Synthetic Neural Data Model for Electronic Communication Networks
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Solution Overview
Problem
Conventional data modeling techniques struggle to create realistic simulation environments that dynamically adapt to participant actions, failing to leverage historical information effectively.
Innovation Solution
A synthetic neural data model utilizing a neural network architecture that generates and trains on historical data, supports various orders, and uses a fixed grid with a first-in-first-out mechanism to simulate electronic communication networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional data modeling techniques are used, then the model structure is simple and easy to implement, but the model cannot effectively learn and adapt to system dynamics based on historical information
Solution Approach 1:
The patent replaces conventional mechanical data modeling approaches with a neural network-based system. The neural network learns system dynamics automatically from historical data through training, substituting traditional explicit modeling methods with a data-driven approach that adapts to complex patterns without requiring manual specification of model structure.
Solution Approach 2:
The patent transforms the modeling approach by changing from fixed conventional parameters to learned neural network parameters. The model evolves its internal parameters through training on historical data, allowing it to adapt to system dynamics. This includes learning from past market conditions, participant behaviors, and system responses to create a more adaptive simulation environment.
2Reliability
If a neural network architecture is introduced to leverage historical information, then the model's ability to learn system dynamics improves, but the computational complexity and training requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-training the neural network on historical data before deployment. The model undergoes an offline training phase where it learns from extensive historical market data, participant behaviors, and system dynamics. This preliminary training ensures the model is already adapted and reliable when deployed for simulation, reducing the need for complex real-time computations during actual use.
3Measurement precision
If the model evolves based on participant actions in real-time, then the simulation accuracy improves, but the computational resources and time required increase
Solution Approach 1:
The patent performs the computationally intensive learning process in advance during an offline training phase. The neural network is trained on historical data containing various participant actions, market conditions, and system responses before deployment. Once trained, the model can rapidly simulate participant actions in real-time without requiring extensive computation during the simulation itself, thus reducing time loss during actual use.
Data Source
AI summary
A method for providing a synthetic neural data model is disclosed. The method includes generating a model that simulates an electronic communication network; appending agents to the model, the agents relating to a software component that sends orders to the model based on a predetermined timestep; assigning a fixed grid to the model, the fixed grid including a tick size that relates to a fixed granularity; calibrating each of the agents by using a calibration data set; inputting the model and historical book data to a neural network; and training, via the neural network, a neural network extension of the model by using an optimizer.