Self-adaptive equivalent amplitude reference market visualization and analysis system

Abstract

The invention belongs to the technical field of market information visualization analysis, and discloses a market information visualization and analysis system based on a self-adaptive equivalent amplitude reference. The system comprises a data preprocessing fluctuation calculation module, a dynamic equivalence amplitude reference generation module, an equivalence line generation maintenance module, a chip distribution dynamic calculation module and a market form analysis module, and is characterized in that based on an original market data set, an equivalence line amplitude is adjusted to carry out adaptive calculation on an object market volatility to obtain a volatility data set; the method comprises the following steps: calculating and adjusting an equivalent amplitude reference value based on a volatility data set to obtain a multi-amplitude equivalent line sequence data set, obtaining an equivalent line sequence data set and a chip distribution data set according to a multi-amplitude equivalent line, and comprehensively analyzing data and visually displaying. The method has the remarkable advantages of high market analysis accuracy, high chip distribution timeliness and good market noise data filtering effect.

Description

Technical Field

[0001] This invention relates to the field of market data visualization and analysis technology, and more specifically, to a market data visualization and analysis system based on an adaptive equivalent amplitude benchmark. Background Technology

[0002] Market data visualization and analysis systems are technical tools that present and interpret abstract and complex price and volume data in the financial market through graphical and structured methods. Their main goal is to help traders identify market trends, judge the strength of bulls and bears, and locate key support and resistance areas, thereby providing a basis for trading decisions.

[0003] However, traditional market visualization and analysis systems often suffer from the following shortcomings: First, they mostly use fixed price ranges as the basis for drawing support and resistance lines. However, the volatility of financial markets is significantly time-varying, and a fixed range benchmark causes the system to fail when market conditions change, resulting in a lag in price trend identification and missing key turning points. Second, traditional systems typically calculate the stock holdings within a price range by simply accumulating trading volume based on all historical transaction data, and use the calculation results as static support or resistance. This makes it difficult for the system to accurately determine the true support and resistance levels, and consequently, it makes it difficult for the system to respond sensitively to recent changes in capital flows and cost structures. Third, traditional systems mostly use fixed time periods as the core of their charting system, and are prone to introducing noise data that is irrelevant to the essence of the market. This makes the traditional system time-dimensional, which can affect the attention of decision-makers and thus affect the reliability of decisions. In summary, how to effectively solve the problems of poor reliability of price range benchmarks, insufficient timeliness of stock distribution, and excessive market noise data in traditional systems has become a key issue that current market visualization and analysis systems need to address.

[0004] In view of this, the present invention proposes an adaptive equivalent amplitude benchmark market visualization and analysis system to solve the above problems. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art and to achieve the above objectives, the present invention provides the following technical solution, including: The basic data acquisition and processing module is used to call external data API interfaces through system commands to collect financial market-related data in real time, and to perform data cleaning and data storage to obtain the raw market data set. Furthermore, the steps of collecting financial market-related data in real time by calling external data API interfaces through system commands, and then performing data cleaning and storage include: S1.1: Configure API interface parameters for U exchanges and third-party data sources in the system; Establish a data source connection pool and support load balancing and failover mechanisms; Different acquisition frequencies are set for high-frequency data, medium-frequency data, and low-frequency data to obtain preset frequencies; S1.2: Execute the market data collection command, automatically retrieve market data of the specified trading instrument from the API interface according to the preset frequency, and obtain the raw dataset, which includes price, trading volume, open interest, buy and sell orders and collection timestamp; S1.3: Perform data cleaning and alignment on the original dataset to obtain the original market data dataset. Data cleaning includes outlier handling and missing value handling, and alignment involves converting the collection timestamps from different data sources in the original dataset into the system's internal time format. S1.4: Store the original market data set in the database and generate a data ready signal to output to the data preprocessing fluctuation calculation module; The data preprocessing volatility calculation module is used to adaptively calculate the amplitude of the benchmark volatility adjustment isoline based on the original market data set to obtain the volatility data set. Furthermore, the steps of preprocessing the original market data dataset and calculating the market volatility indicator include: S2.1: Listen for data ready signal, retrieve the original market data set, obtain the historical equivalent line sequence of the target based on the equivalent line generation and maintenance module, calculate the equivalent volatility by combining multiple amplitude sequences, and obtain the equivalent line amplitude benchmark based on the equivalent volatility; S2.2: Store the equivalence line amplitude benchmark in the database and output it to the equivalence line generation and maintenance module; The dynamic equivalent amplitude benchmark generation module is used to calculate and adjust the equivalent amplitude benchmark value based on the volatility dataset to obtain the benchmark dataset; Furthermore, the steps for calculating and adjusting the equivalent magnitude benchmark based on the volatility dataset include: S3.1: Listen for the volatility ready signal, retrieve the volatility dataset, and calculate the adjustment coefficient based on the standard deviation volatility in the volatility dataset. The specific formula for the calculation is as follows: ; Get the time point adjustment coefficient ,in, Basic adjustment coefficient, To adjust the sensitivity parameters, This represents the recent average volatility. S3.2: Multiply the current standard deviation volatility by the adjustment factor to obtain the dynamic benchmark value; S3.3: The dynamic benchmark value is processed using the exponential moving average smoothing algorithm to obtain the final dynamic benchmark value; S3.4: Package the second calculation timestamp, adjustment coefficient, dynamic baseline value, and final dynamic baseline value to obtain the baseline dataset; S3.5: Store the benchmark dataset in the database and generate a benchmark ready signal to output to the equivalence line generation and maintenance module; The isoequivalence line generation and maintenance module automatically calculates and updates the isoequivalence line feature attributes based on the benchmark dataset to obtain an isoequivalence line sequence dataset. Furthermore, the steps for automatically drawing and updating isoequivalence lines of multiple magnitudes based on a benchmark dataset include: S4.1: Listen for the benchmark ready signal, retrieve the benchmark dataset, and obtain the current price data based on the original market data dataset; S4.2: Use the price at the start time as the standard price, the current dynamic benchmark range as the benchmark price, and define a set of observation ranges based on the benchmark price; Real-time monitoring of price data during transaction events; When the price data in a transaction event is greater than or equal to the standard price multiplied by the observation range in parentheses, a bullish candlestick is generated, and the starting price is replaced with the new transaction price. When the price data in a transaction event is less than or equal to the standard price multiplied by one minus the observed range (in parentheses), a bearish candlestick is generated, and the starting price is replaced with the new transaction price. S4.3: Package the third generation timestamp, start and end time, start and end price, statistical trading volume and open interest, and duration to obtain an isovalue line data; S4.4: Store the isovalue line data from step S4.3 in the database, and generate isovalue line update signals to output to the chip distribution dynamic calculation module and the market pattern analysis module.

