360 lines
14 KiB
Python
360 lines
14 KiB
Python
#!/usr/bin/env python3
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"""
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小唯每日组合信号 — 全市场MA20扫描
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==================================
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每日扫描 ⭐关注 股票，给出综合评分和最优选择
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覆盖: 五粮液/贵州茅台/泸州老窖/洋河股份/招商银行/中国平安/平安银行
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用法:
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python3 stock_portfolio.py # 打印组合信号
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python3 stock_portfolio.py --push # 推送飞书
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"""
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import json, sys, urllib.request, re
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from datetime import datetime
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from pathlib import Path
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OUTPUT = Path.home() / ".hermes" / "stock_backtest"
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OUTPUT.mkdir(exist_ok=True)
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FEISHU_WEBHOOK = "https://open.feishu.cn/open-apis/bot/v2/hook/446db983-e392-4d2c-bfb8-f9060e5df3ad"
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def load_backtest_stats(code):
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"""读取回测结果，用于金叉时的置信度参考"""
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result_file = OUTPUT / f"ma20_result_{code}.json"
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if not result_file.exists():
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return None
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try:
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import ast
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with open(result_file) as f:
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d = json.load(f)
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return d
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except Exception:
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return None
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# MA20参数
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MA_PERIOD = 20
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# ⭐关注股票列表（2026-08-01 行业扫描扩充）
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# 动量正行业优先纳入：新能源/科技/煤炭/半导体/证券
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WATCHED_STOCKS = [
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# 原白酒（弱势观察）
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("000858", "五粮液", "白酒"),
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("600519", "贵州茅台", "白酒"),
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("000568", "泸州老窖", "白酒"),
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("002304", "洋河股份", "白酒"),
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# 原金融
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("600036", "招商银行", "银行"),
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("601318", "中国平安", "保险"),
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("000001", "平安银行", "银行"),
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# 新增：动量正行业（2026-08-01 行业扫描）
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("300750", "宁德时代", "新能源"),
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("002594", "比亚迪", "新能源"),
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("002415", "海康威视", "科技"),
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("000063", "中兴通讯", "科技"),
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("601088", "中国神华", "煤炭"),
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("600188", "兖矿能源", "煤炭"),
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("688981", "中芯国际", "半导体"),
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("600030", "中信证券", "证券"),
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("000333", "美的集团", "家电"),
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# 新增：有色/石油（2026-08-02 扩充扫描）
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("601899", "紫金矿业", "有色"),
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("601857", "中国石油", "石油"),
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]
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# 行业动量过滤（2026-08-01 行业扫描更新）
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# 用 vibe-trading 学术因子引擎扫描 16 行业后确认：
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# 🟢 动量正行业：煤炭+27.7% / 半导体+22.8% / 科技+16.5% / 新能源+12.9% / 军工+5.2%
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# 🔴 真弱势（动量负+年化负+Sharpe负）：白酒-33% / 医药-21.8% / 通信-22.2% / 汽车-36.8% / 地产-46.5%
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# 弱势行业金叉硬拦截，不推荐不开仓
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WEAK_TREND_INDUSTRIES = {"白酒", "医药", "通信", "汽车", "地产"}
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def get_url(url, timeout=8):
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"""统一用curl，避免urllib在hermes/scripts目录下异常"""
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import subprocess, shlex, os
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env = dict(os.environ)
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for k in ["http_proxy", "https_proxy", "HTTP_PROXY", "HTTPS_PROXY"]:
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env.pop(k, None)
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cmd = f"curl -s --max-time {timeout} --compressed {shlex.quote(url)}"
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try:
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r = subprocess.run(cmd, shell=True, capture_output=True, timeout=timeout + 2, env=env)
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return r.stdout.decode("utf-8", errors="ignore")
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except Exception:
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return ""
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def get_url_gbk(url, timeout=8):
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"""GBK编码的请求（腾讯行情）"""
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import subprocess, shlex, os
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env = dict(os.environ)
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for k in ["http_proxy", "https_proxy", "HTTP_PROXY", "HTTPS_PROXY"]:
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env.pop(k, None)
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cmd = f"curl -s --max-time {timeout} --compressed {shlex.quote(url)}"
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try:
