COORDINATED PROCESSING OF DATA BY NETWORKED COMPUTING

RESOURCES

Cross-Reference to Related Applications

[0001] This application claims all benefit, including priority, of:

U.S. Provisional Patent Application No. 62/126,106, filed February 27, 2015, and entitled COORDINATED PROCESSING OF DATA BY NETWORKED COMPUTING RESOURCES;

U.S. Provisional Application No. 62/126,120, filed February 27, 2015, and entitled COORDINATED PROCESSING OF DATA BY NETWORKED COMPUTING RESOURCES; and

U.S. Provisional Application No. 62/132,063, filed March 12, 2015, and entitled COORDINATED PROCESSING OF DATA BY NETWORKED COMPUTING RESOURCES.

[0002] All of the above-noted applications are hereby incorporated by reference in their entireties.

Technical Field

[0003] The present disclosure relates generally to systems, methods, devices and computer-readable media for the management of data processing by multiple networked computing resources. In particular, the disclosure relates to the coordination or synchronization of related or temporal requests for processing of data at distributed network resources.

[0004] Aspects of the material disclosed in this application may underlie or relate to the holding, transfer, and/or administration of securities and other financial interests. Aspects of such holding, transfer, and/or administration may be subject to regulation by governmental and other agencies. The disclosure herein is made solely in terms of logical, programming, and communications possibilities, without regard to statutory, regulatory, or other legal considerations. Nothing herein is intended as a statement or representation that any system, method or process proposed or discussed herein, or the use thereof, does or does not comply with any statute, law, regulation, or other legal requirement in any jurisdiction; nor should it be taken or construed as doing so.

Background

[0005] In various forms of networked or otherwise distributed data processing systems, complex and/or multiple related processes are often routed to multiple computing resources for execution. For example, in financial and other trading systems, orders for purchases, sales, and other transactions in financial interests are often routed to multiple market or exchange servers for fulfillment. For example, when a large order is routed to multiple exchanges (e.g., based on the liquidity available in each market), orders tend to arrive at the faster exchanges (i.e., those having fewer inherent latencies) before they arrive at slower exchanges (i.e., those having greater inherent latencies), and thus show in the books of different exchanges at different times. When orders begin to show on the books of the faster exchanges, other parties can detect the orders and attempt to take advantage of the latency in slower exchanges by cancelling, changing, and or otherwise manipulating quotes (e.g., bids and offers) or other market parameters on the slower exchanges, effectively increasing the implicit trading costs. As a result, orders that may have otherwise executed on any single exchange at a high fill ratio tend to exhibit a lower overall fill ratio when routed to multiple exchanges as a split trade.

[0006] Prior art documents, such as the Rony Kay article "Pragmatic Network Latency Engineering, Fundamental Facts and Analysis, have attempted to address such problems by proposing elimination of one-way communications (i.e., "packet") latencies. Such systems fail to address arbitrage opportunities and other issues caused or facilitated by variations in the time required for multiple processors to execute individual portions of multiple-processor

execution requests (i.e., execution latencies), in addition to (or as part of) communications latencies.

Summary

[0007] In various aspects the present disclosure provides systems, methods, and computer-executable instruction mechanisms (e.g., non-transient machine-readable programming structures) such as software-coded instruction sets and data, for the management of data processing by multiple networked computing resources. In particular, for example, the present disclosure provides systems, methods, and media useful in controlling the synchronization or coordination of related requests for processing of data using distributed network resources.

[0008] For example, in one aspect the present disclosure provides systems, methods, and media for coordinating processing of data by multiple networked computing resources. Such systems, for example, can include at least one processor configured to: receive from one or more data sources signals representing instructions for execution of at least one data process executable by a plurality of networked computing resources; divide the at least one data process into a plurality of data processing segments, each data processing segment to be routed to a different one of a plurality of networked execution processors; based at least partly on latencies in execution of prior data processing requests routed by the system to each of the plurality of networked execution processors, determine a plurality of timing parameters, each of the plurality of timing parameters to be associated with a corresponding one of the plurality of data processing segments, the plurality of timing parameters determined to cause synchronized execution of the plurality of data processing segments by the plurality of networked execution processors; route the plurality of data processing segments to the plurality of corresponding networked execution processors in a timing sequence based on the timing parameters; determining a capture ratio for the data processing segments; and adjusting the timing parameters associated with each of the plurality of networked execution processors based on the capture ratio.

[0009] In some aspects the present disclosure provides systems, methods, and programming or other machine-interpretable instructions for causing synchronized/coordinated processing of data by multiple networked computing resources, such systems, for example, comprising at least one processor configured to execute machine-interpretable instructions and causing the system to: monitor execution of signal processing execution requests by each of the plurality of networked computing resources; determine at least one timing parameter associated with a latency in execution of signal processes between the system and each of the plurality of networked computing resources; and store the at least one timing parameter in machine-readable memory accessible by the at least one processor.

[0010] Monitoring of execution of signal processing execution requests according to such and other embodiments of the invention can be implemented on continual, periodic, and/or other suitable or desirable bases.

[0011] In various embodiments of the various aspects of the invention, the networked computing resources can include one or more exchange servers. The data sources can include one or more broker or trader systems or servers, the controlled signal processes can represent trades in financial interests, and the execution of signal processing execution requests represents the execution of transactions in financial interests, including for example stocks, bonds, options and contract interests, currencies and/or other intangible interests, and/or commodities. In such embodiments requests for execution of data processing procedures can be based wholly or partially on parameters including, for example, any one or more of current market data quotations, order routing rules, order characteristics, displayed liquidity of each networked computing resource, and a probable delay, or latency, in execution of an order quantity at each networked computing resource.

[0012] In the same and further aspects the invention provides systems for controlling or otherwise managing requests for processing of data by distributed computer resources, such systems including one or more processors configured to execute instructions for causing the system to: monitor execution of signal processing execution requests by each of the plurality of networked computing resources; determine at least one timing parameter associated with the latency in execution of signal processes between the system and each of the plurality of networked computing resources; and store the at least one timing parameter for each of the plurality of networked computing resources.

[0013] In accordance with one aspect, there is provided a system for coordinating processing of data by multiple networked computing resources. The system includes at least one processor configured to: monitor data associated with a plurality of networked computing resources, the monitored data including data associated with data processing segments previously routed to the plurality of networked computing resources; receive from one or more data sources signals representing instructions for execution of at least one data process executable by the plurality of networked computing resources; based on the monitored data : divide the at least one data process into at least one data processing segment, each data processing segment to be routed to one of the plurality of networked computing resources; determine a plurality of timing parameters, each of the plurality of timing parameters to be associated with a corresponding one of the plurality of networked computing resources, the plurality of timing parameters determined to cause synchronized execution of the at least one data processing segment by the plurality of networked computing processors; and route the at least one data processing segment to the plurality of corresponding networked computing processors in a timing sequence based on the timing parameters.

[0014] In accordance with another aspect there is provided a method for coordinating processing of data by multiple networked computing resources.

The method includes: monitoring data associated with a plurality of networked computing resources, the monitored data including data associated with data processing segments previously routed to the plurality of networked computing resources; receiving from one or more data sources signals representing instructions for execution of at least one data process executable by the plurality of networked computing resources; based on the monitored data : dividing the at least one data process into at least one data processing segment, each data processing segment to be routed to one of the plurality of networked computing resources; determining a plurality of timing parameters, each of the plurality of timing parameters to be associated with a corresponding one of the plurality of networked computing resources, the plurality of timing parameters determined to cause synchronized execution of the at least one data processing segment by the plurality of networked computing processors; and routing the at least one data processing segment to the plurality of corresponding networked computing processors in a timing sequence based on the timing parameters.

[0015] In accordance with another aspect there is provided a computer-readable medium or media having stored thereon instructions which when executed by at least one processor, configured the at least one processor to: monitor data associated with a plurality of networked computing resources, the monitored data including data associated with data processing segments previously routed to the plurality of networked computing resources; receive from one or more data sources signals representing instructions for execution of at least one data process executable by the plurality of networked computing resources; based on the monitored data : divide the at least one data process into at least one data processing segment, each data processing segment to be routed to one of the plurality of networked computing resources; determine a plurality of timing parameters, each of the plurality of timing parameters to be associated with a corresponding one of the plurality of networked computing resources, the plurality of timing parameters determined to cause synchronized execution of the at least one data processing segment by the plurality of networked computing processors; and route the at least one data processing segment to the plurality of corresponding networked computing processors in a timing sequence based on the timing parameters.

[0016] In accordance with another aspect there is provided : a system for coordinating processing of data by multiple networked computing resources. The system includes at least one processor configured to: obtain a plurality of data processing waves, each data processing wave identifying : one or more data processing segments, one or more networked corresponding computing resources to which the one or more data processing segments are to be routed; and a timing sequence in which the one or more data processing segments are to be routed; obtain a minimum handling interval for each of the networked computing resources; schedule an order for routing the plurality of data processing waves based on the wave timing sequences and the minimum handling intervals for the networked computing resources; and route each of the data processing segments in the plurality of data processing waves based on the order.

[0017] In accordance with another aspect, there is provided : a method for coordinating processing of data by multiple networked computing resources, the method comprising : obtaining a plurality of data processing waves, each data processing wave identifying : one or more data processing segments, one or more networked corresponding computing resources to which the one or more data processing segments are to be routed; and a timing sequence in which the one or more data processing segments are to be routed; obtaining a minimum handling interval for each of the networked computing resources; scheduling an order for routing the plurality of data processing waves based on the wave timing sequences and the minimum handling intervals for the networked computing resources; and routing each of the data processing segments in the plurality of data processing waves based on the order.

[0018] In accordance with another aspect, there is provided : a computer-readable medium or media having stored thereon instructions which when executed by at least one processor, configure the at least one processor to: obtain a plurality of data processing waves, each data processing wave identifying : one or more data processing segments, one or more networked corresponding computing resources to which the one or more data processing segments are to be routed; and a timing sequence in which the one or more data processing segments are to be routed; obtain a minimum handling interval for each of the networked computing resources; schedule an order for routing the plurality of data processing waves based on the wave timing sequences and the minimum handling intervals for the networked computing resources; and route each of the data processing segments in the plurality of data processing waves based on the order.

[0019] In accordance with another aspect, there is provided : a system for coordinating processing of data by multiple networked computing resources, the system comprising at least one processor configured to: receive from one or more data sources signals representing instructions for execution of a plurality of data processes executable by a plurality of networked computing resources, the data processes representing a proposed trade in a financial interest; obtaining data associated with available liquidity of the financial interest at each of the plurality of networked computing resources; divide each of the plurality of data processes into a plurality of data processing segments, each data processing segment divided from a single data process to be routed to at least one of the plurality of networked computing processors; based at least partly on latencies in execution of prior data processing requests routed by the system to each of the plurality of networked computing processors and the available liquidity at each of the plurality of networked computing processors, determine a plurality of timing parameters, each of the plurality of timing parameters to be associated with a corresponding one of the plurality of data processing segments, the plurality of timing parameters determined to cause synchronized execution of the plurality of data processing segments by the plurality of networked computing processors; based on the timing parameters and networked computing processors associated with each of the plurality of data processes, determine a timing sequence for routing the data processing segments for all of the plurality of data processes; and route the plurality of data processing segments to the plurality of corresponding networked computing processors based on the timing sequence.

[0020] In accordance with another aspect, there is provided a method for coordinating processing of data by multiple networked computing resources, the method : receiving from one or more data sources signals representing instructions for execution of a plurality of data processes executable by a plurality of networked computing resources, the data processes representing a proposed trade in a financial interest; obtaining data associated with available liquidity of the financial interest at each of the plurality of networked computing resources; dividing each of the plurality of data processes into a plurality of data processing segments, each data processing segment divided from a single data process to be routed to at least one of the plurality of networked computing processors; based at least partly on latencies in execution of prior data processing requests routed by the system to each of the plurality of networked computing processors and the available liquidity at each of the plurality of networked computing processors, determining a plurality of timing parameters, each of the plurality of timing parameters to be associated with a corresponding one of the plurality of data processing segments, the plurality of timing parameters determined to cause synchronized execution of the plurality of data processing segments by the plurality of networked computing processors; based on the timing parameters and networked computing processors associated with each of the plurality of data processes, determining a timing sequence for routing the data processing segments for all of the plurality of data processes; and routing the plurality of data processing segments to the plurality of corresponding networked computing processors based on the timing sequence.

[0021] In accordance with another aspect, there is provided a computer-readable medium or media having stored thereon instructions which when executed by at least one processor, configure the at least one processor to: receive from one or more data sources signals representing instructions for execution of a plurality of data processes executable by a plurality of networked computing resources, the data processes representing a proposed trade in a financial interest; obtaining data associated with available liquidity of the financial interest at each of the plurality of networked computing resources; divide each of the plurality of data processes into a plurality of data processing segments, each data processing segment divided from a single data process to be routed to at least one of the plurality of networked computing processors; based at least partly on latencies in execution of prior data processing requests routed by the system to each of the plurality of networked computing processors and the available liquidity at each of the plurality of networked computing processors, determine a plurality of timing parameters, each of the plurality of timing parameters to be associated with a corresponding one of the plurality of data processing segments, the plurality of timing parameters determined to cause synchronized execution of the plurality of data processing segments by the plurality of networked computing processors; based on the timing parameters and networked computing processors associated with each of the plurality of data processes, determine a timing sequence for routing the data processing segments for all of the plurality of data processes; and route the plurality of data processing segments to the plurality of corresponding networked computing processors based on the timing sequence.

