# -*- coding: utf-8 -*-
"""
推荐原因生成器：因子截面值 → 带具体数字和方向的中文推荐理由。
"""
import numpy as np
import pandas as pd


def build_reason(code, kline_df, weights=None):
    """
    从单股日K线（时间升序，≥30行）生成多维度推荐原因。
    返回 (reason_str, factor_detail_dict)
    """
    if kline_df is None or len(kline_df) < 30:
        return '', {}
    close = kline_df['close'].reset_index(drop=True)
    vol = kline_df['volume'].reset_index(drop=True)
    high = kline_df['high'].reset_index(drop=True)
    low = kline_df['low'].reset_index(drop=True)
    n = len(close)
    c = close.iloc[-1]

    parts = []

    # 动量
    if n > 21:
        mom = (c / close.iloc[-21] - 1) * 100
        tag = '强' if mom > 5 else ('偏强' if mom > 0 else '偏弱' if mom > -5 else '弱')
        parts.append(f"20日动量{mom:+.1f}%（{tag}）")

    # 趋势（MA20 偏离）
    ma20 = close.rolling(20).mean().iloc[-1]
    if ma20 and ma20 > 0:
        dev = (c - ma20) / ma20 * 100
        tag = '强势区' if dev > 3 else ('偏高水平' if dev > 0 else '偏低水平' if dev > -3 else '弱势区')
        parts.append(f"距MA20 {dev:+.1f}%（{tag}）")

    # 量能
    v20 = vol.rolling(20).mean().iloc[-1]
    if v20 and v20 > 0:
        vr = vol.iloc[-1] / v20
        if vr > 1.5:
            parts.append(f"量比{vr:.1f}（放量）")
        elif vr < 0.5:
            parts.append(f"量比{vr:.1f}（极度缩量）")

    # RSI
    close_s = close
    delta = close_s.diff()
    gain = delta.clip(lower=0).rolling(14).mean()
    loss = (-delta.clip(upper=0)).rolling(14).mean()
    rs = gain / loss.replace(0, np.nan)
    rsi = (100 - 100 / (1 + rs)).iloc[-1]
    if rsi == rsi:
        if rsi > 70:
            parts.append(f"RSI={rsi:.0f}（超买）")
        elif rsi < 30:
            parts.append(f"RSI={rsi:.0f}（超卖）")

    # MACD
    ema12 = close.ewm(span=12, adjust=False).mean()
    ema26 = close.ewm(span=26, adjust=False).mean()
    dif = ema12 - ema26
    dea = dif.ewm(span=9, adjust=False).mean()
    hist = (dif - dea).iloc[-1]
    if hist > 0:
        parts.append("MACD多头")
    else:
        parts.append("MACD空头")

    return '；'.join(parts), {}