A transaction simulation training system based on random historical k-line

By combining cloud computing power filtering and local database caching technologies, the problems of network latency and limited training scenarios in existing financial trading simulation training systems have been solved, achieving an efficient, stable, and diversified K-line training experience and improving practical skills.

CN122288931APending Publication Date: 2026-06-26SHENZHEN HULUWA INFORMATION TECHNOLOGY SERVICE CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
SHENZHEN HULUWA INFORMATION TECHNOLOGY SERVICE CO LTD
Filing Date
2026-03-20
Publication Date
2026-06-26

AI Technical Summary

Technical Problem

Existing financial trading simulation training systems rely on real-time data from the cloud, resulting in high network latency, high data consumption, limited training scenarios, difficulty in covering diverse market conditions, low training efficiency, and inability to meet the needs for efficient, stable, and diversified candlestick chart training.

Method used

It combines cloud computing power filtering units with local database caching units to clean, classify, and standardize historical candlestick data to form a callable dataset, which is then cached locally. It randomly extracts candlestick data with different trends, supports offline training, and provides a diverse trading simulation experience by combining trading simulation interaction units and record statistics units.

Benefits of technology

It significantly improves system response speed and stability, covers various market trends, enhances the comprehensiveness and practicality of training, provides a smooth trading simulation experience and detailed training reports, and helps users quickly improve their candlestick analysis and trading decision-making abilities.

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Abstract

This invention relates to the field of financial trading simulation training technology, and discloses a trading simulation training system based on random historical candlestick charts. The system includes a cloud computing power filtering unit, a local database caching unit, a candlestick chart random extraction unit, and a trading simulation interaction unit. The cloud computing power filtering unit is used to clean and classify the raw historical candlestick chart data. This trading simulation training system based on random historical candlestick charts combines cloud computing power filtering with local database caching, separating candlestick chart data processing from rapid retrieval. This effectively reduces latency and traffic consumption caused by frequent network requests, significantly improving system response speed and operational stability. The local cache supports offline use, eliminating network environment limitations and adapting to more usage scenarios. The overall architecture optimizes computing power allocation, reducing resource waste and allowing the system to operate efficiently with low network overhead, providing users with a smooth and stable candlestick chart trading simulation training experience.
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