529 lines
20 KiB
Python
529 lines
20 KiB
Python
#!/usr/bin/env python3
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"""
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小唯四维选股系统 — 宏观+基本面+技术+消息
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========================================
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综合四个维度筛选股票
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维度说明:
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1. 宏观面 — 美联储政策/人民币汇率/地缘风险
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2. 基本面 — PE/PB/业绩增速/行业景气度
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3. 技术面 — MA20趋势/成交量/均线排列
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4. 消息面 — 政策利好/行业新闻/重大事件
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评分规则:
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- 宏观负面(-1)/中性(0)/正面(+1)
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- 基本面低估(+1)/合理(0)/高估(-1)
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- 技术面多头(+1)/空头(-1)/震荡(0)
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- 消息面利好(+1)/利空(-1)/中性(0)
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- 综合评分: 4分以上关注, 6分以上重点
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用法:
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python3 stock_selector.py scan # 扫描全市场
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python3 stock_selector.py analyze <代码> # 分析单只股票
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python3 stock_selector.py watchlist # 展示关注列表
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"""
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import json, sys, urllib.request, time
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from datetime import datetime
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from pathlib import Path
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import numpy as np
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import pandas as pd
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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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# ===================== 飞书推送 =====================
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def send_feishu(msg):
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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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try:
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with urllib.request.urlopen(req, timeout=10):
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pass
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except Exception:
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pass
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# ===================== 宏观面 — 汇率/利率/风险 =====================
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def get_macro_score():
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"""
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宏观面评分:
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考虑: 人民币汇率, 美联储政策, 地缘风险
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返回: -1(负面) / 0(中性) / +1(正面)
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"""
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score = 0
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reasons = []
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# 人民币汇率
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try:
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url = "https://qt.gtimg.cn/q=usdcnh"
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text = urllib.request.urlopen(url, timeout=5).read().decode("gbk")
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parts = text.split("~")
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if len(parts) > 10:
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usdcnh = float(parts[3])
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if usdcnh > 7.3:
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score -= 1
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reasons.append(f"离岸人民币破7.3({usdcnh}) → 外资流出压力 ⬇️")
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else:
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score += 1
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reasons.append(f"人民币相对稳定({usdcnh}) → 外资流出压力小 ⬆️")
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except Exception:
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reasons.append("人民币汇率获取失败")
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# A股风险偏好(用沪深300判断)
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try:
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url = "https://web.ifzq.gtimg.cn/appstock/app/fqkline/get?_var=kline_dayqfq¶m=sh510300,day,2026-07-01,2026-07-12,10,qfq"
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text = urllib.request.urlopen(url, timeout=5).read().decode("utf-8")
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data = json.loads(text.replace("kline_dayqfq=", "", 1))
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rows = data.get("data", {}).get("sh510300", {}).get("qfqday") or []
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if len(rows) >= 5:
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closes = [float(r[2]) for r in rows]
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if closes[-1] > closes[0]:
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score += 1
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reasons.append("沪深300近期上涨 → 市场风险偏好上升 ⬆️")
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else:
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score -= 1
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reasons.append("沪深300近期下跌 → 市场风险偏好下降 ⬇️")
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except Exception:
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pass
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# 平均
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avg_score = max(-1, min(1, score // max(1, len([r for r in reasons if r]))))
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return avg_score, reasons
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# ===================== 基本面 — PE/PB/业绩 =====================
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def get_fundamental_score(code):
