588 lines
24 KiB
Python
588 lines
24 KiB
Python
"""
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加密货币K线形态策略模块
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基于原有K线形态策略，适配加密货币市场
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"""
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import pandas as pd
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from typing import Dict, List, Any
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from datetime import datetime, timedelta
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from loguru import logger
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from ..data.binance_fetcher import BinanceFetcher
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from ..utils.notification import NotificationManager
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from ..database.mysql_database_manager import MySQLDatabaseManager
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from .kline_pattern_strategy import KLinePatternStrategy
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from .base_strategy import StrategyResult
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class CryptoKLinePatternStrategy(KLinePatternStrategy):
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"""加密货币K线形态策略类"""
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def __init__(self, data_fetcher: BinanceFetcher, notification_manager: NotificationManager,
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config: Dict[str, Any], db_manager: MySQLDatabaseManager = None):
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"""
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初始化加密货币K线形态策略
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Args:
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data_fetcher: Binance数据获取器
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notification_manager: 通知管理器
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config: 策略配置
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db_manager: 数据库管理器
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"""
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# 先调用父类构造函数初始化基本属性
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super().__init__(data_fetcher, notification_manager, config, db_manager)
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# 然后替换和定制加密货币特有属性
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self.data_fetcher = data_fetcher # 替换为Binance数据获取器
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self.strategy_name = "加密货币K线形态策略"
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self.timeframes = config.get('timeframes', ['4hour', 'daily', 'weekly'])
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self.max_turnover_ratio = config.get('max_turnover_ratio', 100.0) # 加密货币换手率通常更高
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# 加密货币特有参数
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self.quote_asset = config.get('quote_asset', 'USDT') # 计价货币
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self.min_volume_usdt = config.get('min_volume_usdt', 1000000) # 最小24h交易量（USDT）
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# 热门币种缓存机制
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self._hot_symbols_cache = None
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self._cache_timestamp = None
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self._cache_duration = 300 # 缓存5分钟
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# 更新策略ID为加密货币版本
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self.strategy_id = self.db_manager.create_or_update_strategy(
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strategy_name=self.strategy_name,
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strategy_type="crypto_kline_pattern",
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description="加密货币强势上涨+多空博弈+突破确认形态识别策略",
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config=config
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)
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logger.info(f"加密货币K线形态策略初始化完成 (策略ID: {self.strategy_id})")
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def _get_cached_hot_symbols(self, max_symbols: int) -> List[str]:
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"""
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获取缓存的热门交易对列表
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Args:
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max_symbols: 最大交易对数量
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Returns:
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交易对列表
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"""
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import time
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current_time = time.time()
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# 检查缓存是否有效
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if (self._hot_symbols_cache is not None and
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self._cache_timestamp is not None and
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current_time - self._cache_timestamp < self._cache_duration):
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logger.info(f"🔄 使用缓存的热门交易对数据 ({len(self._hot_symbols_cache)} 个)")
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return self._hot_symbols_cache[:max_symbols]
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# 缓存失效或不存在，重新获取
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logger.info(f"🔥 获取热门交易对 (TOP{max_symbols})...")
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hot_symbols = self.data_fetcher.get_top_volume_symbols(
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quote_asset=self.quote_asset,
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limit=max_symbols * 2 # 多获取一些以备过滤
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)
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if hot_symbols:
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self._hot_symbols_cache = hot_symbols
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self._cache_timestamp = current_time
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logger.info(f"✅ 热门交易对获取成功，已缓存 {len(self._hot_symbols_cache)} 个")
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return self._hot_symbols_cache[:max_symbols]
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else:
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logger.error("❌ 热门交易对数据为空")
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return []
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def clear_hot_symbols_cache(self):
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"""清除热门交易对缓存，强制下次重新获取"""
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self._hot_symbols_cache = None
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self._cache_timestamp = None
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logger.info("🔄 热门交易对缓存已清除")
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def _check_current_price_validity(self, symbol: str, entry_price: float, tolerance: float = 0.05) -> bool:
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"""
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检查当前价格是否仍然适合入场（不低于入场价的5%）
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Args:
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symbol: 交易对符号
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entry_price: 建议入场价格
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tolerance: 价格下跌容忍度，默认5%
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Returns:
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bool: 当前价格是否仍然有效
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"""
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try:
