quantpylib.hft.features

rolling_vol(mids, n, exp=False)

Calculate rolling volatility of the mid prices.

Parameters:

Name	Type	Description	Default
mids	ndarray	Array of mid prices.	required
n	int	The size of the rolling window.	required
exp	bool	Whether to use exponential weighting. Defaults to False.	False

Returns:

Type	Description
	np.ndarray: An array of rolling volatilities.

sample_vamp(bids, asks, notional=None, bid_notional=None, ask_notional=None)

Calculate the Volume Adjusted Mid Price (VAMP).

Parameters:

Name	Type	Description	Default
bids	ndarray	Array of bid levels as [price, volume].	required
asks	ndarray	Array of ask levels as [price, volume].	required
notional	float	The notional amount for both sides of the orderbook (each). Defaults to None.	None
bid_notional	float	The notional amount for bids. Defaults to notional if not provided.	None
ask_notional	float	The notional amount for asks. Defaults to notional if not provided.	None

Returns:

Name	Type	Description
float		The calculated VAMP.

sample_vol(sample, exp=False)

Calculate the sample volatility.

Parameters:

Name	Type	Description	Default
sample	ndarray	The input sample.	required
exp	bool	Whether to use exponential weighting. Defaults to False.	False

Returns:

Name	Type	Description
float		The calculated sample volatility.

volume_weighted_price(ob_levels, notional)

Calculate the volume-weighted price for a given notional amount.

Parameters:

Name	Type	Description	Default
ob_levels	ndarray	The order book levels as an array of [price, volume].	required
notional	float	The notional amount.	required

Returns:

Name	Type	Description
float		The volume-weighted price.