60).mean()
# 计算MACD
df = self._calculate_macd(df)
# 计算RSI
df = self._calculate_rsi(df, periods=[6, 12, 24])
# 计算KDJ
df = self._calculate_kdj(df)
# 计算布林带
df = self._calculate_bollinger(df)
# 计算量能均线
df['vol_ma5'] = df['成交量'].rolling(window=5).mean()
df['vol_ma10'] = df['成交量'].rolling(window=10).mean()
# 取最后一行数据
latest = df.iloc[-1]
# 判断趋势
current_price = float(latest['收盘'])
ma5 = float(latest['ma5'])
ma20 = float(latest['ma20'])
ma60 = float(latest['ma60'])
if current_price > ma5 > ma20 > ma60:
trend = 'up'
elif current_price < ma5 < ma20 < ma60:
trend = 'down'
else:
trend = 'sideways'
# 布林带位置
boll_upper = float(latest['boll_upper'])
boll_mid = float(latest['boll_mid'])
boll_lower = float(latest['boll_lower'])
if current_price >= boll_upper:
boll_position = '上轨附近（超买）'
elif current_price <= boll_lower:
boll_position = '下轨附近（超卖）'
elif current_price > boll_mid:
boll_position = '中轨上方'
else:
boll_position = '中轨下方'
return {
'ma5': ma5,
'ma20': ma20,
'ma60': ma60,
'trend': trend,
'macd_dif': float(latest['dif']),
'macd_dea': float(latest['dea']),
'macd': float(latest['macd']),
'rsi6': float(latest['rsi6']),
'rsi12': float(latest['rsi12']),
'rsi24': float(latest['rsi24']),
'kdj_k': float(latest['kdj_k']),
'kdj_d': float(latest['kdj_d']),
'kdj_j': float(latest['kdj_j']),
'boll_upper': boll_upper,
'boll_mid': boll_mid,
'boll_lower': boll_lower,
'boll_position': boll_position,
'vol_ma5': float(latest['vol_ma5']),
'volume_ratio': float(latest['成交量']) / float(latest['vol_ma5']) if latest['vol_ma5'] > 0 else 1.0
}
except Exception as e:
self.logger.error(f"计算技术指标失败 {stock_code}: {e}")
import traceback
self.logger.debug(traceback.format_exc())
return None
def get_comprehensive_data(self, stock_code: str) -> Dict:
"""
获取综合数据（实时行情+技术指标）
Args:
stock_code: 股票代码
Returns:
综合数据
"""
result = {}
# 实时行情
quote = self.get_realtime_quote(stock_code)
if quote:
result.update(quote)
# 技术指标
indicators = self.get_technical_indicators(stock_code)
if indicators:
result.update(indicators)
return result
# ========== 技术指标计算方法 ==========
def _calculate_macd(self, df: pd.DataFrame,
fast: int = 12, slow: int = 26, signal: int = 9) -> pd.DataFrame:
"""计算MACD指标"""
ema_fast = df['收盘'].ewm(span=fast, adjust=False).mean()
ema_slow = df['收盘'].ewm(span=slow, adjust=False).mean()
df['dif'] = ema_fast - ema_slow
df['dea'] = df['dif'].ewm(span=signal, adjust=False).mean()
df['macd'] = (df['dif'] - df['dea']) * 2
return df
def _calculate_rsi(self, df: pd.DataFrame, periods: list = [6, 12, 24]) -> pd.DataFrame:
"""计算RSI指标"""
for period in periods:
delta = df['收盘'].diff()
gain = (delta.where(delta > 0, 0)).rolling(window=period).mean()
loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean()
rs = gain / loss
df[f'rsi{period}'] = 100 - (100 / (1 + rs))
return df
def _calculate_kdj(self, df: pd.DataFrame, n: int = 9,
m1: int = 3, m2: int = 3) -> pd.DataFrame:
"""计算KDJ指标"""
low_list = df['最低'].rolling(window=n).min()
high_list = df['最高'].rolling(window=n).max()
rsv = (df['收盘'] - low_list) / (high_list - low_list) * 100
df['kdj_k'] = rsv.ewm(com=m1-1, adjust=False).mean()
df['kdj_d'] = df['kdj_k'].ewm(com=m2-1, adjust=False).mean()
df['kdj_j'] = 3 * df['kdj_k'] - 2 * df['kdj_d']
return df
def _calculate_bollinger(self, df: pd.DataFrame,
period: int = 20, std_num: int = 2) -> pd.DataFrame:
"""计算布林带"""
df['boll_mid'] = df['收盘'].rolling(window=period).mean()
std = df['收盘'].rolling(window=period).std()
df['boll_upper'] = df['boll_mid'] + std_num * std
df['boll_lower'] = df['boll_mid'] - std_num * std
return df
if __name__ == '__main__':
# 测试代码
logging.basicConfig(level=logging.INFO)
# 使用默认地址测试
fetcher = SmartMonitorTDXDataFetcher(base_url="http://192.168.1.222:8181")
# 测试平安银行(000001)
print("测试获取平安银行(000001)数据...")
data = fetcher.get_comprehensive_data('000001')
if data:
print("\n实时行情:")
print(f" 股票名称: {data.get('name')}")
print(f" 当前价: {data.get('current_price')} 元")
print(f" 涨跌幅: {data.get('change_pct')}%")
print(f" 数据源: {data.get('data_source')}")
print("\n技术指标:")
print(f" MA5: {data.get('ma5', 0):.2f}")
print(f" MA20: {data.get('ma20', 0):.2f}")
print(f" MACD: {data.get('macd', 0):.4f}")
print(f" RSI(6): {data.get('rsi6', 0):.2f}")
print(f" 趋势: {data.get('trend')}")
else:
print("获取数据失败")