[0006] S4.5: Repeat steps S4.1 to S4.4 to obtain a continuous dataset of equivalence line sequences, maintain and update multiple equivalence line sequences generated based on different observation amplitudes in parallel; The chip distribution dynamic calculation module is based on the isovalue sequence dataset and uses the decay algorithm to calculate the trading volume distribution of each price range to obtain the chip distribution dataset. Furthermore, the steps for dynamically calculating the chip distribution for each price range based on the isovalue sequence dataset and using the decay algorithm include: S5.1: Retrieve the isoline sequence dataset; S5.2: Generate price ranges based on the price range of the isoprice lines; S5.3: Based on price ranges, according to the characteristics of statistical isoprice lines, and using the decay algorithm, the distribution dataset of trading volume in each price range is obtained. The specific formula for calculation is as follows: ; Get the first Interval weights of price ranges ,in, For the first The original distribution of chips in each price range, This is the time decay coefficient. For the current time and the number The time difference between the most recent transactions within a price range; Adjustments are made based on interval weights to obtain the adjusted interval weights. The specific calculation formula for the adjustment is as follows: ; Get the first Adjustment range weight for each price range ,in, The fluctuation impact coefficient, For the first Historical volatility of a price range This represents the overall average volatility. S5.4: Normalize all adjustment interval weights to the range of 0 to 100% to generate a normalized chip distribution. The specific calculation formula for normalization is as follows: ; Get the first Normalized interval weights for each price range ; S5.5: Pack the fourth generation timestamp and normalized chip distribution to obtain the chip distribution dataset; S5.6: Store the chip distribution dataset in the database; The market pattern analysis module distinguishes between oscillation and trend patterns by the difference between whether the isovalue lines are continuous and in the same direction. It further identifies and subdivides oscillation and trend patterns in an uptrend / downtrend, and statistically analyzes the market data of the isovalue line set for each pattern. Furthermore, based on the isovalue line sequence dataset, the steps of automatically statistically analyzing it into a chip distribution dataset and statistically analyzing the market characteristic data of each pattern include: S6.11: Listen for the update signal of the isovalue line and automatically identify the market pattern based on the same direction between consecutive bars. Specifically, consecutive bars in the same direction are marked as trend patterns, and consecutive bars alternating are marked as oscillation patterns. S6.12: Take the 4th timestamp as the starting time of the column sequence in the above morphology as the morphology label to form a morphology sequence set; S6.13: Statistically analyze the trading volume, open interest, highest price, and lowest price of the bars in the pattern sequence set, and add them to the data attribute features of the pattern sequence set. S6.14: Save the morphological sequence set data features to the database and output them to the long / short pressure analysis module; Furthermore, based on the isovalue sequence dataset and the chip distribution dataset, the steps to analyze the balance of power between buyers and sellers, support and resistance zones in the market, and to identify trend reversal points include: S6.1: Listen for the equivalence line update signal and the chip distribution ready signal, and retrieve the equivalence line sequence dataset and the chip distribution dataset; S6.2: Based on the isoline sequence dataset, the bullish and bearish strength indicators are calculated, which include the strength of bullish forces, the strength of bullish and bearish forces, and the comparison of bullish and bearish forces. S6.3: Based on the chip distribution dataset, perform region identification and combine it with region strength to obtain a list of support and resistance regions; S6.4: Identify potential reversal points based on bullish / bearish strength indicators and a list of support and resistance zones; S6.5: Listen for isoline update signals, retrieve the isoline sequence dataset, and process it according to the isoline sequence dataset to obtain a bullish / bearish pressure report; S6.6: Statistical calculations are performed based on the bullish and bearish pressure reports to obtain a quantitative analysis report of morphological data; S6.7: Generate a comprehensive analysis report based on quantitative analysis of bullish and bearish forces, support and resistance zone lists, potential reversal point sets, and pattern data; S6.8: Store the comprehensive analysis report in the database and generate an analysis report ready signal to output to the visualization and interactive module; The visualization and interactive module is used to generate various visualization charts based on the comprehensive analysis report and the graphical interface, while also supporting user interaction. Furthermore, based on the comprehensive analysis report and the generation of various visualization charts through a graphical interface, the steps that support user interaction include: S7.1: Monitor the readiness signal of the analysis report, retrieve the comprehensive analysis report and chip distribution dataset, and generate the following charts based on the interactive interface: bullish and bearish strength comparison chart, support and resistance area chart, long-term chip distribution display chart, bullish and bearish pressure analysis chart, and trend reversal point annotation chart. S7.2: Based on the interactive interface, it supports user interaction operations and provides data export function; S7.3: Supports users to filter the highlighted area by shape type; Furthermore, a method for visualizing and analyzing market data based on an adaptive equivalent amplitude benchmark includes: S1: By calling the external data API interface through system commands, financial market-related data is collected in real time, and the data is cleaned and stored to obtain the original market data set; S2: Based on the original market data dataset, adaptively calculate the amplitude of the isoline adjustment for the volatility of the target market to obtain the volatility dataset; S3: Calculate and adjust the equivalent amplitude benchmark value based on the volatility dataset to obtain the benchmark dataset; S4: Based on the benchmark dataset, automatically calculate and update the isoline feature attributes of the multi-amplitude isolines to obtain the isoline sequence dataset; S5: Based on the isoline sequence dataset, and using the decay algorithm to calculate the trading volume distribution for each price range, a chip distribution dataset is obtained; S6: Based on the isoline sequence dataset, it automatically identifies market patterns according to the isoline, generates market pattern sequences, and statistically analyzes the market characteristic data of each pattern; based on the isoline sequence dataset and the chip distribution dataset, it analyzes the comparison of bullish and bearish forces, support and resistance areas in the market, identifies trend reversal points, and obtains a comprehensive analysis report; S7: Based on comprehensive analysis reports, it generates various visualization charts using a graphical interface, while also supporting user interaction.

