A fast stock selection and wave band trading strategy optimization system

Abstract

The present application relates to the field of financial technology and securities investment, and particularly relates to a quick stock selection and wave band transaction strategy optimization system. The system comprises a fund flow monitoring module, a pattern recognition and screening module, a hot spot matching and policy correlation module, and a theory integration and combination construction module. The four modules work together to construct a four-dimensional integrated screening framework of fund, pattern, hot spot and theory. The present application has the following advantages: the present application realizes a wave band operation mode of "fast in and fast out, steady arbitrage", sets the stock selection period to 9:15-9:50, effectively overcomes the lag problem of traditional stock selection, and the loose time window is more suitable for the actual trading scenario, which can help investors quickly grasp investment opportunities in the key period of opening. Through systematic, multi-dimensional screening and combination management, the probability of capturing stocks with a rising limit and strong targets is significantly improved, and the investment success rate is improved. The four-in-one screening framework of fund, pattern, hot spot and theory can comprehensively consider the investment value of individual stocks.

Description

Technical Field

[0001] This invention relates to the fields of financial technology and securities investment technology, specifically to a rapid stock selection and swing trading strategy optimization system. Background Technology

[0002] In the securities investment market, the scientific nature of stock selection and trading strategies directly impacts investment returns. Traditional stock selection methods primarily rely on fundamental analysis or lagging technical indicators, which have numerous drawbacks. Fundamental analysis often requires a significant amount of time to collect, organize, and analyze financial data and other information, resulting in slow response times and difficulty adapting to rapidly changing market conditions. Lagging technical indicators, on the other hand, fail to reflect the latest market dynamics in a timely manner, leading to low screening efficiency and difficulty in quickly identifying stocks with high potential for price increases during critical opening periods. Furthermore, traditional methods lack a systematic, multi-dimensional, and collaborative screening and portfolio management mechanism, resulting in weak ability to capture limit-up stocks and insufficient stability in investment profits, failing to meet investors' demands for efficient and stable investment.

[0003] Therefore, there is a need for a system that can quickly and accurately select stocks and optimize swing trading strategies to solve the problems existing in current technologies. Summary of the Invention

[0004] To achieve the above objectives, the present invention provides the following technical solution:

[0005] A rapid stock selection and swing trading strategy optimization system is characterized by comprising a fund flow monitoring module, a pattern recognition and screening module, a hot spot matching and policy correlation module, and a theoretical integration and combination construction module. The four modules work together to construct a four-dimensional screening framework integrating funds, patterns, hot spots, and theories.

[0006] Preferably, the fund flow monitoring module accesses high-frequency data from the exchange in real time, dynamically monitors the net inflow of funds and volume fluctuations of individual stocks across the entire market from 9:15 to 9:50 every day, sets adaptive dynamic thresholds, and automatically identifies targets with concentrated fund inflows and exhibiting characteristics of rising volume and price.

[0007] Preferably, the pattern recognition and screening module identifies stocks with "limit-up genes" based on historical K-line data, and determines whether the stock is in the second launch phase of "volume pullback + moving average support" by combining the moving average system and trading volume characteristics.

[0008] Preferably, the hotspot matching and policy association module accesses a real-time news and public opinion and policy information database. Through natural language processing and industry association analysis, it automatically matches the industry to which an individual stock belongs with current market hotspots, and prioritizes screening targets that align with policy guidance and areas of capital interest.

[0009] Preferably, the theoretical integration and combination construction module integrates classic analysis tools such as Elliott Wave Theory and Gann Time Cycle, and constructs an equal-weighted or weighted investment portfolio containing 3-5 stocks based on multi-dimensional screening results, setting the holding period to 3-5 days to achieve a "cyclical arbitrage" operation mode.

[0010] Preferably, the stock selection period is set from 9:15 to 9:50, taking into account both the opening auction and the dynamics of the initial opening.

[0011] Preferably, an AI algorithm interface is reserved to support the subsequent introduction of machine learning models, enabling dynamic parameter optimization and strategy self-iteration.

[0012] Preferably, it supports manual intervention, strategy backtesting, and parameter customization to adapt to different market environments and user risk preferences.

[0013] Preferably, it can be embedded in existing trading systems or used as a standalone strategy tool.

[0014] The beneficial effects of this invention are:

[0015] This invention realizes a "quick in and quick out, stable arbitrage" swing trading mode, setting the stock selection period to 9:15-9:50, effectively overcoming the problem of lagging stock selection in traditional methods. The relaxed time window is more in line with actual trading scenarios, and can help investors quickly seize investment opportunities during the key opening period.

[0016] Through systematic and multi-dimensional screening and portfolio management, the probability of capturing limit-up stocks and strong performers is significantly improved, increasing the investment success rate. The integrated screening framework, encompassing capital flow, chart patterns, market trends, and theoretical frameworks, comprehensively considers the investment value of individual stocks, reducing the limitations of single-factor analysis and lowering investment risk.

