仿alphaarena项目增加A股ai自动量化
This commit is contained in:
oficcejo
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"""
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智能盯盘 - A股数据获取模块
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使用akshare获取实时行情和技术指标
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支持降级到tushare作为备用数据源
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"""
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import logging
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import os
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import akshare as ak
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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 SmartMonitorDataFetcher:
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"""A股数据获取器（支持多数据源降级）"""
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def __init__(self):
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self.logger = logging.getLogger(__name__)
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# 初始化Tushare（备用数据源）
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self.ts_pro = None
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tushare_token = os.getenv('TUSHARE_TOKEN', '')
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if tushare_token:
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try:
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import tushare as ts
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ts.set_token(tushare_token)
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self.ts_pro = ts.pro_api()
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self.logger.info("Tushare备用数据源初始化成功")
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except Exception as e:
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self.logger.warning(f"Tushare初始化失败: {e}")
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else:
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self.logger.info("未配置Tushare Token，仅使用AKShare数据源")
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def get_realtime_quote(self, stock_code: str, retry: int = 1) -> Optional[Dict]:
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"""
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获取实时行情（带重试和降级机制）
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优先使用AKShare，失败时降级到Tushare
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Args:
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stock_code: 股票代码（如：600519）
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retry: 重试次数（默认1次，避免IP封禁）
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Returns:
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实时行情数据
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"""
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import time
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# 方法1: 组合使用分钟行情 + 基本信息（最可靠）
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for attempt in range(retry):
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try:
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# 1.1 获取股票基本信息（名称）
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info_df = ak.stock_individual_info_em(symbol=stock_code)
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stock_name = 'N/A'
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if not info_df.empty:
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info_dict = dict(zip(info_df['item'], info_df['value']))
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stock_name = info_dict.get('股票简称', 'N/A')
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# 1.2 获取分钟级实时行情
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min_df = ak.stock_zh_a_hist_min_em(symbol=stock_code, period='1', adjust='')
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if min_df.empty:
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self.logger.warning(f"AKShare未找到股票 {stock_code} 的分钟行情数据")
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if attempt < retry - 1:
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time.sleep(2)
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continue
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break
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# 1.3 获取历史数据（计算昨收）
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hist_df = ak.stock_zh_a_hist(symbol=stock_code, period='daily', adjust='')
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# 提取最新分钟数据
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latest = min_df.iloc[-1]
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current_price = float(latest['收盘'])
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# 计算昨收和涨跌幅
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if len(hist_df) >= 2:
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pre_close = float(hist_df.iloc[-2]['收盘'])
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else:
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pre_close = current_price
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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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# 从历史数据获取今天的统计数据
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if len(hist_df) >= 1:
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today_data = hist_df.iloc[-1]
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daily_volume = float(today_data.get('成交量', 0))
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daily_amount = float(today_data.get('成交额', 0))
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daily_high = float(today_data.get('最高', 0))
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daily_low = float(today_data.get('最低', 0))
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daily_open = float(today_data.get('开盘', 0))
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turnover_rate = float(today_data.get('换手率', 0))
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else:
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# 使用分钟数据
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daily_volume = min_df['成交量'].sum()
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daily_amount = min_df['成交额'].sum()
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daily_high = min_df['最高'].max()
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daily_low = min_df['最低'].min()
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daily_open = float(min_df.iloc[0]['开盘'])
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turnover_rate = 0.0
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self.logger.info(f"✅ AKShare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': daily_volume, # 手
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'amount': daily_amount, # 元
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'high': daily_high,
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'low': daily_low,
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'open': daily_open,
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'pre_close': pre_close,
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'turnover_rate': turnover_rate,
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'volume_ratio': 1.0,
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'update_time': str(latest['时间']),
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'data_source': 'akshare'
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}
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except Exception as e:
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if attempt < retry - 1:
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self.logger.warning(f"AKShare获取失败 {stock_code}，第{attempt+1}次重试... 错误: {type(e).__name__}: {str(e)[:50]}")
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time.sleep(2) # 等待2秒后重试
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else:
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self.logger.warning(f"AKShare获取失败 {stock_code}（已重试{retry}次），尝试降级")
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# 降级到Tushare
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if self.ts_pro:
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self.logger.info(f"降级到Tushare获取 {stock_code}...")