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"""
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基本面评分:
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基于估值(PE/PB)和近期趋势
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返回: -1(高估) / 0(合理) / +1(低估)
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"""
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try:
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mc = f"sh{code}" if code.startswith("6") else f"sz{code}"
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url = f"https://qt.gtimg.cn/q={mc}"
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text = urllib.request.urlopen(url, timeout=5).read().decode("gbk")
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parts = text.split("~")
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if len(parts) < 40:
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return 0, [], {}
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pe = float(parts[39]) if parts[39] else 0
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pb = float(parts[46]) if parts[46] else 0
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price = float(parts[3])
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yclose = float(parts[4])
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score = 0
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reasons = []
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# PE判断
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if 0 < pe < 15:
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score += 1
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reasons.append(f"PE={pe:.1f}(历史低位) → 估值有支撑 ⬆️")
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elif 15 <= pe <= 30:
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reasons.append(f"PE={pe:.1f}(合理区间) → 无明显低估/高估")
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elif pe > 30:
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score -= 1
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reasons.append(f"PE={pe:.1f}(历史高位) → 估值偏高风险 ⬇️")
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elif pe <= 0:
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reasons.append(f"PE={pe}(亏损/无效) → 无法判断")
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# PB判断
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if 0 < pb < 3:
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score += 1
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reasons.append(f"PB={pb:.1f}(相对低估) → 净资产有支撑 ⬆️")
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elif pb >= 3:
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reasons.append(f"PB={pb:.1f}(相对高估)")
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# 近期价格位置
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try:
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url2 = f"https://web.ifzq.gtimg.cn/appstock/app/fqkline/get?_var=kline_dayqfq¶m={mc},day,2026-01-01,2026-07-12,200,qfq"
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text2 = urllib.request.urlopen(url2, timeout=5).read().decode("utf-8")
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data2 = json.loads(text2.replace("kline_dayqfq=", "", 1))
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hist = data2.get("data", {}).get(mc, {}).get("qfqday") or []
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if len(hist) >= 60:
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highs = [float(r[3]) for r in hist]
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low = min(highs)
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high = max(highs)
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pos = (price - low) / (high - low) if high > low else 0.5
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if pos < 0.2:
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score += 1
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reasons.append(f"价格处于近半年低位({pos*100:.0f}%) → 相对安全边际 ⬆️")
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elif pos > 0.8:
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score -= 1
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reasons.append(f"价格处于近半年高位({pos*100:.0f}%) → 追高风险 ⬇️")
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except Exception:
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pass
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avg_score = max(-1, min(1, score))
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info = {"pe": pe, "pb": pb, "price": price, "yclose": yclose}
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return avg_score, reasons, info
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except Exception as e:
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return 0, [f"基本面数据获取失败({e})"], {}
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# ===================== 技术面 — MA20趋势 =====================
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def get_technical_score(code):
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"""
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技术面评分:
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基于MA20均线状态
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返回: -1(空头) / 0(震荡) / +1(多头)
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"""
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try:
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mc = f"sh{code}" if code.startswith("6") else f"sz{code}"
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end = datetime.now().strftime("%Y-%m-%d")
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url = f"https://web.ifzq.gtimg.cn/appstock/app/fqkline/get?_var=kline_dayqfq¶m={mc},day,2025-07-01,{end},500,qfq"
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text = urllib.request.urlopen(url, timeout=8).read().decode("utf-8")
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data = json.loads(text.replace("kline_dayqfq=", "", 1))
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rows = data.get("data", {}).get(mc, {}).get("qfqday") or []
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if len(rows) < 30:
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return 0, ["数据不足"], {}
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closes = [float(r[2]) for r in rows]