[0022] As will be appreciated by those skilled in the relevant arts, once they have been made familiar with this disclosure, coordination or synchronization of execution of distributed data processing requests by, for example, synchronized or coordinated transmission of requests for such processing, has a great many possible applications in a large number of data processing fields.

Brief Description of the Drawings

[0023] Reference will now be made to the drawings, which show by way of exam ple embodiments of the present disclosure.

[0024] FIGS. 1A, IB, 1C, 3 and 9 show exam ples of systems suitable for causing processing of data by multiple networked computing resources in accordance with various aspects of the invention.

[0025] FIGS. 2, 4, 8 and 12 show flowcharts illustrating examples of methods for coordinating processing of data by multiple networked com puting resources in accordance with various aspects of the invention.

[0026] FIG. 5 shows an exam ple histogram that may be used in an exam ple method for managing processing of data by m ultiple networked com puting resources in accordance with various aspects of the invention.

[0027] FIGS. 6A and 6B show a com parison of fill ratios using an exam ple method and system for processing of data by m ultiple networked com puting resources versus using a conventional method and system .

[0028] FIG. 7 illustrates the use of an example metric for comparing an example method and system for processing of data by multiple networked computing resources versus results of using a prior art method and system .

[0029] FIG. 10 shows a table illustrating exam ple data associated with networked computing resources.

[0030] FIG. 11A, 11B, 11C, 11D, HE show example schedules for routing data processing segments in data processing waves.

[0031] Throughout the appended drawings, like features are identified by like reference numerals.

Description of Example Embodiments

[0032] In this disclosure, as will be understood by those skilled in the relevant arts, 'synchronized' or 'coordinated' can mean according to any desired tim ing sequence, whether regular, irregular, and/or wholly or partially simultaneous.

[0033] Figure 1 shows an example of a system 100 suitable for coordinating processing of data by m ultiple networked computing resources in accordance with the invention.

[0034] In the example shown, system 100 includes one or more signal or data sources 102 (comprising one or more each of sources 102a, 102b), execution router processor(s) 104, and one or more networked computing resources, or execution processors, 106. In some em bodiments, data sources 102 may include one or more internal data sources 102a, which may comm unicate with the router 104 directly (e.g ., through private local- or wide area network(s) or other secure wireless or wireline com munication, through direct com munication channel(s) or through communication(s) within a single server). In the same and/or other em bodiments, data source(s) 102 may also include one or more external data sources 102b, which may for example communicate with router processor(s) 104 via one or more public networks 108 (e.g ., a public or private telecomm unications network such as the internet), using suitable or otherwise desired network security devices, which may for example include data encryption, etc. In the example shown, router processor(s) 104 comm unicate with each of the one or more networked execution, or computing, resources 106 via a network 110, which may be the same as or different than network(s) 108.

[0035] In various em bodiments, data source(s) 102 may include devices that provide, on behalf of one or more entities that generate trading and/or other data processing requests, signals that com municate data and/or instructions related to execution of data processing processes to router processor(s) 104, which data and/or instructions the router processor(s) 104 may process

(e.g., aggregate by summing, averaging, etc. ; and/or divide into segments, etc.) and use as bases for requests for processing of data by the networked computing resources 106. Data sources 102a, 102b may include, for example, systems, servers, processors and/or any other suitable source(s) of requests for execution of data processing tasks such as offers and/or bids for purchase of commodities, intangible financial interests, etc., and/or other data processing tasks, such as word, image, and/or other communications or document processing tasks. Each or any of data source(s) 102, processor(s) 104, and resources 106 may include multiple such systems, servers or processors.

[0036] In various embodiments, some or all of data source(s) 102 and router processor(s) 104 may be com bined, and/or otherwise configured to implement m ultiple programming or other machine instruction applications running on single machines.

[0037] Networked computing resources 106 may include any devices or other resources that communicate with router processor(s) 104 to receive and carry out any of a very wide variety of data processing requests. Such networked computing resources 106 may include systems, servers, processors or any other suitable devices adapted for execution of any processes suitable for use in implementing the invention, including, for example, processing of offers or bids for purchase of commodities, financial interests, etc., and/or other data processing tasks, such as word or document processing, image, and/or other communications or documentation tasks.

[0038] In various embodiments, the one or more data sources 102 transm it or otherwise provide to or for the router processor(s) 104 signals representing instructions, or requests, for executing data processing functions. Instructions from any given data source(s) 102 may include instructions for signal processes to be executed by any one or more networked computing resources 106. Requested signal processes may

include, for exam ple, computing operations, data manipulations, and/or communications processes or other signal exchanges, among others. In some but not necessarily all exam ples, such instructions may specifically identify networked com puting resource(s) 106 particularly targeted for execution of such processes.

[0039] Router processor(s) 104 may parse instruction signals received from one or more source(s) 102 and use such signals to prepare instructions, or requests, to be forwarded to pluralities of execution processors 106, for execution of data processing and/or other signal processes in accordance with the received instructions. Parsing of such instructions may include, for example, identifying the type of process(es) to be requested, including for example the volume or quantity of an order or bid for a trade or an amount of document processing to be done, and the type, nature, and/or identity(ies) of networked computing resource(s) 106 to be requested to execute, and thereby associated with, a given data processing and/or other signal processing request.

[0040] For example, in order to increase the efficiency of signal and/or other data processing functions, router processor(s) 104 may parse, sort, and aggregate instructions or requests received from multiple sources 102 for relatively smaller execution requests into one or more larger requests for processing, and further divide such aggregated request(s) into pluralities of smaller requests to be distributed to plurality(ies) of execution processors 106, depending, for example, on the current ability of the execution processors 106 to satisfy or complete such processed requests.

[0041] For example, m ultiple instruction signal sets received from different data sources 102a, 102b may be associated with (e.g ., addressed for delivery to and execution by) individual networked computing resource(s) 106, and such instructions may be aggregated into single signal process execution requests for such networked computing resource(s) 106. In some examples, identification of the networked computing resource(s) 106 to be tasked with a given signal processing request may be performed after the aggregating . For example, multiple instructions from different data sources 102a, 102b may be sorted or otherwise associated with a single signal or data process, and such instructions may be aggregated, and the aggregated instructions may be associated with one or more identified networked computing resource(s) 106, such that one or more signal process requests may be accordingly prepared for the identified networked com puting resource(s) 106. Such parsing, sorting, and/or identification may be performed according to predetermined rules or algorithms (e.g ., based on continuing or current processing capabilities of one or more specific networked com puting resource(s) 106), and according to requirements encoded in the instructions or otherwise provided by the originating source(s) 102, where relevant.

[0042] As a further example, single instruction sets for processing of data may be broken down by processor(s) 104 and distributed to a plurality of resources 106 for distributed execution. For example, a relatively large order for trading in one or more financial interests originating from a single source 102a, 102b, might need to be distributed to multiple exchange servers 106 in order to be completely filled ; in such cases request(s) from one or more source(s) 102 may be broken down by processor(s) 104 into suitable orders for execution by a plurality of such resources 106.

[0043] In some embodiments, the instruction sets may be received as parts of various order waves from one or more brokers. These order waves may contain instruction sets, which may be targeted to be transmitted to various venues for trading in one or more financial interests.

[0044] Targeted, or specifically identified, networked computing resources / execution processors 106 comm unicate with the router processor(s) 104 to receive the segmented signal process execution requests and may thereafter execute them accordingly. Execution of such signal processes may include, for exam ple, carrying out a text- or image-processing operation, a

mathematical computation, or a communications signal exchange, among others.

[0045] As will be readily understood by those skilled in the relevant arts, various components of system 100 may combined, or may be implemented in the form of separate systems or devices. In a wide variety of configurations, such combined or separate (sub)systems may be operated by the same or distinct entities. As a particular exam ple, one or more request source(s) 102 may be integrated with, or otherwise associated with, individual router(s) 104.

[0046] In some embodiments, the system may provide one or more intelligent order routers 104 including one or more processors that may be configured for the sequenced, prioritized, scheduled, staggered, segmentation, and/or grouped routing of orders or other data processing segments/requests related to one or more financial interests.

[0047] For example, data processing segments may be grouped in waves, and the routing of the individual segments may be scheduled, staggered, grouped, etc., based on the monitored data associated with destination networked computing resources. In some embodiments, the data processing segments may be routed such that the orders arrive at, are processed by, and/or are executed at one or more networked com puting resources within a timeframe defined at least in part on the monitored data . Figure 1C provides a sample schematic diagram depicting a number of example waves of data processing segments being submitted by Brokers X and Y to Venues 1-5, using router processor(s) 104.

[0048] In some embodiments, multiple instances of a data processing request may be routed to m ultiple exchanges with different quantities, conveyed in a determined tim ing sequence. The timing sequence may be based on timing parameters determ ined from the monitored data, and may define a timed order in which the initiation of the routing of the data processing segments should be performed .

[0049] In routing orders, the system may be configured for the determination of timing parameters, which may include the determination of timing ranges, distributions, etc. The timing parameters may be variable, adaptive, weighted, probabilistic, etc., and may also be tunable depending on various factors, such as network congestion, order load, order priority, venue characteristics, etc.

[0050] In some embodiments, the system may be configured to adapt the routing of orders (data processing segments) based on predicted order flow. For example, the system may have a large number of scheduled orders to be routed at a particular time or during a particular timeframe. Accordingly, the system may be configured to adapt the routing of one or more orders given predicted loads on networking equipment, communication channels, venue systems, routing pathways, intermediary network equipment, etc. The system may be further configured to distribute the routing of orders across various network links and/or across different timeframes in order to load-balance the routing of orders.

[0051] As described herein, the system monitors data associated with one or more networked computing resources. This data can be acquired with or received from with one or more components or devices in the system .

[0052] The system may also be configured for monitoring network data associated with one or more networked computing resources, including monitoring of network performance, monitoring of available network links, among others. Probabilistic distributions and/or models of network performance may be generated, adapted, defined and/or employed in relation to network monitoring. Network monitoring may be utilized for adapting time parameters, etc. Network monitoring may include the determination and/or monitoring of, for example, latency means, maximums, standard deviation, kurtosis, L-kurtosis, skewing, medians, variance, mode, correlations, cross-correlations, covariance, etc., and may be correlated and/or associated with other factors, such as order load, time of day (e.g., the opening of trading, the closing of trading), the occurrence of financial events (e.g., stop trade orders, corporate events, publications of statistics, publication of analyst reports, credit rating changes), the occurrence of network events (e.g., sunspots, denial of service attacks).

[0053] Network monitoring may occur, for example, through the tracking of network performance through 'heartbeats' (e.g., the transmission of ping signals, regularly scheduled transmissions, echo request packets) to measure the transmission time of signals, historical order routing performance, recent order routing flow, the sending of test messages, etc.

[0054] In some embodiments, test messages are utilized for network monitoring and the difference in timing between test messages and routed order messages may be utilized in determining various order routing characteristics, such as time required to process an order, internal latency within a venue, etc.

[0055] In some embodiments, the system utilizes various network prediction techniques in determining order load across various network links, at various venues, etc., including orders scheduled to be routed by the system itself. Execution requests may be rearranged and/or scheduled based in part on the predicted load.

[0056] In some embodiments, the system can access database(s) or may receive or monitor network messages to obtain information regarding the topology of one or more network connection(s) as well as the redundancy scheme. For example, the system may have two or more connections and/or routes to a venue. By accessing and monitoring the performance of these connections and/or routes, when a primary connection/route fails, upon detection, the system can be configured to route all subsequent orders based on latencies associated with a known secondary route/connection so as to minimize undesired timings and likely fill rates caused by the failure.

[0057] In some embodiments, when available, network monitoring can be performed on previously routed trade requests.

[0058] The system may also be configured for the tracking of the trade capture ratio, which may be determined by ratio between the sum of order liquidity as determined at a time (e.g., when a trade decision is made) and the amount of liquidity that was captured in a trade.

[0059] The trade capture ratio may be utilized for various purposes, such as adapting timing parameters, determining that a network component has failed, detecting that third parties may be intercepting orders, etc. For example, a high trade capture ratio may be indicative of reduced information leakage and/or increased fill rate, and a low trade capture ratio may be indicative of increased information leakage and/or a decreased fill rate. A low trade capture ratio may, for example, trigger an alert, a notification, adaption by the system through the application of business logic, implementation of timing parameters, modified routing strategies, etc.

[0060] In some embodiments, the system may include business logic that may be utilized in determining how orders, routing instructions, etc., may be sequenced, prioritized, scheduled, staggered, segmentation, and/or grouped. The business logic may also be utilized for route selection, route determination, route prioritization, etc. The business logic may also be utilized for decision support. In some embodiments, the business logic may be utilized to prioritize orders based on various business rules, such as rules configured to prioritize venues having order books containing larger amounts of liquidity for a particular financial security, etc.

[0061] In some embodiments, the business logic may include intelligent decision support capabilities, such as the ability to traverse decision trees, etc.

[0062] In some examples, the system may access, receive or determine (based on monitored network and order performance) information regarding

different venues. In some examples, this information may include an estimate of the message handling capabilities of venue(s). As illustrated herein, if order falls into a venue queue or processing capabilities of venue(s) are overloaded, the resulting processing delay may affect the effectiveness of timing parameters and may reduce the control the system has on limiting latency arbitrage. Accordingly, in some examples, the system can be configured to use timing parameters to avoid venue queuing or overloading.

