24 results about "Stock trading" patented technology

An artificial intelligence-powered gamified learning management system is disclosed, designed to enhance financial literacy and stock trading education. The system integrates adaptive learning, real-time market analytics, and interactive simulations, including a Fantasy Stock Trading League and Paper Trading Engine. The AI-powered Adaptive Learning Engine dynamically adjusts educational content and trade recommendations based on user performance, utilizing reinforcement learning and collaborative filtering models. The Market Insights Engine provides predictive analytics, sentiment analysis, and risk assessment using advanced AI models. Users engage in competitive trading simulations, sentiment-based challenges, and community-driven learning forums, fostering practical investment skills. The system offers personalized learning paths, real-time feedback, and post-trade analysis to improve decision-making. Principal uses include stock trading education, financial literacy enhancement, and gamified investment simulations. This system addresses the limitations of static financial education platforms by creating an interactive, adaptive, and engaging learning environment.

The invention relates to the field of financial transactions, and particularly discloses a stock transaction risk control method and device, equipment and a medium. The method comprises the following steps: constructing a standardized data set which contains technical indexes and is subjected to LinearSVC sentiment analysis through data acquisition and processing; performing basic stock price prediction by adopting machine learning or a neural network model; constructing a state vector containing VaR and CVaR risk indexes by using reinforcement learning, performing risk perception correction on a prediction result through a TQC algorithm, and generating a transaction signal; and finally calculating and displaying a risk income index and a net value curve. According to the method, deep learning prediction and reinforcement learning risk control are creatively combined, risk factors are subjected to internal biochemical treatment in a prediction stage, forward-looking inhibition of market tail risks and dynamic optimization of transaction signals are realized, the problem of separation of prediction and risk control links in a traditional transaction system is effectively solved, and the risk control efficiency is improved. The earnings after risk adjustment are significantly improved, and an integrated solution with intelligent prediction and robust risk control is provided for security transactions.

An object of a system according to an embodiment is to effectively improve the financial literacy of young people.SOLUTION: A system in accordance with an embodiment includes a micro-learning financial app, a AI guided virtual stock trading game app, and a community platform for financial literacy improvement. The micro-learning financial application provides financial education content that can be learned in a short time. The virtual-guided virtual stock trading game app assists users in learning practical financial knowledge through AI stock trading. A community platform for improving financial literacy provides a place for users to share information and learn from each other.SELECTED DRAWING: Figure 1

1. The name of the design product: stock trading graphical user interface of electronic device. 2. The use of the design product: an electronic device. 3. The design points of the design product: the interface content of the graphical user interface in the electronic device. 4. The picture or photo that best indicates the design points: design 1 front view. 5. Design 1 is designated as the basic design. 6. The use of the graphical user interface: the graphical user interface is used for stock trading and related information viewing. 7. The human-computer interaction mode of the graphical user interface: design 1 front view is a discovery interface. The user clicks to trigger the "opportunity" label in design 1 front view, and the interface switches to design 1 interface change state diagram 1. The user clicks to trigger the square icon button at the lower right of the top title "opportunity" in design 1 interface change state diagram 1, which can switch the interface layout from double row to single row, as shown in design 1 interface change state diagram 2. The user clicks to trigger any one label in design 1 interface change state diagram 2, which can switch to the corresponding quote page. For example, the user clicks to trigger the first label in design 1 interface change state diagram 2, and the interface switches to design 1 interface change state diagram 3. The user can click the heart-shaped icon in the upper right corner to add the current content to the self-selected list. The user clicks to trigger the "transaction" text button at the rightmost side of any stock name, which can switch to the specific information page of the stock. The user slides the interface to the lower part, and the interface switches from design 1 interface change state diagram 3 to design 1 interface change state diagram 4. Design 2 front view is a discovery interface. The user clicks to trigger the "opportunity" label in design 2 front view, and the interface switches to design 2 interface change state diagram 1. The user clicks to trigger the square icon button at the lower right of the top title "opportunity" in design 2 interface change state diagram 1, which can switch the interface layout from double row to single row, as shown in design 2 interface change state diagram 2. The user clicks to trigger any one label in design 2 interface change state diagram 2, which can switch to the corresponding quote page. For example, the user clicks to trigger the first label in design 2 interface change state diagram 2, and the interface switches to design 2 interface change state diagram 3. The user can click the heart-shaped icon in the upper right corner to add the current content to the self-selected list. The user clicks to trigger the "transaction" text button at the rightmost side of any stock name, which can switch to the specific information page of the stock. The user slides the interface to the lower part, and the interface switches from design 2 interface change state diagram 3 to design 2 interface change state diagram 4. In the interface, XXX represents the text content, the content picture is covered by the gray block area in the interface, and the shape of the gray block is only used to cover the content picture and does not represent the shape of the content picture.

