Analytics¶

Format-agnostic post-processing analytics. These functions work with the output of any format's pipeline run (Bitstamp, LOBSTER, or custom).

Trade Analysis¶

order_aggressiveness ¶

order_aggressiveness(
    events: DataFrame, depth_summary: DataFrame
) -> pd.DataFrame

Calculate order aggressiveness with respect to the best bid or ask in BPS.

Parameters:

Name	Type	Description	Default
`events`	`DataFrame`	The events DataFrame (must contain `direction`, `action`, `type`, `timestamp`, `event_id`, `price` columns).	required
`depth_summary`	`DataFrame`	The order book summary statistics DataFrame (must contain `timestamp` and `event_id` columns).	required

Returns:

Type	Description
`DataFrame`	The events DataFrame with an added `aggressiveness_bps` column.

trade_impacts ¶

trade_impacts(trades: DataFrame) -> pd.DataFrame

Generate a DataFrame containing order book impact summaries.

Aggregates trade records by taker order ID to summarise how each aggressive order swept through the book (price range, number of fills, total volume, VWAP, duration).

Parameters:

Name	Type	Description	Default
`trades`	`DataFrame`	The trades DataFrame (must contain `taker`, `price`, `volume`, `timestamp`, `direction` columns).	required

Returns:

Type	Description
`DataFrame`	A DataFrame summarising market order impacts with columns: `id`, `min_price`, `max_price`, `vwap`, `hits`, `vol`, `start_time`, `end_time`, `dir`.

Order Type Classification¶

set_order_types ¶

set_order_types(
    events: DataFrame, trades: DataFrame
) -> pd.DataFrame

Determine limit order types.

Classifies each order as one of: market, resting-limit, flashed-limit, or market-limit, based on how the order interacts with the book over its lifetime.

Parameters:

Name	Type	Description	Default
`events`	`DataFrame`	The limit order events DataFrame.	required
`trades`	`DataFrame`	The executions DataFrame.	required

Returns:

Type	Description
`DataFrame`	The events DataFrame with an updated 'type' column indicating order types.

Order Book Reconstruction¶

order_book ¶

order_book(
    events: DataFrame,
    tp: datetime | None = None,
    max_levels: int | None = None,
    bps_range: int = 0,
    min_bid: float = 0,
    max_ask: float = np.inf,
) -> dict[str, datetime | pd.Timestamp | pd.DataFrame]

Reconstruct the order book at a specific point in time.

Parameters:

Name	Type	Description	Default
`events`	`DataFrame`	DataFrame containing order events.	required
`tp`	`datetime or Timestamp`	The point in time at which to evaluate the order book. If None, uses the latest event timestamp in the data.	`None`
`max_levels`	`int`	The maximum number of price levels to include for bids and asks.	`None`
`bps_range`	`int`	Basis points range to filter the bids and asks. Default is 0.	`0`
`min_bid`	`float`	Minimum bid price. Default is 0.	`0`
`max_ask`	`float`	Maximum ask price. Default is infinity.	`inf`

Returns:

Type	Description
`dict[str, datetime or DataFrame]`	A dictionary containing: - 'timestamp': The evaluation timestamp. - 'asks': DataFrame of active ask orders. - 'bids': DataFrame of active bid orders.