[0063] In some examples, venue information can include physical locations of venues, distances between venues (optical cable distances or transmission times), known or likely presence of a co-located predatory trader, effectiveness or speed of the co-located trader, etc. In some examples, one or more of these factors can be used to determine timing parameters. For example, if an order wave involves a venue with a known, effective co-located trader, the timing parameters may be determined based on a lower timing tolerance/differential. In some examples, routing trades with the highest timing tolerance will occur when the trades are routed in a timing sequence resulting in simultaneous arrival or execution. In some instances, having simultaneous arrival/execution may not be optimal for changing markets, so the processor(s) may be configured to consider different tradeoffs.

[0064] The system may be configured to take into consideration the potential for third parties to impact the fulfillment of transactions and/or trades in financial securities. For example, a high-frequency trader may utilize information relating to a first order at a first venue to determine that a second order will take place at a second venue. The high-frequency trader may then make an opportunistic trade prior to the arrival of the second order, potentially impacting the price and/or quantity available to the second order. As a result, the second order may not be fulfilled or be fulfilled at a lower quantity, impacting trade capture and fill rate. As a second example, a high-frequency trader may utilize information relating to a first order at a first venue in cancelling an order at a second venue and replacing the order with a higher priced order, in anticipation of the arrival of a second order by the entity placing the first order. As a third example, a high-frequency trader may utilize information relating to a first order at a first venue in determining order information (e.g., such as the National Best Bid and Offer) that may be more up to date than the order information available at one or more venues. The high-frequency trader may then utilize this information to place opportunistic orders that cause the execution of transactions where prices and/or quantities may be suboptimal to counterparties to their orders. For example, mispriced orders being left on order books, etc.

[0065] The system may be implemented at various components of a trading network. For example, the system may be implemented at as part of broker electronic systems; as part of a network; as an intermediary gateway and message delivery service; as part of venue electronic systems, etc.

[0066] For example, if the system is implemented as an intermediary gateway and message delivery service, the system may be configured to receive from one or more brokers a plurality of orders, which may be associated with one or more order waves to one or more venues. The intermediary gateway and message delivery service may then coordinate the routing, clustering and/or segmentation of the orders and/or suborders to the one or more venues. For example, a broker may 'give-up' an order and route an order on the broker's behalf.

[0067] In some embodiments, the system is implemented at a broker level. For example, a broker may utilize the system for the routing of client orders. In some embodiments, the system is implemented at a client level. For example, a client may utilize the system for the routing of its own orders.

[0068] The system may be configured for the routing of a large number of orders, and may be designed to for scaling based on the volume of orders. Accordingly, the system may be implemented using various suitable technologies. In some embodiments, the system is a software based solution. In some embodiments, the system is an appliance based solution.

In some embodiments, the system is a combination of both software and an appliance based solution. Scalability may be important as the system may need to be able to handle a large number of waves from multiple brokers simultaneously.

[0069] In some embodiments, the system is implemented using distributed networking technologies, for example, cloud computing techniques. A potential advantage to using distributed networking technologies includes the ability to provision resources to support various instances of the systems based on order routing volumes. Resiliency and/or high availability technologies, such as failover systems, managed backups, hot/cold backup schemes may also be utilized to achieve a consistent level of service and/or uptime. For example, there may be various instances of the system that may be configured to share information among each other, which may allow for the re-establishment of lossless sessions to clients and venues in the event of a failure.

[0070] Some embodiments of the system may provide various benefits, such as increased trade capture rate, increased fill rates, reduced information leakage to third parties, reduced risk of 'moving the market', improved decision making (e.g., achieving a more optimal trade-off between order liquidity and the risk of information leakage), the ability to utilize timing information from various sources, the ability to utilize adaptive and/or probabilistic latency models, the ability to quickly and effectively determine failures in a network or on network equipment, etc.

[0071] Where the system is implemented as an intermediary gateway and message delivery service, there may be potential benefits as there is no/reduced integration required at a venue-level and/or a broker-level, and a broker may be able to utilize the system with reduced need for investment into infrastructure, there may be the ability to operate the system independently of brokers, financial institutions, clients, and/or venues. In some embodiments, existing infrastructure, messaging protocols, networks, etc., may be utilized in conjunction with the system . A reduced need for integration and/or adaptation, as well as ease of interoperation with existing standards and/or protocols may be commercially valuable in view of regulatory requirements, resiliency requirements, security requirements, and the potential expense and/or complexity required for system modifications. The ability to interoperate with existing external systems and/or protocols may lead to increased adoption and/or broker flow.

[0072] In addition or alternatively to determining timing parameters for routing requests to m ultiple venues, in some em bodiments, aspect(s) of the system may be configured to determ ine timing parameters for routing multiple requests to the same venue (i .e. in waves or otherwise) .

[0073] An exam ple of an application of a system 100 for distributed execution of segmented processing requests in accordance with the invention is provided by a financial system 1000 adapted for processing of requests for processing of data representing trades and/or offers for trades, or other transactions, in tangible and/or intangible financial interests such as stocks, bonds, currencies (e.g ., foreign exchange), various forms of natural resources or com modities, options, loans, etc. As shown in Figures 1A and IB, for example, in a financial transaction data processing system 1000 in accordance with the invention, signal or data source(s) 102 may include trader system (s) 1102, which may, for example, include trader/broker systems or servers as well as any other sources of bids, offers, or other transactions in financial interests such as currently provided by known financial trading platforms. In various embodiments, such trader systems 1102 may be referred to as order origination systems.

[0074] Order origination systems 1102, 102a may include systems operated by or on behalf of, for exam ple, entities owned or otherwise controlled by parent or other controlling organizations such as banks or brokerage houses. Order origination systems 1102, 102b may, for example, include systems operated by or on behalf of brokers or other trading entities acting on behalf of, for exam ple, individual investors, trading through or with the assistance of independently-controlled banks, institutional investors, and/or other brokerage houses.

[0075] Router processor(s) 104 in such embodiments may include, for example, server(s) or other system(s) 1104 that comm unicate with trader systems 1102, 102, for example through the receipt and transmission of encoded electronic signals representing requests for processing of data representing execution and/or acknowledgement of transactions in financial interests; and which communicate with broker, exchange, or other market systems or execution processor(s) 1106 for execution of such transactions. In such embodiments a processor 104 may be referred to as a Smart Order Router or Tactical Hybrid Order Router (in either case, "SOR") 1104, 104. An SOR 1104 may, for example, include one or more gateway(s) 1122 and/or router(s) 1124 for facilitating com munications by router(s) 1104 with one or more trader systems 1102, 102 directly (e.g ., through wired comm unication, using one or more dedicated comm unication channel(s), or through communication within a single server) and/or indirectly (e.g. , through wireless communication, through a network 108, 1108 or through an intermediate server). Exchange or market systems 1106, or other execution processor(s) 106 may be in communication with SOR(s) 1104 through, for exam ple, a network 110, 1110, such as the internet or other public network, which may be the same as the network 1108.

[0076] For an embodiment of a system 100 configured as a financial trading or order execution system 1000, requested and executed signal processes provided by source(s) 102 may represent trades or other transactions in financial interests. Such transactions may include, for exam ple, trades and/or offers for trades, or other transactions, in financial interests such as stocks, bonds, currencies (e.g . , foreign exchange), various forms of natural resources or commodities, options, loans, etc. ; and networked computing resources 106 may be, for example, exchange servers 1106, examples of which may include automatic or electronic market systems.

[0077] As will be well understood by those skilled in the relevant arts, an SOR (sub)system, or processor, 1104 receiving such transaction request signal sets can apply a wide variety of processes to the request(s). For example, where the signal sets represent requests for transactions in financial interests, requested transactions can be aggregated, either over time and/or across multiple transaction request sources 1102; and/or processing requests for transactions in one or more interests can be divided for routing to multiple execution handlers or processors 1106, individually or in batches.

[0078] The signal sets representing requests for transactions in financial interests, for example, may be FIX sessions as received from client brokers. As examples, two order types or order instructions that may be accepted include immediate or cancel (IOC) orders, and directed single orders.

[0079] In the context of IOC orders - a broker could send in a set of signals representing orders that the broker intends to be sent out as a single wave to a set of exchanges. Where the orders are always IOCs, many other functions are simplified as there may be no need for the handling of subsequent cancels or replaces. Accordingly, IOC waves may not need to maintain affinity and may be free to use available exchange sessions to best manage congestion.

[0080] In the context of directed single orders, a broker client can use the system to send directed orders to exchanges, taking advantage of network connectivity and exchange sessions. This service can be used by a broker as a primary order gateway to exchanges or as a backup service (reducing their needs for backup connectivity to individual exchanges) .

[0081] In some exam ples other order types such as day orders, good till cancelled orders, or any other order type may be used but may require additional tracking to maintain order affinities and results, and can involve sending and receiving multiple messages (e.g . replace/modify/cancel orders and acknowledgements) to and from the different venues.

[0082] In some em bodiments, using direct or immediate-or-cancel orders, may in some instances allow the SOR system to use a more efficient messaging process which may reduce order messaging loads and/or processing, and/or increase messaging throughput.

[0083] In various embodiments, as described herein, order source(s) 102, 1102 can be implemented together with, or as part of, order router(s) 104, 1104. It will be readily understood by those skilled in the relevant arts that any or all of the various com ponents of system(s) 100, 1000, including for example any or all of processor(s) 102, 104, 106, and methods of operating them in accordance with the disclosure herein, may be implemented using any devices, software, and/or firmware configured for the purposes disclosed herein. A wide variety of components, both hardware and software, as well as firmware, are now known that are suitable, when used singly and/or in various com binations, for implementing such systems, devices, and methods; doubtless others will hereafter be developed .

[0084] Examples of components suitable for use in im plementing exam ples of systems 100, 1000, and the various processes disclosed herein, including for example processes 200 of Figure 2 and 300 of Figure 4, include, for example server-class systems such as the IBM x3850 M2™, the HP ProLiant DL380 G5™ HP ProLiant DL585™, and HP ProLiant DL585 Gl™. A wide variety of other processors, including in some embodiments desktop, laptop, or palm model systems will serve.
1104 adapted to process signals representing requests for execution of trades and/or other transactions in financial interests received from transaction signal source(s) 1102, signal sets representing requests for execution of transactions in one or more financial interests can include signals or signal sets representing, for example, one or more identifiers representing :

• the source(s) of the request, such as a URL or other network address or identifier used by or otherwise associated with a trading system 102, 1102;

• the interest(s) to be traded or otherwise transacted, such as an identifier used by one or more exchanges to identify a stock, a CUSIP number for a bond, a set of currencies to be exchanged, etc. ;

• a type of transaction (e.g., buy, sell, bid, offer, etc.) to be executed or requested;

• one or more quantities (i.e., amounts or volumes) of the interest(s) to be transacted (including for example any total and/or reserve quantities); and

• corresponding price terms.

[0087] Further parameters can include, for example, current and/or historical :

• fill probability for multi-part, or segmented, transaction requests (i.e., the historical proportion of multi-part orders that result in completed transactions);

• amounts of spread between, for example, bid and offer prices, e.g., current and/or relative to historical trends in spread;

• market volatility in specific interests to be traded, or related or corresponding interest(s), or related benchmarks or indexes;

• depth of market book(s), for example current depth relative to historical trends in depth;

• reserve quantities;

• order priority;

• tolerance for information leakage;

• maximum latency period (which may, for example, constrain a maximum size for an associated timing parameter);

• desired routing path;

• desired venue;

• specific routing instructions;

• display quantities; and

• display size and backing, for example on buy and/or sell sides.
[0094] Signal sets received by processors 104, 1104 at 202 can be stored in any volatile and/or persistent memory(ies), as appropriate, for archival and/or further processing purposes.

[0095] At 204, transaction or other data processing execution requests received at 202 can be parsed by router processor(s) 104, 1104 to place them in any suitable or desired form for use in preparing one or more instruction signal sets to be provided to execution processor(s) 106, 1106. Parsing of instruction signals may include, for example, identifying the type of transaction(s) or process(es) to be requested, including for example volumes and/or quantities of orders or bids for trades in specified interest(s), and whether such volumes are to be bought or sold, or offered for sale or purchase; amounts and/or types of document processing to be done; and the type and nature of networked computing resource(s) or execution processor(s) 106 to be requested to execute and thereby be associated with such execution or processing instructions. In various embodiments parsed instruction sets can be stored in temporary or volatile memory(ies) 118, 1018 accessible by the corresponding processor(s) 104, 1104 for aggregation with other processing requests, division for routing to multiple execution processors / resources 106, 1106, and/or preparation and forwarding of batch or other delayed-execution requests.

[0096] In preparing one or more instruction signal sets, an order scheduling module may be utilized that may be configured to handle downstream capacity, queuing, and congestion in the network, exchange gateways, and exchange crossing engine queues.

[0097] The module may be configured to anticipate the maximum message/order flow rate that each exchange session can handle, and then pro-actively avoid exceeding or com ing close to that rate. The SOR router processor(s) 1104 may be configured to re-sequence or delay sending out the early orders in a wave, if it calculates subsequent orders in the wave or in a following wave could become congested or queued.

[0098] The module may be configured with a feedback mechanism (based on ACK round trip latency monitoring) to dynamically determine downstream congestion and queuing delays as these may not be static, consistent, or predictable.

[0099] To handle these capacities, the SOR router processor(s) 1104 may be configured to access exchanges using multiple exchange order sessions and may also be configured to load balance the flow across the order sessions.