1. Name of the product in this design: Options trading panel with graphical user interface for trading platform of electronic device. 2. Purpose of this design: An electronic device. 3. The key design features of this product are the graphical user interface content in the electronic device. 4. The image or photo that best illustrates the design points: Interface change state diagram 1. 5. Purpose of the graphical user interface: This graphical user interface is used as a whole for the stock trading platform to display user accounts and stock information, and is also used in a part to display options trading panel information. 6. Description of the changing states of the graphical user interface: The main view is the custom interface of the stock trading platform. At this time, the options trading panel area is collapsed. When the user clicks the expand icon (arrow icon) on the right side of the options trading panel area in the main view, the interface switches to the interface change state diagram 1. At this time, the options trading panel area of ​​this interface displays the option leg information table. The user can view and adjust the expiration date, strike price, type, direction, and number of contracts on this interface. When the user clicks the remove icon (the icon to the right of the number of contracts information box) on the right side of the option leg information in the interface change state diagram 1, the corresponding option leg information can be removed, as shown in the interface change state diagram 2. When the user clicks the "Add" icon below the option leg information table on this interface, the interface switches to the interface change state diagram 3. When the user clicks the "Sell" option icon below "Add Underlying Stock" in the interface change state diagram 3, the interface switches to the interface change state diagram 4. When a user clicks the “Account Change Preview Entry” icon (the icon to the left of the Transaction Unlock option button) in Interface Change State Figure 4, the interface switches to Interface Change State Figure 5. When the user clicks the “Delete All” icon (the icon to the left of the Order Type option) in Interface Change State Figure 5, the interface switches to Interface Change State Figure 6. The gray areas shown in each interface represent the content screen, and xx in each interface represents the text content. The enlarged views are partial enlarged views of the options trading panel area in each interface view. 7. Other situations requiring explanation: The parts covered by the red semi-transparent color blocks in each interface view do not constitute the parts required for protection in the local appearance.

1. Name of the product in this design: Graphical User Interface for Electronic Devices with Qiyingtong Stock Market Analysis Software Login and Installation. 2. Intended use of this design: for graphical user interfaces of electronic devices. 3. The key design feature of this product is its graphical user interface. 4. The image or photograph that best illustrates the design's key points: the front view. 5. Purpose of the graphical user interface: Displays real-time market data for various futures, options, and financial products, including key information such as price, percentage change, and trading volume. Users can add custom products to watch and set market alerts. 6. Description of the graphical user interface changes: The main view is the basic interface of the Qiyingtong stock trading software. After the user fills in their name, mobile phone number, and WeChat ID, they can click "Try" at the bottom and jump to the interface change state diagram 1. Click "Next" to jump to the interface change state diagram 2. Click "Yes" to continue the installation and jump to the interface change state diagram 3. Click "OK" to install. The Qiyingtong stock trading system icon will be automatically generated on the computer desktop. Double-clicking the icon will enter the system.

The system according to this embodiment aims to efficiently improve the financial literacy of young people. [Solution] The system according to the embodiment comprises a microlearning finance app, a virtual stock trading game app, and a community platform for improving financial literacy. The microlearning finance app provides short daily learning sessions. The virtual stock trading game app provides trading advice from an AI guide. The community platform for improving financial literacy shares information through forums, Q&A sections, and online workshops.

