feat: 初始化黄小瓜AI助手记忆仓库
- 核心配置: IDENTITY, USER, SOUL, AGENTS, TOOLS, HEARTBEAT, MEMORY - memory/: 每日总结和临时记录 - skills/: 所有已安装技能 - notes/: 语音配置笔记
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skills/stock-monitor-skill/scripts/monitor.py
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skills/stock-monitor-skill/scripts/monitor.py
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#!/usr/bin/env python3
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"""
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自选股监控预警工具 - OpenClaw集成版
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支持 A股、ETF 及 国际现货黄金 (伦敦金)
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"""
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import requests
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import json
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import time
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import os
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from datetime import datetime
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from pathlib import Path
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# ============ 配置区 ============
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# 监控列表 - 长期挂机通用配置
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# 注意: 伦敦金使用新浪hf_XAU接口，价格为 人民币/克 (约4800元/克 = $2740/盎司)
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#
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# 预警规则设计原则 (适合长期挂机):
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# 1. 成本百分比预警: 基于持仓成本设置 ±10%/±15% 预警，比固定价格更合理
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# 2. 单日涨跌幅预警:
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# - 个股 ±3%~5% (波动大)
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# - ETF ±1.5%~2.5% (波动小)
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# - 黄金 ±2%~3% (24H特殊)
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# 3. 防骚扰: 同类预警30分钟内只发一次
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# 标的类型定义
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STOCK_TYPE = {
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"INDIVIDUAL": "individual", # 个股
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"ETF": "etf", # ETF
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"GOLD": "gold" # 黄金/贵金属
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}
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WATCHLIST = [
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# ===== 个股: 波动较大，设置较宽的涨跌预警 =====
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{
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"code": "600362",
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"name": "江西铜业",
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"market": "sh",
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"type": "individual",
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"cost": 57.00,
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"alerts": {
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"cost_pct_above": 15.0, # 盈利15%
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"cost_pct_below": -12.0, # 止损12%
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"change_pct_above": 4.0, # 日内异动 ±4%
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"change_pct_below": -4.0,
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"volume_surge": 2.0 # 成交量是5日均量2倍
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}
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},
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{
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"code": "601318",
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"name": "中国平安",
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"market": "sh",
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"type": "individual",
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"cost": 66.00,
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"alerts": {
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"cost_pct_above": 12.0,
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"cost_pct_below": -10.0,
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"change_pct_above": 3.5, # 日内异动 ±3.5%
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"change_pct_below": -3.5,
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"volume_surge": 2.0
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}
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},
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# ===== ETF: 波动相对较小，设置更敏感的预警 =====
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{
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"code": "159892",
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"name": "恒生医疗",
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"market": "sz",
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"type": "etf",
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"cost": 0.80,
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"alerts": {
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"cost_pct_above": 15.0,
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"cost_pct_below": -15.0,
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"change_pct_above": 2.0, # ETF日内异动 ±2%
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"change_pct_below": -2.0,
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"volume_surge": 1.8 # ETF放量阈值更低
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}
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},
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{
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"code": "513180",
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"name": "恒生科技",
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"market": "sh",
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"type": "etf",
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"cost": 0.72,
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"alerts": {
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"cost_pct_above": 15.0,
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"cost_pct_below": -15.0,
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"change_pct_above": 2.0, # ETF日内异动 ±2%
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"change_pct_below": -2.0,
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"volume_surge": 1.8
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}
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},
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{
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"code": "159681",
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"name": "创50ETF",
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"market": "sz",
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"type": "etf",
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"cost": 1.50,
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"alerts": {
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"cost_pct_above": 12.0,
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"cost_pct_below": -12.0,
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"change_pct_above": 2.0, # ETF日内异动 ±2%
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"change_pct_below": -2.0,
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"volume_surge": 1.8
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}
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},
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{
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"code": "516020",
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"name": "化工50ETF",
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"market": "sh",
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"type": "etf",
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"cost": 0.90,
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"alerts": {
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"cost_pct_above": 12.0,
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"cost_pct_below": -12.0,
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"change_pct_above": 2.0, # ETF日内异动 ±2%
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"change_pct_below": -2.0,
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"volume_surge": 1.8
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}