[00100] In some embodiments, instruction sets may consist of FIX messages having built-in mechanisms such as repeating groups having groups of order info stored as multiple constituents within a single message. These messages may often be used to convey waves of baskets - for example, to represent orders of the same stock at different venues, or to represent a portfolio that wants to buy a set of 38 stocks, multiple instances of the same stock order may be transmitted to multiple exchanges with different quantities, conveyed in unison with the SOR router processor(s) determ ining scheduling using timing.

[00101] Instructions received at 202 may be accumulated during defined time intervals, regular or irregular, such as the duration of a business day or any segment thereof, or any other desired time period(s), which may be preset and/or dynamically determined by processor(s) 104, 1104. Instructions may be also be processed individually, as received . If more instructions are to be received prior to processing, or may potentially be received, process 200 can return to 202.

[00102] Transaction requests / instructions may be accumulated during defined time intervals, such as the duration of a business day or any segment thereof, or a desired time period, which may be preset and/or dynamically determined by processor(s) 104, 1104. If more instructions to be received, or may potentially be received, process 200 can return to 202.

[00103] In embodiments of the invention which em ploy sorting / aggregation techniques in parsing or otherwise preparing order or other processing requests, at 206 processor(s) 104, 1104 can repeat process 202 -204 until all needed or desired related or aggregatable processing request signal sets have been received from source(s) 102, 1102. For exam ple, as described above, arbitrary num bers of data records representing orders or requests for purchase of bonds identifiable by CUSIP (Committee on Uniform Security Identification Procedures) numbers can be received from data source(s) 102, 1102, and stored in memory 118, 1018 associated with the processor(s) 104, 1104, for batch processing, for example :

< 10,000 >< price Ax res. 9,000> < buyxCUSIP No. BBx l2,000xprice Cxres. l,000xprice B> < buyxCUSIP No. AAx 18,000 >< price Cxres. 7,000xprice B> < price Ax res. 6,000> < buyxCUSIP No. BBx22,000xprice Cxres. 4,000xprice B>

< price Ax res. 3,000>

[00104] In another example scenario, processor(s) can receive individual large execution instructions from data source(s) 102, 1102. For example :

[00105] In another example scenario, as illustrated by example in Fig . 1C, processor(s) can receive order execution instructions in waves. As illustrated in the example instructions in Fig . 1C, in some examples, the incom ing execution instructions may also include specific venues fields to identify the networked resource or venue to target.

[00106] Upon individual receipt, or at a given periodic rate, a given time, when a given number of orders has been received, when all desired orders have been received, or when any other desired criteria have been satisfied, processor(s) 104, 1104 can, as a part of parsing or otherwise processing instructions at 204, sort and/or group the stored records according to any one or more desired criteria, e.g ., by type of transaction request and interest identifier. For exam ple, in the first example scenario above:

< buy>

< price Bxsource 4>

As shown, various data fields in the transaction request records can be reordered or otherwise reformatted as needed or desired, to suit the processing needs of the routing processor(s) 104, 1104. For example, as shown, the association of a "source" data item associated with or otherwise accorded a different priority, to facilitate efficient ordering while permitting the processor(s) 104, 1104 to report fulfillment of transactions / requests on completion of order processing.

[00107] Process 204 can further include aggregation by processor(s) 104, 1104 of received and sorted transaction requests, into collected or consolidated order(s) for specific types of transactions in specific interest(s), e.g., by summing total or subtotal quantities associated with corresponding transaction requests, thus:

[00108] Fig. 9 shows a process and dataflow diagram of example processes and dataflows for an order router/system 104, 1104. In some examples, the order router/system 104, 1104 may include a client message processing module 2105 whereby processor(s) 104, 1104 and/or other hardware components can be configured to receive waves of client messages. In some examples, client order instructions may be received in waves of multiple messages. In some such examples, the client message processing module may be configured to stage order messages in a buffer or other order wave staging memory/storage device 2110 until all order messages pertaining to a particular wave are received.

[00109] In one example, a data record to be provided by a source 102, 1102 to request a transaction in a given interest, on stated terms, can, in addition to other fields containing transaction request details, include:

[00110] In another exam ple, instead of including a data field indicating the total num ber of orders in wave, a data record may include a flag to indicate that the current order message is the last message in a wave.

[00111] In some examples, the incom ing order messages can be in a FIX (Financial Information exchange) format, or any other standard or proprietary form at/ protocol .

[00112] As illustrated in the example in Fig. 9, once individual orders or complete waves have been received, they may be stored in one or more buffers or other memories/storage devices until they are ready to be processed by a wave timing handler 2115. In the exam ple system in Fig . 9, the com plete order requests (including individual orders and complete wave orders) may be stored in multiple levels or queue. For example, order requests may be initially be stored in a client session queue until they are selected to be moved to a wave queue. In some examples, the system is configured to only allow N waves to be present in the wave queue. In some instances, this may prevent a high volume session from dominating the wave queue and routing resources. In some examples, the system can be configured to allow more than N waves into the wave queue when other session queues have not used up their allotment.

[00113] In other exam ples, complete order requests may be stored directly into one or more wave queues (i.e. without any intermediary client session queue).

[00114] The wave timing handler 2115 can be implemented by one or more hardware device(s) and/or processor(s) 104, 1104 configured to manage queue(s) and determ ine which order(s) in the queue(s) will be transm itted to the venues next.

[00115] In some exam ples, a modified first-in, first-out (FIFO) approach may be used by the wave timing handler. However, rather than a strict FIFO, in some embodiments, the timing handler may be configured to rearrange, cherry-pick, skip, or otherwise select the order(s) in a sequence other than simply traversing the queue. For example, in some embodiments, the first M wave orders in the queue can be processed to determine a sequence for routing those M wave orders, or simply to determine which of the M wave orders will be routed next irrespective of the subsequent ordering or otherwise.

[00116] In some examples, the wave timing handler can be configured to determine a sequence in which to route the wave orders to increase the out of available order routing throughput, to decrease the period of time required to route all of the orders, and/or to optim ize any other operational aspect of the router(s) 104, 1104.

[00117] In some examples as described herein or otherwise, the ordering of the processing of waves can be based on venue latencies, throughputs, market data (e.g. available liquidity, pricing, etc.), etc.

[00118] When all desired signal sets have been received at 202, and optionally sorted, accum ulated, and/or otherwise processed at 204, at 208 processor(s) 104, 1104, using instruction sets processed at 204, can prepare execution-request signal sets for transmission to resources / execution processors 106, 1106. Such execution-request signal sets can comprise any necessary or desirable signals for causing requested processing, including content or data and command signals. For example, in embodiments adapted for processing of requests for transactions in financial interests, requests may be sorted and/or aggregated on the basis of interest(s) to be traded, quantities of interest(s) to be traded, price, etc., and associated with suitable execution command signals. The form of any execution command signals associated with a given request can depend, as those skilled in the relevant arts will recognize, on the nature and type of requests to be executed and the processors 106, 1106 by which they are to be executed, as well any networks 110, 1110 over which signals exchanged between processor(s) 104, 1104 and 106, 1106 are to be sent, including applicable protocols and instruction formatting requirements. Ergo, data pertaining to any or all of systems 106, 1106, 104, 1104, and 110, 1110, protocols used thereby, and/or information related to interests traded, offered, or described thereby may be accessed and used by processor(s) 104, 1104 in parsing and preparing instructions for execution of processing by any of processors or resources 106, 1106. Sources 1126 of such data may include, for example, exchange market data system 1126v (Figure IB) which, for exam ple, in embodiments of the invention adapted for processing of financial transactions, can include information received from various exchange systems 1106, news information sources such as Bloomberg or Reuters, and/or other sources.

[00119] It is sometimes necessary or desirable, in assembling requests for data processing using networked processing resources, including many resources configured for use in executing financial transactions, to break execution and/or other processing requests into multiple parts. Such parts, or segments, can, for example, correspond to portions of larger orders or other data processing requests, to be executed by a plurality of networked resources 106 such as exchange servers or other execution processor or handlers 1106. For example, if a plurality of exchange servers or other markets are available for execution of a transaction request representing a purchase order for a significant amount of a financial interest such as a stock or bond, it may be necessary or desirable to split the order into m ultiple parts, for execution in multiple markets and/or by multiple exchange servers 1106. For exam ple, sufficient quantities of specific interests may not be available, at all or at desirable prices, on a single exchange: in order to fill an order entirely, it may be necessary or desirable to break a single order into smaller segments and route it to multiple exchanges.
[00151] In some examples, the processor(s), at 208 and/or 210, may be configured to select venues to target and/or adjust timings based on other factors as described herein or otherwise. In some examples, the processor(s) to select venues and/or adjust timings based on data associated with a venue. This data can, in some examples, be accessed from a data source and/or can be compiled or observed based on capture ratios, latencies and the like.

[00152] In some exam ples, the processor(s) may be configured to prefer venues or to have longer tim ing differentials for venues which are less likely to have more com petition for trade requests (e.g. by a co-located and/or high-frequency trader). In some examples, the processor(s) may be configured to have different preferential rankings or timing adjustments for different venues based on factors which may be indicative of greater competition or a greater risk of losing trades to arbitrage. In some exam ples, explicit knowledge of an opportunistic co-located trader can be accessed from a data source or can be im plicitly determined based on lower capture ratios and/or lower timing differentials involving the particular venue.

[00153] In some examples, the processor(s) may be configured to have different preferential rankings or timing adjustments for venues based on the fee and/or rebate structure of the venue. For exam ple, venues which provide rebates for posting certain trades may be more likely to have competition from high-frequency traders.

[00154] In some exam ples, when m ultiple venue options are available, the processor(s) may be configured to select venues which are less likely to have greater competition. In some examples, the processor(s) may be configured to generate timings which may risk losing liquidity from the venue with greater com petition (e.g . scheduling it later in the sequence or with a greater delay) if it increases the chances of capturing liquidity at other venue(s). In some examples, the processor(s) may generate tighter timings for waves involving venues with greater competition.

[00155] In some examples, the processor(s) can be configured to select venues and/or adjust timing differentials based on the lost liquidity or lost gross value which would be suffered if a trade request were to arrive too early or too at the venue. This may be, in some examples, based on posted liquidity, posted bid/ask pricing, etc. The processor(s) may be configured to lower the preferential ranking or tighten the timing differential for venue(s) which would result in a greater losses (liquidity or value) if a trade request to the venue(s) were to arrive too early or late resulting in potential arbitrage losses.

[00156] In some embodiments, the SOR router processor(s) 1104 may be configured to automatically adjust the value of timing parameters and/or the size of timing ranges in response to various measured values provided as feedback, such as a trade capture ratio, a recent fill rate, etc. For example, a decreasing trade capture ratio may be indicative of information leakage, and ranges for timing parameters may be automatically tightened and/or timing parameters modified in response.

[00157] For example, in addition to latency and other parameters, the SOR processor(s) can be configured to monitor the fill rate or capture ratio of previous orders routed along a particular a route or to a particular venue. In some examples, the processor(s) can be configured to determine fill rate or capture ratio by comparing the total transaction volume for all routed related-transaction requests with available liquidity. In some examples, the available liquidity can be the total posted liquidity at each of the targeted venues. In some examples, the available liquidity may be the total posted liquidity at any venue. In some examples, the available liquidity may be based on the posted liquidity data available to the SOR processor(s) at the time that the processor(s) prepare execution requests which may include selecting venues to target and/or splitting a large order request. In some

examples, the available liquidity may be based on the posted liquidity data available to the SOR processor(s) when the first transaction request in a wave or sequence is routed.

[00158] In some embodiments, available liquidity can include publicly posted liquidity and/or forecasted liquidity. The processor(s) monitoring the data associated with the networked computing resources can be configured to monitor whether publicly posted liquidity that is captured by a previously routed data processing segment is subsequently replenished with new publicly posted liquidity and/or the timing between the capturing of the original liquidity and the re-posting of the new liquidity. The preparation of data processing segments and determination of their timing parameters can be based on this forecasted liquidity data.

[00159] In another example, the processor(s) may track or identify when multiple data processing segments (unrelated or related to the same data process) target the same posted liquidity. By monitoring data associated with these data processing segments to see if their fill rates exceed the posted liquidity, the processor(s) can compile this data as captured unposted liquidity data.

[00160] Monitoring the data associated with networked computing resources can include monitoring both the re-posting of liquidity and the capturing of un-posted liquidity as forecasted liquidity data which can be used by the processors to prepare data processing segments and their timing parameters to target such forecasted liquidity data.

[00161] In some examples, the processor(s) can be configured to determine the fill rate or capture ratio by compiling the confirmations/acknowledgements or other response messages from the targeted venues to determine the total transaction volume that was successfully captured/completed after all the trade requests in a wave have been routed.

[00162] In some examples, the processor(s) can be configured to adjust timing parameters and/or timing sequences based on the determined fill rate or capture ratio. For example, if a capture ratio for a route or venue falls from its previous, historical and/or typical value, the processor(s) may determine that the timing parameters and/or sequences (e.g. based on latencies and/or other factors) are less effective at avoiding any information leakage or arbitrage trades.

[00163] In some examples, when a capture ratio falls below a defined threshold or by more than a threshold amount, the processor(s) can be configured to adjust timing parameters (e.g. lower or increase time Y or time Z in Fig. 3), adjust timing sequences and/or lower timing threshold(s) for differences in execution times at different venues (e.g. lower Time = A and/or Time = B in Fig. 3).