[0007] The technical effects and advantages of the market data visualization and analysis system based on the adaptive equivalent amplitude benchmark of this invention are as follows: This invention calls an external data API interface via system commands to collect financial market data in real time, performs data cleaning and storage to obtain a raw market data set. Based on the raw market data set, it adaptively calculates the amplitude of the isoprice line for adjusting the volatility of the target market to obtain a volatility data set. Based on the volatility data set, it calculates and adjusts the benchmark value of the isoprice amplitude to obtain a benchmark data set. Based on the benchmark data set, it automatically calculates and updates the isoprice line feature attributes for multiple amplitude isoprice lines to obtain an isoprice line sequence data set. Based on the isoprice line sequence data set, it uses a decay algorithm to calculate the volume distribution of each price range to obtain a chip distribution data set. Based on the isoprice line sequence data set, it automatically identifies market patterns based on the isoprice lines, generates a market pattern sequence, and statistically analyzes the market feature data of each pattern. Based on the isoprice line sequence data set and the chip distribution data set, it analyzes the comparison of bullish and bearish forces, support and resistance areas in the market, identifies trend reversal points, and obtains a comprehensive analysis report. Based on the comprehensive analysis report, it generates various visualization charts through a graphical interface, while also supporting user interaction. This system effectively balances the contradiction between static benchmarks and dynamic markets in traditional systems through its basic data acquisition and processing module, data preprocessing and fluctuation calculation module, dynamic equivalent amplitude benchmark generation module, and equivalent line generation and maintenance module. This fundamentally ensures the accuracy and timeliness of trend identification and structural analysis, effectively solving the failure problem that traditional systems are prone to when facing changes in market conditions. Furthermore, this invention uses a dynamic chip distribution calculation module to dynamically reduce the weight of more distant data based on a decay algorithm, while increasing the importance of recent trading data. This allows the system to reflect a more realistic average holding cost and dense trading areas for current market users, significantly improving the accuracy of risk area warnings. Finally, through a market pattern analysis module and a visualization and interactive module, meaningless market-related noise data caused by time slicing is minimized, allowing users to focus their attention more effectively and improving the reliability of user decisions. Overall, this invention has significant advantages in terms of high market analysis accuracy, strong timeliness of chip distribution, and good filtering effect on market noise data. Attached Figure Description

[0008] Figure 1 This is a flowchart of a market data visualization and analysis system based on an adaptive equivalent amplitude benchmark, according to the present invention. Figure 2 This is a schematic diagram of a market data visualization and analysis method based on an adaptive equivalent amplitude benchmark according to the present invention. Detailed Implementation

[0009] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0010] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” used in the embodiments of this invention are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0011] Depending on the context, the words “if” or “suppose” as used here can be interpreted as “when” or “in response to determination” or “in response to detection.” Similarly, depending on the context, the phrases “if determination” or “if detection (of the stated condition or event)” can be interpreted as “when determination” or “in response to determination” or “when detection (of the stated condition or event)” or “in response to detection (of the stated condition or event).”

[0012] Furthermore, the timing of the steps in the following method embodiments is merely an example and not a strict limitation.

[0013] In practice, the server-side equipment deployed in the adaptive equivalent range benchmark market visualization and analysis system may consist of one or more devices. This system can be implemented as a business instance, a virtual machine, or hardware devices. For example, it can be implemented as a business instance deployed on one or more devices in a cloud node. Simply put, it can be understood as software deployed on a cloud node to provide adaptive equivalent range benchmark market visualization and analysis to various user terminals. Alternatively, it can be implemented as a virtual machine deployed on one or more devices in a cloud node, with application software installed to manage various user terminals. Or, it can also be implemented as a server composed of numerous identical or different types of hardware devices, with one or more devices configured to provide adaptive equivalent range benchmark market visualization and analysis to various user terminals.