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},
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# ===== 伦敦金: 24H特殊标的 =====
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{
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"code": "XAU",
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"name": "伦敦金(人民币/克)",
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"market": "fx",
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"type": "gold",
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"cost": 4650.0,
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"alerts": {
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"cost_pct_above": 10.0, # 盈利10%
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"cost_pct_below": -8.0, # 止损8%
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"change_pct_above": 2.5, # 黄金日内异动 ±2.5%
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"change_pct_below": -2.5
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# 黄金不监控成交量 (外汇市场无成交量概念)
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}
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}
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]
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# 智能频率配置
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SMART_SCHEDULE = {
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"market_open": {"hours": [(9, 30), (11, 30), (13, 0), (15, 0)], "interval": 300}, # 交易时间: 5分钟
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"after_hours": {"interval": 1800}, # 收盘后: 30分钟
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"night": {"hours": [(0, 0), (8, 0)], "interval": 3600}, # 凌晨: 1小时(仅伦敦金)
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}
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# ============ 核心代码 ============
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class StockAlert:
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def __init__(self):
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self.prev_data = {}
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self.alert_log = []
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self.session = requests.Session()
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self.session.headers.update({"User-Agent": "Mozilla/5.0"})
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def should_run_now(self):
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"""智能频率控制: 判断当前是否应该执行监控 (基于北京时间)"""
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# 服务器在纽约(EST)，中国股市用北京时间(CST = EST + 13小时)
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from datetime import timedelta
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now = datetime.now() + timedelta(hours=13) # 转换成北京时间
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hour, minute = now.hour, now.minute
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time_val = hour * 100 + minute
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weekday = now.weekday()
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# 周末只监控伦敦金
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if weekday >= 5: # 周六日
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return {"run": True, "mode": "weekend", "stocks": [s for s in WATCHLIST if s['market'] == 'fx']}
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# 交易时间 (9:30-11:30, 13:00-15:00)
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morning_session = 930 <= time_val <= 1130
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afternoon_session = 1300 <= time_val <= 1500
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if morning_session or afternoon_session:
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return {"run": True, "mode": "market", "stocks": WATCHLIST, "interval": 300}
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# 午休 (11:30-13:00)
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if 1130 < time_val < 1300:
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return {"run": True, "mode": "lunch", "stocks": WATCHLIST, "interval": 600} # 10分钟
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# 收盘后 (15:00-24:00)
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if 1500 <= time_val <= 2359:
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return {"run": True, "mode": "after_hours", "stocks": WATCHLIST, "interval": 1800} # 30分钟
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# 凌晨 (0:00-9:30)
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if 0 <= time_val < 930:
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return {"run": True, "mode": "night", "stocks": [s for s in WATCHLIST if s['market'] == 'fx'], "interval": 3600} # 1小时
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return {"run": False}
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def fetch_eastmoney_kline(self, symbol, market):
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"""获取最新日K线数据 (收盘后也能获取收盘价)"""
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secid = f"{market}.{symbol}"
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url = "https://push2his.eastmoney.com/api/qt/stock/kline/get"
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params = {
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'secid': secid,
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'fields1': 'f1,f2,f3,f4,f5,f6',
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'fields2': 'f51,f52,f53,f54,f55,f56,f57,f58,f59,f60,f61',
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'klt': '101', # 日线
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'fqt': '0',
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'end': '20500101',
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'lmt': '2' # 取最近2天，用于计算涨跌幅
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}
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try:
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resp = self.session.get(url, params=params, timeout=10)
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data = resp.json()
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klines = data.get('data', {}).get('klines', [])
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if len(klines) >= 1:
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# 格式: 日期,开盘,收盘,最高,最低,成交量,成交额,振幅,涨跌幅,涨跌额,换手率
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today = klines[-1].split(',')
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prev_close = float(today[2]) # 昨收
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if len(klines) >= 2:
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prev_close = float(klines[-2].split(',')[2]) # 前一天收盘
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return {
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'name': data.get('data', {}).get('name', symbol),
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'price': float(today[2]), # 收盘
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'prev_close': prev_close,
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'volume': int(float(today[5])),
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'amount': float(today[6]),
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'date': today[0],
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'time': '15:00:00'
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}
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except Exception as e:
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print(f"东财K线获取失败 {symbol}: {e}")
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return None
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def fetch_volume_ma5(self, symbol, market):
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"""获取5日平均成交量"""
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secid = f"{market}.{symbol}"
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url = "https://push2his.eastmoney.com/api/qt/stock/kline/get"
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params = {
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'secid': secid,
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'fields1': 'f1,f2,f3,f4,f5,f6',
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'fields2': 'f51,f52,f53,f54,f55,f56,f57,f58,f59,f60,f61',
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'klt': '101',
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'fqt': '0',
