quantpylib.hft.lob

`LOB`

A class representing a Limit Order Book (LOB).

Attributes:

Name	Type	Description
`timestamp`	`int64_t`	The current timestamp.
`depth`	`int`	The depth of the order book.
`safe_depth`	`int`	The safe depth of the order book.
`bids`	`ndarray`	The bid levels.
`asks`	`ndarray`	The ask levels.
`timestamps_buffer`	`RingBuffer`	The buffer for timestamps.
`bids_buffer`	`RingBuffer`	The buffer for bid levels.
`asks_buffer`	`RingBuffer`	The buffer for ask levels.
`mids_buffer`	`RingBuffer`	The buffer for mid prices.

`init(timestamp=-1, bids=None, asks=None, depth=100, buffer_size=0, apply_shadow_depth=False, **kwargs)`

Initialize the LOB.

Parameters:

Name	Type	Description	Default
`timestamp`	`int64_t`	The initial timestamp in ms. Defaults to -1.	`-1`
`bids`	`ndarray`	The initial bid levels. Defaults to None.	`None`
`asks`	`ndarray`	The initial ask levels. Defaults to None.	`None`
`depth`	`int`	The depth of the order book. Defaults to 100.	`100`
`buffer_size`	`int`	The size of the buffer. Defaults to 0.	`0`
`apply_shadow_depth`	`bool`	Whether to maintain higher levels than depth for accuracy at speed cost. Defaults to False.	`False`
`**kwargs`		Additional keyword arguments.	`{}`

`aggregator(interval=60 * 1000)`

Aggregate the order book buffer into buckets.

Parameters:

Name	Type	Description	Default
`interval`	`int`	The interval for aggregation in milliseconds. Defaults to 60*1000.	`60 * 1000`

Returns:

Name	Type	Description
`list`		A list of aggregated buckets.

`as_dict()`

Get the order book as a dictionary.

Returns:

Name	Type	Description
`dict`		The order book as a dictionary.

`as_tuple()`

Get the order book as a tuple.

Returns:

Name	Type	Description
`tuple`		The order book as a tuple (timestamp, bids, asks).

`buffer_as_list()`

Get the buffer as a list.

Returns:

Name	Type	Description
`list`		The buffer as a list of dictionaries.

`buffer_len()`

Get the length of the buffer.

Returns:

Name	Type	Description
`int`		The length of the buffer.

`clear_buffer()`

Clear the buffers.

`get_ask()`

Get the best ask price.

Returns:

Name	Type	Description
`float`		The best ask price.

`get_ask_sz()`

Get the size of the best ask.

Returns:

Name	Type	Description
`float`		The size of the best ask.

`get_asks()`

Get the ask levels.

Returns:

Type	Description
	np.ndarray: The ask levels.

`get_asks_buffer()`

Get the ask levels buffer.

Returns:

Type	Description
	np.ndarray: The ask levels buffer.

`get_asks_sample(n=-1, tau=-1, T=-1)`

Get the sample of the most recent asks to take.

Parameters:

Name	Type	Description	Default
`n`	`int`	The number of samples to take.	`-1`
`tau`	`int`	`T - t`, where `t` is the earliest sample time.	`-1`
`T`	`int`	The terminal time. Defaults to last timestamp observed.	`-1`

Returns:

Type	Description
	np.ndarray: The sample of the ask levels.

`get_bid()`

Get the best bid price.

Returns:

Name	Type	Description
`float`		The best bid price.

`get_bid_sz()`

Get the size of the best bid.

Returns:

Name	Type	Description
`float`		The size of the best bid.

`get_bids()`

Get the bid levels.

Returns:

Type	Description
	np.ndarray: The bid levels.

`get_bids_buffer()`

Get the bid levels buffer.

Returns:

Type	Description
	np.ndarray: The bid levels buffer.

`get_bids_sample(n=-1, tau=-1, T=-1)`

Get the sample of the most recent bids to take.

Parameters:

Name	Type	Description	Default
`n`	`int`	The number of samples to take.	`-1`
`tau`	`int`	`T - t`, where `t` is the earliest sample time.	`-1`
`T`	`int`	The terminal time. Defaults to last timestamp observed.	`-1`

Returns:

Type	Description
	np.ndarray: The sample of the bid levels.

`get_cumulative_size(dir, price)`

Get the cumulative size and notional that can be instantaneously executed given a limit price.

Parameters:

Name	Type	Description	Default
`dir`	`int`	The direction (-1 for bids, 1 for asks).	required
`price`	`float`	The price level.	required

Returns:

Name	Type	Description
`tuple`		The cumulative size and notional.

`get_impact_price(dir, notional)`

Get the impact price for a given direction and notional amount.

Parameters:

Name	Type	Description	Default
`dir`	`int`	The direction (-1 for bids, 1 for asks).	required
`notional`	`float`	The notional amount.	required

Returns:

Name	Type	Description
`float`		The impact price.

`get_impact_spread(notional, x=False)`

Get the spread between impact prices from top of book.

Parameters:

Name	Type	Description	Default
`notional`	`float`	The notional amount.	required
`x`	`bool`	Whether to normalize by the mid price. Defaults to False.	`False`

Returns:

Name	Type	Description
`float`		The impact spread.

`get_impact_worst(dir, sz)`

Get the worst limit price for a given market order size.

Parameters:

Name	Type	Description	Default
`dir`	`int`	The direction (-1 for bids, 1 for asks).	required
`sz`	`float`	The size of the market order.	required

Returns:

Name	Type	Description
`float`		The worst limit price.

`get_mid()`

Get the mid price.

