Description:FIELD OF THE INVENTION
This invention relates to a predictive stock market analysis system utilizing real-time sentiment analysis and ai-driven pattern recognition
BACKGROUND OF THE INVENTION
The present stock market prediction methods fail to deliver accurate estimates regarding market movements.
The conventional market analysis framework bases its prediction on historical data along with technical indicators that lack sensitivity to modern marketplace changes.
The existing system needs incorporation of multiple datasets combined with advanced AI technologies to enhance market predictions.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
The presented system integrates real-time sentiment analysis from multiple textual data sources which include social media platforms in addition to news feeds and financial reports.
A combination of sophisticated machine learning algorithms and artificial intelligence conducts thorough analysis of fine emotional changes to connect them with market historical data.
Users can use the system to acquire actionable insights along with risk assessment reports which build their capability to take well-informed investment decisions.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: SYSTEM ARCHITECTURE
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The presented system integrates real-time sentiment analysis from multiple textual data sources which include social media platforms in addition to news feeds and financial reports.
A combination of sophisticated machine learning algorithms and artificial intelligence conducts thorough analysis of fine emotional changes to connect them with market historical data.
Users can use the system to acquire actionable insights along with risk assessment reports which build their capability to take well-informed investment decisions.
A Predictive Stock Market Analysis System that combines real-time sentiment analysis and AI-driven pattern recognition to generate buy or sell signals.
The system operates in two main parallel branches:
• Real-Time Sentiment Analysis: This branch focuses on understanding market sentiment by collecting data, scoring it, and determining if the sentiment is positive or negative. Based on this, it predicts price increases or decreases and executes buy or sell orders accordingly.
• AI-Driven Pattern Recognition: This branch utilizes artificial intelligence to detect patterns in market data. It then determines if the identified pattern is bullish or bearish, generating buy or sell signals. The system then monitors the market and adjusts its strategy based on the outcome.
Both branches ultimately lead to executing trades (buy or sell orders) and continuously monitoring the market to refine the trading strategy. The diagram highlights a systematic approach to stock market analysis, leveraging both sentiment and technical indicators for informed decision-making.
NOVELTY:
ADVANTAGES OF THE INVENTION
Improved accuracy in stock market prediction.
Enhanced decision-making for investors.
Reduced risk through personalized assessments.
Increased efficiency in analyzing market data.
 
, Claims:1. A predictive stock market analysis system comprising:
a real-time sentiment analysis module configured to collect textual data from multiple sources including social media platforms, news feeds, and financial reports, and to score said data to determine sentiment polarity;
an AI-driven pattern recognition module configured to detect market patterns from historical and real-time data using machine learning algorithms;
a decision engine operatively connected to both modules to generate buy or sell signals based on sentiment analysis and detected patterns; and
a trade execution module adapted to place trades based on said generated signals, wherein said system continuously monitors the market to refine future trading strategies.
2. The system as claimed in claim 1, wherein the real-time sentiment analysis module employs natural language processing (NLP) techniques to analyze fine emotional variations in the collected textual data for improved prediction of market trends.
3. The system as claimed in claim 1, wherein the AI-driven pattern recognition module classifies detected patterns as bullish or bearish and correlates them with historical market outcomes to determine the probability of price movements.
4. The system as claimed in claim 1, wherein the decision engine integrates sentiment polarity scores and pattern recognition results to generate personalized risk assessment reports for the user.
5. The system as claimed in claim 1, wherein the continuous monitoring mechanism automatically adjusts trading strategies in real time based on feedback from executed trades and updated market data.