1. Name of the product in this design: Graphical User Interface for Bulk Stock Trading on Electronic Devices. 2. Purpose of this design: An electronic device. 3. The key design features of this product are the graphical user interface content in the electronic device. 4. The image or photo that best illustrates the design points: Design 1 Interface Change State Diagram 1. 5. Design 1 is designated as the basic design. 6. Purpose of the graphical user interface: This graphical user interface is used by users to place batch orders when trading stocks. 7. Human-computer interaction method of graphical user interface: The main view of Design 1 is the interface for recommending trading strategies by the trading platform. When the user clicks the "Batch Trading" option button in the main view of Design 1, the interface switches to the interface change state diagram 1 of Design 1. At this time, the order type is a limit order. The user can trigger the order type option box to switch to other order types. When the user clicks the "One-Click Buy" option button in the interface change state diagram 1 of Design 1, the interface is in the unlocked state and switches to the interface change state diagram 2 of Design 1. After the user enters the trading password to unlock the trading, the interface switches to the interface change state diagram 3 of Design 1. When the user clicks the "One-Click Buy" option button in the interface change state diagram 3 of Design 1, the interface switches to the interface change state diagram 4 of Design 1. The user can view the basic information of the order on this interface. Design 2's main view is the trading platform's recommended trading strategy interface. When the user clicks the "Batch Trading" option button in Design 2's main view, the interface switches to Design 2's interface change state diagram 1. At this time, the order type is a market order, and the user can trigger the order type option box to switch to other order types. When the user clicks the "One-Click Buy" option button in Design 2's interface change state diagram 1, the interface is in an unlocked state, and the interface switches to Design 2's interface change state diagram 2. After the user enters the trading password on this interface to unlock the trading, the interface switches to Design 2's interface change state diagram 3. When the user clicks the "One-Click Buy" option button in Design 2's interface change state diagram 3, the interface switches to Design 2's interface change state diagram 4, where the user can view the basic information of the order. In the main views of Design 1 and Design 2, the areas covered by gray blocks represent the content screens, and xx in each interface represents the text content.

1. Name of the product in this design: Electronic device with AI dialogue intelligent monitoring and trading graphical user interface for convenient fund transfer. 2. Intended use of this design: for use in electronic devices. 3. The key design feature of this product is its graphical user interface. 4. The image or photograph that best illustrates the design's key features: the front view. 5. Purpose of the graphical user interface: Intelligent monitoring and trading are sent through a dialogue stream, allowing users to view stock trading assets and perform trading operations at any time during the conversation. This facilitates users to conduct trading activities by combining AI interpretation and dialogue content, ensuring a smooth user experience.

A method for stock trading according to the present invention comprises the steps in which: an interface unit receives an input of a bid quantity corresponding to an item of a stock, one bid price, or two different bid prices; a bid processing unit recognizes as staggered buying when the two different bid prices are input; and the bid processing unit derives a plurality of trade prices between the two different bid prices and performs the staggered buying according to the derived plurality of trade prices.

The present invention acquires information associated with intellectual property competitiveness of an enterprise or the like from patent bulletins and the like. A computer program of the present invention collects at least one of the total number of claims, the total number of claim categories, the total number of independent claims, the total number of dependent claims, the maximum number of independent claims within the same claim category, the total number of claims in a claim category identical to or associated with a specified designated good or specified service, the total number of independent claims in a claim category identical to or associated with a specified designated good or specified service, and the total number of dependent claims in a claim category identical to or associated with a specified designated good or specified service from the claim of each of a plurality of patent bulletins or a plurality of published patent bulletins.

This invention discloses a stock price trend prediction method based on multi-level dynamic graph fusion, relating to the fields of financial technology and artificial intelligence data analysis. The method mainly comprises three parts: multi-channel temporal feature extraction, collaborative modeling of macro and micro spatial structures, and multi-level feature fusion prediction. The steps include: First, constructing a multi-channel technical indicator sequence based on historical stock trading data, and using a multi-channel attention pooling GRU network to extract differentiated temporal features in parallel; Second, constructing a stock association hypergraph based on industry and concept labels, introducing graph information loss (GIL) as a feedback signal to drive an adaptive hyperedge reconstruction mechanism, dynamically adjusting hyperedge weights to filter noise and capture macro market hotspots; Simultaneously, constructing a simple graph within the hyperedge and combining contrastive learning constraints to semantically align stocks with similar technical patterns to enhance the discriminative power of micro local features; Finally, integrating macro hypergraph features and micro simple graph features through a cross-graph fusion module, concatenating them with temporal features, and inputting them into the prediction layer to complete the prediction of the stock price trend at the next moment. This method overcomes the limitations of existing static graph models in dynamically capturing high-order stock correlations and fine-grained technical pattern resonances, providing a new approach for financial spatiotemporal data mining.

