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.
51 lines
1.8 KiB
Python
51 lines
1.8 KiB
Python
"""
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Relative Strength Index (RSI) 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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import numpy as np
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from ..base import BaseIndicator
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from ..result import IndicatorResult
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class RSIIndicator(BaseIndicator):
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"""
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Relative Strength Index (RSI) technical indicator.
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Measures momentum by comparing the magnitude of recent gains to recent losses.
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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 = 14,
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price_column: str = 'close') -> pd.DataFrame:
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"""
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Calculate Relative Strength Index (RSI).
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Args:
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df: DataFrame with OHLCV data
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period: Number of periods for RSI calculation (default: 14)
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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 RSI 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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delta = df[price_column].diff()
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gain = (delta.where(delta > 0, 0)).rolling(window=period).mean()
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loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean()
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rs = gain / loss
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rs = rs.replace([np.inf, -np.inf], np.nan)
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df['rsi'] = 100 - (100 / (1 + rs))
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# Only keep rows with valid RSI, and only 'timestamp' and 'rsi' columns
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result_df = df.loc[df['rsi'].notna(), ['timestamp', 'rsi']].copy()
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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() |