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'end': '20500101',
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'lmt': '6' # 取最近6天(今天+前5天)
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}
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try:
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resp = self.session.get(url, params=params, timeout=10)
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data = resp.json()
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klines = data.get('data', {}).get('klines', [])
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if len(klines) >= 2:
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# 计算前5日平均成交量(不含今天)
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volumes = []
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for k in klines[:-1]: # 排除最后一天(今天)
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p = k.split(',')
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volumes.append(float(p[5])) # 成交量
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return sum(volumes) / len(volumes) if volumes else 0
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except Exception as e:
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print(f"获取均量失败 {symbol}: {e}")
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return 0
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def fetch_ma_data(self, symbol, market):
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"""获取均线数据 (MA5, MA10, MA20) 和 RSI"""
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secid = f"{market}.{symbol}"
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url = "https://push2his.eastmoney.com/api/qt/stock/kline/get"
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params = {
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'secid': secid,
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'fields1': 'f1,f2,f3,f4,f5,f6',
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'fields2': 'f51,f52,f53,f54,f55,f56,f57,f58,f59,f60,f61',
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'klt': '101',
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'fqt': '0',
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'end': '20500101',
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'lmt': '30' # 取最近30天计算MA20和RSI
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}
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try:
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resp = self.session.get(url, params=params, timeout=10)
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data = resp.json()
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klines = data.get('data', {}).get('klines', [])
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if len(klines) >= 20:
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closes = []
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for k in klines:
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p = k.split(',')
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closes.append(float(p[2])) # 收盘价
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# 计算均线
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ma5 = sum(closes[-5:]) / 5
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ma10 = sum(closes[-10:]) / 10
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ma20 = sum(closes[-20:]) / 20
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# 判断均线趋势
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prev_ma5 = sum(closes[-6:-1]) / 5
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prev_ma10 = sum(closes[-11:-1]) / 10
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# 计算RSI(14)
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rsi = self._calculate_rsi(closes, 14)
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return {
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'MA5': ma5,
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'MA10': ma10,
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'MA20': ma20,
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'MA5_trend': 'up' if ma5 > prev_ma5 else 'down',
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'MA10_trend': 'up' if ma10 > prev_ma10 else 'down',
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'golden_cross': prev_ma5 <= prev_ma10 and ma5 > ma10,
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'death_cross': prev_ma5 >= prev_ma10 and ma5 < ma10,
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'RSI': rsi,
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'RSI_overbought': rsi > 70 if rsi else False,
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'RSI_oversold': rsi < 30 if rsi else False
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}
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except Exception as e:
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print(f"获取均线失败 {symbol}: {e}")
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return None
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def _calculate_rsi(self, closes, period=14):
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"""计算RSI指标"""
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if len(closes) < period + 1:
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return None
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gains = []
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losses = []
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for i in range(1, period + 1):
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change = closes[-i] - closes[-i-1]
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if change > 0:
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gains.append(change)
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losses.append(0)
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else:
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gains.append(0)
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losses.append(abs(change))
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avg_gain = sum(gains) / period
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avg_loss = sum(losses) / period
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if avg_loss == 0:
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return 100
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rs = avg_gain / avg_loss
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rsi = 100 - (100 / (1 + rs))
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return round(rsi, 2)
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def fetch_sina_realtime(self, stocks):
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"""获取实时行情 (优先实时，收盘后用日K)"""
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stock_list = [s for s in stocks if s['market'] != 'fx']
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fx_list = [s for s in stocks if s['market'] == 'fx']
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results = {}
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# 1. A股/ETF - 尝试实时接口
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if stock_list:
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codes = [f"{s['market']}{s['code']}" for s in stock_list]
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url = f"https://hq.sinajs.cn/list={','.join(codes)}"
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try:
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resp = self.session.get(url, headers={'Referer': 'https://finance.sina.com.cn'}, timeout=10)
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resp.encoding = 'gb18030'
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for line in resp.text.strip().split(';'):
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if 'hq_str_' not in line or '=' not in line: continue
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key = line.split('=')[0].split('_')[-1]
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if len(key) < 8: continue
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data_str = line[line.index('"')+1 : line.rindex('"')]
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p = data_str.split(',')
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if len(p) > 30 and float(p[3]) > 0:
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# 新浪数据格式: 名称,今日开盘,昨日收盘,当前价,今日最高,今日最低,竞买价,竞卖价,成交量,成交额...
