476 lines
20 KiB
Python
476 lines
20 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.services.telegram_service import get_telegram_service
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from app.services.paper_trading_service import get_paper_trading_service
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from app.services.price_monitor_service import get_price_monitor_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.telegram = get_telegram_service()
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self.analyzer = SignalAnalyzer()
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self.strategy = TrendFollowingStrategy()
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# 模拟交易服务
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self.paper_trading_enabled = self.settings.paper_trading_enabled
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if self.paper_trading_enabled:
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self.paper_trading = get_paper_trading_service()
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self.price_monitor = get_price_monitor_service()
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# 注册价格回调
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self.price_monitor.add_price_callback(self._on_price_update)
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else:
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self.paper_trading = None
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self.price_monitor = None
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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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if self.paper_trading_enabled:
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logger.info(f"模拟交易已启用")
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def _on_price_update(self, symbol: str, price: float):
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"""处理实时价格更新（用于模拟交易）"""
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if not self.paper_trading:
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return
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# 检查是否有订单触发止盈止损
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triggered = self.paper_trading.check_price_triggers(symbol, price)
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for result in triggered:
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# 异步发送平仓通知
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asyncio.create_task(self._notify_order_closed(result))
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async def _notify_order_closed(self, result: Dict[str, Any]):
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"""发送订单平仓通知"""
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is_win = result.get('is_win', False)
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status = result.get('status', '')
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# 确定图标和文本
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if status == 'closed_tp':
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emoji = "🎯"
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status_text = "止盈平仓"
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elif status == 'closed_sl':
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emoji = "🛑"
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status_text = "止损平仓"
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else:
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emoji = "📤"
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status_text = "手动平仓"
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win_text = "盈利" if is_win else "亏损"
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side_text = "做多" if result.get('side') == 'long' else "做空"
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# 构建消息
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message = f"""{emoji} 订单{status_text}
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交易对: {result.get('symbol')}
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方向: {side_text}
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入场: ${result.get('entry_price', 0):,.2f}
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出场: ${result.get('exit_price', 0):,.2f}
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{win_text}: {result.get('pnl_percent', 0):+.2f}% (${result.get('pnl_amount', 0):+.2f})
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持仓时间: {result.get('hold_duration', 'N/A')}"""
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# 发送通知
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await self.feishu.send_text(message)
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await self.telegram.send_message(message)
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logger.info(f"已发送订单平仓通知: {result.get('order_id')}")
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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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# 启动横幅
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logger.info("\n" + "=" * 60)
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logger.info("🚀 加密货币交易信号智能体")
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logger.info("=" * 60)
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logger.info(f" 监控交易对: {', '.join(self.symbols)}")
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logger.info(f" 运行模式: 每5分钟整点执行 (:00, :05, :10, ...)")
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logger.info(f" LLM阈值: {self.llm_threshold * 100:.0f}%")
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if self.paper_trading_enabled:
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logger.info(f" 模拟交易: 已启用")
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logger.info("=" * 60 + "\n")
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# 启动价格监控（用于模拟交易）
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if self.paper_trading_enabled and self.price_monitor:
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for symbol in self.symbols:
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self.price_monitor.subscribe_symbol(symbol)
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logger.info(f"已启动 WebSocket 价格监控: {', '.join(self.symbols)}")
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# 发送启动通知（飞书 + Telegram）
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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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await self.telegram.send_startup_notification(self.symbols)
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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: {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("\n" + "=" * 60)
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logger.info(f"⏰ 定时任务执行 [{run_time.strftime('%Y-%m-%d %H:%M:%S')}]")
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logger.info("=" * 60)
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for symbol in self.symbols:
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await self.analyze_symbol(symbol)
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logger.info("\n" + "─" * 60)
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logger.info(f"✅ 本轮分析完成，共分析 {len(self.symbols)} 个交易对")
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logger.info("─" * 60 + "\n")