[00164] In some examples, when a capture ratio falls below a defined threshold or by more than a threshold amount, it may be indicative of a change in a network device such as failing or close to failing link or component in a router or other switching device. In some examples, the processor(s) can be configured to generate an alert or notification that there may be a potential network issue and/or equipment problem. The alert or notification may include audio alerts, visual alerts on a display or light source, message(s) (e.g. email, instant messaging, etc.), or any other mechanism for alerting an administrator that there may be a network issue and/or equipment problem. In some examples, it has been observed that a dropping capture ratio can provide an early warning of an equipment problem or imminent failure before the router or other affected equipment generates its own warning or failure notification.

[00165] In some examples, the defined threshold or threshold amount may be absolute or relative values. For example, the processor(s) may be configured to adjust timing parameter(s), sequence(s) and/or threshold(s) when the capture ratio falls 2% or falls 2% of its previous or historical value. [00166] In another exam ple, the processor(s) may be configured to adjust tim ing parameter(s), sequence(s) and/or threshold(s) when the capture ratio falls outside its typical variance range or varies by a defined number of standard deviations from its norm (e.g . when the capture ratio is 1-3 standard deviations away from the norm).

[00167] In some exam ples, the processor(s) can be configured to determine the variance range and/or standard deviations by collecting capture ratios over X time periods and building a Gaussian or other distribution .

[00168] Upon determining that a capture ratio for a route/venue or a pair or group of routes/venues is below defined threshold or has fallen by a defined threshold, the processor(s) can be configured to automatically adjust tim ing parameter(s), sequence(s) and/or timing differential threshold(s) for the associated route(s) and/or venue(s) to try to increase the capture ratio for future trade request waves or sequences.

[00169] In some examples, the processor(s), at 208 and/or 210, may be configured to select venues to target and/or adjust timings based on other factors as described herein or otherwise. In some examples, the processor(s) to select venues and/or adjust timings based on data associated with a venue. This data can, in some examples, be accessed from a data source and/or can be compiled or observed based on capture ratios, latencies and the like.

[00170] In some exam ples, the processor(s) may be configured to prefer venues or to have longer tim ing differentials for venues which are less likely to have more com petition for trade requests (e.g. by a co-located and/or high-frequency trader). In some examples, the processor(s) may be configured to have different preferential rankings or timing adjustments for different venues based on factors which may be indicative of greater competition or a greater risk of losing trades to arbitrage. In some exam ples, explicit knowledge of an opportunistic co-located trader can be accessed from a data source or can be im plicitly determined based on lower capture ratios and/or lower timing differentials involving the particular venue.

[00171] In some examples, the processor(s) may be configured to have different preferential rankings or timing adjustments for venues based on the fee and/or rebate structure of the venue. For exam ple, venues which provide rebates for posting certain trades may be more likely to have competition from high-frequency traders.

[00172] In some exam ples, when m ultiple venue options are available, the processor(s) may be configured to select venues which are less likely to have greater competition. In some examples, the processor(s) may be configured to generate timings which may risk losing liquidity from the venue with greater com petition (e.g . scheduling it later in the sequence or with a greater delay) if it increases the chances of capturing liquidity at other venue(s). In some exam ples, the processor(s) may generate tighter timings for waves involving venues with greater com petition.

[00173] In some exam ples, the processor(s) can be configured to select venues and/or adjust timing differentials based on the lost liquidity or lost gross value which would be suffered if a trade request were to arrive too early or too late at the venue. This may be, in some exam ples, based on posted liquidity, posted bid/ask pricing, etc. The processor(s) may be configured to lower the preferential ranking or tighten the tim ing differential for venue(s) which would result in a greater losses (liquidity or value) if a trade request to the venue(s) were to arrive too early or late resulting in potential arbitrage losses.

[00174] In some em bodiments, the router processor 1104 may be configured for optim izing order flow across multiple routes and for multiple orders. For example, increasing fill rate for a single route through a particular routing scheme may disadvantage other routes. The router processor 1104 may be configured to balance the order loads across venues, communication links, network links, time, etc. The router processor 1104

may be configured to determine potential latency impacts that may arise due to congestion caused by the router processor 1104's scheduled routing, and schedule and/or rearrange and/or associate timing parameters accordingly. In some embodiments, the router processor 1104 may be configured to automatically retune and/or shuffle scheduled transmissions.

[00175] In some em bodiments, the router processor 1104 may be configured for the application of one or more business rules representing business logic. The business rules may cause the skewing and/or weighting of various factors used in determining routing characteristics for a particular instruction set associated with an order. For example, the rules may determine how an order is segmented, which venue(s) an order may be transm itted to, the acceptable range of time, etc.

[00176] For exam ple, a particular venue having a particular latency may be favored as the venue may have a high trade capture ratio associated with it, and a higher weight may be associated with the routing of signals to that particular venue.

[00177] As another example, a first venue may provide a deeper order book having more liquidity than a second venue. While the first venue may have some less desirable network characteristics, it may be weighted more heavily for selection given the amount of liquidity that is available.

[00178] In some example embodiments, SOR processor(s) can be configured to determine venues to target as well as the timing parameters for those venues by weighing liquidity and latency variance.

[00179] In some exam ples, latency variance may be caused by factors such as network traffic, poor or varying signal quality of network communications, network node problems, congestion or overloading at the venue (networked execution processor(s)) and the like.

[00180] In some examples, latency variance may be caused by intentionally introduced delay(s) by a venue or aspects of a venue's

communication network or device(s). For example, a venue may randomly introduce a delay (e.g. a "speed bump") to order requests of a particular type, or to all orders. This delay will increase the latency for execution of the affected order request. In some examples, venue(s) may introduce a fixed delay (e.g . 350 ps) to order requests. In some exam ples, venue(s) may introduce a delay having a randomly-selected length (e.g. between 5-25 ms) to order requests.

[00181] In some examples, the order requests to which delay(s) are applied and/or the length of the delay(s) may be randomly selected, may be based on the order type, may be based on order para meters/options/flags/etc. , and/or any combination thereof.

[00182] In some examples, venue(s) may introduce delays having a randomly-selected lengths which are selected from a ranges based on type/parameter/options/flags/etc. of the order requests. For exam ple, one classification of orders may be subject to random delays between 1- 10 ms while another classification or orders may be subject to random delays between 14-24 ms.

[00183] Data associated with previously-routed data processing segments which may have been subject to such random delays that are monitored by the processor(s) can, in some instances, show a m ultimodal distribution illustrative of the 1- lOms range and the 14-24ms range.

[00184] In some examples, the processor(s) can be configured to access defined aspects of intentionally applied delays from one or more database(s) or other data source(s) . In some exam ples, aspects of intentionally applied delays may be observed by tracking historical orders and their respective processing times. Based at least in part on access and/or observed delay information, in some exam ples, the processor(s) can be configured to determine latency ranges, variances, and other statistical values for the latencies.

[00185] Data associated with previously-routed data processing segments which may have been subject to random ly introduced delays that are monitored by the processor(s) can, in some instances, show a multimodal distribution illustrative of a range of latencies without the random delay, and a second range of latencies including the random delay.

[00186] In some examples, the processor(s) can be configured to obtain data associated with the available liquidity of a proposed trade for each networked computing resource (venue) which may potentially be used .

[00187] In some examples, the data associated with available liquidity may be received from publicly available sources and may include the number of shares openly available at a venue. In some examples, the processor(s) can be configured to determine whether there may be more liquidity than what is openly posted (e.g. iceberg or reserve volume) . This determination may be based on data obtained for historical or past orders placed with the venues for similar financial interests, volumes, times of day, counter-parties and/or any other order data.

[00188] At 208 and 210, the processor(s) can be configured to divide a large proposed trade into smaller data processes representing smaller trade requests to different venues or other networked execution processors based on the latency and liquidity data . In some examples, the processor(s) can be configured to select venue(s) and volumes based on liquidity data and latency variance.

[00189] In some exam ples, where a venue has a large available liquidity but a large latency variance, the processor(s) may be configured to balance the chance of capturing that liquidity with the chance of losing liquidity at other venues due to market moves during the potential range of latency periods.

[00190] For exam ple, in a hypothetical situation : venue A has 5000 shares available at price X, and has a latency of ΙΟΟμε +/- 80 ps; and venue B has 500 shares available at price X, latency δθμε +/- 1 \ S. If the SOR is attempting to capture all 5500 shares, the processor(s) may be configured to route a first transaction request to venue A at t = 0, and a second transaction request to venue B 20 ps later. If the transaction requests arrive or are processed in accordance with the mean latency times, the transactions should capture the available volume at both venues at t = 100 ps. However, since the latency variance of venue A is so large, it is possible that the trade at venue A could execute at t = 20 ps, and before the trade executes at venue B at t = 100 ps, the price or availability of the 500 shares at venue B may have changed. Similarly, if the trade at venue A does not execute until t = 180 MS, the order at B which executed at t = lOOps may have triggered the market to change or cancel the order at A before the request is processed at t = 180 ps.

[00191] In some examples, the processor(s) can be configured to select networked computing resource(s) / venue(s) based on a liquidity to latency variance ratio. When the available liquidity at a venue is high, the processor(s) may be configured to target that liquidity even if it has a high latency variance. In some example scenarios, this may involve sacrificing or risking the loss of liquidity at another venue.

[00192] For example, in the above situation, based on the liquidity/latency variance ratio or other metric(s) of venue A versus venue B, the processor(s) may be configured to determine timing parameters which create a higher probability that the liquidity at venue A will be captured. For example, assuming that a competing trading system requires 50 ps to observe an order at venue B and change the order at venue A, the processor(s) may be configured to determine timing parameters and/or a timing sequence such that an order request is sent to venue A at time = 0 and an order request is sent to venue B after time = 51 ps. In this way, if the order at venue A executes at the slowest end of the range at 180 ps, and the order at venue B executes at the fastest end of the range at 130 ps (79 MS latency, 51 ps delay), accounting for the competing trading system time threshold of 50 \ S, the order at venue A will be able to be filled without any risk of arbitrage from the order at venue B. As illustrated by this example, the processor(s) may be configured to target venue(s) having large latency(ies) if they have large available liquidities, even if this may risk losing orders at other venue(s) having smaller available liquidities.

[00193] In this example, the processor(s) were configured to take the worst case scenario; however, based on risk tolerances, the processor(s) may be configured to select more aggressive timing parameters and sequences.

[00194] In some examples, the processor(s) may be configured to select target venues based on liquidity and latency variances. In a first scenario, if a large proposed transaction can be captured by completely avoiding venue(s) having large latency variance(s), the processor(s) can be configured to select and route trade requests to the venue(s) without the large latency variances.

[00195] In some examples, the processor(s) may be configured to split the large proposed transaction between a venue with large latency variance and other venue(s) with smaller latencies.

[00196] If a venue has a large latency variance caused by intentional or engineered delays, in some examples, the processor(s) may be configured to target the liquidity at the venue by sending multiple orders to the same venue in a timing sequence. For example, if a venue processor applies a randomly-assigned delay that is large relative to typical order execution latencies, in some examples, the SOR processor(s) can be configured to detect that a previously sent order has been delayed and may send one or more subsequent orders in the hope that one of the orders will be processed sooner. In some examples, the processor(s) may be configured to detect that a previously sent order has been delayed by observing order confirmations or response messages indicating that the subsequent orders to the same venue have been processed.
[00224] Thereafter, routing processor(s) 104, 1104 can process the transaction segments by using timing parameters, e.g ., delays X', Υ', Ζ', to cause the corresponding transaction segments to be transmitted or otherwise provided to the exchanges 106, 1106 Al, B2, C3 for execution according to a desired timing sequence, for sim ultaneous or otherwise-desired sequential execution.

[00225] Following execution of all or as many portions of routed transaction or processing segments, routing processor(s) 104, 1104 can

receive from corresponding execution processor(s) 106, 1106 data confirming or otherwise indicating such execution, and by accessing data records stored in associated memory(ies), can allocate execution results to the requesting source(s) 102, 1102.

[00226] Reference is now made to FIG. 4, showing an example of a method 300 of determining tim ing parameters to be used in managing processing of data by multiple networked computing resources 106. In the embodiment shown, method 300 is an iterative method, and each loop of the method 300 is denoted as N. Method 300 is suitable for im plementation using, for example, any of various embodiments of systems 100, 1000 and components thereof, including particularly router processor(s) 104, 1104 and data source(s) 1126.

[00227] At 302, each of a plurality of networked computing resources 106, 1106 is monitored, for example by router processor(s) 104, 1104, execution processor(s) 106, 1106, external processor(s) 1126, and/or various components or modules operated by or otherwise associated therewith, for latencies associated with receipt and/or execution of signal processing execution requests. This may be carried out, for example, by a monitoring module (e.g ., an exchange RTL measurement module 1126b, such as for the financial system 1000) in the router processor(s) 104, 1104. Such monitoring may com prise, for exam ple, time stamping outgoing requests for processing of data, and com paring times of receipt of confirmation(s) or results from processing to the corresponding time-stamped outgoing request. The difference in time between the outgoing request and the incoming receipt confirmation and/or data processing results can be defined as a data or signal processing latency, and stored in memory accessible by the router processor(s) 104, 1104. By tim ing differences between outgoing requests and incoming receipts, confirmations, and/or results, such latencies can be monitored on a continual, periodic, and/or other dynamic basis.

[00228] At 306, at least one timing parameter associated with latency(ies) observed in execution of signal processing requests provided to the monitored resources 106, 1106 by the routing processor(s) 104, 1104 is determined . As described herein, such timing parameter(s) may include, for example, latencies due to comm unication delay, such as transmission delays or other signal propagation delays, and/or processing delays, among others. Typically, corresponding tim ing parameter(s) are determ ined for each of the plurality of networked computing resources 106, 1106 to which a transaction order or other data processing request, or a portion thereof, is expected to be sent by routing processor(s) 104, 1104.

