Dynamic Portfolio Selection Database for Real-Time Customization
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Solution Overview
Problem
Current financial portfolio management systems lack customization based on user preferences and real-time updates, making it difficult for individuals to create and maintain optimal portfolios effectively.
Innovation Solution
A dynamically-generated electronic database system that integrates user preferences and real-time data to select financial instruments, using a selector engine and electronic labels to optimize portfolios, while incorporating deep learning and machine learning algorithms for enhanced decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If databases are based solely on various market factors, then the database structure is simple and easy to maintain, but customization based on user selection preferences or needs is not possible
Solution Approach 1:
The database is segmented into multiple independent modules including market factor data, user preference data, selection criteria, and portfolio configurations. Each module can be independently modified without affecting others, enabling customization while maintaining manageable complexity through modular architecture
Solution Approach 2:
The database structure transitions from static to dynamic, allowing real-time updates of user preferences, selection criteria, and portfolio compositions. The system dynamically adapts the database content based on individual user inputs while maintaining a consistent underlying structure
2Productivity
If automatic portfolio selection is limited to exchange-traded funds (ETFs), then the selection process is simplified and automated, but the portfolio performance is solely linked to the performance of that index without clear link to achieving financial goals
Solution Approach 1:
The system applies different selection criteria and weighting schemes to different portions of the portfolio based on specific financial goals. Each goal category (e.g., growth, income, sustainability) has customized local selection rules that can be applied to relevant subsets of the portfolio while maintaining overall automation
Solution Approach 2:
The system dynamically changes selection parameters such as risk tolerance, time horizon, and asset allocation weights based on the user's specific financial goals. These parameter adjustments enable the automated system to adapt its selection criteria to match diverse financial objectives rather than following a fixed index
3Adaptability or versatility
If human beings evaluate all factors needed to optimize and manage a financial instrument portfolio in real time, then complete control and customization are achieved, but it is technically impossible due to the real-time updating exchange
Solution Approach 1:
An automated portfolio management system acts as an intermediary between the real-time exchange data and the investor's financial goals. This intermediary continuously processes market data, applies customized selection criteria, and executes trades without human intervention, achieving both real-time responsiveness and personalized customization
Solution Approach 2:
The system enables self-service portfolio management where the automated platform independently evaluates all market factors, applies user-defined constraints, and makes selection decisions without requiring continuous human evaluation. The system serves itself by automatically monitoring, analyzing, and rebalancing the portfolio in real-time
4Ease of operation
If users are not able to combine investment funds in a way that directly enables them to achieve their goals optimally, then the investment process is simplified, but optimal portfolio construction for individual goals is not achieved
Solution Approach 1:
The system performs preliminary analysis of all available investment funds and their characteristics before the user makes decisions. It pre-calculates optimal combinations based on user goals, risk profiles, and market conditions, then presents ready-to-execute portfolio configurations that combine multiple funds in goal-optimized ways
Data Source
AI summary
A system for creating a financial instrument selection, sharing the selection, and executing trade instructions thereof: a selector engine for creating selection parameters according to a statistical model for weighting desirability of financial instruments combined with entered user selection preferences or objectives for financial instruments, creating investment portfolios of financial instruments based on the selection parameters, and converting the investment portfolios data to output data representing trade instructions of financial instruments; a deep learning decision engine providing predicative input data and an investment sharing and democratization and portfolio monitoring module providing peer investment portfolio shared data input to the selector engine; and an execution platform for executing the trade instructions, the execution platform including electronic selection limiters to prevent execution of specific trades based on electronic flags computed from electronic checks relating to the amounts and types of the trades.