357 lines
12 KiB
Python
357 lines
12 KiB
Python
"""
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美股交易智能体 - 主控制器（LLM 驱动版）
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只进行市场分析和通知，不执行模拟交易
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"""
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import asyncio
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from typing import Dict, Any, List, Optional
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from datetime import datetime, timedelta
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import pandas as pd
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from app.utils.logger import logger
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from app.config import get_settings
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from app.services.yfinance_service import get_yfinance_service
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from app.services.feishu_service import get_feishu_service
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from app.services.telegram_service import get_telegram_service
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from app.crypto_agent.llm_signal_analyzer import LLMSignalAnalyzer
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class StockAgent:
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"""美股交易信号智能体（LLM 驱动，仅分析通知）"""
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def __init__(self):
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"""初始化智能体"""
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self.settings = get_settings()
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self.yfinance = get_yfinance_service()
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self.feishu = get_feishu_service()
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self.telegram = get_telegram_service()
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self.llm_analyzer = LLMSignalAnalyzer()
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# 状态管理
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self.last_signals: Dict[str, Dict[str, Any]] = {}
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self.signal_cooldown: Dict[str, datetime] = {}
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# 配置
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self.symbols = self.settings.stock_symbols.split(',')
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# 运行状态
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self.running = False
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self._event_loop = None
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self._task = None
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logger.info(f"美股智能体初始化完成，监控股票: {self.symbols}")
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async def start(self):
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"""启动智能体"""
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if self.running:
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logger.warning("美股智能体已在运行中")
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return
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self.running = True
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self._event_loop = asyncio.get_event_loop()
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logger.info("美股智能体已启动")
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# 启动分析任务
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self._task = asyncio.create_task(self._analysis_loop())
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async def stop(self):
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"""停止智能体"""
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self.running = False
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if self._task:
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self._task.cancel()
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try:
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await self._task
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except asyncio.CancelledError:
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pass
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logger.info("美股智能体已停止")
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async def _analysis_loop(self):
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"""分析循环 - 只在美股交易时间内运行"""
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while self.running:
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try:
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# 检查是否在美股交易时间
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if not self._is_market_hours():
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# 不在交易时间，等待 10 分钟后再次检查
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logger.debug("非美股交易时间，等待中...")
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await asyncio.sleep(600)
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continue
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# 在交易时间内，分析所有股票
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for symbol in self.symbols:
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if not self.running:
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break
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await self.analyze_symbol(symbol)
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# 等待 1 小时后进行下次分析
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logger.info("本次分析完成，等待 1 小时后进行下次分析...")
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await asyncio.sleep(3600)
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except Exception as e:
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logger.error(f"分析循环出错: {e}")
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await asyncio.sleep(60) # 出错后等待 1 分钟再重试
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def _is_market_hours(self) -> bool:
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"""
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判断当前是否在美股交易时间
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美股交易时间: 周一至周五 9:30-16:00 (EST)
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北京时间:
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- 冬令时 (11月-3月): 22:30-05:00 (次日)
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- 夏令时 (3月-11月): 21:30-04:00 (次日)
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Returns:
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是否在交易时间
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"""
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from datetime import datetime
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# 获取当前时间
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now = datetime.now()
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# 检查是否为周末
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if now.weekday() >= 5: # 5=周六, 6=周日
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return False
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# 获取当前小时和分钟
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hour = now.hour
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minute = now.minute
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current_time = hour * 100 + minute # 转换为数字，如 2130 表示 21:30
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# 判断夏令时/冬令时（简单判断：3-11月为夏令时）
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is_summer = 3 <= now.month <= 11
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if is_summer:
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# 夏令时: 21:30-04:00 (次日)
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# 即 2130-2359 或 0000-0400
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if current_time >= 2130 or current_time < 400:
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return True
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else:
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# 冬令时: 22:30-05:00 (次日)
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# 即 2230-2359 或 0000-0500
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if current_time >= 2230 or current_time < 500:
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return True
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return False
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async def analyze_symbol(self, symbol: str):
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"""
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分析单个股票
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Args:
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symbol: 股票代码
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"""
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try:
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# 1. 获取多时间周期数据
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data = self.yfinance.get_multi_timeframe_data(symbol)
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# 2. 验证数据完整性
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if not self._validate_data(data):
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logger.warning(f"{symbol} 数据不完整，跳过本次分析")
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return
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# 3. 获取当前价格
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ticker = self.yfinance.get_ticker(symbol)
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if not ticker:
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logger.warning(f"无法获取 {symbol} 当前价格")
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return
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current_price = ticker['lastPrice']
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logger.info(f"\n{'='*60}")
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logger.info(f"📊 分析 {symbol} @ ${current_price:,.2f}")
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logger.info(f"{'='*60}")
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# 4. LLM 分析
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logger.info(f"\n🤖 【LLM 分析中...】")
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result = await self.llm_analyzer.analyze(
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symbol, data,
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symbols=self.symbols,
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position_info=None # 美股不跟踪持仓
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)
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# 输出分析摘要
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summary = result.get('analysis_summary', '无')
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logger.info(f" 市场状态: {summary}")
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# 输出新闻情绪
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news_sentiment = result.get('news_sentiment', '')
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news_impact = result.get('news_impact', '')
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if news_sentiment:
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sentiment_icon = {'positive': '📈', 'negative': '📉', 'neutral': '➖'}.get(news_sentiment, '')
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logger.info(f" 新闻情绪: {sentiment_icon} {news_sentiment}")
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if news_impact:
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logger.info(f" 消息影响: {news_impact}")