[00229] In various embodiments, such as in various forms of financial systems 1000, and depending upon the types of system(s) to be used and desired processing results, such timing parameters may be determ ined for one-way and/or round-trip communications between the routing processor(s) 1104 operated by or on behalf of a capital management entity and the exchange server 1106; that is, from generation of a multi-part transaction request by capital management entity's routing processor 1104 to the receipt of a response, such as confirmation of receipt of a part of a larger trading order and/or confirmation of execution of all or part of a requested trade, from the execution resource to which the processing request was directed . With reference to FIG. IB, for example, and explained above, an RTL measurement may include latencies due any or all of transmission of signals within the capital management entity server 1104, processing of signals within the capital management entity 1104, transmission of signals between the capital management entity 1104 and a network 1110, transmission of signals within the network 1110, transmission of signals between the network 1110 and the targeted exchange server 1106, and processing of signals within the exchange server 1106; for both com munications sent from the routing processor(s) 104, 1104 and responses (e.g ., acknowledgement of communication, rejection of a trade request, confirmation of a trade request, etc.) sent from the exchange server 106, 1106. In such embodiments, the tim ing parameter(s) may be simply the total time for the round-trip communication, or a statistical or other mathematical function thereof.

[00230] For example, an exchange RTL measurement module 1126b, such as that associated with SOR 1104 shown in FIG. IB, may determine a tim ing parameter as follows:

1) A time-stamp value Tl is associated by the processor(s) 1104 with a new comm unication M l (e.g ., a trade request) sent to an exchange server 1106.

2) A time-stamp value T2 is associated by the processor(s) 1104 with any response to the request M l received from the exchange processor 1106 to which the request M l was sent. This response can be any response such as acknowledgement, rejection, whole or partial fill, etc., and may depend on the nature of the request represented by M l.

3) The RTL associated with the request M l is calculated as the

difference between T2 and Tl . In some embodiments, as noted above, RTL may be calculated as an average of the time (T2 - Tl) for a past num ber Z (e.g ., 30) of processing requests routed to each of a plurality of targeted exchange processor(s) 1106.

[00231] At 308, timing parameter(s) associated with each networked computing resource 106 may be stored in tim ing data store(s) 214. As described herein, a timing data store 214, in some examples, may be a database or other data structure residing in a memory associated with or otherwise accessible by the router processor(s) 104. Timing parameter(s) stored in tim ing data store(s) 214 may be employed in processes such as those described above in connection with process block 210 of Figure 2. [00232] Timing parameter(s) determ ined by processor(s) 104, 1104 may for exam ple represent rolling histogram(s) representing latencies associated with individual execution processors 106, 1106 and/or other components of system (s) 100, 1000.

[00233] FIG. 5 shows an example of a histogram illustrating stored data representing processing latency time values associated com munications and/or other processing associated with an execution processor 106, 1106 in a system 100, 1000. In the example shown, round-trip latency times (in ms) are stored for the most recent 30 transaction requests or other communications with a given execution server 106. Although the example shows 30 latency times being stored, the number of stored tim ing parameter(s) used in determ ining RTLs or other timing parameters may be greater or fewer, and may vary according to conditions such as the time of day, the season, etc. The results of calculations based on the stored latencies, and other related data, may also be stored in timing data store(s) 214. For exam ple, in the exam ple of FIG. 5, in addition to raw latency times, a rolling average or a rolling mode of the past 30 (or other suitable number) latency times associated with com munications and/or other processing with or by each execution server 106 may also be calculated and stored in timing data store(s) 214.

[00234] As will be readily understood by those skilled in the relevant arts, further factors, including for example desired fix offsets or delays, or scaling factors associated with time of day, day of week, season of year, etc., known trading or other data processing patterns, economic conditions, etc., may be used at 210 in determining timing parameters.

[00235] Timing parameters determined at 210 can be used by routing processor(s) 104, 1104 to synchronize execution of processing requests originated by source(s) 102, 1102 and directed to processor(s) 106, 1106 by, for exam ple, associating with such requests, or portions of them to be forwarded for execution by each of multiple processor(s) 106, 1106, data

items useable by the processor(s) 104, 1104 to cause communication of the requests to the corresponding processor(s) 106, 1106 at desired absolute or relative times, to achieve desired synchronization of the arrival of the requests at the corresponding execution processor(s) 106, 1106. For example, by using data items configured to cause communication of one or more portions of the requests at given time(s) according to a clock associated with the processor(s) 104, 1104, the processor(s) 104, 1104 can cause the request(s) or request portion(s) to be communicated at a desired time of day, or in any desired relative order or sequence without regard to the actual time of day, but rather with respect to each other or some third index.

[00236] At 310, N is incremented by one, or other suitable value, or control is otherwise returned to 302 so that the process 302 - 308 continues. Optionally process 302 - 310 continues until a maximum desired number of iterations has been completed, or until all requests for transactions or other processing by orders have been processed (e.g., routed to execution processors 106, 1106), or until other suitable criteria has been met.

[00237] To aid operators and users of system(s) 100, 1000, or components thereof, understand or evaluate the effect of the disclosed method and system for causing processing of data by multiple networked computing resources, in some aspects, the present disclosure also provides various metrics (e.g., trading benchmarks, in the case of a financial system 1000) which may be determined by, and through the use of data generated from, any or all of the various components of a system 100, 1000.

[00238] Reference is now made to FIG. 6, which shows comparisons of results of transmission of multi-part trade execution requests to pluralities of networked computing resources, or execution processors 106, 1106 according to an example of the disclosed method and system, to results of conventionally-transmitted multi-part trade requests.

[00239] FIG. 6A shows results of execution of a multi-part transaction request using the disclosed methods and systems to obtain synchronized (in the illustrated case, substantially sim ultaneous) execution of the various parts or segments 624 of the multi-part transaction request (a sell order) by a plurality of exchange servers 106, 1106. In the example shown, a fill rate of 94% of an original aggregated order was achieved at the original offer price 630 of $4.21 (shown as "Level 1"). In a second round of transactions (which was filled in a single transaction, as shown at 626) the remaining volume was sold at a less-desired but still acceptable price 632 of $4.20 (shown as "Level 2"). The cost associated with the orders filled below the requested order price (i .e., those orders in Level 2) was $53,000 for the trader systems 1102 (e.g ., client systems) and $10,049 for the capital management entity 1106.

[00240] In FIG. 6B, using prior-art trading methods and systems, an unsynchronized multi-part trade request (m ulti-exchange sell order) consisting of multiple, unsynchronized order segments 624' for the same overall transaction request resulted in an initial fill rate of 47% at the preferred order price 630 of $4.21 (shown as "Level 1"). A further 43% of the request was subsequently filled at the less-desirable price 632 of $4.20 (shown as "Level 2"), with the remainder being filled at a further reduced price 634 of $4.19 (shown as "Level 3") .

[00241] Using methods and systems in accordance with the disclosure, a volume-weighted average sale price (VWAP) 636 of $4.2094/share was realized, as shown at 628. Using prior-art methods and systems, a VWAP 638 of $4.2038/share was realized .

[00242] As will be readily understood by those skilled in the relevant arts, systems 100, 1000 can comprise devices or components suitable for providing a wide variety of further metrics and functionalities. For exam ple, reference is now made to FIG. 7, which illustrates two exam ples of the provision by a routing processor 104, 1104 or other processor of a

benchmark comparison relative to a market average price provided by, for example, a market news service or other market data source 1126v. At 646, performance of a system 100, 1000 in synchronized processing of a multipart transaction request in accordance with the invention is compared to a market performance indicator "Average Price Benchmark." Such average price benchmark, or other benchmark or metric factor, can be obtained from, for exam ple, any or all of components 1126, 1106, etc. At 644, performance of a system 100, 1000 in un-synchronized processing of a multi-part transaction request in accordance with prior art methods is compared to the same market performance indicator "Average Price Benchmark. " Comparison of com parisons 646, 644 indicates that processing of transactions in accordance with the invention provides better results for a seller of financial interests. As will be understood by those skilled in the relevant arts, a wide variety of benchmarks may be used in assessing performance of systems and methods according to the invention. Such benchmarks may be determined at least partially by the nature of the system 100, 1000 used, and the types of transactions or other execution requests processed by such system .

[00243] FIG. 8 is a flowchart illustrating aspects of an exam ple method 800 for coordinating processing of data by multiple networked com puting resources. The method is suitable for implementation by router processor(s) 104 such as, for exam ple, an SOR 1104 or by any one or more processors in system 1000.

[00244] Aspects of the example method 800 in FIG. 8 are similar or the same as those appearing in FIGS. 2 and 4 and described in various example embodiments described herein . Therefore, any examples or implementation details described with respect to those figures or as described herein can be applied to the method of FIG. 8. Similarly, examples and implementation details described with response to FIG. 8 can be similarly applied to the example methods of FIGS. 2 and 4. All of these variants and com binations of these aspects are contemplated by the present description .

[00245] At 810, one or more processors 104 in the system 1000 monitor data associated with the networked computing resources 106. As described herein, in some em bodiments, the data associated with the networked computing resources can, among other things, include data associated with data processing segments previously routed to the networked computing resources.

[00246] In some em bodiments, data associated with previously routed data processing segments can include timing information associated with when a data processing segment is routed (for example, using a timestamp), when a data processing segment is processed at a networked computing resource (for example, a timestamp or processing time field in a response message), when a response message is received at the system (for example, using a timestam p). In some embodiments, monitoring such data can include determining execution latencies for the data processing segments (and/or the different latency components) and associating them with the corresponding networked computing resource.

[00247] In some embodiments, monitoring data can include monitoring and com paring latencies for different types of data processing segments. For example, different types of data processing segments routed to the same venue may have similar transmission latencies, but may have different latencies associated with the actual execution of the data processing segment. In another example, data processing segments having different lengths or even a lack of instruction may have different latencies when routed to the same networked computing resource.

[00248] In another example, an improperly formatted data processing segment may be quickly rejected by a networked computing resource such that the processors can be configured to assume that all or most of the latency associated with such a data processing segment is attributable to transm ission latencies.

[00249] In some embodiments, the processors in the system are configured to determine the different latency components associated with a particular networked computing resource based on differences between the total latencies for different order types/lengths/etc.

[00250] In some examples, latency components can include but are not necessarily limited to outgoing transmission latency, execution latency and return transmission latency (e.g. of a response message).

[00251] Monitored data associated with previously routed data processing segments can, in some examples, include data in one or more response messages indicating whether the data processing segment was successfully processes and/or to what extent it was successful. For example, a data processing segment representing a trade request, data in one or more response messages to the trade request can indicate whether the trade was filled, how much of the request was filled, and/or a price at which the request was filled.

[00252] Monitored data associated with the previously routed data processing segments can include data indicating the liquidity of a financial interest posted or otherwise known to be available at the networked computer resources at the time an initial data processing segment in a wave is routed, or at a time before the initial routing.

[00253] In some embodiments, monitoring the data associated with previously routed data processing segments can include determining one or more capture ratios for each data processing segment and/or for one or more data processes as a whole. Processor(s) can determine a capture ratio by comparing the available liquidity targeted at a networked computing resource with the actual liquidity captured by the trade request. The capture ratio of previously routed data processing segments can be monitored on a segment by segment basis and across all data processing segments divided from the original one or more data processes.

[00254] In some embodiments, the monitored data can include data parameters identifying a risk of information leakage for each of the networked computing resources. For exam ple, parameters can be set to indicate the presence of a co-located or active low-latency third party server, a rebate scheme which encourages one or more trade request types, data associated with data processing segments previously routed to a computing networked computing resource indicating that one or more related data processing segments were unsuccessful, etc. In some exam ples, monitoring the data includes generating a risk of information leakage score based on the monitored data .

[00255] In some embodiments, the system has m ultiple network routes to a networked com puting resource. In these embodiments, the monitored data associated with a particular networked com puting resource may be monitored/associated on a route-by-route basis.

[00256] In addition to routes, in some examples, the monitored data can also include latency data and or status information for route segment(s) along one or more routes and/or device(s) on one or more routes.

[00257] In some embodiments, monitoring the data can include acquiring, measuring or requesting data from one or more com ponents or devices in the system as described herein or otherwise. Some data monitoring may involve various calculations.

[00258] Monitoring data can include storing the data in one or more memories or data storage devices in the system . In some em bodiments, multiple data points associated with networked computing resources can be collected to provide a range and/or distribution/probability for the data .

[00259] At 820, the processor(s) receive, from one or more data sources 102, signals representing instructions for execution of one or more data processes that are executable by one or more of the networked computing resources 106. As discussed above, for example with reference to FIG. 2, the one or more data processes can, in some embodiments, represent requests for execution of trades and/or other transactions in financial interests.

[00260] In some examples, the instructions for the execution of data process(es) may include specific parameters for executing the data process including : process priority, tolerance value for information leakage, a maximum allowable latency period between any data processing segments, a desired routing path, a desired venue, specific routing instructions, etc.

[00261] At 830, based on the monitored data, the processor(s) prepare data processing segments for the one or more data processes. In some examples, preparing the data processing segments can include combining, dividing, or otherwise determining and preparing a number and size of data processing segments for executing the received one or more data processes.

[00262] In some embodiments, the processor(s)' preparation or division of the data processes into the at least one data processing segments includes selecting to which of the networked computing resources to route one or more data processing segments. This selection is based on the monitored data associated with the networked computing resources.

