OpenBB/website/content/platform/reference/technical/cones.md

title, description, keywords

title	description	keywords
cones	Calculate realized volatility quantiles over rolling windows of time using different volatility models. Understand the parameters and their effects, such as index, quantile values, and is_crypto. Examples and code snippets provided.	realized volatility quantiles rolling windows of time calculate volatility parameter data quantile value volatility models standard deviation Parkinson volatility Garman-Klass volatility Hodges-Tompkins volatility Rogers-Satchell volatility Yang-Zhang volatility is_crypto cones data code example

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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.

Examples

from openbb import obb
# Get the cones indicator.
stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp')
cones_data = obb.technical.cones(data=stock_data.results, lower_q=0.25, upper_q=0.75, model='STD')
obb.technical.cones(data=[{'date': '2023-01-02', 'open': 110.0, 'high': 120.0, 'low': 100.0, 'close': 115.0, 'volume': 10000.0}, {'date': '2023-01-03', 'open': 165.0, 'high': 180.0, 'low': 150.0, 'close': 172.5, 'volume': 15000.0}, {'date': '2023-01-04', 'open': 146.67, 'high': 160.0, 'low': 133.33, 'close': 153.33, 'volume': 13333.33}, {'date': '2023-01-05', 'open': 137.5, 'high': 150.0, 'low': 125.0, 'close': 143.75, 'volume': 12500.0}, {'date': '2023-01-06', 'open': 132.0, 'high': 144.0, 'low': 120.0, 'close': 138.0, 'volume': 12000.0}])

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Parameters

Name	Type	Description	Default	Optional
data	List[Data]	The data to use for the calculation.		False
index	str, optional	Index column name to use with data, by default "date"		False
lower_q	float, optional	The lower quantile value for calculations		False
upper_q	float, optional	The upper quantile value for calculations		False
model	Literal["STD", "Parkinson", "Garman-Klass", "Hodges-Tompkins", "Rogers-Satchell", "Yang-Zhang"], optional	The model used to calculate realized volatility		False
is_crypto	bool, optional	Whether the data is crypto or not. If True, volatility is calculated for 365 days instead of 252		False
trading_periods	Optional[int] [default: 252]	Number of trading periods in a year.		True

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Returns

OBBject
    results : List[Data]
        The cones data.