The invention relates to a calculator system and method for predicting stock buying and selling strategies. The calculator system comprises a parameter input unit, a core processing unit and a result output unit. The core processing unit executes a bidirectional closed-loop prediction algorithm based on an initial price, a principal, a fractionation number, a price difference, a non-linear growth rate, a complete transaction cost and financing and coupon fusion parameters input by a user: generating a buying sequence with a declined price and a non-linearly increased number in a buying direction; the total cost including the financing cost and the accurate local selling price are synchronously calculated; and generating a selling sequence of which the price is increased progressively and the number is increased nonlinearly in the selling direction, and synchronously calculating the total income and the reinvestment potential containing the voucher fusion cost. All calculations are integrated with an accurate cost and interest model in real time, an interface supports parameter linkage modification and result instant visual updating, and through combination of a bidirectional closed-loop prediction algorithm and a nonlinear transaction model, risk boundaries of buying and selling decisions are preposed and quantified, so that dynamic controllability of transaction strategies is realized.

The embodiment of the invention provides a method and device for generating MIDI music based on a stock K line. The method comprises the steps that a convolution model of a closing price sequence is constructed to solve a reference pitch sequence, note strength is generated in combination with price fluctuation characteristics, a playing time sequence is determined through Fourier spectrum analysis, and finally music works with market characteristic expression ability are synthesized. According to the method and the device for generating the MIDI music based on the stock K line disclosed by the embodiment of the invention, multi-dimensional mapping from financial data to music parameters is realized, so that the generated melody has higher music performance, better conforms to natural music rhythm and has more dynamic expressive force.

The invention relates to a dragon callback form trend matching method based on causal analysis. The method comprises the steps of 1, acquiring stock transaction data in a specific time window range as an original data set; 2, constructing a geometric form template by defining key structure points of a dragon callback form, and extracting quantitative features by form definition; 3, systematically analyzing the causal association between the obtained Longu callback morphological characteristics and the Longu callback trend by adopting a PC causal discovery algorithm, and carrying out intensity characterization on the identified causal relationship; and 4, constructing a graph convolutional network model by taking the causal intensity graph obtained by calculation in the step 3 as a topological structure basis, carrying out iterative training on the model by taking the mined dragon callback morphological characteristics and trends as data, and finally forming the graph convolutional network model capable of matching the dragon callback morphological trends. The method has the advantages that the dragon callback form in the stock market can be accurately identified, and the trend of the stock is matched according to the actual performance of the stock during the dragon callback period.

SMART STOCK EXCHANGE ORDER SYSTEM The present invention relates to a system (1) which enables users to place smart 5 and flexible orders in the stock exchange market.

The invention provides a screening method and system based on multi-dimensional user information, and relates to the technical field of multi-dimensional user information, and the method comprises the steps: obtaining multi-modal behavior data generated by a plurality of users in a stock transaction software interaction process; performing time sequence alignment and window segmentation processing on the multi-modal behavior data to form a plurality of behavior data fragments of equal-length time windows, and performing statistical feature extraction on each behavior data fragment to form a dense feature vector; inputting the dense feature vector into a GRU network, and performing time sequence modeling on the dense feature vector by using the GRU network to form a time sequence feature sequence; calculating attention weights of different time points in the time sequence feature sequence, and generating a user response capability classification label based on the attention weights; and according to the user response capability classification tag, screening out a target user meeting a preset condition from the plurality of users. According to the method, the accuracy and the timeliness of judging the response capability of the user are improved.