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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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# 停止价格监控
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if self.price_monitor:
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self.price_monitor.stop()
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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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# 分隔线
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logger.info(f"\n{'─' * 50}")
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logger.info(f"📊 {symbol} 分析开始")
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logger.info(f"{'─' * 50}")
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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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# 获取当前价格
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current_price = float(data['5m'].iloc[-1]['close'])
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price_change_24h = self._calculate_price_change(data['1h'])
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logger.info(f"💰 当前价格: ${current_price:,.2f} ({price_change_24h})")
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# 2. 分析趋势（1H + 4H）- 返回详细趋势信息
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logger.info(f"\n📈 【趋势分析】")
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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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trend_strength = trend.get('strength', 'unknown') if isinstance(trend, dict) else 'unknown'
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trend_phase = trend.get('phase', 'unknown') if isinstance(trend, dict) else 'unknown'
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# 趋势方向图标
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trend_icon = {'bullish': '🟢 看涨', 'bearish': '🔴 看跌', 'neutral': '⚪ 震荡'}.get(trend_direction, '❓')
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phase_text = {'impulse': '主升/主跌浪', 'correction': '回调/反弹', 'oversold': '极度超卖',
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'overbought': '极度超买', 'sideways': '横盘'}.get(trend_phase, trend_phase)
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logger.info(f" 方向: {trend_icon} | 强度: {trend_strength} | 阶段: {phase_text}")
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# 3. 检查趋势变化（只有之前有记录时才检查，避免启动时误报）
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if symbol in self.last_trends:
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last_trend = self.last_trends[symbol]
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if isinstance(last_trend, dict):
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last_direction = last_trend.get('direction', 'neutral')
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else:
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last_direction = last_trend
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if 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，传入 1H 数据用于支撑阻力位）
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logger.info(f"\n🎯 【信号分析】")
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signal = self.analyzer.analyze_entry_signal(data['5m'], data['15m'], trend, data['1h'])
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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'] = current_price
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signal['timestamp'] = datetime.now()
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# 输出信号详情
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action_icon = {'buy': '🟢 买入', 'sell': '🔴 卖出', 'hold': '⏸️ 观望'}.get(signal['action'], '❓')
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grade_icon = {'A': '⭐⭐⭐', 'B': '⭐⭐', 'C': '⭐', 'D': ''}.get(signal.get('signal_grade', 'D'), '')
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signal_type = signal.get('signal_type', 'swing')
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type_text = '📈短线' if signal_type == 'short_term' else '📊波段'
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logger.info(f" 信号: {action_icon} | 类型: {type_text} | 置信度: {signal['confidence']}% | 等级: {signal.get('signal_grade', 'D')} {grade_icon}")
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# 输出触发原因
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if signal.get('reasons'):
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logger.info(f" 原因: {', '.join(signal['reasons'][:5])}") # 最多显示5个原因
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# 输出权重详情
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weights = signal.get('signal_weights', {})
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if weights:
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logger.info(f" 权重: 买入={weights.get('buy', 0):.1f} | 卖出={weights.get('sell', 0):.1f}")
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# 输出K线形态
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patterns = signal.get('patterns', {})
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if patterns.get('bullish_patterns') or patterns.get('bearish_patterns'):
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all_patterns = patterns.get('bullish_patterns', []) + patterns.get('bearish_patterns', [])
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logger.info(f" 形态: {', '.join(all_patterns)}")
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# 输出成交量分析
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vol = signal.get('volume_analysis', {})
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if vol:
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vol_icon = {'high': '📈', 'low': '📉', 'normal': '➖'}.get(vol.get('volume_signal', 'normal'), '')
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confirm_text = '✓ 确认' if vol.get('volume_confirms') else '✗ 未确认'
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logger.info(f" 成交量: {vol_icon} {vol.get('volume_signal', 'normal')} | {confirm_text}")
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# 输出支撑阻力位
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levels = signal.get('levels', {})
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if levels.get('nearest_support') or levels.get('nearest_resistance'):
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support = f"${levels['nearest_support']:,.2f}" if levels.get('nearest_support') else '-'
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resistance = f"${levels['nearest_resistance']:,.2f}" if levels.get('nearest_resistance') else '-'
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logger.info(f" 关键位: 支撑={support} | 阻力={resistance}")
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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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logger.info(f"\n📤 【发送通知】")
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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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logger.info(f" 止损: ${signal['stop_loss']:,.2f} | 止盈: ${signal['take_profit']:,.2f}")
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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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logger.info(f" 🤖 触发 LLM 深度分析...")