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# 获取最新价格数据（使用24小时数据）
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current_data = self.data_fetcher.get_historical_klines(
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symbol, '1d', limit=2 # 获取最近2天数据
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)
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if current_data.empty:
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logger.warning(f"无法获取 {symbol} 的最新价格数据")
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return True # 无法获取价格时保守处理，保留信号
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current_price = current_data.iloc[-1]['close']
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min_valid_price = entry_price * (1 - tolerance)
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is_valid = current_price >= min_valid_price
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if not is_valid:
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price_drop_pct = (entry_price - current_price) / entry_price * 100
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logger.info(f"🔻 {symbol} 价格过滤: 当前价 {current_price:.4f} < 入场价 {entry_price:.4f} (下跌{price_drop_pct:.1f}%)")
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return is_valid
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except Exception as e:
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logger.error(f"检查 {symbol} 当前价格失败: {e}")
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return True # 出错时保守处理，保留信号
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def _filter_recent_signals(self, signals: List[Dict[str, Any]], days: int = 7) -> List[Dict[str, Any]]:
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"""
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过滤最近N天内产生的信号，并检查当前价格有效性（加密货币版本）
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Args:
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signals: 信号列表
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days: 最近天数，默认7天
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Returns:
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过滤后的信号列表
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"""
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if not signals:
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return signals
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from datetime import datetime, date
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import pandas as pd
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current_date = datetime.now().date()
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recent_signals = []
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price_filtered_count = 0
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for signal in signals:
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signal_date = signal.get('confirmation_date') or signal.get('date')
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# 处理不同的日期格式
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if isinstance(signal_date, str):
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try:
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signal_date = pd.to_datetime(signal_date).date()
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except:
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continue
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elif hasattr(signal_date, 'date'):
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signal_date = signal_date.date()
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elif not isinstance(signal_date, date):
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continue
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# 计算信号距今天数
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days_ago = (current_date - signal_date).days
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# 只保留最近N天内的信号
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if days_ago <= days:
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# 检查当前价格是否仍然有效
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symbol = signal.get('stock_code', '') # 在加密货币中，stock_code存储的是交易对符号
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entry_price = signal.get('yin_high', 0)
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if symbol and entry_price > 0:
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if self._check_current_price_validity(symbol, entry_price):
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recent_signals.append(signal)
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logger.debug(f"✅ 保留有效信号: {symbol} {signal_date} (距今{days_ago}天)")
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else:
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price_filtered_count += 1
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logger.debug(f"🔻 价格过滤信号: {symbol} {signal_date} (价格已跌破入场价)")
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else:
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# 缺少必要信息时保守处理
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recent_signals.append(signal)
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logger.debug(f"✅ 保留信号(缺少价格信息): {signal_date} (距今{days_ago}天)")
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else:
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logger.debug(f"🗓️ 过滤历史信号: {signal_date} (距今{days_ago}天)")
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# 统计过滤结果
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time_filtered_count = len(signals) - len(recent_signals) - price_filtered_count
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if len(recent_signals) != len(signals):
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logger.info(f"📅 加密货币信号过滤统计: 总共{len(signals)}个 → 保留{len(recent_signals)}个")
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if time_filtered_count > 0:
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logger.info(f" 🗓️ 时间过滤: {time_filtered_count}个")
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if price_filtered_count > 0:
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logger.info(f" 🔻 价格过滤: {price_filtered_count}个")
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return recent_signals
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def analyze_symbol(self, symbol: str, timeframes: List[str] = None,
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session_id: int = None) -> Dict[str, StrategyResult]:
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"""
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分析单个交易对的K线形态
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Args:
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symbol: 交易对符号，如'BTCUSDT'
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timeframes: 时间周期列表，如果为None则使用策略默认周期
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session_id: 扫描会话ID
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Returns:
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时间周期到策略结果的映射
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"""
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if timeframes is None:
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timeframes = self.timeframes
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results = {}
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symbol_name = self.data_fetcher.get_symbol_name(symbol)
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for timeframe in timeframes:
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start_time = datetime.now()
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try:
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# 转换时间周期格式
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binance_interval = self.data_fetcher.convert_timeframe(timeframe)