[0014] In terms of implementation, the adaptive equivalent range benchmark market visualization and analysis system and the user client are mutually compatible. That is, if the adaptive equivalent range benchmark market visualization and analysis system is implemented as an application installed on a cloud service platform, then the user client is a client that establishes a communication connection with the application; or if the adaptive equivalent range benchmark market visualization and analysis system is implemented as a website, then the user client is implemented as a webpage; or if the adaptive equivalent range benchmark market visualization and analysis system is implemented as a cloud service platform, then the user client is implemented as a mini-program in an instant messaging application.

[0015] like Figure 1 The figure shown is a system architecture diagram of a market data visualization and analysis system based on an adaptive equivalent amplitude benchmark provided in an embodiment of the present invention.

[0016] The adaptive equivalent amplitude benchmark market visualization and analysis system described in this invention can be set up on a cloud server. In terms of implementation, it can be implemented as one or more service devices, or as an application installed in the cloud (e.g., a mobile service operator's server or server cluster), or it can be developed as a website. Depending on the functions implemented, the adaptive equivalent amplitude benchmark market visualization and analysis system may include a basic data acquisition and processing module, a data preprocessing and fluctuation calculation module, a dynamic equivalent amplitude benchmark generation module, an equivalent line generation and maintenance module, a dynamic calculation module for chip distribution, a market pattern analysis module, and a visualization and interactive module. The module described in this invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device's processor and perform a fixed function, stored in the electronic device's memory.

[0017] In this embodiment of the invention, in the adaptive equivalent amplitude benchmark market visualization and analysis system, each of the above modules can be implemented independently and can call other modules. Here, "calling" can be understood as a module connecting to multiple modules of another type and providing corresponding services to those connected modules. For example, the visualization and interaction module can call the same information collection module to obtain information collected by that module. Based on the above characteristics, in the adaptive equivalent amplitude benchmark market visualization and analysis system provided in this embodiment of the invention, without modifying the program code, the applicable scope of the adaptive equivalent amplitude benchmark market visualization and analysis system architecture can be adjusted by adding modules and directly calling them, achieving cluster-based horizontal expansion to quickly and flexibly expand the adaptive equivalent amplitude benchmark market visualization and analysis system. In practical applications, the above modules can be set in the same device or different devices, or they can be set in virtual devices, such as service instances in a cloud server.