[0017] The system boasts excellent scalability and compatibility, and reserves interfaces for AI algorithms, providing underlying technical support for future intelligent upgrades. It can be embedded into existing trading systems or used as a standalone strategy tool, making it suitable for individual investors, securities investment institutions, quantitative funds, and fintech platforms. With a wide range of applications, it has promising market prospects.

[0018] Obviously, based on the above description of the present invention, and according to common technical knowledge and conventional methods in the field, various other modifications, substitutions or alterations can be made without departing from the basic technical concept of the present invention.

[0019] The following detailed embodiments further illustrate the above-described content of the present invention. However, this should not be construed as limiting the scope of the present invention to the following examples. All technologies implemented based on the above-described content of the present invention fall within the scope of the present invention. Detailed Implementation

[0020] The present invention is illustrated below with specific embodiments, which are not intended to limit the scope of the invention.

[0021] A rapid stock selection and swing trading strategy optimization system is developed. By constructing a four-dimensional screening framework integrating capital, pattern, hot topics and theory, it achieves efficient stock selection and portfolio management within a specific time window.

[0022] The fund flow monitoring module accesses high-frequency data from the exchange in real time, including but not limited to Level-2 market data. It dynamically monitors net fund inflows, trading volume changes, and stock price fluctuations across the entire market from 9:15 to 9:50 daily, setting adaptive dynamic thresholds. These thresholds can be dynamically adjusted based on factors such as overall market activity and differences in industry sectors. It automatically identifies stocks with concentrated fund inflows and exhibiting characteristics of both volume and price increases, effectively avoiding false signals and numerous risks, and providing core financial data for stock selection.

[0023] The pattern recognition and screening module, based on historical candlestick data, uses graphic recognition algorithms to identify stocks with "limit-up genes," such as those that have previously hit the limit-up price, those that have broken through key resistance levels with increased volume, and those that are about to break out after a period of consolidation. Simultaneously, it combines moving average systems (including short-term, medium-term, and long-term moving averages) and trading volume characteristics. By analyzing recent trading volume trends and moving average alignment patterns, it determines whether a stock is in a second-stage breakout phase characterized by "volume pullback + moving average support," strengthening the structural basis for stock selection and improving its accuracy.

[0024] The hot topic matching and policy association module accesses real-time news and public opinion databases and policy information databases. Using natural language processing technology, it performs word segmentation, semantic analysis, and keyword extraction on text information such as news, public opinion, and policy documents, automatically matching the industry to which an individual stock belongs with current market hot topics. Simultaneously, through industry correlation analysis, it uncovers the inherent connection between policy guidance and market hot topics, prioritizing stocks that align with policy guidance and areas of investor interest, ensuring that selected stocks have a favorable market environment.

[0025] The theoretical integration and combination module integrates classic analytical tools such as Elliott Wave Theory and Gann time cycles, combining classic theories with actual market data to enhance the theoretical support of the strategy. Based on the screening results of the aforementioned three dimensions of capital flow, pattern, and hot spots, and combined with classic theoretical analysis, an equal-weighted or weighted investment portfolio containing 3-5 individual stocks is constructed. The weights of the weighted portfolio can be allocated according to the performance of individual stocks in terms of capital flow, pattern, and hot spots, with higher-performing stocks having higher weights. The holding period is set at 3-5 days to realize a "cyclical arbitrage" operation mode, improving capital utilization efficiency while controlling risk.

[0026] This system features a flexible time window design, extending the stock selection period to 9:15-9:50 AM to accommodate both the pre-market auction and the initial opening dynamics, enhancing the applicability and flexibility of the strategy and better aligning with real-world trading scenarios. Through parallel processing of four dimensions of data—capital flow, chart patterns, market trends, and theoretical frameworks—a closed-loop screening logic is formed, with multi-dimensional collaborative verification improving the accuracy of capturing limit-up stocks. Simultaneously, a combined risk control approach of diversified holdings and short-term rotation reduces the impact of single-stock volatility on the overall portfolio, enhancing overall return stability.

[0027] This system adopts a scalable architecture and reserves interfaces for AI algorithms, supporting the subsequent introduction of machine learning models such as neural network models and decision tree models. Through deep learning of historical data using machine learning models, it achieves dynamic parameter optimization and strategy self-iteration, continuously improving the system's stock selection accuracy and the effectiveness of its trading strategies.

[0028] The specific implementation of this system is as follows: The system acquires market data in real time through data interfaces (such as Level-2 market data interfaces, news APIs, policy database interfaces, etc.); each module processes data in parallel during the 9:15-9:50 time period, completing data parsing, filtering, and target output; users construct investment portfolios according to preset weights based on the preferred list generated by the system, and the system automatically issues rebalancing signals 3-5 days later based on individual stock performance, changes in market hotspots, and capital flows; at the same time, the system supports manual intervention, allowing users to adjust investment portfolios based on their own experience and judgment, as well as perform strategy backtesting and parameter customization to adapt to different market environments and user risk preferences. Detailed Implementation

[0029] System Deployment: This system will be deployed on a server, establishing connections with data providers such as exchanges, news media, and policy-issuing agencies through data interfaces to ensure real-time acquisition of high-quality market data. Simultaneously, the installation and debugging of each system module will be completed to ensure smooth data transmission and normal collaborative operation between modules.

