Abstract: Systems are methods are provided for training an automated agent. The automated agent maintains a reinforcement learning neural network and generates, according to outputs of the reinforcement learning neural network, signals for communicating resource task requests. First and second task data are received. The task data are processed to compute a first performance metric reflective of performance of the automated agent relative to other entities in a first time interval, and a second performance metric reflective of performance of the automated agent relative to other entities in a second time interval. A reward for the reinforcement learning neural network that reflects a difference between the second performance metric and the first performance metric is computed and provided to the reinforcement learning neural network to train the automated agent.

Abstract: Systems are methods are provided for training an automated agent. The automated agent maintains a reinforcement learning neural network and generates, according to outputs of the reinforcement learning neural network, signals for communicating resource task requests. First and second task data are received. The task data are processed to compute a first performance metric reflective of performance of the automated agent relative to other entities in a first time interval, and a second performance metric reflective of performance of the automated agent relative to other entities in a second time interval. A reward for the reinforcement learning neural network that reflects a difference between the second performance metric and the first performance metric is computed and provided to the reinforcement learning neural network to train the automated agent.

Abstract: A smart order router for quantitative trading and order routing and corresponding methods and computer readable media are described. The smart order router includes a machine learning prediction engine configured to, responsive to a control signal received from an upstream trading engine including at least a maximum quantity value and an urgency metric, process input data sets through one or more predictive models to generate the one or more potential combinations of child orders and their associated fill probability metrics, toxicity metrics, and expected gain (loss) metrics and an order placement optimization engine configured to receive the one or more potential combinations of child orders and their associated fill probability metrics, toxicity metrics, and expected gain (loss) metrics and to identify an optimum combination of child orders that maximize an objective function.

Abstract: Systems are methods are provided for training an automated agent. The automated agent maintains a reinforcement learning neural network and generates, according to outputs of the reinforcement learning neural network, signals for communicating resource task requests. First and second task data are received. The task data are processed to compute a first performance metric reflective of performance of the automated agent relative to other entities in a first time interval, and a second performance metric reflective of performance of the automated agent relative to other entities in a second time interval. A reward for the reinforcement learning neural network that reflects a difference between the second performance metric and the first performance metric is computed and provided to the reinforcement learning neural network to train the automated agent.

Abstract: A smart order router for quantitative trading and order routing and corresponding methods and computer readable media are described. The smart order router includes a machine learning prediction engine configured to, responsive to a control signal received from an upstream trading engine including at least a maximum quantity value and an urgency metric, process input data sets through one or more predictive models to generate the one or more potential combinations of child orders and their associated fill probability metrics, toxicity metrics, and expected gain (loss) metrics and an order placement optimization engine configured to receive the one or more potential combinations of child orders and their associated fill probability metrics, toxicity metrics, and expected gain (loss) metrics and to identify an optimum combination of child orders that maximize an objective function.