Machine Learning Model Retraining via Synthetic Data Pre-training
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Solution Overview
Problem
Current machine-learning model optimization methods in finance lack the ability to dynamically adjust asset portfolios in response to changing market conditions and user preferences, leading to suboptimal risk management and returns.
Innovation Solution
A computer-implemented method that continuously trains and re-trains machine-learning models using synthetic and historic data to generate AI-managed portfolios, allowing for dynamic asset allocation and rebalancing based on real-time market data and user objectives, thereby optimizing risk-adjusted returns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine-learning models are continuously trained and re-trained using synthetic and historic data, then the model accuracy and adaptability to changing market conditions improve, but the computational resources and time required for training increase
Solution Approach 1:
The system performs preliminary actions by pre-training machine-learning models using synthetic data before deployment. This pre-training prepares the models in advance so that when real market data becomes available, the models can be quickly fine-tuned rather than trained from scratch, significantly reducing the time loss for continuous re-training while maintaining high accuracy
Solution Approach 2:
The system creates synthetic data copies that mimic real market conditions without requiring actual real-time data. These synthetic copies serve as proxies for expensive and time-consuming real data training, allowing the models to learn patterns and behaviors without the full computational burden of processing authentic market data continuously
2Adaptability or versatility
If machine-learning models are continuously re-trained in real-time, then the adaptability to market changes improves, but the computational complexity and resources required increase
Solution Approach 1:
The training process is segmented into distinct phases: initial training on synthetic data, periodic re-training on historic data, and real-time fine-tuning on new market data. This segmentation allows the system to maintain adaptability while managing computational complexity by not performing full re-training continuously, but rather applying computational resources strategically at appropriate intervals
Solution Approach 2:
The system applies different training qualities to different data types and time periods. Synthetic data receives intensive pre-training computational resources, while real-time market data receives lighter fine-tuning resources. This local differentiation of computational quality maintains adaptability without uniformly high computational complexity across all operations
3Ease of operation
If static methodologies are used for asset portfolio management, then the system simplicity and ease of operation are maintained, but the ability to respond to changing market conditions and user preferences deteriorates
Solution Approach 1:
The machine-learning models perform self-service by automatically adapting to new market conditions and user preferences without requiring manual system reconfiguration. The models self-train on new data and self-adjust their predictions, maintaining system simplicity from the user perspective while achieving high adaptability internally through automated continuous learning processes
Data Source
AI summary
In an embodiment, a one or more non-transitory computer-readable storage media includes receiving account specifications, training a machine-learning model using training data derived from the account specifications to produce a trained machine-learning model capable of outputting a recommendation of a particular asset portfolio, determining an asset portfolio by inputting a particular account specification into the machine-learning model, determining a performance metric for the asset portfolio based on synthetic data and historic data, determining a reference performance metric based on a reference asset portfolio and the synthetic and historic data, comparing the performance metric for the asset portfolio with the reference performance metric to result in a decision of whether to re-train the machine-learning model, re-training the machine-learning model based on updated training data and the synthetic and historic data, automatically executing trades for an updated asset portfolio determined by the re-trained machine-learning model, and repeating the re-training continuously based on a specified frequency or timing.