# US 12,118,614 B1 · Systems and methods for electronic trade order routing > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,118,614 B1 - Document ID: US12118614B1 - Status: Granted - Kind: B1 - Application number: 17/847,055 - Filed: 2022-06-22 - Granted: 2024-10-15 - Earliest claimed: 2021-10-15 - Assignees: BlackRock, Inc. - Applicants: BlackRock, Inc. - Inventors: Shawn Simpson; Jingxin Xi; Donald Weidner; Trevor Hastie; Robert Tibshirani - Web page: https://patentgrep.com/patent/US12118614B1 ## CPC - G06Q 40/04: Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange - G06F 18/2415: based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate - G06Q 40/046: {Trading across multiple trading exchanges or platforms} - G06Q 40/06: Asset management; Financial planning or analysis - G06Q 40/0421: {using artificial intelligence, machine learning or neural networks} - G06Q 40/043: {Tools or interfaces specially adapted for trading or brokering} - G06Q 40/045: {Accepting or processing orders in an exchange} - G06Q 40/0451: {Prioritising, queuing or matching trade orders in an exchange} - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract The present application generally relates to electronic trading systems, and more specifically to systems and methods for electronic trade order routing. Specifically, electronic trade orders may be divided into a first set and a second set. The first set of trade orders are executed with ad hoc execution styles. For the second set, execution style recommendations are generated in a form of a lookup table for the second set of trade orders according to a testing routing strategy. The second set of trade orders are executed with recommended execution styles. A difference between performance metrics among the first set of trade orders and the second set of trade orders is computed. A decision on whether to adopt the recommended execution style is generated based at least in part on the computed difference. ## Claims ### Claim 1 A method of electronically routing an incoming electronic trade order, the method comprising: receiving, at a communication interface of a routing server, information of incoming electronic trade orders and a training dataset of historical trade data; training a machine learning model implemented at the routing server using historical patterns of trade costs from the training dataset of historical trade data; randomly dividing, at the routing server, the incoming electronic trade orders into a first set and a second set; routing, via the communication interface of the routing server, the first set of trade orders to one or more trade execution entities each corresponding to a different execution style in an ad hoc manner; generating, by the machine learning model implemented at the routing server, execution style recommendations in a form of a lookup table for the second set of trade orders according to a testing routing strategy; routing, via the communication interface of the routing server, the second set of trade orders to corresponding trade execution entities based on the execution style recommendations; computing a difference between performance metrics among the first set of trade orders and the second set of trade orders; generating a decision on whether to adopt the machine learning model generated execution style recommendations based at least in part on the computed difference; and routing, via the communication interface of the routing server, a new incoming electronic trade order to a particular trade execution entity based on a recommended execution style predicted by the machine learning model causing an execution of the new incoming electronic trade order using the recommended execution style, when the decision indicates to adopt the machine learning model generated execution style recommendations. ### Claim 2 (depends on 1) The method of claim 1, wherein the execution style recommendations are selected from a set of execution styles including an auto-execution style, a request for quote style, and a direct voice-activated style. ### Claim 3 (depends on 1) The method of claim 1, wherein the performance metrics may include any of: a mean execution implementation shortfall associated with a trade order; or sales of a set of trade orders. ### Claim 4 (depends on 1) The method of claim 1, wherein the execution style recommendations are generated by a statistical model based on attributes including any combination of: a side value, an original trade size, a price change in dollar terms in response to change in spread by a single basis point, a wallet spread, a coupon rate, an amount issued, and a rating. ### Claim 5 (depends on 1) The method of claim 1, further comprising: computing a test statistic based on the difference between mean execution implementation shortfalls and implementation shortfall variances of the first set and the second set. ### Claim 6 (depends on 4) The method of claim 4, wherein the first set or the second set has a sample size no smaller than a threshold computed based on a configurable control level of a test corresponding to a different performance quantile level. ### Claim 7 (depends on 1) The method of claim 1, wherein the execution style recommendations are generated by combining (i) a respective probability indicating a likelihood that a trade order is to be executed under each execution style from a set of execution styles; and (ii) an estimate of an implementation shortfall metric for the trade order under the respective execution style. ### Claim 8 (depends on 6) The method of claim 6, further comprising: retroactively classifying the first set of trade orders and the second set of trade orders depending on the respective probability or the estimate of the performance metrics; and computing an impact differential based on a sample average of performance metric measurements of a subset of trade orders that a classification result matches with the respective probability or the estimate of the performance metric. ### Claim 9 A system of routing an incoming electronic trade order, the system comprising: a communication interface receiving information of incoming electronic trade orders and a training dataset of historical trade data; a memory storing a plurality of processor-executable instructions; and a processor executing the plurality of processor-executable instructions to perform operations comprising: training a machine learning model