204 lines
7.1 KiB
Python
204 lines
7.1 KiB
Python
#!/usr/bin/env python3
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"""
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A股短期题材选股 - 详细诊断版本
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显示每个股票的筛选过程
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"""
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import asyncio
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import sys
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import os
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from datetime import datetime, timedelta
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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from app.utils.logger import logger
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from app.config import get_settings
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from app.astock_agent.tushare_client import get_tushare_client
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async def diagnose_sector_stocks():
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"""诊断板块成分股的筛选过程"""
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print("\n" + "=" * 60)
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print("🔍 A股选股详细诊断")
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print("=" * 60)
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settings = get_settings()
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ts_client = get_tushare_client(settings.tushare_token)
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# 1. 测试获取一个板块的成分股
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print("\n【测试】获取智能电网板块成分股...")
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try:
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# 使用智能电网板块代码（与选股器一致）
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sector_code = "885311.TI"
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members_df = ts_client.get_sector_members(sector_code)
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if members_df.empty:
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print("❌ 无法获取板块成分股")
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return
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stock_codes = members_df['con_code'].tolist()[:10] # 只测试前10只
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print(f"✓ 获取到 {len(stock_codes)} 只成分股（测试前10只）")
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print(f"股票代码: {stock_codes}")
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# 2. 获取这些股票的实时行情
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print("\n【测试】获取实时行情...")
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today = datetime.now().strftime('%Y%m%d')
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yesterday = (datetime.now() - timedelta(days=10)).strftime('%Y%m%d')
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all_stocks_data = []
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for stock_code in stock_codes:
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try:
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daily_df = ts_client.pro.daily(
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ts_code=stock_code,
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start_date=yesterday,
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end_date=today
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)
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if daily_df.empty:
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print(f" ⚠️ {stock_code}: 无历史数据")
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continue
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daily_df = daily_df.sort_values('trade_date')
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latest = daily_df.iloc[-1]
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stock_info = {
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'ts_code': stock_code,
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'name': latest.get('name', ''),
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'close': float(latest['close']),
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'pct_chg': float(latest['pct_chg']),
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'vol': float(latest['vol']),
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'amount': float(latest['amount']) * 1000,
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'trade_date': str(latest['trade_date'])
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}
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all_stocks_data.append(stock_info)
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except Exception as e:
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print(f" ❌ {stock_code}: 获取失败 - {e}")
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continue
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print(f"\n✓ 成功获取 {len(all_stocks_data)} 只股票的行情")
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# 3. 获取每日指标
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print("\n【测试】获取每日指标（换手率等）...")
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basic_df = ts_client.pro.daily_basic(
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ts_code=','.join(stock_codes),
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trade_date=all_stocks_data[0]['trade_date'],
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fields='ts_code,trade_date,turnover_rate,pe,pb'
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)
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if basic_df.empty:
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print("⚠️ 无法获取每日指标数据")
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else:
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print(f"✓ 获取到 {len(basic_df)} 只股票的每日指标")
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# 4. 逐个检查筛选条件
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print("\n【测试】逐个检查筛选条件...")
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print("=" * 80)
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for stock_info in all_stocks_data:
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stock_code = stock_info['ts_code']
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name = stock_info['name']
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close = stock_info['close']
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pct_chg = stock_info['pct_chg']
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vol = stock_info['vol']
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amount = stock_info['amount']
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print(f"\n🔍 {name}({stock_code}):")
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print(f" 日期: {stock_info['trade_date']}")
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print(f" 现价: ¥{close:.2f}, 涨跌幅: {pct_chg:+.2f}%")
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# 检查1: ST股票
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if 'ST' in name or '退' in name:
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print(f" ❌ ST/退市股，被过滤")
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continue
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print(f" ✓ 不是ST/退市股")
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# 检查2: 换手率
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basic_row = basic_df[basic_df['ts_code'] == stock_code]
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if not basic_row.empty:
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turnover = float(basic_row.iloc[0].get('turnover_rate', 0))
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print(f" 换手率: {turnover:.2f}%")
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if 1.0 <= turnover <= 20.0:
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print(f" ✓ 换手率符合")
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else:
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print(f" ❌ 换手率不符合（需要1%-20%）")
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continue
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else:
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print(f" ⚠️ 无换手率数据")
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turnover = 0
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# 检查3: MA多头排列
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try:
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start_date = (datetime.now() - timedelta(days=60)).strftime('%Y%m%d')
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daily_df = ts_client.pro.daily(
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ts_code=stock_code,
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start_date=start_date,
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end_date=today
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)
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if daily_df.empty or len(daily_df) < 30:
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print(f" ❌ 历史数据不足（需要30天以上），无法计算MA")
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continue
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daily_df = daily_df.sort_values('trade_date').reset_index(drop=True)
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close_series = daily_df['close']
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vol_series = daily_df['vol']
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ma5 = close_series.rolling(window=5).mean().iloc[-1]
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ma10 = close_series.rolling(window=10).mean().iloc[-1]
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ma20 = close_series.rolling(window=20).mean().iloc[-1]
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print(f" MA5: ¥{ma5:.2f}, MA10: ¥{ma10:.2f}, MA20: ¥{ma20:.2f}")
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if ma5 > ma20:
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print(f" ✓ MA趋势符合（MA5 > MA20）")
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else:
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print(f" ❌ MA趋势不符合（需要 MA5 > MA20）")
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continue
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# 检查4: 量能
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ma5_vol = vol_series.rolling(window=5).mean().iloc[-1]
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volume_ratio = vol / ma5_vol if ma5_vol > 0 else 1
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print(f" 量比: {volume_ratio:.2f}")
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if volume_ratio >= 0.7:
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print(f" ✓ 量能符合（≥0.7）")
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else:
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print(f" ❌ 量能不足（量比需要≥0.7）")
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continue
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# 检查5: 市值
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if turnover > 0:
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market_cap = amount / (turnover / 100)
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market_cap_yi = market_cap / 100000000
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print(f" 市值: {market_cap_yi:.2f}亿")
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if 30 <= market_cap_yi <= 1000:
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print(f" ✓ 市值符合")
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else:
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print(f" ❌ 市值不符合（需要30-1000亿）")
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continue
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print(f" ✅✅✅ {name}({stock_code}) 通过所有筛选条件！")
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except Exception as e:
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print(f" ❌ 计算技术指标失败: {e}")
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import traceback
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print(traceback.format_exc())
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except Exception as e:
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print(f"❌ 诊断失败: {e}")
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import traceback
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traceback.print_exc()
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async def main():
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"""主函数"""
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await diagnose_sector_stocks()
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print("\n" + "=" * 60)
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print("诊断完成")
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print("=" * 60)
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if __name__ == "__main__":
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asyncio.run(main())
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