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# 保存昨日最高最低价用于跳空检测 (用昨日收盘近似，或用均线数据补充)
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results[key[2:]] = {
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'name': p[0],
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'price': float(p[3]),
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'prev_close': float(p[2]),
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'open': float(p[1]), # 今日开盘
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'high': float(p[4]), # 今日最高
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'low': float(p[5]), # 今日最低
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'volume': int(p[8]),
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'amount': float(p[9]),
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'date': p[30],
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'time': p[31],
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'prev_high': float(p[2]) * 1.02, # 估算昨日最高 (昨收+2%)
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'prev_low': float(p[2]) * 0.98 # 估算昨日最低 (昨收-2%)
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}
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except Exception as e:
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print(f"实时行情获取失败: {e}")
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# 2. 如果实时接口返回空或0，用日K线补数据
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for stock in stock_list:
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code = stock['code']
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if code not in results or results[code]['price'] <= 0:
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kline_data = self.fetch_eastmoney_kline(code, 1 if stock['market'] == 'sh' else 0)
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if kline_data:
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results[code] = kline_data
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print(f" {stock['name']}: 使用日K收盘价 {kline_data['price']}")
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# 3. 伦敦金 (新浪hf_XAU接口，人民币/克)
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if fx_list:
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url = "https://hq.sinajs.cn/list=hf_XAU"
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try:
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resp = self.session.get(url, headers={'Referer': 'https://finance.sina.com.cn'}, timeout=10)
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line = resp.text.strip()
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if '"' in line:
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data_str = line[line.index('"')+1 : line.rindex('"')]
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p = data_str.split(',')
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if len(p) >= 13:
|
||||
# 新浪hf_XAU: 人民币/克 (约4800=2740美元/盎司)
|
||||
price = float(p[0])
|
||||
results['XAU'] = {
|
||||
'name': '伦敦金',
|
||||
'price': price,
|
||||
'prev_close': float(p[7]),
|
||||
'volume': 0, 'amount': 0,
|
||||
'date': p[11] if len(p) > 11 else datetime.now().strftime('%Y-%m-%d'),
|
||||
'time': p[6]
|
||||
}
|
||||
except Exception as e:
|
||||
print(f"伦敦金获取失败: {e}")
|
||||
|
||||
return results
|
||||
|
||||
def check_alerts(self, stock_config, data):
|
||||
"""检查预警条件 (支持成本百分比、单日涨跌幅、分级预警)"""
|
||||
alerts = []
|
||||
alert_weights = [] # 用于计算预警级别
|
||||
code = stock_config['code']
|
||||
cfg = stock_config.get('alerts', {})
|
||||
cost = stock_config.get('cost', 0)
|
||||
stock_type = stock_config.get('type', 'individual')
|
||||
price, prev_close = data['price'], data['prev_close']
|
||||
change_pct = (price - prev_close) / prev_close * 100 if prev_close else 0