Returns:

Name	Type	Description
`float`		The mid price.

`get_mids_buffer()`

Get the mid prices buffer.

Returns:

Type	Description
	np.ndarray: The mid prices buffer.

`get_mids_sample(n=-1, tau=-1, T=-1)`

Get the sample of the most recent mids to take.

Parameters:

Name	Type	Description	Default
`n`	`int`	The number of samples to take.	`-1`
`tau`	`int`	`T - t`, where `t` is the earliest sample time.	`-1`
`T`	`int`	The terminal time. Defaults to last timestamp observed.	`-1`

Returns:

Type	Description
	np.ndarray: The sample of the mid prices.

`get_spread(x=False)`

Get the spread of the top of order book.

Parameters:

Name	Type	Description	Default
`x`	`bool`	Whether to normalize by the mid price. Defaults to False.	`False`

Returns:

Name	Type	Description
`float`		The spread of the order book.

`get_ts_buffer()`

Get the timestamp buffer.

Returns:

Type	Description
	np.ndarray: The timestamp buffer.

`get_vamp(notional=0, bid_notional=0, ask_notional=0)`

Get the Volume Adjusted Mid Price (VAMP).

Parameters:

Name	Type	Description	Default
`notional`	`float`	The notional amount for both sides of the order book. Defaults to 0.	`0`
`bid_notional`	`float`	The notional amount for bids. Defaults to `notional` if not provided.	`0`
`ask_notional`	`float`	The notional amount for asks. Defaults to `notional` if not provided.	`0`

Returns:

Name	Type	Description
`float`		The VAMP.

`get_vol(n=-1, tau=-1, T=-1, x=False, exp=False)`

Get the volatility of the mid prices, excludes zero differences.

Parameters:

Name	Type	Description	Default
`n`	`int`	The number of samples to take.	`-1`
`tau`	`int`	`T - t`, where `t` is the earliest sample time.	`-1`
`T`	`int`	The terminal time. Defaults to last timestamp observed.	`-1`
`x`	`bool`	Whether to normalize by the mid price. Defaults to False.	`False`
`exp`	`bool`	Whether to use exponential weighting. Defaults to False.	`False`

Returns:

Name	Type	Description
`float`		The volatility of the mid prices.

`sample_len(n=-1, tau=-1, T=-1)`

Get the length of the most recent samples to take.

Parameters:

Name	Type	Description	Default
`n`	`int`	The number of samples to take.	`-1`
`tau`	`int`	`T - t`, where `t` is the earliest sample time.	`-1`
`T`	`int`	The terminal time. Defaults to last timestamp observed.	`-1`

Returns:

Name	Type	Description
`int`		The length of the sample.

`update(timestamp, bids, asks, is_snapshot, is_sorted=True)`

Update the order book with new bid and ask levels.

Parameters:

Name	Type	Description	Default
`timestamp`	`int64_t`	The new timestamp.	required
`bids`	`ndarray`	The new bid levels.	required
`asks`	`ndarray`	The new ask levels.	required
`is_snapshot`	`bool`	Whether the update is a snapshot or delta update.	required
`is_sorted`	`bool`	Whether the levels are already sorted (increasing/decreasing). Defaults to True.	`True`

quantpylib.hft.lob

LOB

__init__(timestamp=-1, bids=None, asks=None, depth=100, buffer_size=0, apply_shadow_depth=False, **kwargs)

aggregator(interval=60 * 1000)

as_dict()

as_tuple()

buffer_as_list()

buffer_len()

clear_buffer()

get_ask()

get_ask_sz()

get_asks()

get_asks_buffer()

get_asks_sample(n=-1, tau=-1, T=-1)

get_bid()

get_bid_sz()

get_bids()

get_bids_buffer()

get_bids_sample(n=-1, tau=-1, T=-1)

get_cumulative_size(dir, price)

get_impact_price(dir, notional)

get_impact_spread(notional, x=False)

get_impact_worst(dir, sz)

get_mid()

get_mids_buffer()

get_mids_sample(n=-1, tau=-1, T=-1)

get_spread(x=False)

get_ts_buffer()

get_vamp(notional=0, bid_notional=0, ask_notional=0)

get_vol(n=-1, tau=-1, T=-1, x=False, exp=False)

sample_len(n=-1, tau=-1, T=-1)

update(timestamp, bids, asks, is_snapshot, is_sorted=True)

`LOB`

`init(timestamp=-1, bids=None, asks=None, depth=100, buffer_size=0, apply_shadow_depth=False, **kwargs)`

`aggregator(interval=60 * 1000)`

`as_dict()`

`as_tuple()`

`buffer_as_list()`

`buffer_len()`

`clear_buffer()`

`get_ask()`

`get_ask_sz()`

`get_asks()`

`get_asks_buffer()`

`get_asks_sample(n=-1, tau=-1, T=-1)`

`get_bid()`

`get_bid_sz()`

`get_bids()`

`get_bids_buffer()`

`get_bids_sample(n=-1, tau=-1, T=-1)`

`get_cumulative_size(dir, price)`

`get_impact_price(dir, notional)`

`get_impact_spread(notional, x=False)`

`get_impact_worst(dir, sz)`

`get_mid()`

`get_mids_buffer()`

`get_mids_sample(n=-1, tau=-1, T=-1)`

`get_spread(x=False)`

`get_ts_buffer()`

`get_vamp(notional=0, bid_notional=0, ask_notional=0)`

`get_vol(n=-1, tau=-1, T=-1, x=False, exp=False)`

`sample_len(n=-1, tau=-1, T=-1)`

`update(timestamp, bids, asks, is_snapshot, is_sorted=True)`