Systems and methods for electronic trade order routing

A system of electronically routing an incoming electronic trade order at a routing server, the system comprising:

• a communication interface receiving information of incoming electronic trade orders;
• 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 a training dataset of historical trade data to predict an execution style;
• 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 trained machine learning model trained by historical patterns of trade costs, execution style recommendations 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;
• generating a decision to adopt the recommended execution style based at least in part on a performance difference between the first set of trade orders and the second set of trade orders;
• and
• routing, by the routing server, a new incoming trade order to a particular trade execution entity based on machine learning model generated recommendation.

A non-transitory processor-readable storage medium storing a plurality of processor-executable instructions for electronically routing an incoming electronic trade order at a routing server, the instructions being executed by a processor to perform operations comprising:

• training a machine learning model implemented at the routing server using historical patterns of trade costs from a training dataset of historical trade data to predict an execution style;
• in response to receiving, at a communication interface of the routing server, information of incoming electronic trade orders, 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 trained machine learning model, execution style recommendations 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;
• generating a decision to adopt the recommended execution style based at least in part on a performance difference between the first set of trade orders and the second set of trade orders;
• and
• routing, by the routing server, a new incoming trade order to a particular trade execution entity based on machine learning model generated recommendation.