|
||||
|
||||
# 1. 基于成本的百分比预警 (权重: 高)
|
||||
if cost > 0:
|
||||
cost_change_pct = (price - cost) / cost * 100
|
||||
|
||||
if 'cost_pct_above' in cfg and cost_change_pct >= cfg['cost_pct_above']:
|
||||
target_price = cost * (1 + cfg['cost_pct_above']/100)
|
||||
if not self._alerted_recently(code, 'cost_above'):
|
||||
alerts.append(('cost_above', f"🎯 盈利 {cfg['cost_pct_above']:.0f}% (目标价 ¥{target_price:.2f})"))
|
||||
alert_weights.append(3) # 高权重
|
||||
|
||||
if 'cost_pct_below' in cfg and cost_change_pct <= cfg['cost_pct_below']:
|
||||
target_price = cost * (1 + cfg['cost_pct_below']/100)
|
||||
if not self._alerted_recently(code, 'cost_below'):
|
||||
alerts.append(('cost_below', f"🛑 亏损 {abs(cfg['cost_pct_below']):.0f}% (止损价 ¥{target_price:.2f})"))
|
||||
alert_weights.append(3) # 高权重
|
||||
|
||||
# 2. 基于固定价格的预警 (权重: 中)
|
||||
if 'price_above' in cfg and price >= cfg['price_above'] and not self._alerted_recently(code, 'above'):
|
||||
alerts.append(('above', f"🚀 价格突破 ¥{cfg['price_above']}"))
|
||||
alert_weights.append(2)
|
||||
if 'price_below' in cfg and price <= cfg['price_below'] and not self._alerted_recently(code, 'below'):
|
||||
alerts.append(('below', f"📉 价格跌破 ¥{cfg['price_below']}"))
|
||||
alert_weights.append(2)
|
||||
|
||||
# 3. 单日涨跌幅预警 (权重: 根据幅度)
|
||||
if 'change_pct_above' in cfg and change_pct >= cfg['change_pct_above'] and not self._alerted_recently(code, 'pct_up'):
|
||||
alerts.append(('pct_up', f"📈 日内大涨 {change_pct:+.2f}%"))
|
||||
# 异动越大权重越高
|
||||
if change_pct >= 7:
|
||||
alert_weights.append(3) # 涨停附近
|
||||
elif change_pct >= 5:
|
||||
alert_weights.append(2) # 大涨
|
||||
else:
|
||||
alert_weights.append(1) # 一般异动
|
||||
|
||||
if 'change_pct_below' in cfg and change_pct <= cfg['change_pct_below'] and not self._alerted_recently(code, 'pct_down'):
|
||||
alerts.append(('pct_down', f"📉 日内大跌 {change_pct:+.2f}%"))
|
||||
if change_pct <= -7:
|
||||
alert_weights.append(3) # 跌停附近
|
||||
elif change_pct <= -5:
|
||||
alert_weights.append(2) # 大跌
|
||||
else:
|
||||
alert_weights.append(1) # 一般异动
|
||||
|
||||
# 4. 成交量异动检测 (仅股票和ETF)
|
||||
if stock_type != 'gold' and 'volume_surge' in cfg:
|
||||
current_volume = data.get('volume', 0)
|
||||
if current_volume > 0:
|
||||
# 尝试获取5日均量
|
||||
ma5_volume = self.fetch_volume_ma5(code, 1 if stock_config['market'] == 'sh' else 0)
|
||||
if ma5_volume > 0:
|
||||
volume_ratio = current_volume / ma5_volume
|
||||
threshold = cfg['volume_surge']
|
||||
|
||||
if volume_ratio >= threshold and not self._alerted_recently(code, 'volume_surge'):
|
||||
alerts.append(('volume_surge', f"📊 放量 {volume_ratio:.1f}倍 (5日均量)"))
|
||||
alert_weights.append(2) # 中等权重
|
||||
elif volume_ratio <= 0.5 and not self._alerted_recently(code, 'volume_shrink'):
|
||||
alerts.append(('volume_shrink', f"📉 缩量 {volume_ratio:.1f}倍 (5日均量)"))
|
||||
alert_weights.append(1) # 低权重
|
||||
|
||||
# 5. 均线系统 (MA金叉死叉)
|
||||
if stock_type != 'gold' and cfg.get('ma_monitor', True):
|
||||
ma_data = self.fetch_ma_data(code, 1 if stock_config['market'] == 'sh' else 0)
|
||||
if ma_data:
|
||||
# 金叉: MA5上穿MA10 (短期转强)
|
||||
if ma_data.get('golden_cross') and not self._alerted_recently(code, 'ma_golden'):
|
||||
alerts.append(('ma_golden', f"🌟 均线金叉 (MA5¥{ma_data['MA5']:.2f}上穿MA10¥{ma_data['MA10']:.2f})"))
|
||||
alert_weights.append(3) # 高权重
|
||||
|
||||
# 死叉: MA5下穿MA10 (短期转弱)
|
||||
if ma_data.get('death_cross') and not self._alerted_recently(code, 'ma_death'):
|
||||
alerts.append(('ma_death', f"⚠️ 均线死叉 (MA5¥{ma_data['MA5']:.2f}下穿MA10¥{ma_data['MA10']:.2f})"))
|
||||
alert_weights.append(3) # 高权重
|
||||
|
||||
# RSI超买超卖检测
|
||||
rsi = ma_data.get('RSI')
|
||||
if rsi:
|
||||
if ma_data.get('RSI_overbought') and not self._alerted_recently(code, 'rsi_high'):
|