[0018] Example 1 Please see Figure 1As shown in this embodiment, an adaptive equivalent amplitude benchmark market visualization and analysis system includes: The basic data acquisition and processing module is used to call external data API interfaces through system instructions to collect financial market-related data in real time, and to perform data cleaning and data storage to obtain the original market data set. Furthermore, the steps of calling external data API interfaces through system commands to collect financial market-related data in real time, and then performing data cleaning and storage, include: S1.1: Configure API interface parameters for U exchanges and third-party data sources in the system; It should be explained that API interface parameters include, for example, the URL and authentication key; Establish a data source connection pool and support load balancing and failover mechanisms; Different acquisition frequencies are set for high-frequency data, medium-frequency data, and low-frequency data to obtain preset frequencies; It should be explained that the preset frequency refers to, for example, high-frequency data being collected once per second, medium-frequency data being collected once per minute, and low-frequency data being collected once every five minutes. S1.2: Execute the market data collection command and automatically retrieve market data of the specified trading instrument from the API interface according to the preset frequency to obtain the raw dataset. The market data includes price data, trading volume data, buy and sell order data, order book data and collection timestamp. It should be explained that price data includes opening price, closing price, lowest price, highest price, and latest price; trading volume data includes current trading volume and cumulative trading volume; order book data includes, but is not limited to, the order price and order volume at the five bid and ask levels, total market open interest, and the active buy and sell volume obtained based on tick-by-tick transaction data; buy and sell order data includes the volume at bid 1 to bid 5 and the volume at ask 1 to ask 5. S1.3: Perform data cleaning and alignment on the original dataset to obtain the original market data dataset. Data cleaning includes outlier handling and missing value handling, and alignment involves converting the collection timestamps from different data sources in the original dataset into the system's internal time format. It should be explained that outlier handling refers to marking data items with a single increase or decrease exceeding 30% as outlier data; missing value handling refers to using forward imputation to fill in short-term missing values, while long-term missing values ​​are returned to step S1.2 to re-collect the original dataset. S1.4: Store the original market data set in the database and generate a data ready signal to output to the data preprocessing fluctuation calculation module; The data preprocessing volatility calculation module is used to adaptively calculate the amplitude of the benchmark market volatility adjustment isoline based on the original market data set to obtain a volatility data set. Furthermore, the steps of preprocessing the original market data dataset and calculating the market volatility indicator include: S2.1: Listen for data ready signal, retrieve the original market data set, obtain the historical equivalent line sequence of the target based on the equivalent line generation and maintenance module, calculate the equivalent volatility by combining multiple amplitude sequences, and obtain the equivalent line amplitude benchmark based on the equivalent volatility; S2.2: Store the equivalence line amplitude benchmark in the database and output it to the equivalence line generation and maintenance module; The dynamic equivalent amplitude benchmark generation module is used to calculate and adjust the equivalent amplitude benchmark value based on the volatility dataset to obtain the benchmark dataset; Furthermore, the steps for calculating and adjusting the equivalent magnitude benchmark based on the volatility dataset include: S3.1: Listen for the volatility ready signal, retrieve the volatility dataset, and calculate the adjustment coefficient based on the standard deviation volatility in the volatility dataset. The specific formula for the calculation is as follows: ; Get the time point adjustment coefficient ,in, Basic adjustment coefficient, To adjust the sensitivity parameters, This represents the recent average volatility. It should be explained that "recent" refers to, for example, the past thirty days from the current moment; S3.2: Multiply the current standard deviation volatility by the adjustment factor to obtain the dynamic benchmark value; S3.3: The dynamic benchmark value is processed using the exponential moving average smoothing algorithm to obtain the final dynamic benchmark value; S3.4: Package the second calculation timestamp, adjustment coefficient, dynamic baseline value, and final dynamic baseline value to obtain the baseline dataset; It should be explained that the second generation timestamp refers to the generation time of the benchmark dataset; S3.5: Store the benchmark dataset in the database and generate a benchmark ready signal to output to the equivalence line generation and maintenance module; The equivalence line generation and maintenance module automatically calculates and updates the equivalence line feature attributes for multiple amplitudes based on the benchmark dataset, thereby obtaining an equivalence line sequence dataset. Furthermore, the steps for automatically drawing and updating isoequivalence lines of multiple magnitudes based on a benchmark dataset include: S4.1: Listen for the benchmark ready signal, retrieve the benchmark dataset, and obtain the current price data based on the original market data dataset; S4.2: Use the price at the start time as the standard price, the current dynamic benchmark range as the benchmark price, and define a set of observation ranges based on the benchmark price; It should be explained that the observation range includes a set of 0.5 times the benchmark price, 1 times the benchmark price, 2 times the benchmark price, and 4 times the benchmark price; Real-time monitoring of price data during transaction events; When the price data in a transaction event is greater than or equal to the standard price multiplied by the observation range in parentheses, a bullish candlestick is generated, and the starting price is replaced with the new transaction price. It needs to be explained that a bullish candlestick refers to an upward-moving equivalent line; the data structure of a bullish candlestick is as follows: the opening price is the standard price, the lowest price is the standard price, and the closing price is the trigger line price, i.e., the highest price; When the price data in a transaction event is less than or equal to the standard price multiplied by one minus the observed range (in parentheses), a bearish candlestick is generated, and the starting price is replaced with the new transaction price. It needs to be explained that a bearish candlestick refers to a falling price line; the data structure of a bearish candlestick is as follows: the opening price is the standard price, the lowest price is the standard price, and the closing price is the trigger price, i.e., the lowest price. It needs to be explained that the isovalue line is driven by price space rather than time period. Its "closing price" is the price data in the transaction event generated when a new isovalue line is triggered, and each isovalue line needs to record the duration, that is, the time interval from generation to being replaced by the next isovalue line. S4.3: Package the third generation timestamp, start and end time, start and end price, statistical trading volume and open interest, and duration to obtain an isovalue line data; S4.4: Store the isovalue line data from step S4.3 in the database, and generate isovalue line update signals to output to the chip distribution dynamic calculation module and the market pattern analysis module; It should be explained that the third generation timestamp refers to the generation time of the isoequivalence line sequence dataset; S4.5: Repeat steps S4.1 to S4.4 to obtain a continuous dataset of equivalence line sequences, maintain and update multiple equivalence line sequences generated based on different observation amplitudes in parallel; The chip distribution dynamic calculation module is based on the isovalue sequence dataset and uses the decay algorithm to calculate the trading volume distribution of each price range to obtain the chip distribution dataset. Furthermore, the steps for dynamically calculating the chip distribution for each price range based on the isovalue sequence dataset and using the decay algorithm include: S5.1: Retrieve the isoline sequence dataset; S5.2: Generate price ranges based on the price range of the isoprice lines; It should be explained that the expression for the price range is: ,in, For the first The price of the equivalence lines For prices lower than Adjacent isolines For prices higher Adjacent isolines; S5.3: Based on price ranges, according to the characteristics of statistical isoprice lines, and using the decay algorithm, the distribution dataset of trading volume in each price range is obtained. The specific formula for calculation is as follows: ; Get the first Interval weights of price ranges ,in, For the first The original distribution of chips in each price range, This is the time decay coefficient. For the current time and the number The time difference between the most recent transactions within a price range; Adjustments are made based on interval weights to obtain the adjusted interval weights. The specific calculation formula for the adjustment is as follows: ; Get the first Adjustment range weight for each price range ,in, The fluctuation impact coefficient, For the first Historical volatility of a price range This represents the overall average volatility. S5.4: Normalize all adjustment interval weights to the range of 0 to 100% to generate a normalized chip distribution. The specific calculation formula for normalization is as follows: ; Get the first Normalized interval weights for each price range ; S5.5: Pack the fourth generation timestamp and normalized chip distribution to obtain the chip distribution dataset; It should be explained that the fourth generation timestamp refers to the generation time of the chip distribution dataset; S5.6: Store the chip distribution dataset in the database; The market pattern analysis module distinguishes between oscillation and trend patterns by the difference between whether the isovalue lines are continuous and in the same direction. It further identifies and subdivides the oscillation and trend patterns in the upward / downward movement, and statistically analyzes the market data of the isovalue line set for each pattern. Furthermore, based on the isovalue line sequence dataset, the steps of automatically statistically analyzing it into a chip distribution dataset and statistically analyzing the market characteristics of each pattern include: S6.11: Listen for the update signal of the isovalue line and automatically identify the market pattern based on the same direction between consecutive bars. Specifically, consecutive bars in the same direction are marked as trend patterns, and consecutive bars alternating are marked as oscillation patterns. S6.12: Take the 4th timestamp as the starting time of the column sequence in the above morphology as the morphology label to form a morphology sequence set; S6.13: Statistically analyze the trading volume, open interest, highest price, and lowest price of the bars in the pattern sequence set, and add them to the data attribute features of the pattern sequence set. S6.14: Save the morphological sequence set data features to the database and output them to the long / short pressure analysis module; Furthermore, based on the isovalue sequence dataset and the chip distribution dataset, the steps to analyze the balance of power between buyers and sellers, support and resistance zones in the market, and to identify trend reversal points include: S6.1: Listen for the equivalence line update signal and the chip distribution ready signal, and retrieve the equivalence line sequence dataset and the chip distribution dataset; S6.2: Based on the isoline sequence dataset, the bullish and bearish strength indicators are calculated, which include the strength of bullish forces, the strength of bullish and bearish forces, and the comparison of bullish and bearish forces. It should be explained that the strength of the bullish forces is calculated by dividing the number of upward breakouts by the total number of breakouts and then multiplying by 100%; the strength of the bullish and bearish forces is calculated by dividing the number of downward breakouts by the total number of breakouts and then multiplying by 100%; and the comparison of bullish and bearish forces is calculated by dividing the strength of the bullish forces by the quotient of the sum of the strength of the bullish forces and the strength of the bullish and bearish forces. S6.3: Based on the chip distribution dataset, perform region identification and combine it with region strength to obtain a list of support and resistance regions; It needs to be explained that zone identification refers to designating price ranges with dense trading volume below the current price as support zones, and price ranges with dense trading volume above the current price as resistance zones; zone strength is calculated by substituting into the formula: get; S6.4: Identify potential reversal points based on bullish / bearish strength indicators and a list of support and resistance zones; It should be explained that the identification includes: when the balance of power between buyers and sellers is greater than 0.8 or less than 0.2, it is considered the first reversal point; when the price approaches the support or resistance zone, it is considered the second reversal point; and when there is an abnormal increase in trading volume, it is considered the third reversal point. S6.5: Listen for isoline update signals, retrieve the isoline sequence dataset, and process it according to the isoline sequence dataset to obtain a bullish / bearish pressure report; It needs to be explained that the processing in step S6.1 refers to scanning the sequence of equivalence lines, identifying continuous segments with alternating positive and negative equivalence lines and continuous segments with consecutive equivalence lines in the same direction, to obtain oscillation pattern equivalence lines and trend pattern equivalence lines, and assigning a label to each identified pattern. Among them, the consecutive equivalence lines in the trend pattern equivalence lines refer to, for example, consecutive positive lines, which indicate an upward trend, and consecutive negative lines, which indicate a downward trend. S6.6: Statistical calculations are performed based on the bullish and bearish pressure reports to obtain a quantitative analysis report of morphological data; It should be explained that the statistical calculations in step S6.6 include price statistics, trading volume statistics, isoprice line statistics, bullish and bearish pressure statistics, and duration statistics. Taking price statistics as an example, price statistics include the highest price, lowest price, and upper and lower price edges within the pattern. S6.7: Generate a comprehensive analysis report based on quantitative analysis of bullish and bearish forces, support and resistance zone lists, potential reversal point sets, and pattern data; It should be explained that the comprehensive analysis report includes, but is not limited to, price trend direction, price volatility level, distribution of bullish and bearish forces, list of key support and resistance levels, and warnings of high-risk reversal points; S6.8: Store the comprehensive analysis report in the database and generate an analysis report ready signal to output to the visualization and interactive module; The visualization and interactive module is used to generate various visualization charts based on the comprehensive analysis report and the graphical interface, while also supporting user interaction. Furthermore, based on the comprehensive analysis report and the generation of various visual charts through the graphical interface, the steps that support user interaction include: S7.1: Monitor the readiness signal of the analysis report, retrieve the comprehensive analysis report and chip distribution dataset, and generate the following charts based on the interactive interface: a comparison chart of bullish and bearish forces, a support and resistance area chart, a long-term chip distribution display chart, a pattern-based bullish and bearish pressure analysis chart, and a trend reversal point annotation chart. It needs to be explained that a bullish / bearish strength comparison chart refers to a chart that can be displayed on the interactive interface, such as a chart using two-color bar charts to represent the comparison between bullish and bearish forces; a support / resistance zone chart refers to a chart that can be displayed on the interactive interface, such as a chart that overlays support and resistance zones on the main price chart; a pattern-based bullish / bearish pressure analysis chart refers to a chart where patterns are divided into oscillations and trends, further subdivided into oscillations / trends in an uptrend and oscillations / trends in a downtrend. Alternating distribution of bullish and bearish bars indicates oscillation, while continuous arrangement indicates a trend. Pattern analysis is a statistical analysis of a set of bars in an oscillation / trend. For example, for an oscillation pattern, it involves analyzing a set of bars arranged in a continuous bullish / bearish pattern, including the highest price, lowest price, upper and lower limits, active buying and selling volume, active buying and selling positions, the number of bars (actually the number of times the oscillation crosses the upper and lower limits), areas of high trading volume, and bullish / bearish pressure; a trend reversal point annotation chart refers to a chart that can be displayed on the interactive interface, such as a chart using different shaped icons on the price chart to distinguish different types of reversal points. S7.2: Based on the interactive interface, it supports user interaction operations and provides data export function; It should be explained that interactive operations include, but are not limited to, time range selection, trading instrument switching, chart type selection, chart zooming, and data point hovering tips; S7.3: Supports users to filter the highlighted area by shape type; It should be explained that pattern types refer to upward trends and downward fluctuations, etc. The beneficial effects of this embodiment are as follows: By calling external data API interfaces through system instructions, relevant data from the financial market is collected in real time, and data cleaning and storage are performed to obtain an original market data set. Based on the original market data set, the volatility of the target market is adjusted using an adaptive calculation of the isoprice line amplitude to obtain a volatility data set. Based on the volatility data set, the isoprice amplitude benchmark value is calculated and adjusted to obtain a benchmark data set. Based on the benchmark data set, the isoprice lines of multiple amplitudes are automatically calculated and their characteristic attributes are updated to obtain an isoprice line sequence data set. Based on the isoprice line sequence data set, the volume distribution of each price range is calculated using a decay algorithm to obtain a chip distribution data set. Based on the isoprice line sequence data set, market patterns are automatically identified based on the isoprice lines, generating a market pattern sequence, and statistically analyzing the market characteristic data of each pattern. Based on the isoprice line sequence data set and the chip distribution data set, the market's bullish and bearish forces, support and resistance areas are analyzed, and trend reversal points are identified to obtain a comprehensive analysis report. Based on the comprehensive analysis report, various visualization charts are generated using a graphical interface, while also supporting user interaction. This system, through its basic data acquisition and processing module, data preprocessing and fluctuation calculation module, dynamic equivalent amplitude benchmark generation module, and equivalent line generation and maintenance module, effectively balances the contradiction between static benchmarks and dynamic markets present in traditional systems. This fundamentally ensures the accuracy and timeliness of trend identification and structural analysis, effectively solving the failure problem that traditional systems are prone to when facing changes in market conditions. Furthermore, this embodiment uses a dynamic chip distribution calculation module to dynamically reduce the weight of more distant data based on a decay algorithm, while increasing the importance of recent trading data. This allows the system to reflect a more realistic average holding cost and dense trading areas for current market users, significantly improving the accuracy of risk area warnings. Finally, through a market pattern analysis module and a visualization and interactive module, meaningless market-related noise data caused by time slicing is minimized, allowing users to focus their attention more effectively and improving the reliability of user decisions. Overall, this embodiment has significant advantages in terms of high market analysis accuracy, strong timeliness of chip distribution, and good filtering effect on market noise data.