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# 计算需要获取的K线数量(使用limit更稳定)
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if timeframe == 'daily':
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limit = 60 # 获取60根日线
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elif timeframe == '4hour':
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limit = 180 # 获取180根4小时线(约30天)
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elif timeframe == 'weekly':
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limit = 26 # 获取26根周线(约半年)
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else:
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limit = 100 # 默认100根
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logger.info(f"🔍 分析交易对: {symbol} | 周期: {timeframe}")
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# 获取历史数据(使用limit参数,更可靠)
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df = self.data_fetcher.get_historical_klines(
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symbol, binance_interval, limit=limit
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)
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if df.empty:
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logger.warning(f"{symbol} {timeframe} 数据为空")
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results[timeframe] = StrategyResult(
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strategy_name=self.strategy_name,
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stock_code=symbol,
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timeframe=timeframe,
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signals=[],
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success=False,
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error="数据为空",
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execution_time=(datetime.now() - start_time).total_seconds()
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)
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continue
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# 计算K线特征
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df_with_features = self.calculate_kline_features(df)
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# 检测形态（返回已确认信号和形态形成）
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confirmed_signals, formed_patterns = self.detect_pattern(df_with_features)
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# 过滤3天内的信号，7天内的形态
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recent_signals = self._filter_recent_signals(confirmed_signals, days=3)
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recent_patterns = self._filter_recent_signals(formed_patterns, days=7)
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# 处理确认信号格式
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formatted_signals = []
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for signal in recent_signals:
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formatted_signal = {
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'date': signal['date'],
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'signal_type': signal['pattern_type'],
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'price': signal['breakout_price'],
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'confidence': signal['final_yang_entity_ratio'],
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'stock_code': symbol, # 添加交易对代码
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'stock_name': symbol_name,
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'status': 'confirmed',
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'details': {
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'yin_high': signal['yin_high'],
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'breakout_amount': signal['breakout_amount'],
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'breakout_pct': signal['breakout_pct'],
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'ema20_price': signal['ema20_price'],
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'turnover_ratio': signal['turnover_ratio'],
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'breakout_position': signal.get('breakout_position', 4),
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'pattern_subtype': signal.get('pattern_subtype', '') # 添加形态子类型
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}
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}
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# 添加回踩确认信息
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if not signal.get('confirmation_pending', True):
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formatted_signal['details'].update({
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'new_high_confirmed': signal.get('new_high_confirmed', True),
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'new_high_price': signal.get('new_high_price'),
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'new_high_date': signal.get('new_high_date'),
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'confirmation_date': signal.get('confirmation_date'),
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'confirmation_days': signal.get('confirmation_days'),
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'pullback_distance': signal.get('pullback_distance')
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})
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formatted_signals.append(formatted_signal)
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# 将信号添加到监控列表
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signal['stock_code'] = symbol
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signal['stock_name'] = symbol_name
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signal['timeframe'] = timeframe
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self.add_triggered_signal(signal)
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# 保存信号到数据库(标记为加密货币)
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if session_id is not None:
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try:
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signal_id = self.db_manager.save_stock_signal(
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session_id=session_id,
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strategy_id=self.strategy_id,
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signal=signal,
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asset_type='crypto' # 标记为加密货币
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)
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logger.debug(f"加密货币信号已保存到数据库: signal_id={signal_id}")
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except Exception as e:
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logger.error(f"保存加密货币信号到数据库失败: {e}")
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# 处理形态形成格式
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formatted_patterns = []
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for pattern in recent_patterns:
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formatted_pattern = {
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'date': pattern['date'],
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'pattern_type': pattern['pattern_type'],
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'price': pattern['breakout_price'],
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'confidence': pattern['final_yang_entity_ratio'],
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'stock_code': symbol, # 添加交易对代码
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'stock_name': symbol_name,
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'status': 'formed',
