286 lines
11 KiB
Python
286 lines
11 KiB
Python
"""
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加密货币交易智能体 - 主控制器
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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.binance_service import binance_service
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from app.services.feishu_service import get_feishu_service
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from app.crypto_agent.signal_analyzer import SignalAnalyzer
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from app.crypto_agent.strategy import TrendFollowingStrategy
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class CryptoAgent:
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"""加密货币交易信号智能体"""
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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.binance = binance_service
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self.feishu = get_feishu_service()
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self.analyzer = SignalAnalyzer()
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self.strategy = TrendFollowingStrategy()
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# 状态管理
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self.last_signals: Dict[str, Dict[str, Any]] = {} # 上次信号
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self.last_trends: Dict[str, str] = {} # 上次趋势
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self.signal_cooldown: Dict[str, datetime] = {} # 信号冷却时间
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# 配置
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self.symbols = self.settings.crypto_symbols.split(',')
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self.analysis_interval = self.settings.crypto_analysis_interval
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self.llm_threshold = self.settings.crypto_llm_threshold
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# 运行状态
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self.running = False
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logger.info(f"加密货币智能体初始化完成，监控交易对: {self.symbols}")
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def _get_seconds_until_next_5min(self) -> int:
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"""计算距离下一个5分钟整点的秒数"""
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now = datetime.now()
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current_minute = now.minute
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current_second = now.second
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# 计算下一个5的倍数分钟
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minutes_past = current_minute % 5
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if minutes_past == 0 and current_second == 0:
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# 刚好在整点，立即执行
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return 0
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minutes_to_wait = 5 - minutes_past if minutes_past > 0 else 5
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seconds_to_wait = minutes_to_wait * 60 - current_second
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return seconds_to_wait
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async def run(self):
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"""主运行循环 - 在5的倍数分钟执行"""
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self.running = True
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logger.info("加密货币智能体开始运行...")
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# 发送启动通知
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await self.feishu.send_text(
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f"🚀 加密货币智能体已启动\n"
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f"监控交易对: {', '.join(self.symbols)}\n"
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f"运行时间: 每5分钟整点 (:00, :05, :10, ...)"
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)
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while self.running:
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try:
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# 等待到下一个5分钟整点
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wait_seconds = self._get_seconds_until_next_5min()
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if wait_seconds > 0:
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next_run = datetime.now() + timedelta(seconds=wait_seconds)
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logger.info(f"等待 {wait_seconds} 秒，下次运行时间: {next_run.strftime('%H:%M:%S')}")
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await asyncio.sleep(wait_seconds)
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# 执行分析
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run_time = datetime.now()
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logger.info(f"=== 开始分析 [{run_time.strftime('%H:%M:%S')}] ===")
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for symbol in self.symbols:
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await self.analyze_symbol(symbol)
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# 等待几秒确保不会在同一分钟内重复执行
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await asyncio.sleep(2)
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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(10) # 出错后等待10秒再继续
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def stop(self):
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"""停止运行"""
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self.running = False
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logger.info("加密货币智能体已停止")
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async def analyze_symbol(self, symbol: str):
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"""
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分析单个交易对
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Args:
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symbol: 交易对，如 'BTCUSDT'
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"""
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try:
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logger.info(f"开始分析 {symbol}...")