[0019] Example 2 Please see Figure 2 As shown, the parts not described in detail in this embodiment are described in Embodiment 1. An adaptive equivalent amplitude benchmark market visualization and analysis method is provided. The method includes: S1: calling an external data API interface through system instructions to collect financial market-related data in real time, and performing data cleaning and data storage to obtain the original market dataset; S2: Based on the original market data dataset, adaptively calculate the amplitude of the isoline adjustment for the volatility of the target market to obtain the volatility dataset; S3: Calculate and adjust the equivalent amplitude benchmark value based on the volatility dataset to obtain the benchmark dataset; S4: Based on the benchmark dataset, automatically calculate and update the isoline feature attributes of the multi-amplitude isolines to obtain the isoline sequence dataset; S5: Based on the isoline sequence dataset, and using the decay algorithm to calculate the trading volume distribution for each price range, a chip distribution dataset is obtained; S6: Based on the isoline sequence dataset, it automatically identifies market patterns according to the isoline, generates market pattern sequences, and statistically analyzes the market characteristic data of each pattern; based on the isoline sequence dataset and the chip distribution dataset, it analyzes the comparison of bullish and bearish forces, support and resistance areas in the market, identifies trend reversal points, and obtains a comprehensive analysis report; S7: Based on comprehensive analysis reports, it generates various visualization charts using a graphical interface, while also supporting user interaction.

