"""个股资金流向分析

在热门板块成分股中，通过主力净流入、换手率、量比、市值
筛选被主力资金真正介入的个股。
"""

import logging
import pandas as pd

from app.data.tushare_client import tushare_client
from app.data import tencent_client
from app.data.models import SectorInfo, CapitalFlow
from app.config import settings
from app.analysis.sector_alignment import build_hot_theme_membership

logger = logging.getLogger(__name__)


def _score_capital_flow(
    main_net_inflow: float,
    inflow_ratio: float,
    turnover_rate: float,
    volume_ratio: float | None,
    circ_mv: float | None,
) -> float:
    """资金维度评分，返回 0-100"""
    score = 0.0

    # 主力净流入金额归一化评分 (35%)
    # 这里不做跨个股归一化，而是基于绝对值打分
    if main_net_inflow > 10000:       # >1亿
        score += 35
    elif main_net_inflow > 5000:      # >5000万
        score += 28
    elif main_net_inflow > 2000:      # >2000万
        score += 22
    elif main_net_inflow > 500:       # >500万
        score += 15
    elif main_net_inflow > 0:
        score += 8

    # 主力净流入占比 (20%)
    if inflow_ratio > 15:
        score += 20
    elif inflow_ratio > 10:
        score += 16
    elif inflow_ratio > 5:
        score += 12
    elif inflow_ratio > 2:
        score += 8
    elif inflow_ratio > 0:
        score += 4

    # 换手率 (15%) - 3%-15% 最佳
    if settings.min_turnover_rate <= turnover_rate <= settings.max_turnover_rate:
        # 5%-10% 最佳
        if 5 <= turnover_rate <= 10:
            score += 15
        else:
            score += 10
    elif turnover_rate > 0:
        score += 3

    # 量比 (15%)
    if volume_ratio is not None:
        if volume_ratio > 3.0:
            score += 15
        elif volume_ratio > 2.0:
            score += 12
        elif volume_ratio > 1.5:
            score += 9
        elif volume_ratio > 1.0:
            score += 5

    # 市值适配 (15%)
    if circ_mv is not None:
        mv_b = circ_mv  # 已经是亿
        if settings.min_circ_mv <= mv_b <= settings.max_circ_mv:
            if 100 <= mv_b <= 300:  # 最佳区间
                score += 15
            else:
                score += 10
        elif mv_b > 0:
            score += 3

    return score


def _score_valuation(pe: float | None, pb: float | None) -> float:
    """估值安全评分，返回 0-100

    PE/PB 越低越安全（分数越高）。
    过高估值会被大幅惩罚，避免追高买在估值泡沫中。
    """
    score = 0.0

    # PE_TTM 评分 (60%)
    if pe is not None and pe > 0:
        if pe <= 15:
            score += 60     # 低估
        elif pe <= 25:
            score += 50     # 合理偏低
        elif pe <= 40:
            score += 35     # 合理
        elif pe <= 60:
            score += 20     # 偏高
        elif pe <= 100:
            score += 8      # 高估
        else:
            score += 0      # 极度高估
    elif pe is not None and pe < 0:
        score += 5          # 亏损股，低分
    else:
        score += 30         # 无数据，给中间分

    # PB 评分 (40%)
    if pb is not None and pb > 0:
        if pb <= 1.5:
            score += 40     # 破净或接近，极度低估
        elif pb <= 3:
            score += 32     # 合理
        elif pb <= 5:
            score += 22     # 偏高
        elif pb <= 8:
            score += 12     # 高估
        else:
            score += 4      # 极度高估
    else:
        score += 20         # 无数据，给中间分

    return score


async def filter_stocks_by_capital(
    hot_sectors: list[SectorInfo],
    trade_date: str = None,
) -> list[dict]:
    """
    从热门板块中筛选资金流入个股。
    返回: [{ts_code, name, sector, capital_score, turnover_rate, ...}]
    """
    if not trade_date:
        trade_date = tushare_client.get_latest_trade_date()

    # 收集所有热门板块的成分股（去重）
    stock_sectors: dict[str, list[str]] = {}
    sector_member_codes, sector_code_map, _, _, _ = build_hot_theme_membership(hot_sectors)
    for code in sector_member_codes:
        if not code or "." not in str(code):
            continue
        stock_sectors.setdefault(code, []).append(sector_code_map.get(code, ""))

    if not stock_sectors:
        logger.warning("热门板块成分股为空")
        return []

    logger.info(f"热门板块共 {len(stock_sectors)} 只成分股待筛选")

