Execution Optimizer for Trading Order Automation
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Solution Overview
Problem
In investment banking, delays in executing trades can lead to price slippage and inconsistencies due to manual processing and volatility in market conditions, impacting the execution of securities orders.
Innovation Solution
A computer-implemented method and system, known as the Execution Optimizer, which processes trading orders by applying profiles based on portfolio managers' historical trading habits, using prediction models and real-time feedback to optimize order execution through a rules engine, ensuring automatic and efficient execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual order execution is used, then flexibility in handling complex orders is improved, but execution speed and consistency deteriorate
Solution Approach 1:
The system enables automatic self-execution of orders through the EO platform, which autonomously processes orders by applying prediction models and rules without requiring manual intervention. This resolves the contradiction by maintaining operational flexibility through programmable logic while dramatically improving execution speed through automation.
Solution Approach 2:
The patent replaces manual mechanical order execution with an automated electronic system that uses prediction models, rules engines, and algorithmic processing. This substitution eliminates human response time limitations while maintaining flexibility through configurable parameters and adaptive algorithms.
2Adaptability or versatility
If manual order execution is used, then adaptability to individual trader preferences is improved, but execution consistency and accuracy deteriorate
Solution Approach 1:
The system applies personalized profiles to individual portfolio managers that capture their specific trading preferences, risk tolerances, and strategic approaches. Each trader receives customized execution treatment while the system maintains overall consistency through standardized processing frameworks and uniform application of rules.
Solution Approach 2:
The patent utilizes configurable parameters within the prediction models and rules engine that can be adjusted to reflect individual trader preferences. These parameters are systematically managed to ensure consistency in how preferences are applied across different orders and market conditions.
3Loss of time
If faster execution is implemented, then price slippage is reduced, but system complexity increases
Solution Approach 1:
The system performs preliminary analysis by applying prediction models to forecast price movements and optimal execution timing before actually executing orders. This advance preparation enables faster response to market opportunities while managing complexity through pre-computed predictions and predefined execution strategies.
Solution Approach 2:
The patent introduces an intermediary layer consisting of prediction models and rules engines that mediate between order reception and execution. This intermediary processing layer manages system complexity by handling the computational burden of real-time analysis while maintaining simple, clean interfaces for order input and execution output.
4Productivity
If automated execution systems are used, then execution speed is improved, but adaptability to changing market conditions deteriorates
Solution Approach 1:
The system incorporates dynamic elements through real-time market data feeds that continuously update prediction models and trigger rule-based adjustments. The automated execution adapts to changing market conditions by dynamically recalculating optimal execution parameters while maintaining high-speed automated processing.
Solution Approach 2:
The patent implements feedback mechanisms where execution results and market responses are continuously monitored and fed back into the prediction models. This closed-loop system enables the automated execution to learn from past performance and adapt its strategies in real-time based on actual market conditions and execution outcomes.
Data Source
AI summary
An embodiment of the present invention provides computer-implemented methods and systems for optimizing the executing an order, such as trading orders. An order may be electronically routed to an Execution Optimizer (“EO”). The EO may apply a particular profile to the order, corresponding to a particular portfolio manager. Next, the order, with the profile, may be routed, electronically, to a third party where a prediction model may be applied to the order, indicating trading parameters for the order. The order, with the trading parameters from the prediction model, may be passed back to the EO, where a rules engine may apply rules, specific to the executing financial institution, to the order. The order may then be passed to a selected broker for market trading.