quantpylib.hft.lob
LOB
__init__(timestamp=-1, bids=None, asks=None, depth=100, buffer_size=100, apply_shadow_depth=False)
A class representing a Limit Order Book (LOB) for financial trading.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
timestamp
|
float
|
The initial timestamp in milliseconds. Defaults to current time. |
-1
|
bids
|
ndarray
|
Initial bid array. Defaults to None. |
None
|
asks
|
ndarray
|
Initial ask array. Defaults to None. |
None
|
depth
|
int
|
The maximum depth of the order book. Defaults to 100. |
100
|
buffer_size
|
int
|
The size of the buffers for storing historical data. Defaults to 100. |
100
|
as_dict()
Represent the current state of the order book as a dictionary.
Returns:
| Name | Type | Description |
|---|---|---|
dict |
The current timestamp, bids, and asks. |
as_tuple()
Represent the current state of the order book as a tuple.
Returns:
| Name | Type | Description |
|---|---|---|
tuple |
The current timestamp, bids, and asks. |
buffer_as_list()
Obtain the historical data buffers as a list of dictionaries.
buffer_len()
Get the length of the buffers.
Returns:
| Name | Type | Description |
|---|---|---|
int |
The length of the buffers. |
clear_buffer()
Clear all historical data buffers.
get_ask()
Get the best ask price.
get_ask_sz()
Get the best ask size.
get_asks()
Get the current ask array.
get_asks_buffer()
Get the buffer of three-dimensional (time,price,size) ask arrays.
Returns:
| Type | Description |
|---|---|
|
np.ndarray: The array of ask arrays in the buffer. |
get_asks_sample(n=None, tau=None, T=None)
Get the most recent sample of ask arrays.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
n
|
int
|
The number of samples to take. |
None
|
tau
|
int
|
|
None
|
T
|
int
|
The terminal time. Defaults to last timestamp observed. |
None
|
get_bid()
Get the best bid price.
get_bid_sz()
Get the best bid size.
get_bids()
Get the current bid array.
get_bids_buffer()
Get the buffer of three-dimensional (time,price,size) bid arrays.
Returns:
| Type | Description |
|---|---|
|
np.ndarray: The array of bid arrays in the buffer. |
get_bids_sample(n=None, tau=None, T=None)
Get the most recent sample of bid arrays.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
n
|
int
|
The number of samples to take. |
None
|
tau
|
int
|
|
None
|
T
|
int
|
The terminal time. Defaults to last timestamp observed. |
None
|
get_cumulative_size(dir, price)
Returns the size and volume that can be executed given a limit price instantaneously.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
dir
|
int
|
The direction of the trade. 1 for buy, -1 for sell. |
required |
price
|
float
|
The limit price. |
required |
get_impact_price(dir, notional)
Calculate the volume-weighted price for a given notional amount.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
dir
|
int
|
The direction of the trade. 1 for buy, -1 for sell. |
required |
notional
|
float
|
The notional amount. |
required |
Returns:
| Name | Type | Description |
|---|---|---|
float |
The volume-weighted 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 for the calculation. |
required |
x
|
bool
|
Whether to normalize by price. |
False
|
get_impact_worst(dir, sz)
Returns the worst limit price for a given market order size.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
dir
|
int
|
The direction of the trade. 1 for buy, -1 for sell. |
required |
sz
|
float
|
The size of the market order. |
required |
get_mid()
Get the mid price of the current order book.
Returns:
| Name | Type | Description |
|---|---|---|
float |
The mid price. |
get_mids_buffer()
Get the buffer of one-dimensional mid prices.
Returns:
| Type | Description |
|---|---|
|
np.ndarray: The array of mid prices in the buffer. |
get_mids_sample(n=None, tau=None, T=None)
Get the most recent sample of mid prices.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
n
|
int
|
The number of samples to take. |
None
|
tau
|
int
|
|
None
|
T
|
int
|
The terminal time. Defaults to last timestamp observed. |
None
|
get_spread(x=False)
Get the spread at top of book.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x
|
bool
|
Whether to normalize by price. |
False
|
get_ts_buffer()
Get the buffer of one-dimensional timestamps.
Returns:
| Type | Description |
|---|---|
|
np.ndarray: The array of timestamps in the buffer. |
get_vamp(notional=None, bid_notional=None, ask_notional=None)
Calculate the volume-adjusted mid price.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
notional
|
float
|
The notional amount for the calculation. |
None
|
bid_notional
|
float
|
The notional amount for the bid side. Defaults to |
None
|
ask_notional
|
float
|
The notional amount for the ask side. Defaults to |
None
|
Returns:
| Name | Type | Description |
|---|---|---|
float |
The volume-adjusted mid price. |
get_vol(n=None, tau=None, T=None, x=False, exp=False)
Get mid-price volatility. Excludes zero differences.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
n
|
int
|
The number of samples to use. |
None
|
tau
|
int
|
|
None
|
T
|
int
|
The terminal time. Defaults to last timestamp observed. |
None
|
x
|
bool
|
Whether to normalize by price. |
False
|
exp
|
bool
|
Whether to use exponential weighting. Defaults to False. |
False
|
sample_len(n=None, tau=None, T=None)
Get the length of the most recent samples to take.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
n
|
int
|
The number of samples to take. |
None
|
tau
|
int
|
|
None
|
T
|
int
|
The terminal time. Defaults to last timestamp observed. |
None
|
Returns:
| Name | Type | Description |
|---|---|---|
int |
The length of the samples. |
update(timestamp, bids, asks, is_snapshot, sort_levels=False)
Update the order book with new bid and ask data.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
timestamp
|
float
|
The current timestamp in milliseconds. |
required |
bids
|
ndarray
|
The new bid array. |
required |
asks
|
ndarray
|
The new ask array. |
required |
is_snapshot
|
bool
|
Whether the update is a snapshot (true) or an incremental update (false). |
required |
a_in_b(a, b)
Check if elements of array a are in array b.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
a
|
ndarray
|
The array of elements to check. |
required |
b
|
ndarray
|
The array to check against. |
required |
Returns:
| Type | Description |
|---|---|
|
np.ndarray: A boolean array indicating if elements of |