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# 输出关键价位
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levels = result.get('key_levels', {})
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if levels.get('support') or levels.get('resistance'):
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support_str = ', '.join([f"${s:,.2f}" for s in levels.get('support', [])[:2]])
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resistance_str = ', '.join([f"${r:,.2f}" for r in levels.get('resistance', [])[:2]])
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logger.info(f" 支撑位: {support_str or '-'}")
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logger.info(f" 阻力位: {resistance_str or '-'}")
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# 5. 处理信号
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signals = result.get('signals', [])
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if not signals:
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logger.info(f"\n⏸️ 结论: 无交易信号，继续观望")
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return
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# 输出所有信号
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logger.info(f"\n🎯 【发现 {len(signals)} 个信号】")
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for sig in signals:
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signal_type = sig.get('type', 'unknown')
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type_map = {'short_term': '短线', 'medium_term': '中线', 'long_term': '长线'}
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type_text = type_map.get(signal_type, signal_type)
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action = sig.get('action', 'wait')
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action_map = {'buy': '🟢 做多', 'sell': '🔴 做空'}
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action_text = action_map.get(action, action)
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grade = sig.get('grade', 'D')
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confidence = sig.get('confidence', 0)
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grade_icon = {'A': '⭐⭐⭐', 'B': '⭐⭐', 'C': '⭐', 'D': ''}.get(grade, '')
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logger.info(f"\n {type_text} {action_text} [{grade}级{grade_icon}] {confidence}%")
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# 6. 过滤并通知最佳信号
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best_signal = self._get_best_signal(signals)
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if not best_signal:
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logger.info(f"\n⏸️ 信号质量不高，不发送通知")
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return
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# 检查置信度阈值
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threshold = self.settings.stock_llm_threshold * 100
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if best_signal.get('confidence', 0) < threshold:
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logger.info(f"\n⏸️ 置信度不足 ({best_signal.get('confidence', 0)}% < {threshold}%)")
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return
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# 检查冷却时间
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if not self._should_send_signal(symbol, best_signal):
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logger.info(f"\n⏸️ 信号冷却中，不发送通知")
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return
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# 发送通知
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await self._send_signal_notification(symbol, best_signal, current_price)
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# 更新状态
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self.last_signals[symbol] = best_signal
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self.signal_cooldown[symbol] = datetime.now()
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except Exception as e:
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logger.error(f"❌ 分析 {symbol} 出错: {e}")
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import traceback
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logger.error(traceback.format_exc())
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def _get_best_signal(self, signals: List[Dict[str, Any]]) -> Optional[Dict[str, Any]]:
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"""获取最佳信号"""
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# 过滤掉 D 级信号
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valid_signals = [s for s in signals if s.get('grade', 'D') != 'D']
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if not valid_signals:
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return None
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# 按等级和置信度排序
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grade_order = {'A': 0, 'B': 1, 'C': 2}
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valid_signals.sort(key=lambda x: (
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grade_order.get(x.get('grade', 'C'), 3),
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-x.get('confidence', 0)
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))
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return valid_signals[0]
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def _should_send_signal(self, symbol: str, signal: Dict[str, Any]) -> bool:
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"""判断是否应该发送信号"""
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action = signal.get('action', 'wait')
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if action == 'wait':
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return False
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# 检查冷却时间（60分钟内不重复发送相同方向的信号）
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if symbol in self.signal_cooldown:
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cooldown_end = self.signal_cooldown[symbol] + timedelta(minutes=60)
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if datetime.now() < cooldown_end:
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if symbol in self.last_signals:
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if self.last_signals[symbol].get('action') == action:
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logger.debug(f"{symbol} 信号冷却中，跳过")
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return False
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return True
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async def _send_signal_notification(
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self,
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symbol: str,
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signal: Dict[str, Any],
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current_price: float
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):
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"""发送信号通知"""
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try:
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# 使用正确的方法格式化信号
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card = self.llm_analyzer.format_feishu_card(signal, symbol)
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title = card['title']
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content = card['content']
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# 发送到飞书
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await self.feishu.send_markdown(title, content)
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# 发送到 Telegram
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await self.telegram.send_message(self.llm_analyzer.format_signal_message(signal, symbol))
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logger.info(f"✅ 信号通知已发送: {title}")
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except Exception as e:
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logger.error(f"发送通知失败: {e}")
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def _validate_data(self, data: Dict[str, pd.DataFrame]) -> bool:
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"""验证数据完整性"""
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required_intervals = ['1d', '1h']
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for interval in required_intervals:
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if interval not in data or data[interval].empty:
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return False
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if len(data[interval]) < 20:
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return False
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return True
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async def analyze_once(self, symbol: str) -> Dict[str, Any]:
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"""单次分析（用于测试或手动触发）"""
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data = self.yfinance.get_multi_timeframe_data(symbol)
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if not self._validate_data(data):
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return {'error': '数据不完整'}
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result = await self.llm_analyzer.analyze(
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symbol, data,
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symbols=self.symbols,
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position_info=None
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)
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return result
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def get_status(self) -> Dict[str, Any]:
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"""获取智能体状态"""
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return {
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'running': self.running,
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'symbols': self.symbols,
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'mode': 'LLM 驱动（仅分析通知）',
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'last_signals': {
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symbol: {
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'type': sig.get('type'),
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'action': sig.get('action'),
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'confidence': sig.get('confidence'),
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'grade': sig.get('grade')
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}
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for symbol, sig in self.last_signals.items()
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}
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}
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# 全局单例
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_stock_agent: Optional[StockAgent] = None
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def get_stock_agent() -> StockAgent:
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"""获取美股智能体单例"""
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global _stock_agent
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if _stock_agent is None:
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_stock_agent = StockAgent()
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return _stock_agent
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