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r = subprocess.run(cmd, shell=True, capture_output=True, timeout=timeout + 2, env=env)
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return r.stdout.decode("gbk", errors="ignore")
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except Exception:
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return ""
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def get_kline_data(code, min_needed=60):
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"""获取足够的日K线数据（前复权）"""
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import subprocess, shlex, os, re, json
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from datetime import timedelta
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market = "sz" if code.startswith(("00", "30")) else "sh"
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full = f"{market}{code}"
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# 实际交易日只有2/3，请求足够多的数据确保有min_needed个
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end_d = datetime.now()
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start_d = end_d - timedelta(days=int(min_needed * 1.8))
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start_str = start_d.strftime("%Y-%m-%d")
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end_str = end_d.strftime("%Y-%m-%d")
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# 请求足够多的条数
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url = (f"https://web.ifzq.gtimg.cn/appstock/app/fqkline/get"
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f"?_var=kline_dayqfq¶m={full},day,{start_str},{end_str},300,qfq")
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env = dict(os.environ)
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for k in ["http_proxy", "https_proxy", "HTTP_PROXY", "HTTPS_PROXY"]:
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env.pop(k, None)
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cmd = f"curl -s --max-time 8 --compressed {shlex.quote(url)}"
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try:
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r = subprocess.run(cmd, shell=True, capture_output=True, timeout=10, env=env)
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text = r.stdout.decode("utf-8", errors="ignore")
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except Exception:
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return []
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if not text or len(text) < 50:
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print(f"[DEBUG] {code} empty response, len={len(text)}")
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return []
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try:
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json_str = re.sub(r"^[^=]+=", "", text, count=1)
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data = json.loads(json_str)
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raw = data.get("data", {}).get(full, {}).get("qfqday", [])
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if not raw:
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raw = data.get("data", {}).get(full, {}).get("day", [])
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if raw:
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raw = sorted(raw, key=lambda x: x[0])[-min_needed:]
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return raw
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except Exception:
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return []
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def validate_klines(raw, min_needed=25):
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"""验证K线数据质量，返回有效数据或空列表"""
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if not raw or len(raw) < min_needed:
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return []
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# 检查日期连续性（允许周末/节假日跳跃）
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dates = [r[0] for r in raw]
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prices = [float(r[2]) for r in raw if len(r) > 2]
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if len(prices) < min_needed:
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return []
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# 检查价格合理性：过滤价格为0或异常值
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valid = [r for r in raw if len(r) > 5 and float(r[2]) > 0]
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return valid[-min_needed:] if len(valid) >= min_needed else []
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def analyze_ma20(code, name, industry):
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"""分析单只股票的MA20状态 — 含数据质量验证"""
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raw_klines = get_kline_data(code, MA_PERIOD + 10)
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klines = validate_klines(raw_klines, MA_PERIOD + 2)
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if len(klines) < MA_PERIOD + 2:
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return None
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try:
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closes = [float(k[2]) for k in klines] # index 2 = close
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ma20 = sum(closes[-MA_PERIOD:]) / MA_PERIOD
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current_price = closes[-1]
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prev_price = closes[-2]
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above_ma = current_price > ma20
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golden_cross = prev_price <= ma20 < current_price # 今天刚上穿
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dead_cross = prev_price >= ma20 > current_price # 今天刚下穿
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chg_pct = (current_price - closes[0]) / closes[0] * 100 if closes else 0
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return {
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"code": code,
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"name": name,
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"industry": industry,
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"price": current_price,
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"ma20": ma20,
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"above_ma": above_ma,
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"golden_cross": golden_cross,
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"dead_cross": dead_cross,
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"chg_pct": chg_pct,
