For two pandas series with DateTimeIndex , return corresponding series with the same numer of entries, forward-filled. :type s1: pd.Series

Get the true price of a symbol at the requested time. :param symbol: which symbol to query :type symbol: str :par

Process ABIDES output data into format suitable for aggregation and cache. :param params: Data structure constructed by __name__.generate_plot_d

Momentum Agent actions logic

Given a mid-price, compute new orders that need to be placed, then send the orders to the Exchange. :param mid: mid-price :t

Given a mid-price, compute new orders that need to be placed, then send the orders to the Exchange. :param mid: mid-price :t

Given a mid-price, compute new orders that need to be placed, then send the orders to the Exchange. :param mid: mid-price :t

Plot binned counts of trade volume.

Prepares and caches POV market impact experiment output files for further aggregation and processing. :param plot_data: Data structure holding p

:param args: superclass args :param wake_freq: Frequency of wakeup -- str to be parsed by pd.Timedelta :param order_size: siz

Update window size and tick size relative to internal spread estimate. :return:

Clusters the union of the data points and returns the cluster distribution. Clusters the union of eval_data and ref_data into num_clusters using mi

It computes the log-likelihood log(p(x_0)) that is the probability that x was generated by the predicted distribution. We need it whe

Loads the data.

Gets portion of self.history that has arrived since last call of self.get_transacted_volume. Also updates self._transacted_volume[self.h

Compute the KL divergence between two gaussians. Shapes are automatically broadcasted, so batches can be compared to scalars

Get the mean of the prior p(x_{t-1} | x_t).

we need to go from the output of the diffusion model to an actual order

we need to go from the output of the cgan model to an actual order

Get the mean and variance of the posterior q(x_{t-1} | x_t, x_0). :param x_0: the initial image. :param x_t: the image at ti

Returns a DataFrame with columns ['execution_time', 'quantity'] from a dictionary with same format as self.history, describing executed t

Updates self._transacted_volume["unrolled_transactions"] with data from recent_history :return:

Get a term for the variational lower-bound. The resulting units are bits. This allows for comparison to other papers.

Compute quantiles for mid-price and liquidity measures. :param saved_orderbooks: output of __name__.process_orderbooks_for_liquidity_plots

Method augments orderbook with volume weighted average price.

Sums the values of a pandas Series indexed by Datetime according to specific binwidth. :param s: series of values to process :type s

Returns a pandas DataFrame constructed from the order book log, to be consumed by agent.ExchangeAgent.logOrderbookSnapshots.

From bundled streams return dict with interarrival times collated by symbol.

Cancels all resting limit orders placed by the market maker

Cancels all resting limit orders placed by the market maker

Cancels all resting limit orders placed by the market maker