[00263] Preparing/dividing the data processes also includes determining a size of each of the data processing segments. In some embodiments, this may include a size of a processing task, or a size of a trade request. The processors are configured to determine the size of each of the data processing segments based on the monitored data.

[00264] The selection and size determinations, in some embodiments, can be based on monitored data including one or more of: available liquidity at the networked computing resources of financial interest(s) associated with the data process, a risk of information leakage, and latency(ies) associated with the networked computing resources.

[00265] While the preparation of data processing segments may be based on targeting available liquidity at multiple networked computing resources, in some examples, the processor(s) may determine that a single data processing segment is to be sent to a single networked computing resource. Alternatively, this may be viewed as sending a size 0 or no data processing segment to other networked computing resources.

[00266] At 840, the processor(s) determine timing parameters for the data processing segments. The timing parameters are based on the monitored data, and can identify differences in the timing of initiating the routing of the data processing segments. In some examples, the timing parameters can define timing offsets or delays between which a first data processing segment is to be routed relative to the routing time of a second data processing segment. In some examples, the timing parameters can define a range of times within which a first data processing segment is to be routed relative to a routing time of a second data processing segment.

[00267] In some embodiments, the timing parameters can define a probability or risk factor for information leakage associated with different time sub-ranges within the range of times.

[00268] The timing parameters are determined to as to cause synchronized execution of the data processing segments. In some examples, the timing parameters can be determined with the aim of having the data processing segments execute as closely together as possible. In some examples, the timing parameters can be determined with the aim of having the data processing segments executing in a desired sequence and/or with desired relative timings. In some examples, the timing parameters can be determined with the aim of capturing as much liquidity as possible and/or at as desirable a price as possible. In some examples, the timing parameters can be determined based on the distribution of a randomly-introduced delay at one or more of the networked computing resources.

[00269] At 850, the processor(s) initiate the routing of the data processing segments to their respective networking computing resources based on the timing parameters.

[00270] As described herein or otherwise, the division of data processes, the determination of the number, size and destination networked computing resources of data processing segments is based on the monitored data associated with the networked computing resources.

[00271] In some situations, the processors are configured to consider different tradeoffs between available liquidity, risk of information leakage and/or latency variance for one or more networked computing resources.

[00272] For example, by default or based on a risk tolerance value associated with a data source from which a data process has been received, the processor(s) can prepare data processing segments and timing parameters to target as much liquidity as possible despite a higher risk of data leakage and of losing part of the liquidity or a better price. Conversely, based on a different default or lower risk tolerance value associated with a data source, the processor(s) can prepare data processing segments and timing parameters to target a smaller amount of liquidity with a lower risk of data leakage.
[00323] In some embodiments, the selection of waves from a queue may be based on the total routing/handling time for the particular wave. For example, if a wave has tight timing parameters (i.e. small delays between data processing segments, and therefore a shorter handling time), it may be prioritized over a wave having a longer handling time if it can easily fit in a next schedule or otherwise result in higher throughput.

[00324] At 850, the system routes each of the data processing segments in the ordered data processing waves based on the schedule.

[00325] As described herein, the processors monitor data associated with the networked computing resources. In some embodiments, the processors obtain a minimum handling interval time by identifying with the monitored data when two or more data processing segments routed to the same networked computing resource resulted in one or more latencies which are longer than a historical latency value for the networked computing resource. The difference in time between the involved data processing segments can be used to define a minimum handling time for the networked computing resource. In some examples, the longer latency may be indicative that one of the data processing segments was queued at the networked computing resource (or along the path).

[00326] Similarly, in some embodiments, the processors can identify a number of data processing segments that can be typically routed to the same networked computing resource within a minimum handling time before a longer than typical latency is observed. In some examples, this number may be indicative with a number of processors, etc. at a networked computing resource that must be busy before a subsequent request is queued.

[00327] In some embodiments, when the monitored data (e.g. latency, capture ratio) indicates that a networked computing resource (or route) is congested, the processors can delay, reschedule or re-prepare any wave that has at least one data processing segment for routing to the affected networked computing resource.

[00328] In some embodiments, monitoring the data includes identifying in various data processing segments from the scheduled wave orders which are to be routed to the same destination or otherwise have the potential to act as a good test case to determine timing parameters, handling times, congestion thresholds, etc. In some examples, by identifying and specifically monitoring these data processing segments with test capabilities, the system may, in some instances reduce the need to send test data processing segments.

[00329] While the disclosure has been provided and illustrated in connection with specific, presently-preferred embodiments, many variations and modifications may be made without departing from the spirit and scope of the invention(s) disclosed herein. The disclosure and invention(s) are therefore not to be limited to the exact components or details of methodology or construction set forth above. Except to the extent necessary or inherent in the processes themselves, no particular order to steps or stages of methods or processes described in this disclosure, including the Figures, is intended or implied. In many cases the order of process steps may be varied without changing the purpose, effect, or import of the methods described. The scope of the claims is to be defined solely by the appended claims, giving due consideration to the doctrine of equivalents and related doctrines.
What is claimed is:

1. A system for coordinating processing of data by multiple networked computing resources, the system comprising at least one processor configured to:

monitor data associated with a plurality of networked computing resources, the monitored data including data associated with data processing segments previously routed to the plurality of networked computing resources;

receive from one or more data sources signals representing instructions for execution of at least one data process executable by the plurality of networked computing resources;

based on the monitored data :

divide the at least one data process into at least one data processing segment, each data processing segment to be routed to one of the plurality of networked computing resources;

determine a plurality of timing parameters, each of the plurality of timing parameters to be associated with a corresponding one of the plurality of networked computing resources, the plurality of timing parameters determined to cause synchronized execution of the at least one data processing segment by the plurality of networked computing processors; and

route the at least one data processing segment to the plurality of corresponding networked computing processors in a timing sequence based on the timing parameters.

2. The system of claim 1 wherein monitoring the data associated with the plurality of networked computing resources comprises:

receiving messages in response to the routing of the at least one data processing segment to the plurality of corresponding networked computing processors; and

determining, from the received messages, at least one capture ratio for the at least one data process;

wherein determining the plurality of timing parameters for at least one subsequent data processing segments is based at least in part on the at least one capture ratio.

3. The system of claim 1 wherein monitoring the data associated with the plurality of networked computing resources comprises:

receiving messages in response to the routing of the at least one data processing segment to the plurality of corresponding networked computing processors;

determining, from the received messages, at least one capture ratio for the at least one data process; and

generating an alert indicative of a potential hardware failure when the at least one capture ratio is below a defined threshold or changes from a historical average by a defined threshold.

4. The system of claim 1 wherein monitoring the data associated with the plurality of networked computing resources comprises:

determining components of a latency associated with routing a data processing segment to one of the plurality of networked computing resources;

wherein determining the components is based on differences in latencies associated with routing different types of data processing segments to the one of the plurality of networked computing resources.

5. The system of claim 1 wherein the monitored data associated with the plurality of networked computing resources includes data associated with multiple routes for routing data processing segments from the system to the networked computing resources.

6. The system of claim 5, wherein the data associated with the multiple routes includes latency data or status information for at least one route segment or device in at least one of the multiple routes.

7. The system of claim 1 wherein the monitored data associated with the plurality of networked computing resources includes data for identifying a risk of information leakage from a corresponding one of the networked computing resources.

8. The system of claim 1, wherein dividing the at least one data process into the at least one of data processing segments comprises:

selecting to which of the plurality networked computing resources at least one of the at least one data processing segment is to be routed; and

for each of the selected networked computing resources, determining a size of the corresponding at least one data process processing segments.

9. The system of claim 8, wherein the selection and the size determination are based on at least one of: an available liquidity, a risk of information leakage, and a latency associated with the plurality of computing resources.

10. The system of claim 9, wherein the available liquidity includes publicly posted liquidity and forecasted liquidity.

11. The system of claim 1, wherein the timing parameters define an allowable time range within which a first data processing segment is to be routed relative to a routing time of a second data processing segment.

12. The system of claim 1, wherein the at least one processor is configured to:

introduce random timing variations into the timing sequence for routing the plurality of data processing segments, the random timing variations falling within a range that satisfies the plurality of timing parameters;

monitor data associated with the data processing segments routed with the random timing variations; and

adjusting the timing parameters associated with the plurality of corresponding networked computing processors.

13. The system of claim 1 wherein monitoring the data associated with the plurality of networked computing resources comprises:

receiving messages in response to the routing of the plurality of data processing segments to the plurality of corresponding networked computing processors; and

determining a distribution of execution latencies associated with one of the networked computing processors based on the received messages associated with data processing segments routed to the one of the networked computing processors; and

wherein the at least one processor is configured to determine the timing sequence based on one or more time ranges within which a first data processing segment can be routed relative to a time at which a second data processing segment is routed, the one or more time ranges based on the distribution.

14. A method for coordinating processing of data by multiple networked computing resources, the method comprising :

monitoring data associated with a plurality of networked computing resources, the monitored data including data associated with data processing segments previously routed to the plurality of networked computing resources;

receiving from one or more data sources signals representing instructions for execution of at least one data process executable by the plurality of networked computing resources;

based on the monitored data :

dividing the at least one data process into at least one data processing segment, each data processing segment to be routed to one of the plurality of networked computing resources;

determining a plurality of timing parameters, each of the plurality of timing parameters to be associated with a corresponding one of the plurality of networked computing resources, the plurality of timing parameters determined to cause synchronized execution of the at least one data processing segment by the plurality of networked computing processors; and

routing the at least one data processing segment to the plurality of corresponding networked computing processors in a timing sequence based on the timing parameters.

15. The method of claim 14 wherein monitoring the data associated with the plurality of networked computing resources comprises:

receiving messages in response to the routing of the at least one data processing segment to the plurality of corresponding networked computing processors; and

determining, from the received messages, at least one capture ratio for the at least one data process;

wherein determining the plurality of timing parameters for at least one subsequent data processing segments is based at least in part on the at least one capture ratio.

16. The method of claim 14 wherein monitoring the data associated with the plurality of networked computing resources comprises:

receiving messages in response to the routing of the at least one data processing segment to the plurality of corresponding networked computing processors;

determining, from the received messages, at least one capture ratio for the at least one data process; and

generating an alert indicative of a potential hardware failure when the at least one capture ratio is below a defined threshold or changes from a historical average by a defined threshold.

17. The method of claim 14 wherein monitoring the data associated with the plurality of networked computing resources comprises:

determining components of a latency associated with routing a data processing segment to one of the plurality of networked computing resources;

wherein determining the components is based on differences in latencies associated with routing different types of data processing segments to the one of the plurality of networked computing resources.

18. The method of claim 14 wherein the monitored data associated with the plurality of networked computing resources includes data associated with multiple routes for routing data processing segments to the networked computing resources.

19. The method of claim 18, wherein the data associated with the multiple routes includes latency data or status information for at least one route segment or device in at least one of the multiple routes.

20. The method of claim 14 wherein the monitored data associated with the plurality of networked computing resources includes data for identifying a risk of information leakage from a corresponding one of the networked computing resources.

21. The method of claim 14, wherein dividing the at least one data process into the at least one of data processing segments comprises:

selecting to which of the plurality networked computing resources at least one of the at least one data processing segment is to be routed; and

for each of the selected networked computing resources, determining a size of the corresponding at least one data process processing segments.

22. The method of claim 21, wherein the selection and the size determination are based on at least one of: an available liquidity, a risk of information leakage, and a latency associated with the plurality of computing resources.

23. The method of claim 22, wherein the available liquidity includes publicly posted liquidity and forecasted liquidity.

24. The method of claim 14, wherein the timing parameters define an allowable time range within which a first data processing segment is to be routed relative to a routing time of a second data processing segment.

25. The method of claim 14, comprising :

introducing random timing variations into the timing sequence for routing the plurality of data processing segments, the random timing variations falling within a range that satisfies the plurality of timing parameters;

monitoring data associated with the data processing segments routed with the random timing variations; and

adjusting the timing parameters associated with the plurality of corresponding networked computing processors.

26. The method of claim 14 wherein monitoring the data associated with the plurality of networked computing resources comprises:

receiving messages in response to the routing of the plurality of data processing segments to the plurality of corresponding networked computing processors; and

determining a distribution of execution latencies associated with one of the networked computing processors based on the received messages associated with data processing segments routed to the one of the networked computing processors; and

wherein the method comprises: determining the timing sequence based on one or more time ranges within which a first data processing segment can be routed relative to a time at which a second data processing segment is routed, the one or more time ranges based on the distribution.

27. A computer-readable medium or media having stored thereon instructions which when executed by at least one processor, configure the at least one processor to:

monitor data associated with a plurality of networked computing resources, the monitored data including data associated with data processing segments previously routed to the plurality of networked computing resources;

receive from one or more data sources signals representing instructions for execution of at least one data process executable by the plurality of networked computing resources;

based on the monitored data :

divide the at least one data process into at least one data processing segment, each data processing segment to be routed to one of the plurality of networked computing resources;

determine a plurality of timing parameters, each of the plurality of timing parameters to be associated with a corresponding one of the plurality of networked computing resources, the plurality of timing parameters determined to cause synchronized execution of the at least one data processing segment by the plurality of networked computing processors; and

route the at least one data processing segment to the plurality of corresponding networked computing processors in a timing sequence based on the timing parameters.

