Skip to main content

strategy.utils.volatility_model

pd​

BaseModel​

VolatilityModel Objects​

class VolatilityModel(BaseModel)

A class for calculating various volatility models on financial data.

This class inherits from BaseModel and provides methods to calculate different volatility indicators such as ATR, Standard Deviation, Bollinger Bands, RVI, and Normalized Standard Deviation.

Attributes:

  • data pd.DataFrame or pd.Series - The input financial data.
  • data_format str - The type of input data ('ohlcv' or 'close').

__init__​

def __init__(data)

Initialize the VolatilityModel with the given data.

Arguments:

  • data pd.DataFrame or pd.Series - The input financial data.

atr​

def atr(window, scalar=1)

Calculate the Average True Range (ATR) and its bounds.

Arguments:

  • window int - The rolling window for ATR calculation.
  • scalar float, optional - Multiplier for the upper and lower bounds. Defaults to 1.

Returns:

  • pd.DataFrame - A DataFrame containing ATR, Upper Bound, and Lower Bound.

std​

def std(window, scalar=1)

Calculate the Standard Deviation (STD) and its bounds.

Arguments:

  • window int - The rolling window for STD calculation.
  • scalar float, optional - Multiplier for the upper and lower bounds. Defaults to 1.

Returns:

  • pd.DataFrame - A DataFrame containing STD, Upper Bound, and Lower Bound.

bollinger_bands​

def bollinger_bands(window, scalar=2)

Calculate the Bollinger Bands (BB) and Bollinger Bandwidth (BBW).

Arguments:

  • window int - The rolling window for BB calculation.
  • scalar float, optional - Number of standard deviations for the bands. Defaults to 2.

Returns:

  • pd.DataFrame - A DataFrame containing BBW, Upper Bound, and Lower Bound.

rvi​

def rvi(window, scalar=1)

Calculate the Relative Volatility Index (RVI) and its bounds.

Arguments:

  • window int - The rolling window for RVI calculation.
  • scalar float, optional - Multiplier for the upper and lower bounds. Defaults to 1.

Returns:

  • pd.DataFrame - A DataFrame containing RVI, Upper Bound, and Lower Bound.

normalized_std​

def normalized_std(window, scalar=1)

Calculate the Normalized Standard Deviation and its bounds.

The Normalized STD is calculated as mean(std) / max(std) over the given window.

Arguments:

  • window int - The rolling window for the calculation.
  • scalar float, optional - Multiplier for the upper and lower bounds. Defaults to 1.

Returns:

  • pd.DataFrame - A DataFrame containing Normalized STD, Upper Bound, and Lower Bound.

model​

def model(model_type, window, scalar=1)

Select and calculate the specified volatility model.

Arguments:

  • model_type str - The type of model to use.
  • Options - 'std', 'atr', 'bb', 'rvi', or 'normalized_std'.
  • window int - The rolling window for the model calculation.
  • scalar float, optional - Multiplier for bounds or bands. Defaults to 1.

Returns:

  • pd.DataFrame - A DataFrame containing the calculated model values and bounds.

Raises:

  • ValueError - If an unrecognized model type is provided.

get_data_window​

@classmethod
def get_data_window(cls, model_type, window)

Calculate the required minimum data length for each model type.

Arguments:

  • model_type: String, the type of model (e.g. 'std', 'atr', 'bb')
  • window: Integer, the window size for the model.

Returns:

Integer, the required data length for the specified model.