52 lines
2.6 KiB
Markdown
52 lines
2.6 KiB
Markdown
# Analysis Module
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This document provides an overview of the `Analysis` module and its components, which are typically used for technical analysis of financial market data.
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## Modules
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The `Analysis` module includes classes for calculating common technical indicators:
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- **Relative Strength Index (RSI)**: Implemented in `cycles/Analysis/rsi.py`.
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- **Bollinger Bands**: Implemented in `cycles/Analysis/boillinger_band.py`.
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## Class: `RSI`
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Found in `cycles/Analysis/rsi.py`.
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Calculates the Relative Strength Index.
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### `__init__(self, period: int = 14)`
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- **Description**: Initializes the RSI calculator.
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- **Parameters**:
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- `period` (int, optional): The period for RSI calculation. Defaults to 14. Must be a positive integer.
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### `calculate(self, data_df: pd.DataFrame, price_column: str = 'close') -> pd.DataFrame`
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- **Description**: Calculates the RSI and adds it as an 'RSI' column to the input DataFrame. Handles cases where data length is less than the period by returning the original DataFrame with a warning.
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- **Parameters**:
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- `data_df` (pd.DataFrame): DataFrame with historical price data. Must contain the `price_column`.
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- `price_column` (str, optional): The name of the column containing price data. Defaults to 'close'.
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- **Returns**: `pd.DataFrame` - The input DataFrame with an added 'RSI' column (containing `np.nan` for initial periods where RSI cannot be calculated). Returns a copy of the original DataFrame if the period is larger than the number of data points.
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## Class: `BollingerBands`
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Found in `cycles/Analysis/boillinger_band.py`.
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Calculates Bollinger Bands for given financial data.
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### `__init__(self, period: int = 20, std_dev_multiplier: float = 2.0)`
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- **Description**: Initializes the BollingerBands calculator.
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- **Parameters**:
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- `period` (int, optional): The period for the moving average and standard deviation. Defaults to 20. Must be a positive integer.
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- `std_dev_multiplier` (float, optional): The number of standard deviations for the upper and lower bands. Defaults to 2.0. Must be positive.
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### `calculate(self, data_df: pd.DataFrame, price_column: str = 'close') -> pd.DataFrame`
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- **Description**: Calculates Bollinger Bands and adds 'SMA' (Simple Moving Average), 'UpperBand', and 'LowerBand' columns to the DataFrame.
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- **Parameters**:
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- `data_df` (pd.DataFrame): DataFrame with price data. Must include the `price_column`.
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- `price_column` (str, optional): The name of the column containing the price data (e.g., 'close'). Defaults to 'close'.
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- **Returns**: `pd.DataFrame` - The original DataFrame with added columns: 'SMA', 'UpperBand', 'LowerBand'.
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