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https://github.com/silencesdg/mt5_python_ea_suite.git
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songkunling
39 lines
1.6 KiB
Python
39 lines
1.6 KiB
Python
import pandas as pd
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from .base_strategy import BaseStrategy
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from config import STRATEGY_CONFIG
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class BollingerStrategy(BaseStrategy):
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def __init__(self, data_provider, symbol, timeframe, period=None, std_dev=None):
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super().__init__(data_provider, symbol, timeframe)
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# 从配置中获取参数，如果传入参数则使用传入的参数
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config = STRATEGY_CONFIG.get('bollinger', {})
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self.period = period if period is not None else config.get('period', 20)
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self.std_dev = std_dev if std_dev is not None else config.get('std_dev', 2.0)
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def _calculate_indicators(self, df):
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mean = df['close'].rolling(self.period).mean()
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std = df['close'].rolling(self.period).std()
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df['upper_band'] = mean + self.std_dev * std
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df['lower_band'] = mean - self.std_dev * std
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return df
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def generate_signal(self):
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rates = self.data_provider.get_historical_data(self.symbol, self.timeframe, self.period + 5)
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if rates is None or len(rates) < self.period:
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return 0
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df = pd.DataFrame(rates)
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df = self._calculate_indicators(df)
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if df['close'].iloc[-1] < df['lower_band'].iloc[-1]:
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return 1
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elif df['close'].iloc[-1] > df['upper_band'].iloc[-1]:
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return -1
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return 0
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def run_backtest(self, df):
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df = df.copy()
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df = self._calculate_indicators(df)
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signals = pd.Series(0, index=df.index)
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signals[df['close'] < df['lower_band']] = 1
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signals[df['close'] > df['upper_band']] = -1
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return signals |