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return self._get_realtime_quote_from_tushare(stock_code)
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else:
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self.logger.error(f"AKShare失败且未配置Tushare，无法获取 {stock_code} 行情")
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return None
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def get_technical_indicators(self, stock_code: str, period: str = 'daily', retry: int = 1) -> Optional[Dict]:
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"""
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计算技术指标（带降级机制）
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优先使用AKShare，失败时降级到Tushare
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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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retry: 重试次数（默认2次）
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Returns:
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技术指标数据
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"""
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import time
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# 方法1: 尝试使用AKShare
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for attempt in range(retry):
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try:
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# 获取历史数据（最近200个交易日，用于计算指标）
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end_date = datetime.now().strftime('%Y%m%d')
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start_date = (datetime.now() - timedelta(days=300)).strftime('%Y%m%d')
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# 获取历史数据
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df = ak.stock_zh_a_hist(
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symbol=stock_code,
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period=period,
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start_date=start_date,
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end_date=end_date,
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adjust="qfq" # 前复权
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)
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if df.empty or len(df) < 60:
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if attempt < retry - 1:
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self.logger.warning(f"AKShare历史数据不足 {stock_code}，第{attempt+1}次重试...")
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time.sleep(1)
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continue
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else:
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self.logger.warning(f"AKShare历史数据不足 {stock_code}，尝试降级")
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break
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# 数据充足，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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if attempt < retry - 1:
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self.logger.warning(f"AKShare获取历史数据失败 {stock_code}，第{attempt+1}次重试... 错误: {type(e).__name__}: {str(e)[:50]}")
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time.sleep(1)
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else:
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self.logger.warning(f"AKShare获取历史数据失败 {stock_code}（已重试{retry}次），尝试降级到Tushare")
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break
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# 方法2: 降级到Tushare
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if self.ts_pro:
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self.logger.info(f"降级到Tushare获取 {stock_code} 历史数据...")
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return self._get_technical_indicators_from_tushare(stock_code, period)
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else:
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self.logger.error(f"AKShare失败且未配置Tushare，无法获取 {stock_code} 技术指标")
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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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return None
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def _get_technical_indicators_from_tushare(self, stock_code: str, period: str = 'daily') -> Optional[Dict]:
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"""
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使用Tushare获取历史数据并计算技术指标
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Args:
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stock_code: 股票代码（6位）
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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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# 转换股票代码格式（Tushare格式：600519.SH, 000001.SZ）
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if stock_code.startswith('6'):
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ts_code = f"{stock_code}.SH"
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elif stock_code.startswith(('0', '3')):
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ts_code = f"{stock_code}.SZ"
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else:
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ts_code = stock_code
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# 计算日期范围
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end_date = datetime.now().strftime('%Y%m%d')
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start_date = (datetime.now() - timedelta(days=400)).strftime('%Y%m%d')
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# 获取历史数据
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df = self.ts_pro.daily(
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ts_code=ts_code,
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start_date=start_date,
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end_date=end_date
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)
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if df is None or df.empty:
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self.logger.error(f"Tushare未返回 {stock_code} 的历史数据")
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return None
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# Tushare数据是从新到旧，需要反转
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df = df.sort_values('trade_date', ascending=True).reset_index(drop=True)
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if len(df) < 60:
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self.logger.warning(f"Tushare历史数据不足 {stock_code}（仅{len(df)}条）")
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return None
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# 统一列名为AKShare格式（完整映射）
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df = df.rename(columns={
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'open': '开盘',
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'high': '最高',
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'low': '最低',
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'close': '收盘',
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'vol': '成交量',
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'amount': '成交额',
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'trade_date': '日期'
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})
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# 如果没有关键列，尝试使用其他可能的列名
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column_mapping = {
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'开盘': ['open', 'Open', 'OPEN'],
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'最高': ['high', 'High', 'HIGH'],
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'最低': ['low', 'Low', 'LOW'],
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'收盘': ['close', 'Close', 'CLOSE'],
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'成交量': ['vol', 'volume', 'Volume', 'VOLUME'],
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'成交额': ['amount', 'Amount', 'AMOUNT']
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}
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for target_col, possible_cols in column_mapping.items():
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if target_col not in df.columns:
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for col in possible_cols:
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if col in df.columns:
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df[target_col] = df[col]
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break
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# 确认必需的列存在
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required_cols = ['开盘', '最高', '最低', '收盘', '成交量']
