#!/usr/bin/env python3
"""
测试单个股票的筛选逻辑
"""
import asyncio
import sys
import os

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
from app.astock_agent.short_term_thematic_selector import get_thematic_selector


async def test_single_stock():
    """测试单个股票"""
    print("\n" + "=" * 80)
    print("🔍 测试单个股票筛选")
    print("=" * 80)

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

    # 测试股票代码（从诊断脚本中找到的通过股票）
    test_stock = "000682.SZ"

    # 获取该股票所属的板块
    sectors_df = ts_client.get_concept_sectors()
    smart_grid = sectors_df[sectors_df['name'] == '智能电网']

    if smart_grid.empty:
        print("未找到智能电网板块")
        return

    sector_code = smart_grid.iloc[0]['ts_code']
    sector_name = smart_grid.iloc[0]['name']

    # 获取板块成分股
    members_df = ts_client.get_sector_members(sector_code)
    stock_codes = members_df['con_code'].tolist()

    if test_stock not in stock_codes:
        print(f"{test_stock} 不在智能电网板块中")
        return

    print(f"\n测试股票: {test_stock}")
    print(f"所属板块: {sector_name} ({sector_code})")
    print(f"板块成分股数量: {len(stock_codes)}")
    print(f"测试股票在板块中的位置: {stock_codes.index(test_stock) + 1}/{len(stock_codes)}")

    # 获取实时行情 - 检查更多股票
    check_count = min(200, len(stock_codes))
    print(f"\n获取前 {check_count} 只股票的实时行情...")

    realtime_df = ts_client.get_realtime_data(stock_codes[:check_count])

    if realtime_df.empty:
        print("无法获取实时行情")
        return

    print(f"实时行情数据获取成功，共 {len(realtime_df)} 只股票")

    # 检查目标股票是否在行情数据中
    if test_stock not in realtime_df['ts_code'].values:
        print(f"❌ {test_stock} 不在行情数据中")
        print(f"行情数据中的股票: {realtime_df['ts_code'].tolist()[:10]}")
        return

    stock_row = realtime_df[realtime_df['ts_code'] == test_stock].iloc[0]
    print(f"\n✓ {test_stock} 行情数据:")
    print(f"  现价: {stock_row['close']}")
    print(f"  涨跌幅: {stock_row['pct_chg']}%")
    print(f"  成交量: {stock_row['vol']}")
    print(f"  成交额: {stock_row['amount']}千元")

    # 获取每日指标
    trade_date = realtime_df.iloc[0]['trade_date']
    basic_df = ts_client.get_stock_daily_basic([test_stock], str(trade_date))

    print(f"\n每日指标数据: {'有' if not basic_df.empty else '无'}")
    if not basic_df.empty:
        basic_row = basic_df[basic_df['ts_code'] == test_stock]
        if not basic_row.empty:
            print(f"  换手率: {basic_row.iloc[0]['turnover_rate']}%")

    # 调用选股器的内部检查函数
    print("\n开始筛选检查...")
    print("=" * 80)

    # 检查所有股票
    passed_stocks = []
    for idx, stock_code in enumerate(stock_codes[:check_count], 1):
        try:
            result = selector._check_single_stock(
                stock_code=stock_code,
                sector_name=sector_name,
                sector_change=2.77,
                realtime_df=realtime_df,
                basic_df=basic_df
            )

            if result:
                passed_stocks.append((stock_code, result.get('name', '')))
                print(f"  ✓ [{idx}] {stock_code}: {result.get('name', '')}")

        except Exception as e:
            print(f"  ✗ [{idx}] {stock_code}: 检查失败 - {e}")

    print("=" * 80)
    print(f"\n检查了 {check_count} 只股票，通过筛选: {len(passed_stocks)} 只")

    if passed_stocks:
        print("\n✅ 通过的股票:")
        for stock_code, name in passed_stocks[:20]:  # 只显示前20只
            print(f"  - {stock_code}: {name}")
        if len(passed_stocks) > 20:
            print(f"  ... 还有 {len(passed_stocks) - 20} 只")
    else:
        print("\n❌ 没有股票通过筛选")


async def main():
    await test_single_stock()


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