| Artificial intelligence-driven predictive analytics and algorithmic trading system for financial market optimization, has multiple interconnected components that work together to optimize financial market trading | |
| 2025-03-29 | |
| 专利权人 | CHENNAMSETTY C S (CHEN-Individual) ; MANDA P (MAND-Individual) ; GANGONE S (GANG-Individual) ; PANDIPATI S (PAND-Individual) ; MITTAPALLY R (MITT-Individual) ; KANDULA N (KAND-Individual) ; BALLAMUDI S (BALL-Individual) ; DACHEPALLI V (DACH-Individual) ; RAYARAO S R (RAYA-Individual) |
| 申请日期 | 2025-03-29 |
| 专利号 | IN202541031002-A |
| 成果简介 | NOVELTY - The system comprises multiple interconnected components that work together to optimize financial market trading. The system starts with data collection, where market data e.g. stock prices, are gathered. The raw data serves as foundation for predictive analysis, where adaptive filtering is applied to preprocess and refine the collected information, for removing noise and ensuring reliable input for further analysis. The system employs Convolutional neural network (CNN)(Deep learning network) to analyze historical and real-time financial data. The CNN(Deep learning network) detect complex price patterns and predict future movements with high accuracy, for enabling traders to make informed decisions. USE - Artificial intelligence (Al)-driven predictive analytics and algorithmic trading system for financial market optimization. ADVANTAGE - The system enhances decision-making, reduces risks, and boost profitability by processing large datasets in real time, thus increasing trading efficiency and market stability. The system evaluates and optimizes effectiveness of artificial intelligence (AI)-based trading systems by minimizing risks and maximizing profitability, thus assessing impact of adaptive filtering on data quality, improving market trend forecasting using CNN (Deep learning network), and enhancing automated trading efficiency through deep deterministic policy gradient (DDPG)-based performance monitoring, ultimately leading to more adaptive and intelligent financial trading strategies. |
| IPC 分类号 | G06N-020/00 ; G06N-003/045 ; G06N-003/08 ; G06Q-040/04 ; G06Q-040/08 |
| 国家 | 印度 |
| 专业领域 | 信息技术 |
| 语种 | 英语 |
| 成果类型 | 专利 |
| 文献类型 | 科技成果 |
| 条目标识符 | http://119.78.100.226:8889/handle/3KE4DYBR/13379 |
| 专题 | 中国科学院新疆生态与地理研究所 |
| 作者单位 | 1.CHENNAMSETTY C S (CHEN-Individual) 2.MANDA P (MAND-Individual) 3.GANGONE S (GANG-Individual) 4.PANDIPATI S (PAND-Individual) 5.MITTAPALLY R (MITT-Individual) 6.KANDULA N (KAND-Individual) 7.BALLAMUDI S (BALL-Individual) 8.DACHEPALLI V (DACH-Individual) 9.RAYARAO S R (RAYA-Individual) |
| 推荐引用方式 GB/T 7714 | CHENNAMSETTY C S,MANDA P,GANGONE S,et al. Artificial intelligence-driven predictive analytics and algorithmic trading system for financial market optimization, has multiple interconnected components that work together to optimize financial market trading. IN202541031002-A[P]. 2025. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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