Probabilistic Analysis Trading Platform Using Bayesian Networks
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Solution Overview
Problem
Current trading platforms lack efficient methods to predict securities prices and make informed trading decisions based on probabilistic analysis, especially in high-frequency trading and portfolio management, due to limitations in integrating probabilistic graphical models with financial analysis and trend analysis.
Innovation Solution
The Probabilistic Analysis Trading Platform (PATP) merges Probabilistic Graphical Models (PGMs) with financial and trend analysis techniques to model relationships between input parameters and goal probabilities, using Bayesian networks and historical data to predict securities prices and take trading actions, and is self-adjusted with new data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional trading platforms use basic analysis methods, then system complexity remains low, but prediction accuracy and trading decision quality deteriorate
Solution Approach 1:
The patent combines probabilistic graphical models with fundamental financial analysis and trend analysis techniques into a unified trading platform. This integration merges multiple analytical approaches (PGMs, fundamental analysis, trend analysis) to simultaneously improve prediction accuracy while managing system complexity through structured combination of components.
Solution Approach 2:
The trading platform is designed to perform multiple functions: it conducts probabilistic analysis, fundamental analysis, and trend analysis within a single system. This multi-functionality allows the platform to address various aspects of securities price prediction and trading decision-making without requiring separate specialized systems.
2Measurement precision
If the platform processes more data for better predictions, then prediction accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The platform pre-processes and structures financial data, fundamental data, and trend data before they are needed for prediction. By organizing data in advance using probabilistic graphical models and establishing relationships between variables beforehand, the system reduces the computational burden during actual prediction and trading decision-making.
Solution Approach 2:
The system dynamically adjusts its processing based on market conditions and available data. It can adaptively select which analytical methods to apply and how deeply to process different types of data, allowing it to maintain high prediction accuracy while optimizing processing time based on current requirements.
Data Source
AI summary
The Probabilistic Analysis Trading Platform Apparatuses, Methods and Systems (“PATP”) transforms model training request and security analysis request inputs via PATP components into model parameters data, model training response, order request, and security analysis response outputs. A security analysis request associated with a security may be obtained via security analysis component. Model parameters of a model associated with the security may be retrieved. Modified nodes of the model for which probabilities have been modified by expert input data associated with the security analysis request may be determined. Dependent nodes for each of the modified nodes may be determined. Probabilities associated with the dependent nodes may be recalculated and an output value associated with a result node of the model may be determined based on the recalculated probabilities. A trading action may be facilitated based on the output value.