||||
alerts.append(('rsi_high', f"🔥 RSI超买 ({rsi})，可能回调"))
|
||||
alert_weights.append(2)
|
||||
elif ma_data.get('RSI_oversold') and not self._alerted_recently(code, 'rsi_low'):
|
||||
alerts.append(('rsi_low', f"❄️ RSI超卖 ({rsi})，可能反弹"))
|
||||
alert_weights.append(2)
|
||||
|
||||
# 5. 跳空缺口检测 (需要昨日数据)
|
||||
if stock_type != 'gold':
|
||||
prev_high = data.get('prev_high', 0)
|
||||
prev_low = data.get('prev_low', 0)
|
||||
current_open = data.get('open', price) # 当前价近似开盘价
|
||||
|
||||
# 向上跳空: 今日开盘 > 昨日最高
|
||||
if prev_high > 0 and current_open > prev_high * 1.01: # 1%以上算跳空
|
||||
gap_pct = (current_open - prev_high) / prev_high * 100
|
||||
if not self._alerted_recently(code, 'gap_up'):
|
||||
alerts.append(('gap_up', f"⬆️ 向上跳空 {gap_pct:.1f}%"))
|
||||
alert_weights.append(2)
|
||||
|
||||
# 向下跳空: 今日开盘 < 昨日最低
|
||||
elif prev_low > 0 and current_open < prev_low * 0.99:
|
||||
gap_pct = (prev_low - current_open) / prev_low * 100
|
||||
if not self._alerted_recently(code, 'gap_down'):
|
||||
alerts.append(('gap_down', f"⬇️ 向下跳空 {gap_pct:.1f}%"))
|
||||
alert_weights.append(2)
|
||||
|
||||
# 6. 动态止盈/移动止损 (当盈利达到一定幅度后启动)
|
||||
if cost > 0:
|
||||
profit_pct = (price - cost) / cost * 100
|
||||
|
||||
# 当盈利 >= 10% 时，启动移动止盈
|
||||
if profit_pct >= 10:
|
||||
# 计算回撤幅度 (从最高点回撤)
|
||||
high_since_cost = data.get('high', price)
|
||||
drawdown = (high_since_cost - price) / high_since_cost * 100 if high_since_cost > cost else 0
|
||||
|
||||
# 回撤5%提醒减仓
|
||||
if drawdown >= 5 and not self._alerted_recently(code, 'trailing_stop_5'):
|
||||
alerts.append(('trailing_stop_5', f"📉 利润回撤 {drawdown:.1f}%，建议减仓保护利润"))
|
||||
alert_weights.append(2)
|
||||
|
||||
# 回撤10%提醒清仓
|
||||
elif drawdown >= 10 and not self._alerted_recently(code, 'trailing_stop_10'):
|
||||
alerts.append(('trailing_stop_10', f"🚨 利润回撤 {drawdown:.1f}%，建议清仓止损"))
|
||||
alert_weights.append(3)
|
||||
|
||||
# 6. 计算预警级别
|
||||
level = self._calculate_alert_level(alerts, alert_weights, stock_type)
|
||||
|
||||
return alerts, level
|
||||
|
||||
def _calculate_alert_level(self, alerts, weights, stock_type):
|
||||
"""计算预警级别: info(提醒) / warning(警告) / critical(紧急)"""
|
||||
if not alerts:
|
||||
return None
|
||||
|
||||
total_weight = sum(weights)
|
||||
alert_count = len(alerts)
|
||||
|
||||
# 紧急: 多条件共振 或 高权重单一条件
|
||||
if total_weight >= 5 or alert_count >= 3:
|
||||
return "critical"
|
||||
|
||||
# 警告: 中等权重 或 2个条件
|
||||
if total_weight >= 3 or alert_count >= 2:
|
||||
return "warning"
|
||||
|
||||
# 提醒: 单一低权重条件
|
||||
return "info"
|
||||
|
||||
def _alerted_recently(self, code, atype):
|
||||
now = time.time()
|
||||
self.alert_log = [l for l in self.alert_log if now - l['t'] < 1800] # 30分钟有效期
|
||||
for l in self.alert_log:
|
||||
if l['c'] == code and l['a'] == atype: return True
|
||||
return False
|
||||
|
||||
def record_alert(self, code, atype):
|
||||
self.alert_log.append({'c': code, 'a': atype, 't': time.time()})
|
||||
|
||||
def fetch_news(self, symbol):
|
||||
"""抓取个股最近新闻 (新浪/东财聚合) - 简化版"""
|
||||
try:
|
||||
# 使用东财个股新闻API
|
||||
url = f"https://emweb.securities.eastmoney.com/PC_HSF10/CompanySurvey/CompanySurveyAjax"
|
||||
params = {"code": symbol}
|
||||
resp = self.session.get(url, params=params, timeout=5)
|
||||
return ["新闻模块已就绪 (市场收盘中)"]
|
||||
except:
|
||||
return []
|
||||
|
||||
def run_once(self, smart_mode=True):
|
||||
"""执行监控 (支持智能频率)"""
|
||||
if smart_mode:
|
||||
schedule = self.should_run_now()
|
||||
if not schedule.get("run"):
|
||||
return []
|
||||
|
||||
stocks_to_check = schedule.get("stocks", WATCHLIST)
|
||||
mode = schedule.get("mode", "normal")
|
||||
|
||||
# 只在特定模式打印日志
|
||||
if mode in ["market", "weekend"]:
|
||||
print(f"[{datetime.now().strftime('%H:%M')}] {mode}模式扫描 {len(stocks_to_check)} 只标的...")
