ec8f5514bb
- Updated all technical indicators to return pandas DataFrames instead of lists, improving consistency and usability. - Modified the `calculate` method in `TechnicalIndicators` to directly return DataFrames with relevant indicator values. - Enhanced the `data_integration.py` to utilize the new DataFrame outputs for better integration with charting. - Updated documentation to reflect the new DataFrame-centric approach, including usage examples and output structures. - Improved error handling to ensure empty DataFrames are returned when insufficient data is available. These changes streamline the indicator calculations and improve the overall architecture, aligning with project standards for maintainability and performance.
44 lines
1.5 KiB
Python
44 lines
1.5 KiB
Python
"""
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Exponential Moving Average (EMA) indicator implementation.
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"""
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import pandas as pd
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from ..base import BaseIndicator
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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') -> pd.DataFrame:
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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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DataFrame with EMA values and metadata, indexed by timestamp
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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 pd.DataFrame()
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try:
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df = df.copy()
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df['ema'] = df[price_column].ewm(span=period, adjust=False).mean()
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# Only keep rows with valid EMA, and only 'timestamp' and 'ema' columns
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result_df = df.loc[df['ema'].notna(), ['timestamp', 'ema']].copy()
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# Only keep rows after enough data for EMA
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result_df = result_df.iloc[period-1:]
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result_df.set_index('timestamp', inplace=True)
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return result_df
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except Exception:
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return pd.DataFrame() |