Financial Commodity Price Analysis System Using Aggregated Tick Data
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Solution Overview
Problem
Existing financial commodity price analysis systems lack the ability to utilize user search volume, transaction volume, aggregated prices, and competitor prices as reference factors, leading to insufficient accuracy and fairness in price references.
Innovation Solution
A computer-based financial commodity price analysis system that collects user search volume data, transaction volumes, competitor price data, and volume price indicators through web crawlers and APIs, calculates statistical indicators, and generates price suggestions based on these data points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional price statistical methods (OHLC) are used, then the analysis is simple to implement, but the accuracy and fairness of price references are insufficient due to susceptibility to manipulation
Solution Approach 1:
The patent changes the parameter used for price reference from traditional OHLC prices to aggregated prices calculated from maximum weighted tick data. This parameter change increases measurement precision by using a more robust metric that is less susceptible to manipulation, while the complexity increase is managed through automated calculation systems.
Solution Approach 2:
The patent introduces an intermediary calculation layer that aggregates tick data into weighted prices before using them for analysis. This intermediary step filters out manipulative individual transactions and produces a more accurate representative price, resolving the contradiction between accuracy and complexity.
2Measurement precision
If multiple reference factors (user search volume, transaction volume, aggregated prices, competitor prices) are incorporated, then the pricing strategy accuracy is improved, but the data collection and processing complexity increases
Solution Approach 1:
The patent creates a multi-functional data collection system that simultaneously gathers user search volume, transaction volume, aggregated prices, and competitor prices through unified web crawlers and APIs. This universal approach handles multiple data types through consistent methods, improving pricing accuracy while managing complexity through standardized processes.
Solution Approach 2:
The patent merges multiple data collection methods (web crawlers and APIs) into a unified system that simultaneously processes diverse reference factors. By combining these data sources and processing them through integrated analysis modules, the system achieves comprehensive pricing accuracy without proportionally increasing operational complexity.
3Measurement precision
If real-time microsecond-level tick trading data is collected and analyzed, then the price trend analysis precision is improved, but the data processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary aggregation of tick data into weighted prices and pre-calculates statistical indicators before they are needed for analysis. This preliminary processing reduces the computational burden during real-time analysis, maintaining high precision while reducing actual processing time when decisions are required.
Solution Approach 2:
The patent implements continuous real-time collection and processing of tick data, maintaining constant analysis readiness. By continuously processing data streams rather than batch processing, the system keeps computational resources efficiently utilized and provides immediate high-precision analysis when needed, reducing effective processing delays.
Data Source
AI summary
A computer system for implementing a financial commodity price analysis. This addresses the issue by real-time collection of microsecond-level transaction data for financial commodities, calculating the maximum weighted price for any period, and converting quantitative data into charts to display the most densely traded prices, trends, and price support and breakout points that stimulate changes in investor sentiment. The system accurately analyzes financial commodity price trends, serving as a reference for investors and a standard for bulk commodity price trading. The financial commodity price analysis system of this invention provides price analysis services based on big data, allowing for a more accurate assessment of market demand and competitive dynamics, thereby formulating more reasonable pricing strategies. The price suggestion generation module can flexibly adjust price suggestions based on different market positioning and cost considerations.