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"diff_pct": (current_price - ma20) / ma20 * 100,
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}
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except Exception:
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return None
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def get_macro():
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"""宏观指标：上证 + 原油 + 沪深300（大盘风险代理）"""
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result = {}
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# 上证
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text = get_url_gbk("https://qt.gtimg.cn/q=sh000001")
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parts = text.split("~")
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if len(parts) > 5:
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try:
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p = float(parts[3])
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y = float(parts[4])
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result["上证"] = {"price": p, "change": (p-y)/y*100}
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except:
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pass
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# 原油
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text = get_url_gbk("https://qt.gtimg.cn/q=hf_OIL")
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parts = text.split("~")
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if len(parts) > 5 and parts[3]:
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try:
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p = float(parts[3])
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y = float(parts[4])
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result["原油"] = {"price": p, "change": p-y}
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except:
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pass
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# 沪深300（大盘风险代理）
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text = get_url_gbk("https://qt.gtimg.cn/q=sh510300")
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parts = text.split("~")
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if len(parts) > 5:
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try:
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p = float(parts[3])
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y = float(parts[4])
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chg = (p-y)/y*100
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result["沪深300"] = {"price": p, "change": chg,
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"level": "强势(>0)" if chg > 0 else "弱势(<0)"}
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except:
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pass
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# 离岸人民币（新浪 ifzq 备用，失败则跳过）
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try:
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url = "https://web.ifzq.gtimg.cn/appstock/app/fqkline/get?_var=kline_dayqfq¶m=usdcny,day,2026-07-01,2026-07-12,5,qfq"
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text = get_url(url)
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# 解析失败则跳过，不影响其他指标
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except Exception:
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pass
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return result
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def build_portfolio_report(push=False):
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today = datetime.now().strftime("%Y-%m-%d")
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macro = get_macro()
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print(f"\n{'='*60}")
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print(f"小唯股票组合信号 {today}")
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print(f"{'='*60}")
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# 宏观
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print("\n【宏观】")
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for name, d in macro.items():
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e = "📈" if d["change"] > 0 else "📉"
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print(f" {e} {name}: {d['price']:.2f} ({d['change']:+.2f})")
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# 个股扫描
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print(f"\n【MA20扫描】MA周期={MA_PERIOD}日")
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print(f"{'代码':<8} {'名称':<8} {'行业':<6} {'价格':>8} {'MA20':>8} {'偏离':>7} {'信号'}")
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print("-" * 65)
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signals = [] # 有信号的股票
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for code, name, industry in WATCHED_STOCKS:
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r = analyze_ma20(code, name, industry)
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if not r:
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print(f"{code} {name}: 数据不足")
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continue
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above = "✅在MA20上方" if r["above_ma"] else "❌在MA20下方"
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diff = r["diff_pct"]
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signal = "持仓"
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signal_icon = "🟢"
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if r["golden_cross"]:
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signal = "⭐金叉买入"
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signal_icon = "🟡"
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elif r["dead_cross"]:
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signal = "🔴死叉卖出"
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signal_icon = "🔴"
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elif not r["above_ma"]:
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signal = "空仓"
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signal_icon = "⚪"
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print(f" {code} {name:<6} {industry:<5} {r['price']:>8.2f} {r['ma20']:>8.2f} {diff:>+6.1f}% {signal_icon}{signal}")
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if r["golden_cross"]:
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is_weak = r.get("industry", "") in WEAK_TREND_INDUSTRIES
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signals.append({"code": code, "name": name, "price": r["price"], "ma20": r["ma20"], "diff": diff, "industry": r.get("industry", ""), "weak": is_weak})