[0020] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the present invention.

Claims

1. A market data visualization and analysis system based on an adaptive equivalent amplitude benchmark, characterized in that, The system includes: a data preprocessing fluctuation calculation module, a dynamic equivalent amplitude benchmark generation module, an equivalent line generation and maintenance module, a chip distribution dynamic calculation module, and a market pattern analysis module, wherein: The data preprocessing volatility calculation module is used to adaptively calculate the amplitude of the benchmark market volatility adjustment isoline based on the original market data set to obtain a volatility data set. The dynamic equivalent amplitude benchmark generation module is used to calculate and adjust the equivalent amplitude benchmark value based on the volatility dataset to obtain the benchmark dataset; The equivalence line generation and maintenance module automatically calculates and updates the equivalence line feature attributes for multiple amplitudes based on the benchmark dataset, thereby obtaining an equivalence line sequence dataset. The chip distribution dynamic calculation module is based on the isovalue sequence dataset and uses the decay algorithm to calculate the trading volume distribution of each price range to obtain the chip distribution dataset. The market pattern analysis module distinguishes between oscillation and trend patterns by the difference between whether the isovalue lines are continuous and in the same direction. It further identifies and subdivides oscillation and trend patterns in an uptrend / downtrend, and statistically analyzes the market data of the isovalue line set for each pattern.

2. The market data visualization and analysis system based on an adaptive equivalent amplitude benchmark according to claim 1, characterized in that, The system also includes: a basic data acquisition and processing module and a visualization and interactive module, wherein: The basic data acquisition and processing module is used to call external data API interfaces through system instructions to collect financial market-related data in real time, and to perform data cleaning and data storage to obtain the original market data set. The visualization and interactive module is used to generate various visualization charts based on the comprehensive analysis report and the graphical interface, while also supporting user interaction.

