#!/usr/bin/env python3
"""
小唯宏观指标自动化 — 真实数据驱动四维评分的宏观维度
==================================================
获取上证/沪深300/创业板/原油，计算宏观趋势评分。

评分规则：
  上证 20日涨幅 > 0 → +1（大盘向上）
  上证 20日涨幅 < -3% → -1（大盘走弱）
  沪深300 20日涨幅 > 0 → +1
  原油 20日涨幅 > 10% → -1（通胀压力）
  综合：sum 后 clip 到 [-1, 1]

用法：
  python3 stock_macro.py          # 输出宏观评分
  python3 stock_macro.py --json   # JSON 输出（供矛盾周报引用）
"""
import json, sys, urllib.request
from datetime import datetime, timedelta
from pathlib import Path

OUTPUT = Path.home() / ".hermes" / "stock_backtest"
OUTPUT.mkdir(exist_ok=True)


def get_url(url, timeout=8):
    import subprocess, shlex, os
    env = dict(os.environ)
    for k in ["http_proxy", "https_proxy", "HTTP_PROXY", "HTTPS_PROXY"]:
        env.pop(k, None)
    cmd = f"curl -s --max-time {timeout} --compressed {shlex.quote(url)}"
    try:
        r = subprocess.run(cmd, shell=True, capture_output=True, timeout=timeout + 2, env=env)
        return r.stdout.decode("utf-8", errors="ignore")
    except Exception:
        return ""


def get_url_gbk(url, timeout=8):
    import subprocess, shlex, os
    env = dict(os.environ)
    for k in ["http_proxy", "https_proxy", "HTTP_PROXY", "HTTPS_PROXY"]:
        env.pop(k, None)
    cmd = f"curl -s --max-time {timeout} --compressed {shlex.quote(url)}"
    try:
        r = subprocess.run(cmd, shell=True, capture_output=True, timeout=timeout + 2, env=env)
        return r.stdout.decode("gbk", errors="ignore")
    except Exception:
        return ""


def get_kline_change(symbol, days=20):
    """获取指数/品种过去 N 日涨幅"""
    today = datetime.now().strftime("%Y-%m-%d")
    start = (datetime.now() - timedelta(days=days * 2)).strftime("%Y-%m-%d")
    url = (f"https://web.ifzq.gtimg.cn/appstock/app/fqkline/get"
           f"?_var=kline_dayqfq&param={symbol},day,{start},{today},{days},qfq")
    text = get_url(url)
    if not text or len(text) < 50:
        return None
    try:
        import re
        json_str = re.sub(r"^[^=]+=", "", text, count=1)
        data = json.loads(json_str)
        key = list(data.get("data", {}).keys())
        if not key:
            return None
        raw = data["data"][key[0]].get("qfqday") or data["data"][key[0]].get("day") or []
        if len(raw) < 2:
            return None
        closes = [float(r[2]) for r in raw if len(r) > 2 and float(r[2]) > 0]
        if len(closes) < 2:
            return None
        return (closes[-1] - closes[0]) / closes[0] * 100
    except Exception:
        return None


def get_macro_score():
    """
    计算宏观评分
    返回: {"macro": int, "details": {指标: 涨幅}, "reasons": [...]}
    """
    # 上证指数
    shanghai = get_kline_change("sh000001", days=20)
    # 沪深300（用 ETF 510300 代理）
    hs300 = get_kline_change("sh510300", days=20)
    # 创业板
    chinext = get_kline_change("sz399006", days=20)
    # 原油
    oil = get_kline_change("hf_OIL", days=20)

    score = 0
    details = {}
    reasons = []

    if shanghai is not None:
        details["上证20日"] = f"{shanghai:+.1f}%"
        if shanghai > 0:
            score += 1
            reasons.append(f"上证20日{shanghai:+.1f}% → 大盘向上")
        elif shanghai < -3:
            score -= 1
            reasons.append(f"上证20日{shanghai:+.1f}% → 大盘走弱")
        else:
            reasons.append(f"上证20日{shanghai:+.1f}% → 震荡")

    if hs300 is not None:
        details["沪深300"] = f"{hs300:+.1f}%"
        if hs300 > 0:
            score += 1
            reasons.append(f"沪深300 {hs300:+.1f}% → 蓝筹强")
        elif hs300 < -3:
            score -= 1
            reasons.append(f"沪深300 {hs300:+.1f}% → 蓝筹弱")

    if chinext is not None:
        details["创业板"] = f"{chinext:+.1f}%"
        if chinext < -5:
            score -= 1
            reasons.append(f"创业板{chinext:+.1f}% → 成长弱")

    if oil is not None:
        details["原油20日"] = f"{oil:+.1f}%"
        if oil > 10:
            score -= 1
            reasons.append(f"原油{oil:+.1f}% → 通胀压力")
        elif oil < -10:
            score += 1
            reasons.append(f"原油{oil:+.1f}% → 通缩缓解")

    # clip 到 [-1, 1]
    macro = max(-1, min(1, score))
    if macro == 0 and not reasons:
        macro = 0
        reasons.append("宏观数据获取不足，中性")

    return {"macro": macro, "details": details, "reasons": reasons, "date": datetime.now().strftime("%Y-%m-%d")}


def main():
    result = get_macro_score()
    if "--json" in sys.argv:
        out = OUTPUT / "macro_score.json"
        with open(out, "w", encoding="utf-8") as f:
            json.dump(result, f, ensure_ascii=False, indent=2)
        print(json.dumps(result, ensure_ascii=False))
        print(f"📁 已保存: {out}")
        return

    print("=" * 50)
    print(f"小唯宏观评分 {result['date']}")
    print("=" * 50)
    for k, v in result["details"].items():
        print(f"  {k}: {v}")
    print(f"\n宏观评分: {result['macro']:+d}")
    for r in result["reasons"]:
        print(f"  • {r}")


if __name__ == "__main__":
    main()