"""
CCI技术指标筛选器

基于商品通道指数(CCI)筛选超卖标的
"""

import pandas as pd
from datetime import datetime

from .base import DataFrameScreener
from core.factors.technical import calculate_cci, resample_to_weekly
from core.common.db import DatabaseManager
from core.common.notify import NotificationManager


class CCIScreener(DataFrameScreener):
    """CCI超卖筛选器"""

    def __init__(self, config: dict = None):
        super().__init__("CCI超卖筛选", config)
        self.day_period = config.get("day_period", 14)
        self.week_period = config.get("week_period", 14)
        self.threshold = config.get("threshold", -100)
        self.use_weekly = config.get("use_weekly", True)
        self.db_manager = DatabaseManager()
        self.notifier = NotificationManager()

    def screen(self, df: pd.DataFrame) -> dict:
        """
        对单只标的进行CCI筛选

        Args:
            df: DataFrame with OHLCV data

        Returns:
            dict: {
                'triggered': bool,
                'day_cci': float,
                'week_cci': float or None,
                'current_price': float,
            }
        """
        if not self.validate_data(df):
            return {"triggered": False, "error": "数据格式错误"}

        # 计算日线CCI
        day_cci = calculate_cci(df, period=self.day_period).iloc[-1]
        current_price = df["close"].iloc[-1]

        result = {
            "triggered": day_cci < self.threshold,
            "day_cci": round(day_cci, 2),
            "week_cci": None,
            "current_price": round(current_price, 2),
        }

        # 计算周线CCI
        if self.use_weekly:
            weekly_df = resample_to_weekly(df)
            if len(weekly_df) >= self.week_period:
                week_cci = calculate_cci(weekly_df, period=self.week_period).iloc[-1]
                result["week_cci"] = round(week_cci, 2)
                # 日线或周线任一超卖即触发
                result["triggered"] = (
                    day_cci < self.threshold or week_cci < self.threshold
                )

        return result

    def get_data_from_db(self, code: str, limit: int = 100) -> pd.DataFrame:
        """从数据库获取数据"""
        sql = f"""
            SELECT date, open, high, low, close, volume
            FROM public.index_kline
            WHERE code = '{code}'
            ORDER BY date DESC
            LIMIT {limit}
        """
        result = self.db_manager.execute_query(sql)

        if not result:
            return pd.DataFrame()

        df = pd.DataFrame(result)
        df["date"] = pd.to_datetime(df["date"])

        # 转换数值类型
        for col in ["open", "high", "low", "close", "volume"]:
            df[col] = pd.to_numeric(df[col], errors="coerce")

        df = df.sort_values("date").reset_index(drop=True)
        return df

    def run_screening(self, code_list: list = None) -> list:
        """
        执行批量筛选

        Args:
            code_list: 标的代码列表，None则从配置文件读取

        Returns:
            list: 符合条件的标的列表
        """
        if code_list is None:
            # 从CSV文件读取指数列表
            import os
            csv_path = self.config.get("index_fund_info_file", "index_fund_info.csv")
            if os.path.exists(csv_path):
                df = pd.read_csv(csv_path, encoding="utf-8-sig")
                code_list = df.drop_duplicates(subset=["指数代码"]).to_dict("records")
            else:
                raise ValueError(f"找不到标的列表文件: {csv_path}")

        signals = []
        today_str = datetime.now().strftime("%Y-%m-%d")

        print(f"开始CCI筛选，共 {len(code_list)} 个标的...")

        for i, code_info in enumerate(code_list):
            if isinstance(code_info, dict):
                code = code_info.get("指数代码")
                name = code_info.get("指数名称", code)
            else:
                code = code_info
                name = code

            try:
                df = self.get_data_from_db(code, limit=self.config.get("lookback_days", 100))

                if len(df) < self.day_period:
                    continue

                # 检查最新日期
                if df["date"].max().strftime("%Y-%m-%d") != today_str:
                    continue

                result = self.screen(df)

                if result["triggered"]:
                    signals.append({
                        "code": code,
                        "name": name,
                        "day_cci": result["day_cci"],
                        "week_cci": result["week_cci"],
                        "price": result["current_price"],
                    })
                    print(f"  ✓ {code} ({name}): 日线CCI={result['day_cci']:.2f}")

            except Exception as e:
                print(f"  ✗ {code}: {e}")
                continue

        print(f"\n筛选完成，{len(signals)} 个标的符合CCI超卖条件")

        # 发送通知
        if signals:
            self.notifier.notify_signal(signals, signal_type="CCI超卖")

        return signals

    def run_daily(self):
        """每日定时运行"""
        from datetime import datetime

        # 检查是否为交易日
        if datetime.today().weekday() >= 5 and self.config.get("skip_weekend", True):
            print("非交易日，跳过")
            return

        self.run_screening()


def create_cci_screener_from_config(config_path: str = None) -> CCIScreener:
    """从配置文件创建CCI筛选器"""
    import yaml
    import os

    if config_path is None:
        config_path = os.path.join(
            os.path.dirname(__file__), "..", "..", "config", "strategies", "cci.yaml"
        )

    with open(config_path, "r", encoding="utf-8") as f:
        config = yaml.safe_load(f)

    return CCIScreener(config)