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# 1. 获取多周期数据
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data = self.binance.get_multi_timeframe_data(symbol)
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if not self._validate_data(data):
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logger.warning(f"{symbol} 数据不完整，跳过分析")
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return
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# 2. 分析趋势（1H + 4H）- 返回详细趋势信息
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trend = self.analyzer.analyze_trend(data['1h'], data['4h'])
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trend_direction = trend.get('direction', 'neutral') if isinstance(trend, dict) else trend
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# 3. 检查趋势变化
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last_direction = self.last_trends.get(symbol, {})
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if isinstance(last_direction, dict):
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last_direction = last_direction.get('direction', 'neutral')
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if last_direction and last_direction != trend_direction:
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await self._handle_trend_change(symbol, last_direction, trend_direction, data)
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self.last_trends[symbol] = trend
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# 4. 分析进场信号（15M 为主，5M 辅助）
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signal = self.analyzer.analyze_entry_signal(data['5m'], data['15m'], trend)
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signal['symbol'] = symbol
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signal['trend'] = trend_direction
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signal['trend_info'] = trend if isinstance(trend, dict) else {'direction': trend}
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signal['price'] = float(data['5m'].iloc[-1]['close'])
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signal['timestamp'] = datetime.now()
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# 5. 检查是否需要发送信号
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if self._should_send_signal(symbol, signal):
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# 6. 计算止损止盈
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atr = float(data['15m'].iloc[-1].get('atr', 0))
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if atr > 0:
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sl_tp = self.analyzer.calculate_stop_loss_take_profit(
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signal['price'], signal['action'], atr
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)
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signal.update(sl_tp)
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# 7. LLM 深度分析（置信度超过阈值时）
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if signal['confidence'] >= self.llm_threshold * 100:
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llm_result = await self.analyzer.llm_analyze(data, signal, symbol)
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# 处理 LLM 分析结果
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if llm_result.get('parsed'):
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parsed = llm_result['parsed']
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# 新格式使用 signal 而不是 recommendation
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recommendation = parsed.get('signal', parsed.get('recommendation', {}))
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# 如果 LLM 建议观望，降低置信度
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if recommendation.get('action') == 'wait':
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signal['confidence'] = min(signal['confidence'], 40)
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signal['llm_analysis'] = llm_result.get('summary', 'LLM 建议观望')
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else:
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# 使用 LLM 的止损止盈建议
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if recommendation.get('stop_loss'):
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signal['stop_loss'] = recommendation['stop_loss']
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if recommendation.get('targets'):
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signal['take_profit'] = recommendation['targets'][0]
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elif recommendation.get('take_profit'):
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signal['take_profit'] = recommendation['take_profit']
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signal['llm_analysis'] = llm_result.get('summary', '')
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else:
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signal['llm_analysis'] = llm_result.get('summary', llm_result.get('raw', '')[:200])
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# 8. 发送飞书通知（置信度仍然足够高时）
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if signal['confidence'] >= 50:
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await self.feishu.send_trading_signal(signal)
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# 9. 更新状态
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self.last_signals[symbol] = signal
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self.signal_cooldown[symbol] = datetime.now()
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logger.info(f"{symbol} 发送{signal['action']}信号，置信度: {signal['confidence']}%")
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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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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 = ['5m', '15m', '1h', '4h']
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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: # 至少需要20条数据
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return False
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return True
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def _should_send_signal(self, symbol: str, signal: Dict[str, Any]) -> bool:
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"""
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判断是否应该发送信号
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Args:
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symbol: 交易对
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signal: 信号数据
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Returns:
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是否发送
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"""
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# 如果是观望，不发送
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if signal['action'] == 'hold':
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return False
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# 置信度太低，不发送
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if signal['confidence'] < 50:
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return False
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# 检查冷却时间（同一交易对30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=30)
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if datetime.now() < cooldown_end:
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# 检查是否是相同方向的信号
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if symbol in self.last_signals:
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if self.last_signals[symbol]['action'] == signal['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 _handle_trend_change(self, symbol: str, old_trend: str, new_trend: str,
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data: Dict[str, pd.DataFrame]):
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"""处理趋势变化"""
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price = float(data['1h'].iloc[-1]['close'])
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await self.feishu.send_trend_change(symbol, old_trend, new_trend, price)
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logger.info(f"{symbol} 趋势变化: {old_trend} -> {new_trend}")
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async def analyze_once(self, symbol: str) -> Dict[str, Any]:
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"""
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单次分析（用于测试或手动触发）
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Args:
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symbol: 交易对
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Returns:
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分析结果
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"""
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data = self.binance.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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trend = self.analyzer.analyze_trend(data['1h'], data['4h'])
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signal = self.analyzer.analyze_entry_signal(data['5m'], data['15m'], trend)
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signal['symbol'] = symbol
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signal['trend'] = trend
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signal['price'] = float(data['5m'].iloc[-1]['close'])
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# 计算止损止盈
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atr = float(data['15m'].iloc[-1].get('atr', 0))
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if atr > 0:
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sl_tp = self.analyzer.calculate_stop_loss_take_profit(
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signal['price'], signal['action'], atr
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)
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signal.update(sl_tp)
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return signal
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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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'analysis_interval': self.analysis_interval,
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'last_signals': {
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symbol: {
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'action': sig.get('action'),
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'confidence': sig.get('confidence'),
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'timestamp': sig.get('timestamp').isoformat() if sig.get('timestamp') else None
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}
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for symbol, sig in self.last_signals.items()
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},
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'last_trends': self.last_trends
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}
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# 全局实例
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crypto_agent = CryptoAgent()
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