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missing_cols = [col for col in required_cols if col not in df.columns]
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if missing_cols:
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self.logger.error(f"Tushare数据缺少列 {stock_code}: {missing_cols}")
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return None
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self.logger.info(f"✅ Tushare成功获取 {stock_code} 历史数据，共{len(df)}条")
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# 使用统一的计算方法
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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"Tushare获取历史数据失败 {stock_code}: {type(e).__name__}: {str(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_main_force_flow(self, stock_code: str, retry: int = 2) -> Optional[Dict]:
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"""
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获取主力资金流向（带重试机制）
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Args:
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stock_code: 股票代码
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retry: 重试次数（默认2次）
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Returns:
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主力资金数据
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"""
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import time
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for attempt in range(retry):
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try:
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# 获取个股资金流（新版AKShare API参数调整）
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try:
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df = ak.stock_individual_fund_flow_rank(market="今日")
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except TypeError:
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# 如果market参数也不支持，尝试无参数调用
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try:
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df = ak.stock_individual_fund_flow_rank()
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except TypeError as te:
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self.logger.warning(f"AKShare API参数不兼容: {te}")
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return None
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stock_data = df[df['代码'] == stock_code]
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if stock_data.empty:
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self.logger.warning(f"未找到股票 {stock_code} 的资金流向数据")
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return None
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row = stock_data.iloc[0]
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# 主力净额
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main_net = float(row.get('主力净流入-净额', 0)) / 10000 # 转换为万元
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main_net_pct = float(row.get('主力净流入-净占比', 0))
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# 判断主力动向
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if main_net > 0 and main_net_pct > 5:
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trend = '大幅流入'
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elif main_net > 0:
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trend = '小幅流入'
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elif main_net < 0 and main_net_pct < -5:
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trend = '大幅流出'
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elif main_net < 0:
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trend = '小幅流出'
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||||
else:
|
||||
trend = '观望'
|
||||
|
||||
return {
|
||||
'main_net': main_net, # 万元
|
||||
'main_net_pct': main_net_pct, # 百分比
|
||||
'super_net': float(row.get('超大单净流入-净额', 0)) / 10000,
|
||||
'big_net': float(row.get('大单净流入-净额', 0)) / 10000,
|
||||
'mid_net': float(row.get('中单净流入-净额', 0)) / 10000,
|
||||
'small_net': float(row.get('小单净流入-净额', 0)) / 10000,
|
||||
'trend': trend,
|
||||
'data_source': 'akshare'
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
if attempt < retry - 1:
|
||||
self.logger.warning(f"AKShare获取资金流向失败 {stock_code}，第{attempt+1}次重试... 错误: {type(e).__name__}")
|
||||
time.sleep(1) # 等待1秒后重试