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'details': {
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'yin_high': pattern['yin_high'],
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'breakout_amount': pattern['breakout_amount'],
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'breakout_pct': pattern['breakout_pct'],
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'ema20_price': pattern['ema20_price'],
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'turnover_ratio': pattern['turnover_ratio'],
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'breakout_position': pattern.get('breakout_position', 4),
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'pattern_subtype': pattern.get('pattern_subtype', '') # 添加形态子类型
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}
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}
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formatted_patterns.append(formatted_pattern)
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execution_time = (datetime.now() - start_time).total_seconds()
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results[timeframe] = StrategyResult(
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strategy_name=self.strategy_name,
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stock_code=symbol,
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timeframe=timeframe,
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signals=formatted_signals,
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patterns=formatted_patterns,
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success=True,
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execution_time=execution_time
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)
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# 美化统计日志
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if formatted_signals or formatted_patterns:
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logger.info(f"✅ {symbol} {timeframe}周期: 信号={len(formatted_signals)}个, 形态={len(formatted_patterns)}个")
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if formatted_signals:
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logger.info(f" 🎯 已确认信号:")
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for i, signal in enumerate(formatted_signals, 1):
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logger.info(f" {i}. {signal['date']} | 价格: {signal['price']:.4f} | {signal['signal_type']}")
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if formatted_patterns:
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logger.info(f" 📊 形态形成:")
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for i, pattern in enumerate(formatted_patterns, 1):
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logger.info(f" {i}. {pattern['date']} | 价格: {pattern['price']:.4f} | {pattern['pattern_type']}")
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else:
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logger.debug(f"📭 {symbol} {timeframe}周期: 无信号和形态")
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except Exception as e:
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logger.error(f"分析交易对 {symbol} {timeframe}周期失败: {e}")
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execution_time = (datetime.now() - start_time).total_seconds()
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results[timeframe] = StrategyResult(
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strategy_name=self.strategy_name,
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stock_code=symbol,
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timeframe=timeframe,
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signals=[],
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patterns=[],
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success=False,
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error=str(e),
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execution_time=execution_time
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)
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return results
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def scan_market(self, symbol_list: List[str] = None, max_symbols: int = 100) -> Dict[str, Dict[str, List[Dict[str, Any]]]]:
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"""
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扫描加密货币市场
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Args:
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symbol_list: 交易对列表，如果为None则使用热门交易对
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max_symbols: 最大扫描交易对数量
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Returns:
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所有交易对的分析结果
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"""
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logger.info("🚀" + "="*70)
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logger.info("🌍 开始加密货币市场K线形态扫描")
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logger.info("🚀" + "="*70)
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# 创建扫描会话
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scan_config = {
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'max_symbols': max_symbols,
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'data_source': 'Binance',
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'quote_asset': self.quote_asset,
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'timeframes': self.timeframes
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}
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session_id = self.db_manager.create_scan_session(
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strategy_id=self.strategy_id,
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scan_config=scan_config
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)
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if symbol_list is None:
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# 使用缓存的热门交易对数据
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symbol_list = self._get_cached_hot_symbols(max_symbols)
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if symbol_list:
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logger.info(f"📊 数据源: Binance热门交易对 | 扫描交易对: {len(symbol_list)} 个")
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else:
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logger.error("❌ 热门交易对数据为空，无法进行扫描")
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return {}
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||
results = {}
|
||
total_signals = 0
|
||
total_patterns = 0
|
||
|
||
for i, symbol in enumerate(symbol_list):
|
||
logger.info(f"⏳ 扫描进度: [{i+1:3d}/{len(symbol_list):3d}] 🔍 {symbol}")
|
||
|
||
try:
|
||
symbol_results = self.analyze_symbol(symbol, session_id=session_id)
|
||
|
||
# 统计信号和形态数量
|
||
symbol_signal_count = sum(result.get_signal_count() for result in symbol_results.values())
|
||
symbol_pattern_count = sum(result.get_pattern_count() for result in symbol_results.values())
|
||
|
||
# 只保留有确认信号的交易对结果，不包括仅有形态的交易对
|
||
if symbol_signal_count > 0:
|
||
results[symbol] = symbol_results
|
||
total_signals += symbol_signal_count
|
||
total_patterns += symbol_pattern_count
|
||
|
||
except Exception as e:
|
||
logger.error(f"扫描交易对 {symbol} 失败: {e}")
|
||
continue
|
||
|
||
# 更新扫描会话统计
|
||
try:
|
||
self.db_manager.update_scan_session_stats(
|
||
session_id=session_id,
|
||
total_scanned=len(symbol_list),
|
||
total_signals=total_signals
|
||
)
|
||
logger.debug(f"扫描会话统计已更新: {session_id}")
|
||
except Exception as e:
|
||
logger.error(f"更新扫描会话统计失败: {e}")
|
||
|
||
# 美化最终扫描结果
|
||
logger.info("🎉" + "="*70)
|
||
logger.info(f"🌍 加密货币市场K线形态扫描完成!")