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dates = [r[0] for r in rows]
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closes_s = pd.Series(closes)
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ma20 = closes_s.rolling(20).mean()
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ma60 = closes_s.rolling(60).mean()
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ma120 = closes_s.rolling(120).mean()
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last_close = closes[-1]
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last_ma20 = ma20.iloc[-1]
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last_ma60 = ma60.iloc[-1]
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last_ma120 = ma120.iloc[-1]
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# 均线排列
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if last_ma20 > last_ma60 > last_ma120:
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ma_arrangement = "多头排列"
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t_score = 1
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elif last_ma20 < last_ma60 < last_ma120:
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ma_arrangement = "空头排列"
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t_score = -1
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else:
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ma_arrangement = "均线混乱"
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t_score = 0
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# 价格与MA20关系
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pct_above = (last_close - last_ma20) / last_ma20 * 100
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# 近期趋势
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trend_20d = (closes[-1] - closes[-20]) / closes[-20] * 100 if len(closes) >= 20 else 0
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reasons = [
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f"{ma_arrangement}(价格{'' if last_close>last_ma20 else ''}{pct_above:+.1f}%vsMA20)",
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f"近20日涨跌: {trend_20d:+.1f}%",
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]
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info = {
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"price": last_close,
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"ma20": last_ma20,
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"ma60": last_ma60,
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"ma120": last_ma120,
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"ma_arrangement": ma_arrangement,
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"pct_above_ma20": pct_above,
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"trend_20d": trend_20d,
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}
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return t_score, reasons, info
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except Exception as e:
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return 0, [f"技术面数据获取失败({e})"], {}
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# ===================== 消息面 — 行业/政策 =====================
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def get_stock_news(code, name, max_news=3):
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"""获取个股最新新闻（东方财富）"""
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try:
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url = f"https://np-anotice-stock.eastmoney.com/api/security/ann?sr=-1&page_size={max_news}&page_index=1&ann_type=SZA&stock_list={code}"
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import subprocess, shlex, os, json
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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 6 --compressed {shlex.quote(url)}"
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r = subprocess.run(cmd, shell=True, capture_output=True, timeout=8, env=env)
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data = json.loads(r.stdout.decode("utf-8", errors="ignore"))
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notices = data.get("data", {}).get("list", [])
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news = []
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for n in notices[:max_news]:
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title = n.get("title", "")[:50]
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notice_date = n.get("notice_date", "")[:10]
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if title:
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news.append(f"· {notice_date} {title}")
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return news if news else [f"近{max_news}日无重大公告"]
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except Exception:
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return ["新闻获取失败"]
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def get_sector_news(industry, max_news=2):
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"""获取行业相关新闻 — 回退到指数情绪代理（行业API不可用时）"""
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try:
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# 先尝试东财快讯
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keyword_map = {"白酒": "白酒", "银行": "银行", "保险": "保险", "新能源": "新能源"}
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keyword = keyword_map.get(industry, industry) or industry or ""
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base = "https://search-api-web.eastmoney.com/search/jsonp"
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param = ('{"uid":"","keyword":"' + keyword + '","type":["cmsArticleListNew"],'
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'"client":"web","clientVersion":"curr","clientType":"web",'
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'"param":{"cmsArticleListNew":{"pageIndex":1,"pageSize":' + str(max_news) + '}}}')
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url = base + "?param=" + param
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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 = ["curl", "-s", "--max-time", "6",
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"-H", "Referer: https://so.eastmoney.com/",
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"-H", "User-Agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 Chrome/120 Safari/537.36",