|
||||
else:
|
||||
self.logger.warning(f"AKShare获取资金流向失败 {stock_code}（已重试{retry}次），尝试降级到Tushare")
|
||||
break
|
||||
|
||||
# 降级到Tushare
|
||||
if self.ts_pro:
|
||||
return self._get_main_force_from_tushare(stock_code)
|
||||
else:
|
||||
self.logger.error(f"AKShare失败且未配置Tushare，无法获取 {stock_code} 资金流向")
|
||||
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）
|
||||
注意：已移除主力资金流向数据，因为该接口不稳定且AI决策不依赖此数据
|
||||
|
||||
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)
|
||||
|
||||
# 主力资金（已禁用 - 接口不稳定）
|
||||
# main_force = self.get_main_force_flow(stock_code)
|
||||
# if main_force:
|
||||
# result['main_force'] = main_force
|
||||
|
||||
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
|
||||
|
||||
|
||||
# ========== Tushare备用数据源方法 ==========
|
||||
|
||||
def _get_realtime_quote_from_tushare(self, stock_code: str) -> Optional[Dict]:
|
||||
"""
|
||||
从Tushare获取实时行情（备用数据源）
|
||||
使用免费接口，无需积分
|
||||
|
||||
Args:
|
||||
stock_code: 股票代码
|
||||
|
||||
Returns:
|
||||
实时行情数据
|
||||
"""
|
||||
try:
|
||||
# 转换股票代码格式（Tushare格式：600519.SH）
|
||||
if stock_code.startswith('6'):
|
||||
ts_code = f"{stock_code}.SH"
|
||||
elif stock_code.startswith(('0', '3')):
|
||||
ts_code = f"{stock_code}.SZ"
|
||||
else:
|
||||
self.logger.warning(f"无法识别股票代码市场: {stock_code}")
|
||||
return None
|
||||
|
||||
# 方法1: 尝试使用daily_basic（基础日线，无需积分）
|
||||
try:
|
||||
df = self.ts_pro.daily_basic(ts_code=ts_code,
|
||||
trade_date=datetime.now().strftime('%Y%m%d'),
|
||||
fields='ts_code,trade_date,close,turnover_rate,volume_ratio,pe,pb')
|
||||
|
||||
if df.empty:
|
||||
# 获取最近交易日
|
||||
end_date = datetime.now().strftime('%Y%m%d')
|
||||
df = self.ts_pro.daily_basic(ts_code=ts_code,
|
||||
end_date=end_date,
|
||||
fields='ts_code,trade_date,close,turnover_rate,volume_ratio,pe,pb')
|
||||
df = df.head(1)
|
||||
|
||||
if not df.empty:
|
||||
row = df.iloc[0]
|
||||
|
||||
# 获取日线数据补充价格信息
|
||||
df_daily = self.ts_pro.daily(ts_code=ts_code,
|
||||
trade_date=row['trade_date'],
|
||||
fields='open,high,low,pre_close,change,pct_chg,vol,amount')
|
||||
|
||||
if not df_daily.empty:
|
||||
daily_row = df_daily.iloc[0]
|
||||
|
||||
# 获取股票名称
|
||||
stock_basic = self.ts_pro.stock_basic(ts_code=ts_code, fields='name')
|
||||
stock_name = stock_basic.iloc[0]['name'] if not stock_basic.empty else 'N/A'
|
||||
|
||||
self.logger.info(f"✅ Tushare降级成功（基础接口），获取到 {stock_code} 数据")
|
||||
|
||||
return {
|
||||
'code': stock_code,
|
||||
'name': stock_name,
|
||||
'current_price': float(row['close']),
|
||||
'change_pct': float(daily_row.get('pct_chg', 0)),
|
||||
'change_amount': float(daily_row.get('change', 0)),
|
||||
'volume': float(daily_row.get('vol', 0)) * 100,
|
||||
'amount': float(daily_row.get('amount', 0)) * 1000,
|
||||
'high': float(daily_row.get('high', 0)),
|
||||
'low': float(daily_row.get('low', 0)),
|
||||
'open': float(daily_row.get('open', 0)),
|
||||
'pre_close': float(daily_row.get('pre_close', 0)),
|
||||
'turnover_rate': float(row.get('turnover_rate', 0)),
|
||||
'volume_ratio': float(row.get('volume_ratio', 1.0)),
|
||||
'update_time': row['trade_date'],
|
||||
'data_source': 'tushare'
|
||||
}
|
||||
except Exception as e:
|
||||
self.logger.warning(f"Tushare基础接口失败: {str(e)[:100]}")
|
||||
|
||||
# 方法2: 降级使用更基础的stock_basic+pro_bar
|
||||
try:
|
||||
# 获取股票名称
|
||||
stock_basic = self.ts_pro.stock_basic(ts_code=ts_code, fields='name')
|
||||
stock_name = stock_basic.iloc[0]['name'] if not stock_basic.empty else 'N/A'
|
||||
|
||||
# 使用pro_bar获取行情（社区版免费）
|
||||
import tushare as ts
|
||||
df = ts.pro_bar(ts_code=ts_code, adj='qfq', ma=[5, 20])
|
||||
|
||||
if df is not None and not df.empty:
|
||||
row = df.iloc[0]
|
||||
|
||||
self.logger.info(f"✅ Tushare降级成功（pro_bar接口），获取到 {stock_code} 数据")
|
||||
|
||||
return {
|
||||
'code': stock_code,
|
||||
'name': stock_name,
|
||||
'current_price': float(row['close']),
|
||||
'change_pct': float(row.get('pct_chg', 0)),
|
||||
'change_amount': float(row.get('change', 0)),
|
||||
'volume': float(row.get('vol', 0)) * 100,
|
||||
'amount': float(row.get('amount', 0)) * 1000,
|
||||
'high': float(row.get('high', 0)),
|
||||
'low': float(row.get('low', 0)),
|
||||
'open': float(row.get('open', 0)),
|
||||
'pre_close': float(row.get('pre_close', 0)),
|
||||
'turnover_rate': float(row.get('turnover_rate', 0)),
|
||||
'volume_ratio': 1.0,
|
||||
'update_time': row['trade_date'],
