cones
- Model
- Chart
Returns a DataFrame of realized volatility quantiles.
Source Code: [link]
openbb.ta.cones(data: pd.DataFrame, lower_q: float = 0.25, upper_q: float = 0.75, is_crypto: bool = False, model: str = "STD")
Parameters
This function does not take any parameters.
Returns
| Type | Description |
|---|---|
| cones | DataFrame of realized volatility quantiles. |
Examples
df = openbb.stocks.load("AAPL")
cones_df = openbb.ta.cones(data = df, lower_q = 0.10, upper_q = 0.90)
cones_df = openbb.ta.cones(df,0.15,0.85,False,"Garman-Klass")
Plots the realized volatility quantiles for the loaded ticker.
Source Code: [link]
openbb.ta.cones_chart(data: pd.DataFrame, symbol: str = "", lower_q: float = 0.25, upper_q: float = 0.75, model: str = "STD", is_crypto: bool = False, export: str = "", sheet_name: Optional[str] = None, external_axes: bool = False)
Parameters
| Name | Type | Description | Default | Optional |
|---|---|---|---|---|
| data | pd.DataFrame | DataFrame of OHLC prices. | None | False |
| symbol | str (default = "") | The ticker symbol. | True | |
| lower_q | float (default = 0.25) | The lower quantile to calculate for. | 0.25 | True |
| upper_q | float (default = 0.75) | The upper quantile to for. | 0.75 | True |
| is_crypto | bool (default = False) | If true, volatility is calculated for 365 days instead of 252. | False | True |
| model | str (default = "STD") | The model to use for volatility calculation. Choices are: ["STD", "Parkinson", "Garman-Klass", "Hodges-Tompkins", "Rogers-Satchell", "Yang-Zhang"] Standard deviation measures how widely returns are dispersed from the average return. It is the most common (and biased) estimator of volatility. Parkinson volatility uses the high and low price of the day rather than just close to close prices. It is useful for capturing large price movements during the day. Garman-Klass volatility extends Parkinson volatility by taking into account the opening and closing price. As markets are most active during the opening and closing of a trading session; it makes volatility estimation more accurate. Hodges-Tompkins volatility is a bias correction for estimation using an overlapping data sample. It produces unbiased estimates and a substantial gain in efficiency. Rogers-Satchell is an estimator for measuring the volatility with an average return not equal to zero. Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates a drift term, mean return not equal to zero. Yang-Zhang volatility is the combination of the overnight (close-to-open volatility). It is a weighted average of the Rogers-Satchell volatility and the open-to-close volatility. | STD | True |
| export | str | Format of export file | True | |
| sheet_name | str | Optionally specify the name of the sheet the data is exported to. | None | True |
| external_axes | bool | Whether to return the figure object or not, by default False | False | True |
Returns
This function does not return anything
Examples
df_ta = openbb.stocks.load('XLY') openbb.ta.cones_chart(data = df_ta, symbol = 'XLY')
df_ta = openbb.stocks.load('XLE') openbb.ta.cones_chart(data = df_ta, symbol = "XLE", lower_q = 0.10, upper_q = 0.90)
openbb.ta.cones_chart(data = df_ta, symbol = "XLE", model = "Garman-Klass")