Returns the short-window and long-window moving averages of mid price. :return:

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

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

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

Computes dictionary of run statistics for comparison. :param orderbook_df: preprocessed orderbook (see __name__.make_orderbook_for_analysis)

Computes the PRD curve for discrete distributions. This function computes the PRD curve for the discrete distribution eval_dist with respect to t

Concats a pd.Series object to a pd.DataFrame horizontally. They need to each have a DatetimeIndex

Bins trades into specified-width bins and counts the number. :param interarrival_times_series: pandas Series object corresponding to the int

Creates orderbook DataFrames from ABIDES exchange output file and orderbook output file.

Helper function: returns *real* cube root of a float

Saves files of the form orders_{symbol}_{date}.pkl

Extracts specific event from stream.

Clip to requested level and fill NaNs according to LOBSTER spec.

Reparametrized forward diffusion process, takes in input x_0 and returns x_t after t steps of noise x_t(x_0, ϵ) = √(α̅_t)x_0 + √(1 -

Generates data structure containing information about ABIDES output paths and experimental parameters, for downstream processing :param

Retrieve mid price from most recent subscription data. :return:

Get the price at current_time, linearly interpolated between price_low and price_high measured at times time_low and time_high

Set next wakeup time for agent.

Get time increment corresponding to wakeup period.

Get time increment corresponding to wakeup period.

getWeights initializes a logistic regresssion model with the average weights from the previous iteration, then trains the model using mini-ba

Get mean spread for the day's trading. :param orderbook_df: preprocessed orderbook (see __name__.make_orderbook_for_analysis) for data witho

https://stackoverflow.com/a/7274316

Function computes the VWAP for an execution agent's orders, when ran from the `execution_iabs_plots` config. :param experiment_name: name fo

LatencyModel.get_latency() samples and returns the final latency for a single Message according to the model specified during initialization.

https://matplotlib.org/3.1.1/gallery/lines_bars_and_markers/linestyles.html

Creates dictionary of parameters used by intraday seasonality plots.

Get the value of s corresponding to closest datetime to ts. :param s: pandas Series with pd.DatetimeIndex :type s: pd.Series

Handles Market Data messages for agents using subscription mechanism

Initialise the current_bids and current_asks object attributes, which internally keep track of the limit orders sent to the Exchange.

Retrives fundamental path from orderbook path.

Method extracts fundamentals for each symbol into DataFrames. Note that input files must be of the form generated by util/formatting/mid_

Log full depth quotes (price, volume) from this order book at some pre-determined frequency. Here we are looking at the actual log for this o

Loads a plot config file for a single day POV market impact experiment and plots the result. :param config_path: 'Name of config file to execute

Computes aggregated statistics (median) across multiple market impact experiments and saves the result. :param path_glob: Glob pattern for p

Load config file for multiday POV market impact experiment and draws plots, doing necessary data processing if necessary. :param config_path: pa