To provide more accurate predictions of future levels of interest on the internet compared to conventional technologies. [Solution] A prediction system (S) that predicts the future level of interest in a keyword based on time-series data of frequency information related to the keyword transmitted to a server (3,4), comprising: data acquisition means (Ca2) for acquiring time-series data; data division means (Ca4) for dividing the time-series data into predetermined time intervals; frequency analysis means (Ca5) for analyzing the frequency components of the time-series data for each time interval; and determination means (Ca6) for determining whether or not there will be an increase in future frequency information based on a comparison of the amplitude intensity of low-frequency components in the target time interval with the amplitude intensity of low-frequency components in past time intervals.

The invention relates to the technical field of financial science and technology, and discloses an automatic stock transaction execution system and method, and the system comprises a data collection module, a data processing module, a prediction analysis module, a transaction decision module, a transaction execution module, and a monitoring report module. The method comprises the steps of collecting multi-source stock market data and performing data cleaning and preprocessing to generate a standardized stock market data sequence; stock trend prediction analysis is carried out through a machine learning algorithm based on the data sequence, and a stock price prediction signal is generated; generating and executing an automatic transaction instruction according to the prediction signal in combination with a risk control model; and monitoring a transaction execution state in real time, dynamically adjusting a strategy, and finally generating a transaction execution report. According to the invention, the robustness and accuracy of stock price prediction signals are improved, and the adaptability and anti-risk capability of the system to market dynamics are enhanced.

This invention discloses a cell rendering method, system, device, and storage medium based on individual stock momentum, relating to the fields of financial data visualization and human-computer interaction technology. It aims to solve the technical problems of fragmented information display, spatial redundancy, and single visualization dimensions in existing stock trading terminals. This invention acquires multi-period historical market data of securities, extracts multi-scale moving average sequences, constructs a logarithmic benchmark to eliminate differences in stock price dimensions, determines the overall trend of the target, quantifies short-term momentum intensity and maps it to multi-level discrete energy values, matches corresponding feature background colors and rendering opacity, and finally combines the multi-period rendering results into a cell's underlying array grid color blocks. This invention achieves high-density display of multi-dimensional market information within a single cell, significantly improving the human-computer interaction efficiency and page space utilization of financial data terminals.

This application discloses a restricted stock trading system, including a user interaction layer, a control layer, a business layer, and an execution layer. The control layer, connected to the user interaction layer, receives order requests from the user interaction layer and, based on the target batch of restricted stock sales tasks associated with the order request and the order method indicated in the order request, sends a first order instruction corresponding to the target batch. The business layer, connected to the control layer, receives and responds to the first order instruction, generates a aggregate order associated with the target batch of restricted stock sales tasks, the aggregate order including the transaction targets corresponding to the order method; and generates a second order instruction based on the aggregate order. The execution layer, connected to the business layer, receives and, based on the second order instruction, performs restricted stock sales transactions according to the transaction targets corresponding to the aggregate order, obtaining the transaction result. The trading system can reduce the number of second order instructions and improve server resource utilization.

The invention provides a customer management method and system based on stock investment user feature and behavior data mining, and relates to the technical field of customer management based on stock investment user feature and behavior data mining. Based on the user information data, mining the interaction touch data through a feature engineering algorithm to obtain user interaction behavior feature data; performing association mining on the data to obtain an adaptive stock investment platform service list and an updated platform interface; and finally, according to the service use frequency and satisfaction data of the users, customer groups are determined, and differentiated customer management strategies are formulated, so that adaptive service list, interface updating and customer group differentiated management can be realized by mining stock investment user interaction and information data, and a complete technical path is provided for precise service users of a stock investment platform.

The embodiment of the invention provides a model-based stock price acquisition method and related equipment, and belongs to the technical field of deep learning. According to the embodiment of the invention, the method can achieve the multi-scale decomposition of the stock transaction data through a VMD technology, enables a complex non-stationary signal to be divided into a plurality of intrinsic mode functions which represent different frequency features, employs a Transformers model as a core frame, carries out the channel-level fusion of the multi-mode features after VMD decomposition with the original stock transaction data, and achieves the recognition of the original stock transaction data. And global dependency modeling is carried out on a time sequence dimension through a multi-head self-attention mechanism, so that the model can simultaneously capture long-term trend and short-term fluctuation characteristics, and the prediction precision is remarkably improved.