28. A system for coordinating processing of data by multiple networked computing resources, the system comprising at least one processor configured to:

obtain a plurality of data processing waves, each data processing wave identifying : one or more data processing segments, one or more networked corresponding computing resources to which the one or more data processing segments are to be routed; and a timing sequence in which the one or more data processing segments are to be routed;

obtain a minimum handling interval for each of the networked computing resources;

schedule an order for routing the plurality of data processing waves based on the wave timing sequences and the minimum handling intervals for the networked computing resources; and

route each of the data processing segments in the plurality of data processing waves based on the order.

29. The system of claim 28 wherein scheduling the order for routing the plurality of data processing waves comprises determining an order which intersperses the data processing segments of different data processing waves without violating the minimum handling intervals of the plurality of networked computing resources or the timing sequences of the plurality of data processing waves.

30. The system of claim 28 wherein the timing sequences identify one or more time ranges within which a first data processing segment in a particular data processing wave can be routed relative to a time at which a second data processing segment in the particular data processing wave is routed.

31. The system of claim 28 wherein obtaining the plurality of data processing waves comprises obtaining M data processing waves from a wave queue; and wherein scheduling the order for routing the M data processing waves comprises: determining a total handling time for each possible arrangement of the M data processing waves, and scheduling the order as an arrangement of the possible arrangements having the shortest total handling time.

32. The system of claim 31 wherein M is selected based on a scheduling computation time and total handling times for one or more of the plurality of data processing waves.

33. The system of claim 28 wherein obtaining the plurality of data processing waves comprises obtaining M data processing waves from a wave queue; and wherein scheduling the order for routing the M data processing waves comprises: determining total handling times for different arrangements of the N data processing waves until a defined computation time expires, and scheduling the order as an arrangement from the different arrangements having the shortest total handling time determined before the defined computation time expired.

34. The system of claim 28, wherein the at least one processor is configured to: monitor data associated with the plurality of networked computing resources, the monitored data including data associated with data processing segments previously routed to the plurality of networked computing resources.

35. The system of claim 34, wherein obtaining the minimum handling interval time comprises: determining when two or more data processing segments routed to one of the networked computing resources resulted in one or more execution latencies which are longer than a historical latency value; and using a relative timing of the routing of the two or more data processing segments to define the minimum handling time for the one of the networked computing resources.

36. The system of claim 28 wherein the at least one processor is configured to: identify two or more data processing segments which are scheduled to be routed to one of the plurality of networked computing resources with a relative timing that is within a threshold of the minimum handling interval of the one of the plurality of networked computing resources; and after routing the two or more data processing segments, monitoring execution latencies for the two or more data processing segments to determine whether to adjust the minimum handling interval of the one of the plurality of networked computing resources.

37. The system of claim 28, wherein the at least one processor is configured to identify a plurality of data processing segments which are scheduled to be routed to one of the plurality of networked computing resources with a relative timing that is within a threshold of the minimum handling interval of the one of the plurality of networked computing resources; and after routing the plurality of data processing segments, monitoring execution latencies for the plurality of data processing segments to determine a number of data processing segments which will trigger the minimum handling interval of the one of the plurality of networked computing resources.

38. The system of claim 28 wherein the plurality of data processing waves is obtained from a wave queue; wherein the at least one processor is configured to fill the wave queue with at most N data processing waves from each of a plurality of session queues associated with one or more corresponding data sources.

39. The system of claim 38 wherein filing the wave queue comprises selecting data processing waves from the plurality of session queues using a priority scheme which is based at least in part on timing parameters associated with the data processing waves.

40. A method for coordinating processing of data by multiple networked computing resources, the method comprising :

obtaining a plurality of data processing waves, each data processing wave identifying : one or more data processing segments, one or more networked corresponding computing resources to which the one or more data processing segments are to be routed; and a timing sequence in which the one or more data processing segments are to be routed;

obtaining a minimum handling interval for each of the networked computing resources;

scheduling an order for routing the plurality of data processing waves based on the wave timing sequences and the minimum handling intervals for the networked computing resources; and

routing each of the data processing segments in the plurality of data processing waves based on the order.

41. The method of claim 40 wherein scheduling the order for routing the plurality of data processing waves comprises determining an order which intersperses the data processing segments of different data processing waves without violating the minimum handling intervals of the plurality of networked computing resources or the timing sequences of the plurality of data processing waves.

42. The method of claim 40 wherein the timing sequences identify one or more time ranges within which a first data processing segment in a particular data processing wave can be routed relative to a time at which a second data processing segment in the particular data processing wave is routed.

43. The method of claim 40 wherein obtaining the plurality of data processing waves comprises obtaining M data processing waves from a wave queue; and wherein scheduling the order for routing the M data processing waves comprises: determining a total handling time for each possible arrangement of the M data processing waves, and scheduling the order as an arrangement of the possible arrangements having the shortest total handling time.

44. The method of claim 40 wherein M is selected based on a scheduling computation time and total handling times for one or more of the plurality of data processing waves.

45. The method of claim 40 wherein obtaining the plurality of data processing waves comprises obtaining M data processing waves from a wave queue; and wherein scheduling the order for routing the M data processing waves comprises: determining total handling times for different arrangements of the N data processing waves until a defined computation time expires, and scheduling the order as an arrangement from the different arrangements having the shortest total handling time determined before the defined computation time expired.

46. The method of claim 40, comprising : monitoring data associated with the plurality of networked computing resources, the monitored data including data associated with data processing segments previously routed to the plurality of networked computing resources.

47. The method of claim 46, wherein obtaining the minimum handling interval time comprises: determining when two or more data processing segments routed to one of the networked computing resources resulted in one or more execution latencies which are longer than a historical latency value; and using a relative timing of the routing of the two or more data processing segments to define the minimum handling time for the one of the networked computing resources.

48. The method of claim 40 comprising : identifying two or more data processing segments which are scheduled to be routed to one of the plurality of networked computing resources with a relative timing that is within a threshold of the minimum handling interval of the one of the plurality of networked computing resources; and after routing the two or more data processing segments, monitoring execution latencies for the two or more data processing segments to determine whether to adjust the minimum handling interval of the one of the plurality of networked computing resources.

49. The method of claim 40, comprising identifying a plurality of data processing segments which are scheduled to be routed to one of the plurality of networked computing resources with a relative timing that is within a threshold of the minimum handling interval of the one of the plurality of networked computing resources; and after routing the plurality of data processing segments, monitoring execution latencies for the plurality of data processing segments to determine a number of data processing segments which will trigger the minimum handling interval of the one of the plurality of networked computing resources.

50. The method of claim 40 wherein the plurality of data processing waves is obtained from a wave queue; wherein the at least one processor is configured to fill the wave queue with at most N data processing waves from each of a plurality of session queues associated with one or more corresponding data sources.

51. The method of claim 50 wherein filing the wave queue comprises selecting data processing waves from the plurality of session queues using a priority scheme which is based at least in part on timing parameters associated with the data processing waves.

52. A computer-readable medium or media having stored thereon instructions which when executed by at least one processor, configure the at least one processor to:

obtain a plurality of data processing waves, each data processing wave identifying : one or more data processing segments, one or more networked corresponding computing resources to which the one or more data processing segments are to be routed; and a timing sequence in which the one or more data processing segments are to be routed;

obtain a minimum handling interval for each of the networked computing resources;

schedule an order for routing the plurality of data processing waves based on the wave timing sequences and the minimum handling intervals for the networked computing resources; and

route each of the data processing segments in the plurality of data processing waves based on the order.

53. A system for coordinating processing of data by multiple networked computing resources, the system comprising at least one processor configured to:

receive from one or more data sources signals representing instructions for execution of a plurality of data processes executable by a plurality of networked computing resources, the data processes representing a proposed trade in a financial interest;

obtaining data associated with available liquidity of the financial interest at each of the plurality of networked computing resources;

divide each of the plurality of data processes into a plurality of data processing segments, each data processing segment divided from a single data process to be routed to at least one of the plurality of networked computing processors;

based at least partly on latencies in execution of prior data processing requests routed by the system to each of the plurality of networked computing processors and the available liquidity at each of the plurality of networked computing processors, determine a plurality of timing parameters, each of the plurality of timing parameters to be associated with a corresponding one of the plurality of data processing segments, the plurality of timing parameters determined to cause synchronized execution of the plurality of data processing segments by the plurality of networked computing processors;

based on the timing parameters and networked computing processors associated with each of the plurality of data processes, determine a timing sequence for routing the data processing segments for all of the plurality of data processes; and

route the plurality of data processing segments to the plurality of corresponding networked computing processors based on the timing sequence.

54. The system of claim 53, wherein dividing the at least one data process into a plurality of data processing segments comprises:

selecting to which of the plurality networked computing resources at least one of the plurality of data processing segments is to be routed; and

for each of the selected networked computing resources, determining a size of the corresponding at least one data process processing segments.

55. The system of claim 54 wherein the selection and the size determination are based at least partly on the available liquidity and a distribution of historical execution latencies associated with the plurality of computing resources.

56. The system of claim 55, wherein the at least one processor is configured to:

when the distribution for a particular networked computing resource is a multimodal distribution :

the plurality of data processing segments include at least two data processing segments to be routed to the particular networked computing resource based on the multimodal distribution, and

determine the timing parameters for the at least two data processing segments based on the multimodal distribution.

57. The system of claim 55, wherein dividing the at least one data process into a plurality of data processing segments comprises dividing the at least one data process into at least two data processing segments to be routed to the same networked computing resource; wherein the at least one processor is configured to determine the timing parameters for each of the at least two data processing segments.

58. The system of claim 55, wherein the selection and the size determination are a function of the variance of the distributions of historical execution latencies for the plurality of networked computing resources.

59. The system of claim 53 wherein the at least one processor is configured to determine whether liquidity in addition to posted available liquidity is historically available at a particular networked computing resource; and wherein dividing the at least one data process includes dividing the at least one data process into at least two data processing segments to be routed to the particular networked computing resource, the at least two data processing segments having timing parameters to target the posted available trade liquidity and the additional liquidity.

60. A method for coordinating processing of data by multiple networked computing resources, the method comprising :

receiving from one or more data sources signals representing instructions for execution of a plurality of data processes executable by a plurality of networked computing resources, the data processes representing a proposed trade in a financial interest;

obtaining data associated with available liquidity of the financial interest at each of the plurality of networked computing resources;

dividing each of the plurality of data processes into a plurality of data processing segments, each data processing segment divided from a single data process to be routed to at least one of the plurality of networked computing processors;

based at least partly on latencies in execution of prior data processing requests routed by the system to each of the plurality of networked computing processors and the available liquidity at each of the plurality of networked computing processors, determining a plurality of timing parameters, each of the plurality of timing parameters to be associated with a corresponding one of the plurality of data processing segments, the plurality of timing parameters determined to cause synchronized execution of the plurality of data processing segments by the plurality of networked computing processors;

based on the timing parameters and networked computing processors associated with each of the plurality of data processes, determining a timing sequence for routing the data processing segments for all of the plurality of data processes; and

routing the plurality of data processing segments to the plurality of corresponding networked computing processors based on the timing sequence.

61. The method of claim 60, wherein dividing the at least one data process into a plurality of data processing segments comprises:

selecting to which of the plurality networked computing resources at least one of the plurality of data processing segments is to be routed; and

for each of the selected networked computing resources, determining a size of the corresponding at least one data process processing segments.

62. The method of claim 61 wherein the selection and the size determination are based at least partly on the available liquidity and a distribution of historical execution latencies associated with the plurality of computing resources.

63. The method of claim 62, wherein

when the distribution for a particular networked computing resource is a multimodal distribution :

the plurality of data processing segments include at least two data processing segments to be routed to the particular networked computing resource based on the multimodal distribution, and

the method comprises determining the timing parameters for the at least two data processing segments based on the multimodal distribution.

64. The method of claim 62, wherein dividing the at least one data process into a plurality of data processing segments comprises dividing the at least one data process into at least two data processing segments to be routed to the same networked computing resource; wherein the method comprises determining the timing parameters for each of the at least two data processing segments.

65. The method of claim 62, wherein the selection and the size determination are a function of the variance of the distributions of historical execution latencies for the plurality of networked computing resources.

66. The method of claim 60 wherein the method comprises determining whether liquidity in addition to posted available liquidity is historically available at a particular networked computing resource; and wherein dividing the at least one data process includes dividing the at least one data process into at least two data processing segments to be routed to the particular networked computing resource, the at least two data processing segments having timing parameters to target the posted available trade liquidity and the additional liquidity.

67. A computer-readable medium or media having stored thereon instructions which when executed by at least one processor, configure the at least one processor to:

receive from one or more data sources signals representing instructions for execution of a plurality of data processes executable by a plurality of networked computing resources, the data processes representing a proposed trade in a financial interest;

obtaining data associated with available liquidity of the financial interest at each of the plurality of networked computing resources;

divide each of the plurality of data processes into a plurality of data processing segments, each data processing segment divided from a single

data process to be routed to at least one of the plurality of networked computing processors;

based at least partly on latencies in execution of prior data processing requests routed by the system to each of the plurality of networked computing processors and the available liquidity at each of the plurality of networked computing processors, determine a plurality of timing parameters, each of the plurality of timing parameters to be associated with a corresponding one of the plurality of data processing segments, the plurality of timing parameters determined to cause synchronized execution of the plurality of data processing segments by the plurality of networked computing processors;

based on the timing parameters and networked computing processors associated with each of the plurality of data processes, determine a timing sequence for routing the data processing segments for all of the plurality of data processes; and

route the plurality of data processing segments to the plurality of corresponding networked computing processors based on the timing sequence.