"""东方财富分钟K线数据客户端

通过 push2his.eastmoney.com 获取 A 股分钟级 K 线数据，
用于盘中量能分布分析和短线进场时机判断。

免费接口，无需认证，注意限频。
"""

import logging
import httpx
import pandas as pd
from datetime import datetime

from app.data.cache import cache
from app.config import settings

logger = logging.getLogger(__name__)

# 东方财富分钟K线接口
EASTMONEY_KLINE_URL = "http://push2his.eastmoney.com/api/qt/stock/kline/get"
HEADERS = {
    "Referer": "http://finance.eastmoney.com",
    "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36",
}


def _ts_code_to_eastmoney(ts_code: str) -> str:
    """600519.SH -> 1.600519  (上海=1, 深圳=0)"""
    code, market = ts_code.split(".")
    prefix = "1" if market == "SH" else "0"
    return f"{prefix}.{code}"


async def get_min_kline(
    ts_code: str,
    period: str = "5",
    count: int = 48,
) -> pd.DataFrame:
    """获取分钟级 K 线数据

    Args:
        ts_code: Tushare 格式代码，如 600519.SH
        period: 分钟周期 ("1", "5", "15", "30", "60")
        count: 请求条数（5分钟×48=4小时≈一个交易日）

    Returns:
        DataFrame with columns: time, open, close, high, low, volume, amount
        空 DataFrame if failed
    """
    cache_key = f"min_kline:{ts_code}:{period}"
    cached = cache.get(cache_key)
    if cached is not None:
        return cached

    secid = _ts_code_to_eastmoney(ts_code)

    params = {
        "secid": secid,
        "fields1": "f1,f2,f3,f4,f5,f6",
        "fields2": "f51,f52,f53,f54,f55,f56,f57,f58,f59,f60,f61",
        "klt": period,       # 分钟周期
        "fqt": "1",          # 前复权
        "beg": "0",
        "end": "20500101",
        "lmt": str(count),   # 返回条数限制
    }

    try:
        async with httpx.AsyncClient() as client:
            resp = await client.get(
                EASTMONEY_KLINE_URL,
                params=params,
                headers=HEADERS,
                timeout=10,
            )
            data = resp.json()

        klines = data.get("data", {}).get("klines", [])
        if not klines:
            logger.debug(f"东方财富分钟K线 {ts_code} 无数据")
            return pd.DataFrame()

        # 解析：格式 "2026-04-15 09:30,5.71,5.73,5.74,5.70,12345,70000.00"
        rows = []
        for line in klines:
            parts = line.split(",")
            if len(parts) < 7:
                continue
            rows.append({
                "time": parts[0],
                "open": float(parts[1]),
                "close": float(parts[2]),
                "high": float(parts[3]),
                "low": float(parts[4]),
                "volume": float(parts[5]),
                "amount": float(parts[6]),
            })

        df = pd.DataFrame(rows)
        if df.empty:
            return df

        # 缓存（分钟数据盘中3分钟过期，盘后30分钟过期）
        ttl = 180 if _is_trading_hours() else 1800
        cache.set(cache_key, df, ttl)
        return df

    except Exception as e:
        logger.error(f"东方财富分钟K线获取失败 {ts_code}: {e}")
        return pd.DataFrame()


def analyze_intraday_volume_distribution(min_df: pd.DataFrame) -> dict:
    """分析盘中量能分布（基于5分钟K线）

    Returns:
        {
            "morning_volume_ratio": float,  # 上午成交额占比
            "afternoon_volume_ratio": float, # 下午成交额占比
            "opening_strength": float,  # 开盘30分钟成交额/全天占比
            "closing_strength": float,  # 尾盘30分钟成交额/全天占比
            "volume_trend": str,  # "morning_dominant" / "afternoon_dominant" / "balanced"
            "key_periods": list[str],  # 关键放量时段描述
        }
    """
    if min_df.empty or len(min_df) < 4:
        return {
            "morning_volume_ratio": 0.5,
            "afternoon_volume_ratio": 0.5,
            "opening_strength": 0.25,
            "closing_strength": 0.25,
            "volume_trend": "balanced",
            "key_periods": [],
        }

    total_amount = min_df["amount"].sum()
    if total_amount == 0:
        return {
            "morning_volume_ratio": 0.5,
            "afternoon_volume_ratio": 0.5,
            "opening_strength": 0.25,
            "closing_strength": 0.25,
            "volume_trend": "balanced",
            "key_periods": [],
        }

    # 按时间段分割
    morning = min_df[min_df["time"].str.contains("09:|10:|11:0", na=False)]
    afternoon = min_df[min_df["time"].str.contains("13:|14:0|14:1|14:2|14:3|14:4|14:5", na=False)]

    morning_ratio = morning["amount"].sum() / total_amount if not morning.empty else 0.5
    afternoon_ratio = afternoon["amount"].sum() / total_amount if not afternoon.empty else 0.5

    # 开盘30分钟（9:30-10:00）
    opening_30 = min_df[min_df["time"].str.contains("09:3|09:4|09:5|10:0", na=False)]
    opening_strength = opening_30["amount"].sum() / total_amount if not opening_30.empty else 0.25

    # 尾盘30分钟（14:30-15:00）
    closing_30 = min_df[min_df["time"].str.contains("14:3|14:4|14:5|15:0", na=False)]
    closing_strength = closing_30["amount"].sum() / total_amount if not closing_30.empty else 0.25

    # 量能趋势判断
    if morning_ratio > 0.6:
        volume_trend = "morning_dominant"
    elif afternoon_ratio > 0.6:
        volume_trend = "afternoon_dominant"
    else:
        volume_trend = "balanced"

    # 关键放量时段
    key_periods = []
    avg_amount = min_df["amount"].mean()
    hot_bars = min_df[min_df["amount"] > avg_amount * 2]
    for _, bar in hot_bars.iterrows():
        time_str = bar["time"].split(" ")[1] if " " in bar["time"] else bar["time"]
        pct = bar["amount"] / total_amount * 100
        key_periods.append(f"{time_str}放量(占比{pct:.0f}%)")

    return {
        "morning_volume_ratio": round(morning_ratio, 2),
        "afternoon_volume_ratio": round(afternoon_ratio, 2),
        "opening_strength": round(opening_strength, 2),
        "closing_strength": round(closing_strength, 2),
        "volume_trend": volume_trend,
        "key_periods": key_periods[:3],
    }


def _is_trading_hours() -> bool:
    """简单判断是否在交易时间"""
    now = datetime.now()
    if now.weekday() >= 5:  # 周六周日
        return False
    hour = now.hour
    minute = now.minute
    # 9:30 - 11:30 或 13:00 - 15:00
    if (hour == 9 and minute >= 30) or (hour == 10) or (hour == 11 and minute <= 30):
        return True
    if (hour == 13) or (hour == 14) or (hour == 15 and minute == 0):
        return True
    return False