    # 获取股票基本信息（用于过滤 ST 和次新）
    stock_basic = tushare_client.get_stock_basic()
    st_codes = set()
    new_codes = set()
    if not stock_basic.empty:
        st_codes = set(stock_basic[stock_basic["name"].str.contains("ST", na=False)]["ts_code"])
        # 上市不足 60 天
        from datetime import datetime, timedelta
        cutoff = (datetime.now() - timedelta(days=settings.min_list_days)).strftime("%Y%m%d")
        new_codes = set(stock_basic[stock_basic["list_date"] > cutoff]["ts_code"])

    # 获取当日全市场资金流向
    mf_df = tushare_client.get_moneyflow_batch(trade_date)

    # 获取当日 daily_basic（换手率、市值等）
    basic_df = tushare_client.get_daily_basic(trade_date)

    results = []

    for ts_code, sector_names in stock_sectors.items():
        # 过滤 ST 和次新
        if ts_code in st_codes or ts_code in new_codes:
            continue

        # 资金流向
        main_net = 0.0
        inflow_ratio = 0.0
        if not mf_df.empty:
            row = mf_df[mf_df["ts_code"] == ts_code]
            if not row.empty:
                r = row.iloc[0]
                main_net = (
                    (r.get("buy_elg_amount", 0) or 0) - (r.get("sell_elg_amount", 0) or 0) +
                    (r.get("buy_lg_amount", 0) or 0) - (r.get("sell_lg_amount", 0) or 0)
                )
                total = (
                    (r.get("buy_elg_amount", 0) or 0) + (r.get("sell_elg_amount", 0) or 0) +
                    (r.get("buy_lg_amount", 0) or 0) + (r.get("sell_lg_amount", 0) or 0) +
                    (r.get("buy_md_amount", 0) or 0) + (r.get("sell_md_amount", 0) or 0) +
                    (r.get("buy_sm_amount", 0) or 0) + (r.get("sell_sm_amount", 0) or 0)
                )
                if total > 0:
                    inflow_ratio = main_net / total * 100

        # 硬性条件：主力净流入 > 0
        if main_net <= 0:
            continue

        # 换手率和市值
        turnover_rate = 0.0
        volume_ratio = None
        circ_mv = None
        pe = None
        pb = None
        if not basic_df.empty:
            b_row = basic_df[basic_df["ts_code"] == ts_code]
            if not b_row.empty:
                b = b_row.iloc[0]
                turnover_rate = float(b.get("turnover_rate", 0) or 0)
                volume_ratio = float(b["volume_ratio"]) if pd.notna(b.get("volume_ratio")) else None
                circ_mv_raw = b.get("circ_mv", 0) or 0
                circ_mv = float(circ_mv_raw) / 10000  # Tushare 单位是万元，转为亿
                pe = float(b["pe_ttm"]) if pd.notna(b.get("pe_ttm")) else None
                pb = float(b["pb"]) if pd.notna(b.get("pb")) else None

        # 换手率硬性过滤
        if turnover_rate < settings.min_turnover_rate or turnover_rate > settings.max_turnover_rate:
            continue

        # 市值硬性过滤
        if circ_mv is not None and (circ_mv < settings.min_circ_mv or circ_mv > settings.max_circ_mv):
            continue

        capital_score = _score_capital_flow(
            main_net_inflow=main_net,
            inflow_ratio=inflow_ratio,
            turnover_rate=turnover_rate,
            volume_ratio=volume_ratio,
            circ_mv=circ_mv,
        )

        valuation_score = _score_valuation(pe, pb)

        # 获取股票名称
        name = ts_code
        if not stock_basic.empty:
            name_row = stock_basic[stock_basic["ts_code"] == ts_code]
            if not name_row.empty:
                name = name_row.iloc[0]["name"]

        results.append({
            "ts_code": ts_code,
            "name": name,
            "sector": sector_names[0],  # 取第一个板块
            "sectors": sector_names,
            "main_net_inflow": round(main_net, 2),
            "inflow_ratio": round(inflow_ratio, 2),
            "turnover_rate": round(turnover_rate, 2),
            "volume_ratio": volume_ratio,
            "circ_mv": round(circ_mv, 2) if circ_mv else None,
            "pe": round(pe, 2) if pe else None,
            "pb": round(pb, 2) if pb else None,
            "capital_score": round(capital_score, 1),
            "valuation_score": round(valuation_score, 1),
        })

    # 按资金评分排序
    results.sort(key=lambda x: x["capital_score"], reverse=True)

    top = results[:settings.top_stock_count]
    for r in top:
        logger.info(f"资金筛选: {r['name']}({r['ts_code']}) 板块={r['sector']} "
                     f"主力净流入={r['main_net_inflow']}万 评分={r['capital_score']}")

    return top