84 lines
3.3 KiB
Python
84 lines
3.3 KiB
Python
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"""
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Moving Average Convergence Divergence (MACD) indicator implementation.
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"""
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from typing import List
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import pandas as pd
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from ..base import BaseIndicator
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from ..result import IndicatorResult
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class MACDIndicator(BaseIndicator):
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"""
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Moving Average Convergence Divergence (MACD) technical indicator.
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Calculates trend-following momentum indicator that shows the relationship
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between two moving averages of a security's price.
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Handles sparse data appropriately without interpolation.
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"""
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def calculate(self, df: pd.DataFrame, fast_period: int = 12,
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slow_period: int = 26, signal_period: int = 9,
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price_column: str = 'close') -> List[IndicatorResult]:
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"""
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Calculate Moving Average Convergence Divergence (MACD).
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Args:
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df: DataFrame with OHLCV data
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fast_period: Fast EMA period (default 12)
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slow_period: Slow EMA period (default 26)
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signal_period: Signal line EMA period (default 9)
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price_column: Price column to use ('open', 'high', 'low', 'close')
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Returns:
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List of indicator results with MACD, signal, and histogram values
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"""
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# Validate input data
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if not self.validate_dataframe(df, slow_period):
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return []
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try:
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# Calculate fast and slow EMAs
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df['ema_fast'] = df[price_column].ewm(span=fast_period, adjust=False).mean()
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df['ema_slow'] = df[price_column].ewm(span=slow_period, adjust=False).mean()
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# Calculate MACD line
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df['macd'] = df['ema_fast'] - df['ema_slow']
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# Calculate signal line (EMA of MACD)
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df['signal'] = df['macd'].ewm(span=signal_period, adjust=False).mean()
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# Calculate histogram
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df['histogram'] = df['macd'] - df['signal']
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# Convert results to IndicatorResult objects
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results = []
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for i, (timestamp, row) in enumerate(df.iterrows()):
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# Only return results after minimum period
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if i >= slow_period - 1:
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if not (pd.isna(row['macd']) or pd.isna(row['signal']) or pd.isna(row['histogram'])):
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result = IndicatorResult(
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timestamp=timestamp,
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symbol=row['symbol'],
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timeframe=row['timeframe'],
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values={
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'macd': row['macd'],
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'signal': row['signal'],
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'histogram': row['histogram']
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},
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metadata={
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'fast_period': fast_period,
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'slow_period': slow_period,
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'signal_period': signal_period,
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'price_column': price_column
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}
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)
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results.append(result)
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return results
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except Exception as e:
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if self.logger:
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self.logger.error(f"Error calculating MACD: {e}")
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return []
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