implemented at the routing server using historical patterns of trade costs from the training dataset of historical trade data; routing, via the communication interface, the first set of trade orders to one or more trade execution entities each corresponding to a different execution style in an ad hoc manner; generating, by a machine learning model implemented on one or more processors, execution style recommendations in a form of a lookup table for the second set of trade orders according to a testing routing strategy; routing, via the communication interface, the second set of trade orders to corresponding trade execution entities based on the execution style recommendations; computing a difference between performance metrics among the first set of trade orders and the second set of trade orders; generating a decision on whether to adopt the machine learning model generated execution style recommendations based at least in part on the computed difference; and routing, via the communication interface, a new incoming electronic trade order to a particular trade execution entity based on a recommended execution style predicted by the machine learning model causing an execution of the new incoming electronic trade order using the recommended execution style, when the decision indicates to adopt the machine learning model generated execution style recommendations. ### Claim 10 (depends on 9) The system of claim 9, wherein the execution style recommendations are selected from a set of execution styles including an auto-execution style, a request for quote style, and a direct voice-activated style. ### Claim 11 (depends on 9) The system of claim 9, wherein the performance metrics may include any of: a mean execution implementation shortfall associated with a trade order; or sales of a set of trade orders. ### Claim 12 (depends on 9) The system of claim 9, wherein the execution style recommendations are generated by a statistical model based on attributes including any combination of: a side value, an original trade size, a price change in dollar terms in response to change in spread by a single basis point, a wallet spread, a coupon rate, an amount issued, and a rating. ### Claim 13 (depends on 9) The system of claim 9, wherein the operations further comprise: computing a test statistic based on the difference between mean execution implementation shortfalls and implementation shortfall variances of the first set and the second set. ### Claim 14 (depends on 13) The system of claim 13, wherein the first set or the second set has a sample size no smaller than a threshold computed based on a configurable control level of a test corresponding to a different performance quantile level. ### Claim 15 (depends on 9) The system of claim 9, wherein the execution style recommendations are generated by combining (i) a respective probability indicating a likelihood that a trade order is to be executed under each execution style from a set of execution styles; and (ii) an estimate of an implementation shortfall metric for the trade order under the respective execution style. ### Claim 16 (depends on 15) The system of claim 15, wherein the operations further comprise: retroactively classifying the first set of trade orders and the second set of trade orders depending on the respective probability or the estimate of the performance metrics; and computing an impact differential based on a sample average of performance metric measurements of a subset of trade orders that a classification result matches with the respective probability or the estimate of the performance metric. ### Claim 17 A non-transitory processor-readable storage medium storing a plurality of processor-executable instructions for routing an incoming electronic trade order, the instructions being executed by a processor to perform operations comprising: receiving, at a communication interface of a routing server, information of incoming electronic trade orders and a training dataset of historical trade data; training a machine learning model implemented at the routing server using historical patterns of trade costs from the training dataset of historical trade data; randomly dividing, at the routing server, the incoming electronic trade orders into a first set and a second set; routing, via the communication interface of the routing server, the first set of trade orders to one or more trade execution entities each corresponding to a different execution style in an ad hoc manner; generating, by a machine learning model implemented at the routing server, execution style recommendations in a form of a lookup table for the second set of trade orders according to a testing routing strategy; routing, via the communication interface of the routing server, the second set of trade orders to corresponding trade execution entities based on the execution style recommendations; computing a difference between performance metrics among the first set of trade orders and the second set of trade orders; generating a decision on whether to adopt the machine learning model generated execution style recommendations based at least in part on the computed difference; and routing, via the communication interface of the routing server, a new incoming electronic trade order to a particular trade execution entity based on a recommended execution style predicted by the machine learning model causing an execution of the new incoming electronic trade order using the recommended execution style, when the decision indicates to adopt the machine learning model generated execution style recommendations. ### Claim 18 (depends on 17) The non-transitory processor-readable storage medium of claim 17, wherein the execution style recommendations are selected from a set of execution styles including an auto-execution style, a request for quote style, and a direct voice-activated style. ### Claim 19 (depends on 17) The non-transitory processor-readable storage medium of claim 17, wherein the performance metrics may include any of: a mean execution implementation shortfall associated with a trade order; or sales of a set of trade orders. ### Claim 20 (depends on 17) The non-transitory processor-readable storage medium of claim 17, wherein the operations further comprise: computing a test statistic based on the difference between mean execution implementation shortfalls and implementation shortfall variances of the first set and the second set.