REFINITIV US ORGANIZATION LLC

The present disclosure relates to methods of, and systems for, authorisation. Aspects of the present disclosure relate to methods of, and systems for: token key management, set-based authorisation and synchronising an authorisation system.

The present disclosure relates to methods of, and systems for, authorisation. Aspects of the present disclosure relate to methods of, and systems for: token key management, set-based authorisation and synchronising an authorisation system.

The present disclosure relates to methods of, and systems for, authorisation. Aspects of the present disclosure relate to methods of, and systems for: token key management, set-based authorisation and synchronising an authorisation system.

The present disclosure relates to methods of, and systems for, authorisation. Aspects of the present disclosure relate to methods of, and systems for: token key management, set-based authorisation and synchronising an authorisation system.

Systems and methods of pauseless end-of-interval rollovers are described to provide rollover processing with fault tolerance. A system may execute first and second instances of a matching engine for fault tolerance. Each instance by design is deterministically programmed to perform paused end-of-interval (EOI) rollover processing in which orders may be received without blocking by transmitting loopback messages that indicate to-be-expired messages. The sequencer may sequence the loopback messages along with new order messages. Each instance may continue to receive sequenced messages, which may include loopback messages it or the other instance sent, and expire the order corresponding to the loopback message or the counterpart loopback message, wherein expiration of the order occurs on both the first instance and the second instance without blocking new messages from the sequencer.

Systems and methods may use natural language processing (NLP) and machine-learning techniques to detect an impact that an event will have on a domain-specific topic. For example, the system may use multi-stage cleaning using a rules-based and an artificial intelligence (AI)-based filter to filter large quantities of event items that may not be relevant to a domain of interest. The AI-based filter may be trained using labeled event items that were previously known to be impactful. The system may cluster the cleaned event items to group similar event items and eliminate redundancy. The system may then predict and quantify the impact that events described by clustered event items will have on the domain-specific topic. Such prediction may be based on a classifier trained using various model features that correlate with impactful events, including prior similar events.

An electronic trading venue (ETV) may prioritize maker orders that specify or are associated with cancel delay values by selecting a maker order to be matched with taker orders before competing maker orders, at the same price level for a given instrument, that have smaller or no cancel delay values. Such prioritization may occur even if the competing maker orders were received before the maker order. A cancel delay value indicates an amount of time that must pass after a request to cancel a maker order is received before the ETV actually cancels the maker order. Thus, in exchange for prioritization, maker orders will be subject to a cancelation delay that starts after a cancel request is received and during which a maker order will not be canceled. Takers may benefit from this by being able to match with a maker order before it can be canceled.

The invention relates to systems and methods for distributing encrypted market data updates to market participants via private multicast channels. Among other factors, characteristics of modern computer networks may cause participants to be sent (and to receive) their encrypted data for that update at different times. Thus, after the participants have all been sent their data for that update the system may then simultaneously transmit to those participants the key that will enable them to decipher their data via a public multicast channel such that all recipients receive the key at the same time. In this manner the invention may ensure that although participants receive their data in a given update at different times, they are unable to decipher that data until substantially the same time.

A system may access a first message for processing over the electronic communication network, the first message being associated with at least two potentially conflicting message states. The system may generate, based on the first message, a phantom message for transmission to a message receiver, the phantom message indicating the two potentially conflicting message states. The system may transmit the phantom message to the message receiver and to a data distributor that provides data updates regarding messages to one or more participants. The data distributor may not provide a data update for phantom messages. The system may access a second message that would otherwise block the phantom message until a response is received from the message receiver regarding a processing result for the phantom message. The system may generate a non-blocking message based on the second message, and transmit the non-blocking message to the message receiver and the data distributor.

The disclosure relates to multi-objective optimization based on Deep First Search (“DFS”) with backtracking and search space reduction. For example, a system may access a message comprising an order and a plurality of codes, wherein unique combinations of codes each define a choice of codes that is to be searched against a second plurality of codes, and wherein permutations of pairwise checks between the choice of codes and the second plurality of codes define a search space. The system may execute a Deep-First Search (“DFS”) with backtracking on the reduced search space until an optimum objective for each choice of code from among the plurality of codes is found. The system may identify a permutation of orders based on the codes that gave the optimum objective for each choice of code derived from the unique combinations of one or more codes.