60 lines
2.1 KiB
Python
60 lines
2.1 KiB
Python
"""
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Exponential Moving Average (EMA) 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 EMAIndicator(BaseIndicator):
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"""
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Exponential Moving Average (EMA) technical indicator.
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Calculates weighted moving average giving more weight to recent prices.
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Handles sparse data appropriately without interpolation.
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"""
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def calculate(self, df: pd.DataFrame, period: int = 20,
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price_column: str = 'close') -> List[IndicatorResult]:
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"""
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Calculate Exponential Moving Average (EMA).
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Args:
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df: DataFrame with OHLCV data
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period: Number of periods for moving average (default: 20)
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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 EMA values
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"""
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# Validate input data
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if not self.validate_dataframe(df, period):
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return []
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try:
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# Calculate EMA using pandas exponential weighted moving average
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df['ema'] = df[price_column].ewm(span=period, adjust=False).mean()
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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 >= period - 1 and not pd.isna(row['ema']):
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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={'ema': row['ema']},
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metadata={'period': period, 'price_column': price_column}
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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 EMA: {e}")
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return [] |