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url]
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r = subprocess.run(cmd, capture_output=True, timeout=8, env=env)
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raw = r.stdout.decode("utf-8", errors="ignore")
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m = re.search(r'\(\[.*\]\)', raw, re.DOTALL)
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if m:
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items = json.loads(m.group(1))
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news = [f"· {it.get('title','')[:40]}" for it in items[:max_news] if it.get('title')]
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if news:
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return news
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except Exception:
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pass
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# 回退：使用指数涨跌作为行业情绪代理
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try:
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sector_codes = {"白酒": "sh000858", "银行": "sh000001", "保险": "sh601318", "新能源": "sz399808"}
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code = sector_codes.get(industry, "sh000001")
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mc = code[:2] + code[2:]
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url = f"https://qt.gtimg.cn/q={mc}"
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text = get_url_gbk(url)
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parts = text.split("~")
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if len(parts) > 5:
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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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sentiment = "📈 行业指数上涨" if chg > 0 else "📉 行业指数下跌"
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return [f"· {sentiment} ({chg:+.2f}%) — {parts[1]}"]
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except Exception:
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pass
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return ["· 行业新闻获取失败（回退到静态规则）"]
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def get_sentiment_score_from_news(code, name="", industry=""):
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"""消息面评分 — 基于真实新闻 + 静态规则回退"""
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import time
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news = get_stock_news(code, name)
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sector_news = get_sector_news(industry) if industry else []
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score = 0
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reasons = []
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# 真实新闻分析
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all_news = news + sector_news
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for item in all_news:
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text = item.lower()
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if any(k in text for k in ["违规", "调查", "处罚", "减持", "业绩预亏", "暴雷"]):
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score -= 1
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reasons.append(f"利空公告: {item[:30]}")
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elif any(k in text for k in ["回购", "增持", "业绩预增", "中标", "合作", "突破"]):
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score += 1
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reasons.append(f"利好公告: {item[:30]}")
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# 静态规则回退（无新闻时）
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if not reasons:
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if name in ["五粮液", "贵州茅台", "泸州老窖", "洋河股份"]:
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score -= 1; reasons.append("白酒消费降级压制 ⬇️")
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score += 1; reasons.append("估值低位有支撑 ⬆️")
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elif name in ["平安银行", "招商银行"]:
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score += 1; reasons.append("高股息防御 ⬆️")
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score += 1; reasons.append("低估值稳健 ⬆️")
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elif "宁德" in name:
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score -= 1; reasons.append("新能源产能过剩担忧 ⬇️")
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score += 1; reasons.append("行业龙头壁垒 ⬆️")
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else:
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reasons.append("消息面无明显驱动")
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avg_score = max(-1, min(1, score))
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return avg_score, reasons, news, sector_news
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# ===================== 综合评分 =====================
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def analyze_stock(code, name=""):
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"""对单只股票进行四维评分"""
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print(f"\n{'='*60}")
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print(f"四维选股分析: {name}({code})")
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print(f"{'='*60}")
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# 1. 宏观面
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m_score, m_reasons = get_macro_score()
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print(f"[宏观面] 评分: {m_score:+d}")
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for r in m_reasons:
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print(f" - {r}")
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# 2. 基本面
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f_score, f_reasons, f_info = get_fundamental_score(code)
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print(f"[基本面] 评分: {f_score:+d}")
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for r in f_reasons:
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print(f" - {r}")
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# 3. 技术面
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t_score, t_reasons, t_info = get_technical_score(code)