|
||
logger.info(f"📊 扫描统计:")
|
||
logger.info(f" 🔍 总扫描交易对: {len(symbol_list)} 个")
|
||
logger.info(f" 🎯 确认信号: {total_signals} 个")
|
||
logger.info(f" 📊 形态形成: {total_patterns} 个")
|
||
logger.info(f" 📈 涉及交易对: {len(results)} 个")
|
||
logger.info(f" 💾 扫描会话ID: {session_id}")
|
||
|
||
if results:
|
||
logger.info(f"📋 详细结果:")
|
||
signal_count = 0
|
||
pattern_count = 0
|
||
|
||
for symbol, symbol_results in results.items():
|
||
for timeframe, result in symbol_results.items():
|
||
# 显示确认信号
|
||
for signal in result.signals:
|
||
signal_count += 1
|
||
logger.info(f" 🎯 信号#{signal_count}: {symbol} | {timeframe} | {signal['date']} | {signal['price']:.4f}")
|
||
|
||
# 显示形态形成
|
||
for pattern in result.patterns:
|
||
pattern_count += 1
|
||
logger.info(f" 📊 形态#{pattern_count}: {symbol} | {timeframe} | {pattern['date']} | {pattern['price']:.4f}")
|
||
|
||
logger.info("🎉" + "="*70)
|
||
|
||
# 发送汇总通知
|
||
if results:
|
||
scan_stats = {
|
||
'total_scanned': len(symbol_list),
|
||
'data_source': f'Binance-{self.quote_asset}'
|
||
}
|
||
|
||
try:
|
||
success = self.notification_manager.send_strategy_summary(results, scan_stats)
|
||
if success:
|
||
logger.info("📱 策略信号汇总通知发送完成")
|
||
else:
|
||
logger.warning("📱 策略信号汇总通知发送失败")
|
||
except Exception as e:
|
||
logger.error(f"发送汇总通知失败: {e}")
|
||
|
||
return results
|
||
|
||
def get_strategy_description(self) -> str:
|
||
"""获取策略描述"""
|
||
trend_desc = ""
|
||
if self.strong_trend_enabled:
|
||
trend_desc = f"""
|
||
【多头排列先决条件】✅
|
||
- EMA5 > EMA10 > EMA20（三重多头排列）
|
||
- 在形态识别、突破确认、回踩信号三个阶段都进行检查
|
||
- 确保整个过程中始终保持强势趋势
|
||
"""
|
||
else:
|
||
trend_desc = "\n【多头排列先决条件】❌ 未启用\n"
|
||
|
||
return f"""加密货币K线形态策略 - 强势上涨+多空博弈+突破确认
|
||
{trend_desc}
|
||
新形态设计：强势上涨 → 多空博弈 → 突破确认
|
||
|
||
【形态A：双阳+博弈】
|
||
1. 强势阶段：2根连续阳线
|
||
2. 博弈阶段：2-3根K线，平均实体≤40%（阴线/十字星/小阳线）
|
||
3. 突破确认：博弈后1-3根K线内，大阳线（实体≥55%）突破博弈阶段最高价
|
||
|
||
【形态B：高实体+博弈】
|
||
1. 强势阶段：1根高实体阳线（实体≥60%）
|
||
2. 博弈阶段：2-3根K线，平均实体≤40%（阴线/十字星/小阳线）
|
||
3. 突破确认：博弈后1-3根K线内，大阳线（实体≥55%）突破博弈阶段最高价
|
||
|
||
【严格约束条件】
|
||
- EMA5 > EMA10 > EMA20（三重多头排列）全程检查
|
||
- 价格不能跌破EMA10支撑（博弈、突破、回踩全程）
|
||
- 价格必须创新高后回踩到博弈阶段最高点附近才产生正式信号
|
||
- 回踩容忍度：{self.pullback_tolerance:.0%}
|
||
- 确认窗口：{self.pullback_confirmation_days}天
|
||
|
||
【加密货币市场特点】
|
||
- 计价货币: {self.quote_asset}
|
||
- 最小24h交易量: ${self.min_volume_usdt:,.0f}
|
||
- 支持时间周期：{', '.join(self.timeframes)}
|
||
|
||
【策略优势】
|
||
- 真实市场心理：强势→博弈→突破的完整过程
|
||
- 多重验证：EMA多头排列 + EMA10支撑 + 创新高回踩确认
|
||
- 降低假突破：严格的形态和趋势验证
|
||
- 优质入场时机：回踩确认提供更好的风险收益比
|
||
"""
|
||
|
||
|
||
if __name__ == "__main__":
|
||
# 测试代码
|
||
from ..data.binance_fetcher import BinanceFetcher
|
||
from ..utils.notification import NotificationManager
|
||
|
||
# 模拟配置
|
||
strategy_config = {
|
||
'min_entity_ratio': 0.55,
|
||
'timeframes': ['daily', '4hour'],
|
||
'quote_asset': 'USDT'
|
||
}
|
||
|
||
notification_config = {
|
||
'dingtalk': {
|
||
'enabled': False,
|
||
'webhook_url': ''
|
||
}
|
||
}
|
||
|
||
# 初始化组件
|
||
data_fetcher = BinanceFetcher()
|
||
notification_manager = NotificationManager(notification_config)
|
||
strategy = CryptoKLinePatternStrategy(data_fetcher, notification_manager, strategy_config)
|
||
|
||
print("加密货币K线形态策略初始化完成")
|
||
print(strategy.get_strategy_description())
|