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cones

Calculate the realized volatility quantiles over rolling windows of time.

The cones indicator is designed to map out the ebb and flow of price movements through a detailed analysis of volatility quantiles. By examining the range of volatility within specific time frames, it offers a nuanced view of market behavior, highlighting periods of stability and turbulence.

The model for calculating volatility is selectable and can be one of the following:

  • Standard deviation
  • Parkinson
  • Garman-Klass
  • Hodges-Tompkins
  • Rogers-Satchell
  • Yang-Zhang

Read more about it in the model parameter description.

Parameters​

data: ForwardRef('Data') | ForwardRef('DataFrame') | ForwardRef('Series') | ForwardRef('ndarray') | dict | list
index: str
Default: date
lower_q: float
Default: 0.25
upper_q: float
Default: 0.75
model: Literal['std', 'parkinson', 'garman_klass', 'hodges_tompkins', 'rogers_satchell', 'yang_zhang']
Default: std
is_crypto: bool
Default: False
trading_periods: int | None
chart: bool
Default: False

Returns​

results: list[Data]

Serializable results.

provider: str

Provider name.

warnings: Optional[list[Warning_]]

list of warnings.

chart: Optional[Chart]

Chart object.

extra: dict[str, Any]

Extra info.

Data​

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