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print(f"[技术面] 评分: {t_score:+d}")
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for r in t_reasons:
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print(f" - {r}")
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# 4. 消息面
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industry_map = {"000858": "白酒", "600519": "白酒", "000568": "白酒", "002304": "白酒",
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"600036": "银行", "601318": "保险", "000001": "银行", "300750": "新能源", "510300": ""}
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industry = industry_map.get(code, "")
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s_score, s_reasons, stock_news, sector_news = get_sentiment_score_from_news(code, name, industry)
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print(f"[消息面] 评分: {s_score:+d}")
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for r in s_reasons:
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print(f" - {r}")
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if stock_news:
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for n in stock_news[:2]:
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print(f" 📰 {n}")
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if sector_news:
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for n in sector_news[:1]:
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print(f" 📢 {n}")
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# ===========================================================
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# 组合评分逻辑（修正简单相加的缺陷）
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# ===========================================================
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# 规则1: 技术面空头 + 基本面低估 = 买入机会(逆向)
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# 规则2: 技术面空头 + 基本面高估 = 危险加倍(双杀)
|
||
# 规则3: 宏观负面时，技术面多头无法持续
|
||
# 规则4: 综合评分考虑方向匹配
|
||
|
||
f_strong = f_score >= 1 # 基本面好(低估)
|
||
f_weak = f_score <= -1 # 基本面差(高估)
|
||
t_bear = t_score <= -1 # 技术面空头(超跌)
|
||
t_bull = t_score >= 1 # 技术面多头
|
||
m_bad = m_score <= -1 # 宏观差
|
||
|
||
# 逆向机会: 技术面空头+基本面好 = 低估买入机会
|
||
if t_bear and f_strong:
|
||
adjusted_total = 2 # 视为关注机会
|
||
combo_reason = "逆向机会: 技术超跌+基本面低估"
|
||
# 双杀: 技术差+基本面也差
|
||
elif t_bear and f_weak:
|
||
adjusted_total = -2
|
||
combo_reason = "双杀风险: 技术超跌+基本面高估"
|
||
# 技术多头+基本面好 + 宏观不差
|
||
elif t_bull and f_strong and not m_bad:
|
||
adjusted_total = 3
|
||
combo_reason = "共振: 技术+基本面+宏观同向"
|
||
# 强势股(宏观好时)
|
||
elif t_bull and m_score >= 0:
|
||
adjusted_total = 2
|
||
combo_reason = "技术多头，宏观中性支撑"
|
||
# 常规计算
|
||
else:
|
||
adjusted_total = m_score + f_score + t_score + s_score
|
||
combo_reason = "常规评分"
|
||
|
||
total = adjusted_total
|
||
max_score = 4
|
||
|
||
print(f"\n组合逻辑: {combo_reason}")
|
||
print(f"\n{'='*60}")
|
||
print(f"综合评分: {total:+d} / {max_score} ({(total/max_score*100):.0f}%)")
|
||
if total >= 6:
|
||
verdict = "⭐⭐⭐ 重点关注 — 四维共振，强烈看多"
|
||
elif total >= 4:
|
||
verdict = "⭐⭐ 关注 — 多维度偏多"
|
||
elif total >= 2:
|
||
verdict = "⭐ 关注 — 逆向机会或温和看多"
|
||
elif total >= 0:
|
||
verdict = "⚠️ 观察 — 方向不明"
|
||
else:
|
||
verdict = "❌ 不碰 — 双杀风险"
|
||
print(f"结论: {verdict}")
|
||
print(f"{'='*60}")
|
||
|
||
# 保存
|
||
result = {
|
||
"code": code, "name": name, "date": datetime.now().strftime("%Y-%m-%d %H:%M"),
|
||
"scores": {"macro": m_score, "fundamental": f_score, "technical": t_score, "sentiment": s_score},
|
||
"total": total,
|
||
"verdict": verdict,
|
||
"macro_reasons": m_reasons,
|
||
"fundamental_reasons": f_reasons,
|
||
"fundamental_info": f_info,
|
||
"technical_reasons": t_reasons,
|
||
"technical_info": t_info,
|
||
"sentiment_reasons": s_reasons,
|
||
}
|
||
result_file = OUTPUT / f"四维_{code}.json"
|
||
with open(result_file, "w") as f:
|
||
json.dump(result, f, ensure_ascii=False, indent=2)
|
||
print(f"数据存: {result_file}")
|
||
|
||
return result
|
||
|
||
|
||
def scan_watchlist():
|
||
"""扫描关注列表"""
|
||
stocks = [
|
||
("000858", "五粮液"),
|
||
("600519", "贵州茅台"),
|
||
("000001", "平安银行"),
|
||
("300750", "宁德时代"),
|
||
("510300", "沪深300ETF"),
|
||
]
|
||
|
||
print(f"\n{'='*70}")
|
||
print(f"{'股票':<12} {'宏观':>5} {'基本面':>6} {'技术面':>6} {'消息面':>6} {'综合':>5} {'结论'}")
|
||
print(f"{'-'*70}")
|
||
|
||
results = []
|
||
for code, name in stocks:
|
||
r = analyze_stock(code, name)
|
||
results.append(r)
|
||
|
||
sc = r["scores"]
|
||
ts = r["total"]
|
||
print(f"{name:<12} {sc['macro']:>+4} {sc['fundamental']:>+5} {sc['technical']:>+5} {sc['sentiment']:>+5} {ts:>+4} {r['verdict'].split(' ')[0]}")
|
||
|
||
time.sleep(0.5) # 避免请求过快
|
||
|
||
print(f"{'='*70}")
|
||
|
||
# 汇总
|
||
print(f"\n📊 宏观面: {'负面 ⬇️' if results[0]['scores']['macro'] < 0 else '正面 ⬆️' if results[0]['scores']['macro'] > 0 else '中性'}")
|
||
good = [r for r in results if r["total"] >= 2]
|
||
print(f"可选股票({len(good)}只): {[r['name'] for r in good]}")
|
||
|
||
msg = f"""📊 四维选股扫描
|
||
|
||
宏观面: {'负面 ⬇️' if results[0]['scores']['macro'] < 0 else '正面 ⬆️'}
|
||
|
||
| 股票 | 宏观 | 基本面 | 技术 | 消息 | 综合 |
|
||
|------|------|--------|------|------|------|
|
||
"""
|
||
for r in results:
|
||
sc = r["scores"]
|
||
msg += f"| {r['name']} | {sc['macro']:>+2} | {sc['fundamental']:>+2} | {sc['technical']:>+2} | {sc['sentiment']:>+2} | **{r['total']:>+2}** |\n"
|
||
|
||
good = [r for r in results if r["total"] >= 2]
|
||
msg += f"\n可选股票({len(good)}): "
|
||
msg += " / ".join([r["name"] for r in good]) if good else "无"
|
||
|
||
msg += f"\n\n生成: {datetime.now().strftime('%Y-%m-%d %H:%M')}"
|
||
send_feishu(msg)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
if "--scan" in sys.argv or "scan" in sys.argv:
|
||
scan_watchlist()
|
||
elif "--watchlist" in sys.argv:
|
||
scan_watchlist()
|
||
elif len(sys.argv) >= 3 and sys.argv[1] == "analyze":
|
||
code = sys.argv[2]
|
||
name = sys.argv[3] if len(sys.argv) > 3 else code
|
||
analyze_stock(code, name)
|
||
elif len(sys.argv) >= 2 and sys.argv[1] not in ["--scan", "--watchlist"]:
|
||
code = sys.argv[1]
|
||
name = sys.argv[2] if len(sys.argv) > 2 else code
|
||
analyze_stock(code, name)
|
||
else:
|
||
print("用法:")
|
||
print(" python3 stock_selector.py --scan # 扫描关注列表")
|
||
print(" python3 stock_selector.py analyze <代码> [名称] # 分析单只")
|
||
print(" python3 stock_selector.py 000858 五粮液 # 同上简写") |