新增加项目文件，
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strategy.py
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187
strategy.py
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"""
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行情策略管理模块 - 支持多种策略
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"""
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import logging
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import pandas as pd
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import numpy as np
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import pandas as pd
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from quote_manager import QuoteManager, DataSource
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from logger_utils_new import log_info, log_warning, log_error, log_trigger, log_debug, setup_logger, LOG_STYLES
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from abc import ABC, abstractmethod
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from enum import Enum, auto
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class StrategyType(Enum):
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TU_STRATEGY = auto() # 土策略
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BREAKOUT_STRATEGY = auto() # 突破策略
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MEAN_REVERSION_STRATEGY = auto() # 均值回归策略
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class TradingStrategy(ABC):
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def __init__(self, quote_manager):
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self.quote_manager = quote_manager
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self.history_data = {}
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self.STOP_LOSS = 0.03
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self.COMMISSION = 0.0003
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self.MIN_TRADE_AMOUNT = 1e7
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@abstractmethod
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def _analyze_signal(self, quote_data):
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"""分析交易信号"""
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pass
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class TuStrategy(TradingStrategy):
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"""土策略实现"""
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def analyze_signal(self, quote_data):
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# 现有的土策略分析逻辑
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# ... 保留原有代码 ...
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signal = {
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'code': quote_data['TS_CODE'],
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'signal': 'buy' if quote_data['PRICE'] < quote_data['OPEN'] else 'sell',
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'price': quote_data['PRICE'],
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'volume': quote_data['VOLUME'],
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'timestamp': pd.Timestamp.now().strftime('%Y-%m-%d %H:%M:%S')
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}
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return signal
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class BreakoutStrategy(TradingStrategy):
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"""突破策略实现"""
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def analyze_signal(self, quote_data):
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# 实现突破策略逻辑
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# ... 新增代码 ...
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signal = {
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'code': quote_data['TS_CODE'],
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'signal': 'buy' if quote_data['PRICE'] > quote_data['OPEN'] else'sell',
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'price': quote_data['PRICE'],
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'volume': quote_data['VOLUME'],
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'timestamp': pd.Timestamp.now().strftime('%Y-%m-%d %H:%M:%S')
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}
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return signal
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class StrategyManager:
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def __init__(self, quote_manager):
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self.quote_manager = quote_manager
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self.strategies = {
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StrategyType.TU_STRATEGY: TuStrategy(quote_manager),
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StrategyType.BREAKOUT_STRATEGY: BreakoutStrategy(quote_manager)
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}
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self.current_strategy = StrategyType.TU_STRATEGY # 默认策略
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def set_strategy(self, strategy_type):
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"""设置当前使用的策略"""
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if strategy_type in self.strategies:
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self.current_strategy = strategy_type
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return True
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return False
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def monitor_stocks(self, stock_codes):
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"""使用当前策略监控股票"""
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strategy = self.strategies[self.current_strategy]
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return strategy.monitor_stocks(stock_codes)
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class TradingStrategy(ABC):
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def __init__(self, quote_manager):
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self.quote_manager = QuoteManager()
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def monitor_stocks(self, stock_codes):
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"""监控股票行情"""
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try:
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quotes = self.quote_manager.get_realtime_quotes(stock_codes)
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log_info(f"行情更新: {pd.Timestamp.now()}")
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signals = []
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for code, data in quotes.items():
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signal = self._analyze_signal(data)
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log_info(f"{code}: 最新价 {data['PRICE']} 成交量 {data['VOLUME']}")
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signals.append({
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'code': code,
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'signal': signal,
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'price': data['PRICE'],
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'volume': data['VOLUME'],
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'timestamp': pd.Timestamp.now().strftime('%Y-%m-%d %H:%M:%S')
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})
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return signals
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except Exception as e:
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log_error(f"获取行情失败: {str(e)}")
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return []
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def __init__(self, quote_manager):
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self.quote_manager = quote_manager
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self.history_data = {}
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self.STOP_LOSS = 0.03 # 止损比例
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self.COMMISSION = 0.0003 # 单边佣金
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self.MIN_TRADE_AMOUNT = 1e7 # 最小成交金额
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def _calculate_slope(self, x):
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"""计算斜率"""
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if len(x) < 2:
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return np.nan
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n = len(x)
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sum_x = (n - 1) * n / 2
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sum_xx = (n - 1) * n * (2 * n - 1) / 6
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sum_y = np.sum(x)
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sum_xy = np.sum(np.arange(n) * x)
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denom = n * sum_xx - sum_x * sum_x
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if denom == 0:
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return np.nan
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return (n * sum_xy - sum_x * sum_y) / denom
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def _update_history_data(self, code, data):
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"""更新历史数据"""
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if code not in self.history_data:
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self.history_data[code] = []
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self.history_data[code].append(data)
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# 只保留最近30天数据
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if len(self.history_data[code]) > 30:
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self.history_data[code] = self.history_data[code][-30:]
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return pd.DataFrame(self.history_data[code])
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def _analyze_signal(self, quote_data):
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"""基于土策略分析交易信号"""
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code = quote_data['TS_CODE']
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df = self._update_history_data(code, quote_data)
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if len(df) < 25: # 确保有足够数据计算指标
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return 'HOLD'
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# 计算技术指标
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df = df.sort_values('TRADE_DATE')
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df['X_1'] = df['LOW'].rolling(10, min_periods=5).min()
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df['X_2'] = df['HIGH'].rolling(25, min_periods=10).max()
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df['X_6'] = ((df['CLOSE'] - df['X_1']) / (df['X_2'].replace(0, np.nan) - df['X_1']) * 4)
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df['X_6'] = df['X_6'].ewm(span=4, adjust=False).mean().shift(1)
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# 信号过滤条件
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df['X_7'] = df['X_6'].rolling(5).apply(
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lambda x: 0 if (np.diff(x > 3.5) == 1).any() else 1, raw=True
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).fillna(1)
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# 动量计算
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df['X_10'] = df['CLOSE'].pct_change(2).shift(1) * 100
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df['X_43'] = df['X_10'].rolling(2).sum() * df['X_7']
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# 复合指标
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ema_open_12 = df['OPEN'].ewm(span=12, adjust=False).mean()
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df['X_15'] = df['X_7'] * (df['OPEN'] - ema_open_12) / ema_open_12 * 200
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# 斜率计算
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df['X_41'] = df['X_15'].rolling(2).apply(self._calculate_slope)
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df['X_42'] = df['X_6'].rolling(2).apply(self._calculate_slope)
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# 最终土值计算
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df['tu_value'] = (df['X_41'] * 0.02 - df['X_42'] * df['X_7']) * df['X_7']
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# 过滤条件
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current = df.iloc[-1]
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if (current['X_43'] > 8 and
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not current['CODE'].startswith(('313', '314', '315', '688')) and
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current['AMOUNT'] > self.MIN_TRADE_AMOUNT and
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not np.isnan(current['tu_value'])):
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return 'BUY' if current['tu_value'] > 0 else 'SELL'
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return 'HOLD'
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