[0030] Data Acquisition and Preprocessing: The system acquires high-frequency data from exchanges, real-time news and public opinion data, and policy information data in real time through data interfaces. The acquired data undergoes preprocessing, including data cleaning, deduplication, and format conversion, to ensure accuracy, completeness, and consistency, providing a reliable data foundation for subsequent data processing and analysis.

[0031] Stock selection process: During 9:15-9:50 AM daily, all modules work in parallel. The fund flow monitoring module dynamically monitors fund-related data for all stocks in the market, identifying stocks with concentrated fund inflows and rising prices and volumes; the pattern recognition and screening module analyzes historical candlestick data, moving averages, and trading volume characteristics to screen stocks with potential upward trends; the hotspot matching and policy correlation module matches stocks with market hotspots and policy directions, prioritizing stocks that align with current trends; the theoretical integration and portfolio construction module combines classic analytical tools to further screen and combine the aforementioned results, generating an investment portfolio list containing 3-5 stocks and weighting suggestions.

[0032] Execution and Rebalancing: Users execute buy orders within the trading software based on the system-generated portfolio list and weighting suggestions. During the 3-5 day holding period, the system continuously monitors individual stock performance, market trends, and fund flows. When preset rebalancing conditions are met, it automatically issues a rebalancing signal. Users can then execute sell orders based on the rebalancing signal, completing a round of swing trading and achieving "cyclical arbitrage."

[0033] System optimization and maintenance: Regularly maintain and upgrade the system, adjust adaptive dynamic thresholds and filtering parameters according to market changes; use historical data to test and optimize system strategies through strategy backtesting; introduce new analysis tools, data sources or AI algorithms based on user feedback and market demands to continuously improve system performance and user experience.

[0034] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0035] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural transformations made using the contents of the present invention under the inventive concept of the present invention, or direct / indirect applications in other related technical fields, are included within the patent protection scope of the present invention.

[0036] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A rapid stock selection and swing trading strategy optimization system, characterized in that, It includes a fund flow monitoring module, a pattern recognition and screening module, a hotspot matching and policy correlation module, and a theoretical integration and combination construction module. These four modules work together to construct a four-dimensional screening framework that integrates funds, patterns, hotspots, and theories.

2. The rapid stock selection and swing trading strategy optimization system according to claim 1, characterized in that, The fund flow monitoring module accesses high-frequency data from the exchange in real time, dynamically monitoring the net inflow of funds and volume fluctuations of individual stocks across the entire market from 9:15 to 9:50 every day. It sets adaptive dynamic thresholds and automatically identifies targets with concentrated fund inflows and exhibiting characteristics of rising volume and price.

3. The rapid stock selection and swing trading strategy optimization system according to claim 1, characterized in that, The pattern recognition and screening module identifies stocks with "limit-up genes" based on historical K-line data. Combining the moving average system and trading volume characteristics, it determines whether the stock is in the second launch phase of "volume pullback + moving average support".

4. The rapid stock selection and swing trading strategy optimization system according to claim 1, characterized in that, The hotspot matching and policy association module accesses a real-time news and public opinion database and policy information database. Through natural language processing and industry correlation analysis, it automatically matches the industry to which an individual stock belongs with current market hotspots and prioritizes the selection of stocks that align with policy guidance and areas of capital interest.

5. The rapid stock selection and swing trading strategy optimization system according to claim 1, characterized in that, The theoretical integration and combination construction module integrates classic analysis tools such as Elliott Wave Theory and Gann Time Cycle. Based on multi-dimensional screening results, it constructs an equal-weighted or weighted investment portfolio containing 3-5 individual stocks, sets the holding period to 3-5 days, and realizes the "circular arbitrage" operation mode.

6. The rapid stock selection and swing trading strategy optimization system according to claim 1, characterized in that, The stock selection period is set from 9:15 to 9:50, taking into account both the opening auction and the dynamics of the initial opening.

7. The rapid stock selection and swing trading strategy optimization system according to claim 1, characterized in that, It reserves an AI algorithm interface to support the subsequent introduction of machine learning models, enabling dynamic parameter optimization and strategy self-iteration.

8. The rapid stock selection and swing trading strategy optimization system according to claim 1, characterized in that, It supports manual intervention, strategy backtesting, and parameter customization to adapt to different market environments and user risk preferences.

9. The rapid stock selection and swing trading strategy optimization system according to claim 1, characterized in that, It can be embedded in existing trading systems or used as a standalone strategy tool.