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# 总结
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print(f"\n{'='*60}")
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strong_signals = [s for s in signals if not s.get("weak")]
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weak_signals = [s for s in signals if s.get("weak")]
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if strong_signals:
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print(f"⭐ 今日出现MA20金叉 ({len(strong_signals)}只):")
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for s in strong_signals:
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stats = load_backtest_stats(s["code"])
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stat_line = ""
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if stats:
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stat_line = f" | 历史胜率{stats.get('win_rate',0):.0f}% α{stats.get('alpha',0):+.1f}% 最大回撤{stats.get('max_drawdown',0):.0f}%"
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print(f" → {s['name']}({s['code']}) 价格{s['price']:.2f} MA20={s['ma20']:.2f} 偏离{s['diff']:+.1f}%{stat_line}")
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if weak_signals:
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print(f"⚠️ 弱势行业金叉建议半仓 ({len(weak_signals)}只):")
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for s in weak_signals:
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print(f" → {s['name']}({s['code']}) 价格{s['price']:.2f} MA20={s['ma20']:.2f} 偏离{s['diff']:+.1f}% (行业动量负,半仓)")
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if not strong_signals and not weak_signals:
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print("今日无金叉信号。若已持仓则继续持有，未持仓保持空仓等待。")
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print(f"\n小唯股票投研 · 组合扫描")
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# 飞书推送
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if push:
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msg = f"📊 股票组合信号 {today}\n\n"
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for name, d in macro.items():
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e = "📈" if d["change"] > 0 else "📉"
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msg += f"{e} {name}: {d['price']:.2f} ({d['change']:+.2f}%)\n"
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msg += f"\nMA20扫描 ({MA_PERIOD}日):\n"
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for code, name, industry in WATCHED_STOCKS:
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r = analyze_ma20(code, name, industry)
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if not r:
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continue
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diff = r["diff_pct"]
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if r["golden_cross"]:
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if industry in WEAK_TREND_INDUSTRIES:
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# v2：弱势行业金叉半仓参与（回测证明全拦截损失 α，半仓最优）
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msg += f"🟡 {name}({code}) 金叉! 弱势行业建议半仓 (价格{r['price']:.2f} 偏离MA20 {diff:+.1f}%)\n"
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else:
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stats = load_backtest_stats(code)
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stat_line = ""
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if stats:
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stat_line = f"\n 📊 MA20策略: 历史胜率{stats.get('win_rate',0):.0f}% | α{stats.get('alpha',0):+.1f}% | 最大回撤{stats.get('max_drawdown',0):.0f}%"
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msg += f"🟡 {name}({code}) 金叉! 价格{r['price']:.2f} 偏离MA20 {diff:+.1f}%{stat_line}\n"
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# 多账户自动路由（2026-08-01 新增，v2 弱势半仓）
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if "--multi" in sys.argv:
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try:
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import stock_multi_account as sma
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action, detail = sma.execute_signal(
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{"industry": industry, "signal": "BUY", "close": r["price"], "code": code})
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msg += f" 📝 多账户: [{action}] {detail}\n"
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except Exception as e:
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msg += f" ⚠️ 多账户执行失败: {e}\n"
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elif r["dead_cross"]:
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msg += f"🔴 {name}({code}) 死叉! 平仓\n"
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if "--multi" in sys.argv:
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try:
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import stock_multi_account as sma
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action, detail = sma.execute_signal(
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{"industry": industry, "signal": "SELL", "close": r["price"], "code": code})
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msg += f" 📝 多账户: [{action}] {detail}\n"
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except Exception as e:
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msg += f" ⚠️ 多账户执行失败: {e}\n"
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if not signals:
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msg += "\n暂无金叉，继续空仓等待。\n"
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msg += f"\n生成: {datetime.now().strftime('%H:%M')}"
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try:
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payload = json.dumps({"msg_type": "text", "content": {"text": msg}}).encode()
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req = urllib.request.Request(FEISHU_WEBHOOK, data=payload,
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headers={"Content-Type": "application/json"})
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urllib.request.urlopen(req, timeout=8)
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print("✅ 已推送飞书")
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except Exception:
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print("⚠️ 飞书推送失败")
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return signals
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if __name__ == "__main__":
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build_portfolio_report(push="--push" in sys.argv) |