|
||||
'data_source': 'tushare'
|
||||
}
|
||||
except Exception as e:
|
||||
self.logger.warning(f"Tushare pro_bar接口失败: {str(e)[:100]}")
|
||||
|
||||
# 所有方法都失败
|
||||
self.logger.error(f"Tushare所有接口都失败 {stock_code}，可能是积分不足或网络问题")
|
||||
self.logger.info("💡 提示：访问 https://tushare.pro/user/token 查看积分和权限")
|
||||
return None
|
||||
|
||||
except Exception as e:
|
||||
error_msg = str(e)
|
||||
if "权限" in error_msg or "积分" in error_msg:
|
||||
self.logger.error(f"Tushare权限不足 {stock_code}: 需要更多积分")
|
||||
self.logger.info("💡 获取积分方法：")
|
||||
self.logger.info(" 1. 完善个人信息 +100积分")
|
||||
self.logger.info(" 2. 每日签到 +1积分")
|
||||
self.logger.info(" 3. 参与社区互动")
|
||||
self.logger.info(" 详情访问: https://tushare.pro/document/1?doc_id=13")
|
||||
else:
|
||||
self.logger.error(f"Tushare获取失败 {stock_code}: {error_msg[:100]}")
|
||||
return None
|
||||
|
||||
def _get_main_force_from_tushare(self, stock_code: str) -> Optional[Dict]:
|
||||
"""
|
||||
从Tushare获取主力资金流向（备用数据源）
|
||||
注意：资金流向接口需要较高积分
|
||||
|
||||
Args:
|
||||
stock_code: 股票代码
|
||||
|
||||
Returns:
|
||||
主力资金数据
|
||||
"""
|
||||
try:
|
||||
# 转换股票代码格式
|
||||
if stock_code.startswith('6'):
|
||||
ts_code = f"{stock_code}.SH"
|
||||
elif stock_code.startswith(('0', '3')):
|
||||
ts_code = f"{stock_code}.SZ"
|
||||
else:
|
||||
return None
|
||||
|
||||
# 尝试获取资金流向数据（需要120积分）
|
||||
today = datetime.now().strftime('%Y%m%d')
|
||||
df = self.ts_pro.moneyflow(ts_code=ts_code, start_date=today, end_date=today)
|
||||
|
||||
if df.empty:
|
||||
# 获取最近一个交易日
|
||||
df = self.ts_pro.moneyflow(ts_code=ts_code, end_date=today)
|
||||
df = df.head(1)
|
||||
|
||||
if df.empty:
|
||||
self.logger.warning(f"Tushare未找到股票 {stock_code} 的资金流向数据")
|
||||
return None
|
||||
|
||||
row = df.iloc[0]
|
||||
|
||||
# 计算主力净额（大单+超大单）
|
||||
buy_lg_amount = float(row.get('buy_lg_amount', 0))
|
||||
buy_elg_amount = float(row.get('buy_elg_amount', 0))
|
||||
sell_lg_amount = float(row.get('sell_lg_amount', 0))
|
||||
sell_elg_amount = float(row.get('sell_elg_amount', 0))
|
||||
|
||||
main_net = (buy_lg_amount + buy_elg_amount - sell_lg_amount - sell_elg_amount) / 10000
|
||||
|
||||
# 计算净占比
|
||||
net_mf_amount = float(row.get('net_mf_amount', 0))
|
||||
main_net_pct = (main_net / net_mf_amount * 100) if net_mf_amount != 0 else 0
|
||||
|
||||
# 判断主力动向
|
||||
if main_net > 0 and main_net_pct > 5:
|
||||
trend = '大幅流入'
|
||||
elif main_net > 0:
|
||||
trend = '小幅流入'
|
||||
elif main_net < 0 and main_net_pct < -5:
|
||||
trend = '大幅流出'
|
||||
elif main_net < 0:
|
||||
trend = '小幅流出'
|
||||
else:
|
||||
trend = '观望'
|
||||
|
||||
self.logger.info(f"✅ Tushare降级成功，获取到 {stock_code} 资金流向")
|
||||
|
||||
return {
|
||||
'main_net': main_net,
|
||||
'main_net_pct': main_net_pct,
|
||||
'super_net': (buy_elg_amount - sell_elg_amount) / 10000,
|
||||
'big_net': (buy_lg_amount - sell_lg_amount) / 10000,
|
||||
'mid_net': float(row.get('buy_md_amount', 0) - row.get('sell_md_amount', 0)) / 10000,
|
||||
'small_net': float(row.get('buy_sm_amount', 0) - row.get('sell_sm_amount', 0)) / 10000,
|
||||
'trend': trend
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
error_msg = str(e)
|
||||
if "权限" in error_msg or "积分" in error_msg:
|
||||
self.logger.warning(f"⚠️ Tushare资金流向接口需要120积分，当前积分不足")
|
||||
self.logger.info("💡 获取积分方法：")
|
||||
self.logger.info(" 1. 完善个人信息 +100积分")
|
||||
self.logger.info(" 2. 每日签到累积 +30积分（30天）")
|
||||
self.logger.info(" 3. 参与社区互动获得积分")
|
||||
self.logger.info(" 详情: https://tushare.pro/document/1?doc_id=13")
|
||||
self.logger.info(" 智能盯盘会继续运行，仅缺少资金流向数据")
|
||||
else:
|
||||
self.logger.error(f"Tushare获取资金流向失败 {stock_code}: {error_msg[:100]}")
|
||||
return None
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
# 测试代码
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
|
||||
fetcher = SmartMonitorDataFetcher()
|
||||
|
||||
# 测试贵州茅台
|
||||
print("测试获取贵州茅台(600519)数据...")
|
||||
data = fetcher.get_comprehensive_data('600519')
|
||||
|
||||
if data:
|
||||
print("\n实时行情:")
|
||||
print(f" 当前价: {data.get('current_price')} 元")
|
||||
print(f" 涨跌幅: {data.get('change_pct')}%")
|
||||
|
||||
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}")
|
||||
|
||||
if 'main_force' in data:
|
||||
print("\n主力资金:")
|
||||
print(f" 主力净额: {data['main_force']['main_net']:.2f}万")
|
||||
print(f" 主力动向: {data['main_force']['trend']}")
|
||||
|
||||
Reference in New Issue
Block a user