Used by any Trading Agent subclass to cancel any order. The order must currently appear in the agent's open orders list.

Generate a _Order object for a particular price level :param price: :type price: int

Used by any trading agent subclass to query the total transacted volume in a given lookback period

Returns variables that keep track of whether spread and transacted volume have been observed.

Internal function to extract correct values for a sender->recipient pair from parameters that can be specified as scalar, 1-D ndarray, or 2-D

Returns variables that keep track of whether spread and transacted volume have been observed.

Context manager that wraps the code block in a try except statement, catching specified exceptions and printing warning supplied by user.

Checks if orderbook dataframe is in wide or skinny format.

Make orderbook amenable to mid-price + liquidity plots from ABIDES input. :param stream_path: path to ABIDES Exchange output, e.g. Exchang

Place a market order at the exchange. :param quantity (int): order quantity :param is_buy_order (bool): True if Buy else Fal

The exchange agents sends an order book update to the agents using the subscription API if one of the following conditions are met: 1) ag

Converts a pandas Series object of datetime64[ns] timestamps to Series of seconds from midnight on that day. Inspired by https://stackoverfl

Aggregates cached experimental data. :param data_to_process: output of __name__.load_cached :return:

cancels all resting limit orders placed by the market maker

Keep only rows within certain time bounds of dataframe. :param df: DataFrame with DatetimeIndex :param start: lower bound :p

Converts imported ABIDES DataFrame into LOBSTER FORMAT.

https://stackoverflow.com/a/42266882

Extracts first element of pandas Series s, or returns s if not a series.

Get time increment corresponding to wakeup period.

Calculates the average price paid (weighted by the order size)

Returns first and last timestamp of pandas DataFrame in plot-scripts format or LOBSTER format.

Returns date as string from pandas series of timestamps. :param s: :type s: pandas.Series(datetime64[ns]) :return s_date_st

Loads cached execution experiment data from __name__.make_cached into memory :return:

Used by any Trading Agent subclass to modify any existing limit order. The order must currently appear in the agent's open orders list.

Method takes orderbook log and transforms into format amenable to "LOBSTER-ification" :param df: pd.DataFrame orderbook output by ABIDES :pa

Get a numpy array of weights, one per diffusion step. The weights needn't be normalized, but must be positive.

DONE: remember to use the mean/std of the training set, to z-normalize the test set.

Update internal spread estimate with exponentially weighted moving average :param spread: :return:

A fast approximation of the cumulative distribution function of the standard normal.

Take the mean over all non-batch dimensions.

Given a list of _Order objects, remove the corresponding orders from ExchangeAgent's orderbook :param orders_to_cancel: orders to remove fro

Check if date in format YYYY-MM-DD in a string.

Given a mid price, computes the orders that need to be removed from orderbook, and pops these orders from bid and ask deques. :p

Args: input_features: numpy.ndarray([N, feature_dim], dtype=np.float32) nearest_k: int Returns: Distances to kth near

Args: data_x: numpy.ndarray([N, feature_dim], dtype=np.float32) data_y: numpy.ndarray([N, feature_dim], dtype=np.float32) Ret

args: X: np.array of shape N x dim Y: np.array of shape N x dim returns: N x N symmetric np.array

Creates a sequential model of convolutional layers to progressively reduce the input size and the number of channels to 1, followed b

Compute the reverse diffusion process for the current time step

Deduplicate index values of pd.Series

Extracts random seed and date from path of ABIDES output file :param p: path from which to extract seed and date :param yes_base: regex patt