#!/usr/bin/env python3
"""
A股短期题材选股 - 详细诊断版本
显示每个股票的筛选过程
"""
import asyncio
import sys
import os
from datetime import datetime, timedelta

sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))

from app.utils.logger import logger
from app.config import get_settings
from app.astock_agent.tushare_client import get_tushare_client


async def diagnose_sector_stocks():
    """诊断板块成分股的筛选过程"""
    print("\n" + "=" * 60)
    print("🔍 A股选股详细诊断")
    print("=" * 60)

    settings = get_settings()
    ts_client = get_tushare_client(settings.tushare_token)

    # 1. 测试获取一个板块的成分股
    print("\n【测试】获取智能电网板块成分股...")
    try:
        # 使用智能电网板块代码（与选股器一致）
        sector_code = "885311.TI"
        members_df = ts_client.get_sector_members(sector_code)

        if members_df.empty:
            print("❌ 无法获取板块成分股")
            return

        stock_codes = members_df['con_code'].tolist()[:10]  # 只测试前10只
        print(f"✓ 获取到 {len(stock_codes)} 只成分股（测试前10只）")
        print(f"股票代码: {stock_codes}")

        # 2. 获取这些股票的实时行情
        print("\n【测试】获取实时行情...")
        today = datetime.now().strftime('%Y%m%d')
        yesterday = (datetime.now() - timedelta(days=10)).strftime('%Y%m%d')

        all_stocks_data = []
        for stock_code in stock_codes:
            try:
                daily_df = ts_client.pro.daily(
                    ts_code=stock_code,
                    start_date=yesterday,
                    end_date=today
                )

                if daily_df.empty:
                    print(f"  ⚠️  {stock_code}: 无历史数据")
                    continue

                daily_df = daily_df.sort_values('trade_date')
                latest = daily_df.iloc[-1]

                stock_info = {
                    'ts_code': stock_code,
                    'name': latest.get('name', ''),
                    'close': float(latest['close']),
                    'pct_chg': float(latest['pct_chg']),
                    'vol': float(latest['vol']),
                    'amount': float(latest['amount']) * 1000,
                    'trade_date': str(latest['trade_date'])
                }
                all_stocks_data.append(stock_info)

            except Exception as e:
                print(f"  ❌ {stock_code}: 获取失败 - {e}")
                continue

        print(f"\n✓ 成功获取 {len(all_stocks_data)} 只股票的行情")

        # 3. 获取每日指标
        print("\n【测试】获取每日指标（换手率等）...")
        basic_df = ts_client.pro.daily_basic(
            ts_code=','.join(stock_codes),
            trade_date=all_stocks_data[0]['trade_date'],
            fields='ts_code,trade_date,turnover_rate,pe,pb'
        )

        if basic_df.empty:
            print("⚠️  无法获取每日指标数据")
        else:
            print(f"✓ 获取到 {len(basic_df)} 只股票的每日指标")

        # 4. 逐个检查筛选条件
        print("\n【测试】逐个检查筛选条件...")
        print("=" * 80)

        for stock_info in all_stocks_data:
            stock_code = stock_info['ts_code']
            name = stock_info['name']
            close = stock_info['close']
            pct_chg = stock_info['pct_chg']
            vol = stock_info['vol']
            amount = stock_info['amount']

            print(f"\n🔍 {name}({stock_code}):")
            print(f"  日期: {stock_info['trade_date']}")
            print(f"  现价: ¥{close:.2f}, 涨跌幅: {pct_chg:+.2f}%")

            # 检查1: ST股票
            if 'ST' in name or '退' in name:
                print(f"  ❌ ST/退市股，被过滤")
                continue
            print(f"  ✓ 不是ST/退市股")

            # 检查2: 换手率
            basic_row = basic_df[basic_df['ts_code'] == stock_code]
            if not basic_row.empty:
                turnover = float(basic_row.iloc[0].get('turnover_rate', 0))
                print(f"  换手率: {turnover:.2f}%")
                if 1.0 <= turnover <= 20.0:
                    print(f"  ✓ 换手率符合")
                else:
                    print(f"  ❌ 换手率不符合（需要1%-20%）")
                    continue
            else:
                print(f"  ⚠️  无换手率数据")
                turnover = 0

            # 检查3: MA多头排列
            try:
                start_date = (datetime.now() - timedelta(days=60)).strftime('%Y%m%d')
                daily_df = ts_client.pro.daily(
                    ts_code=stock_code,
                    start_date=start_date,
                    end_date=today
                )

                if daily_df.empty or len(daily_df) < 30:
                    print(f"  ❌ 历史数据不足（需要30天以上），无法计算MA")
                    continue

                daily_df = daily_df.sort_values('trade_date').reset_index(drop=True)
                close_series = daily_df['close']
                vol_series = daily_df['vol']

                ma5 = close_series.rolling(window=5).mean().iloc[-1]
                ma10 = close_series.rolling(window=10).mean().iloc[-1]
                ma20 = close_series.rolling(window=20).mean().iloc[-1]

                print(f"  MA5: ¥{ma5:.2f}, MA10: ¥{ma10:.2f}, MA20: ¥{ma20:.2f}")

                if ma5 > ma20:
                    print(f"  ✓ MA趋势符合（MA5 > MA20）")
                else:
                    print(f"  ❌ MA趋势不符合（需要 MA5 > MA20）")
                    continue

                # 检查4: 量能
                ma5_vol = vol_series.rolling(window=5).mean().iloc[-1]
                volume_ratio = vol / ma5_vol if ma5_vol > 0 else 1
                print(f"  量比: {volume_ratio:.2f}")

                if volume_ratio >= 0.7:
                    print(f"  ✓ 量能符合（≥0.7）")
                else:
                    print(f"  ❌ 量能不足（量比需要≥0.7）")
                    continue

                # 检查5: 市值
                if turnover > 0:
                    market_cap = amount / (turnover / 100)
                    market_cap_yi = market_cap / 100000000
                    print(f"  市值: {market_cap_yi:.2f}亿")

                    if 30 <= market_cap_yi <= 1000:
                        print(f"  ✓ 市值符合")
                    else:
                        print(f"  ❌ 市值不符合（需要30-1000亿）")
                        continue

                print(f"  ✅✅✅ {name}({stock_code}) 通过所有筛选条件！")

            except Exception as e:
                print(f"  ❌ 计算技术指标失败: {e}")
                import traceback
                print(traceback.format_exc())

    except Exception as e:
        print(f"❌ 诊断失败: {e}")
        import traceback
        traceback.print_exc()


async def main():
    """主函数"""
    await diagnose_sector_stocks()
    print("\n" + "=" * 60)
    print("诊断完成")
    print("=" * 60)


if __name__ == "__main__":
    asyncio.run(main())