|
||||
else:
|
||||
stocks_to_check = WATCHLIST
|
||||
|
||||
data_map = self.fetch_sina_realtime(stocks_to_check)
|
||||
triggered = []
|
||||
|
||||
for stock in stocks_to_check:
|
||||
code = stock['code']
|
||||
if code not in data_map: continue
|
||||
|
||||
data = data_map[code]
|
||||
|
||||
# 数据有效性检查
|
||||
if data['price'] <= 0 or data['prev_close'] <= 0:
|
||||
continue
|
||||
|
||||
alerts, level = self.check_alerts(stock, data)
|
||||
|
||||
if alerts:
|
||||
change_pct = (data['price'] - data['prev_close']) / data['prev_close'] * 100 if data['prev_close'] else 0
|
||||
|
||||
# 中国习惯: 红色=上涨, 绿色=下跌
|
||||
if change_pct > 0:
|
||||
color_emoji = "🔴" # 红涨
|
||||
elif change_pct < 0:
|
||||
color_emoji = "🟢" # 绿跌
|
||||
else:
|
||||
color_emoji = "⚪"
|
||||
|
||||
# 预警级别标识
|
||||
level_icons = {
|
||||
"critical": "🚨", # 紧急
|
||||
"warning": "⚠️", # 警告
|
||||
"info": "📢" # 提醒
|
||||
}
|
||||
level_icon = level_icons.get(level, "📢")
|
||||
level_text = {"critical": "【紧急】", "warning": "【警告】", "info": "【提醒】"}.get(level, "")
|
||||
|
||||
msg = f"<b>{level_icon} {level_text}{color_emoji} {stock['name']} ({code})</b>\n"
|
||||
msg += f"━━━━━━━━━━━━━━━━━━━━\n"
|
||||
msg += f"💰 当前价格: <b>{data['price']:.2f}</b> ({change_pct:+.2f}%)\n"
|
||||
|
||||
# 显示持仓盈亏
|
||||
cost = stock.get('cost', 0)
|
||||
if cost > 0:
|
||||
cost_change = (data['price'] - cost) / cost * 100
|
||||
profit_icon = "🔴+" if cost_change > 0 else "🟢"
|
||||
msg += f"📊 持仓成本: ¥{cost:.2f} | 盈亏: {profit_icon}{cost_change:.2f}%\n"
|
||||
|
||||
msg += f"\n🎯 触发预警 ({len(alerts)}项):\n"
|
||||
for _, text in alerts:
|
||||
msg += f" • {text}\n"
|
||||
self.record_alert(code, _)
|
||||
|
||||
# Pro版：集成智能分析
|
||||
try:
|
||||
from analyser import StockAnalyser
|
||||
analyser = StockAnalyser()
|
||||
insight = analyser.generate_insight(stock, {
|
||||
'price': data['price'],
|
||||
'change_pct': change_pct
|
||||
}, alerts)
|
||||
msg += f"\n{insight}"
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
triggered.append(msg)
|
||||
|
||||
return triggered
|
||||
|
||||
if __name__ == '__main__':
|
||||
monitor = StockAlert()
|
||||
for alert in monitor.run_once():
|
||||
print(alert)
|
||||
Reference in New Issue
Block a user