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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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logger.info(f" 🤖 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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logger.info(f" 🤖 LLM 建议: {recommendation.get('action', 'N/A')}")
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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. 发送通知（飞书 + Telegram，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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await self.telegram.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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action_text = '买入' if signal['action'] == 'buy' else '卖出'
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logger.info(f" ✅ 已发送 {action_text} 信号通知（飞书+Telegram）")
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# 10. 创建模拟订单
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if self.paper_trading_enabled and self.paper_trading:
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if signal.get('signal_grade', 'D') != 'D':
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order = self.paper_trading.create_order_from_signal(signal)
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if order:
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logger.info(f" 📝 已创建模拟订单: {order.order_id}")
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else:
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logger.info(f" ⏸️ 置信度不足({signal['confidence']}%)，不发送通知")
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else:
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# 输出为什么不发送
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if signal['action'] == 'hold':
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logger.info(f"\n⏸️ 结论: 观望，无交易机会")
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elif signal['confidence'] < 50:
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logger.info(f"\n⏸️ 结论: 置信度不足({signal['confidence']}%)，继续观望")
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else:
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logger.info(f"\n⏸️ 结论: 信号冷却中，跳过")
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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 _calculate_price_change(self, h1_data: pd.DataFrame) -> str:
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"""计算24小时价格变化"""
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if len(h1_data) < 24:
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return "N/A"
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price_now = h1_data.iloc[-1]['close']
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price_24h_ago = h1_data.iloc[-24]['close']
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change = ((price_now - price_24h_ago) / price_24h_ago) * 100
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if change >= 0:
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return f"+{change:.2f}%"
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return f"{change:.2f}%"
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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:
|
||
是否发送
|
||
"""
|
||
# 如果是观望，不发送
|
||
if signal['action'] == 'hold':
|
||
return False
|
||
|
||
# 置信度太低，不发送
|
||
if signal['confidence'] < 50:
|
||
return False
|
||
|
||
# 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）
|
||
if symbol in self.signal_cooldown:
|
||
cooldown_end = self.signal_cooldown[symbol] + timedelta(minutes=30)
|
||
if datetime.now() < cooldown_end:
|
||
# 检查是否是相同方向的信号
|
||
if symbol in self.last_signals:
|
||
if self.last_signals[symbol]['action'] == signal['action']:
|
||
logger.debug(f"{symbol} 信号冷却中，跳过")
|
||
return False
|
||
|
||
return True
|
||
|
||
async def _handle_trend_change(self, symbol: str, old_trend: str, new_trend: str,
|
||
data: Dict[str, pd.DataFrame]):
|
||
"""处理趋势变化"""
|
||
price = float(data['1h'].iloc[-1]['close'])
|
||
await self.feishu.send_trend_change(symbol, old_trend, new_trend, price)
|
||
await self.telegram.send_trend_change(symbol, old_trend, new_trend, price)
|
||
logger.info(f"{symbol} 趋势变化: {old_trend} -> {new_trend}")
|
||
|
||
async def analyze_once(self, symbol: str) -> Dict[str, Any]:
|
||
"""
|
||
单次分析（用于测试或手动触发）
|
||
|
||
Args:
|
||
symbol: 交易对
|
||
|
||
Returns:
|
||
分析结果
|
||
"""
|
||
data = self.binance.get_multi_timeframe_data(symbol)
|
||
|
||
if not self._validate_data(data):
|
||
return {'error': '数据不完整'}
|
||
|
||
trend = self.analyzer.analyze_trend(data['1h'], data['4h'])
|
||
signal = self.analyzer.analyze_entry_signal(data['5m'], data['15m'], trend)
|
||
|
||
signal['symbol'] = symbol
|
||
signal['trend'] = trend
|
||
signal['price'] = float(data['5m'].iloc[-1]['close'])
|
||
|
||
# 计算止损止盈
|
||
atr = float(data['15m'].iloc[-1].get('atr', 0))
|
||
if atr > 0:
|
||
sl_tp = self.analyzer.calculate_stop_loss_take_profit(
|
||
signal['price'], signal['action'], atr
|
||
)
|
||
signal.update(sl_tp)
|
||
|
||
return signal
|
||
|
||
def get_status(self) -> Dict[str, Any]:
|
||
"""获取智能体状态"""
|
||
return {
|
||
'running': self.running,
|
||
'symbols': self.symbols,
|
||
'analysis_interval': self.analysis_interval,
|
||
'last_signals': {
|
||
symbol: {
|
||
'action': sig.get('action'),
|
||
'confidence': sig.get('confidence'),
|
||
'timestamp': sig.get('timestamp').isoformat() if sig.get('timestamp') else None
|
||
}
|
||
for symbol, sig in self.last_signals.items()
|
||
},
|
||
'last_trends': self.last_trends
|
||
}
|
||
|
||
|
||
# 全局实例
|
||
crypto_agent = CryptoAgent()
|