3. The market data visualization and analysis system based on an adaptive equivalent amplitude benchmark according to claim 2, characterized in that, The steps for collecting financial market-related data in real time by calling external data API interfaces through system commands, and then performing data cleaning and storage include: S1.1: Configure API interface parameters for U exchanges and third-party data sources in the system; Establish a data source connection pool and support load balancing and failover mechanisms; Different acquisition frequencies are set for high-frequency data, medium-frequency data, and low-frequency data to obtain preset frequencies; S1.2: Execute the market data collection command, automatically retrieve market data of the specified trading instrument from the API interface according to the preset frequency, and obtain the raw dataset, which includes price, trading volume, open interest, buy and sell orders and collection timestamp; S1.3: Perform data cleaning and alignment on the original dataset to obtain the original market data dataset. Data cleaning includes outlier handling and missing value handling, and alignment involves converting the collection timestamps from different data sources in the original dataset into the system's internal time format. S1.4: Store the original market data set in the database and generate a data ready signal to output to the data preprocessing fluctuation calculation module.

4. The market data visualization and analysis system based on an adaptive equivalent amplitude benchmark according to claim 3, characterized in that, The steps for preprocessing the original market data dataset and calculating market volatility indicators include: S2.1: Listen for data ready signal, retrieve the original market data set, obtain the historical equivalent line sequence of the target based on the equivalent line generation and maintenance module, calculate the equivalent volatility by combining multiple amplitude sequences, and obtain the equivalent line amplitude benchmark based on the equivalent volatility; S2.2: Store the equivalence line amplitude benchmark in the database and output it to the equivalence line generation and maintenance module.

5. The market data visualization and analysis system based on an adaptive equivalent amplitude benchmark according to claim 4, characterized in that, The steps for automatically drawing and updating isoequivalence lines of multiple magnitudes based on a benchmark dataset include: S4.1: Listen for the benchmark ready signal, retrieve the benchmark dataset, and obtain the current price data based on the original market data dataset; S4.2: Use the price at the start time as the standard price, the current dynamic benchmark range as the benchmark price, and define a set of observation ranges based on the benchmark price; Real-time monitoring of price data during transaction events; When the price data in a transaction event is greater than or equal to the standard price multiplied by the observation range in parentheses, a bullish candlestick is generated, and the starting price is replaced with the new transaction price. When the price data in a transaction event is less than or equal to the standard price multiplied by one minus the observed range (in parentheses), a bearish candlestick is generated, and the starting price is replaced with the new transaction price. S4.3: Package the third generation timestamp, start and end time, start and end price, statistical trading volume and open interest, and duration to obtain an isovalue line data; S4.4: Store the isovalue line data from step S4.3 in the database, and generate isovalue line update signals to output to the chip distribution dynamic calculation module and the market pattern analysis module; S4.5: Repeat steps S4.1 to S4.4 to obtain a continuous dataset of equivalence line sequences, maintain multiple equivalence line sequences generated based on different observation amplitudes in parallel, and update them.

6. The market data visualization and analysis system based on an adaptive equivalent amplitude benchmark according to claim 5, characterized in that, The steps for dynamically calculating the chip distribution for each price range based on the isovalue sequence dataset and using the decay algorithm include: S5.1: Retrieve the isoline sequence dataset; S5.2: Generate price ranges based on the price range of the isoprice lines; S5.3: Based on price ranges, according to the characteristics of statistical isoprice lines, and using the decay algorithm, the distribution dataset of trading volume in each price range is obtained; Adjustments are made based on interval weights to obtain the adjusted interval weights; S5.4: Normalize all adjustment interval weights to the range of 0 to 100% to generate a normalized chip distribution; S5.5: Pack the fourth generation timestamp and normalized chip distribution to obtain the chip distribution dataset; S5.6: Store the chip distribution dataset in the database.

7. The market data visualization and analysis system based on an adaptive equivalent amplitude benchmark according to claim 5, characterized in that, The steps for automatically analyzing a dataset of isovalue line sequences into a chip distribution dataset and statistically analyzing the market characteristics of each pattern include: S6.11: Listen for the update signal of the isovalue line and automatically identify the market pattern based on the same direction between consecutive bars. Specifically, consecutive bars in the same direction are marked as trend patterns, and consecutive bars alternating are marked as oscillation patterns. S6.12: Take the 4th timestamp as the starting time of the column sequence in the above morphology as the morphology label to form a morphology sequence set; S6.13: Statistically analyze the trading volume, open interest, highest price, and lowest price of the bars in the pattern sequence set, and add them to the data attribute features of the pattern sequence set. S6.14: Save the morphological sequence set data features to the database and output them to the long / short pressure analysis module.

8. The market data visualization and analysis system based on an adaptive equivalent amplitude benchmark according to claim 7, characterized in that, Based on isovalue sequence datasets and volume distribution datasets, the steps for analyzing the balance of power between buyers and sellers, support and resistance zones, and identifying trend reversal points in the market include: S6.1: Listen for the equivalence line update signal and the chip distribution ready signal, and retrieve the equivalence line sequence dataset and the chip distribution dataset; S6.2: Based on the isoline sequence dataset, the bullish and bearish strength indicators are calculated, which include the strength of bullish forces, the strength of bullish and bearish forces, and the comparison of bullish and bearish forces. S6.3: Based on the chip distribution dataset, perform region identification and combine it with region strength to obtain a list of support and resistance regions; S6.5: Listen for isoline update signals, retrieve the isoline sequence dataset, and process it according to the isoline sequence dataset to obtain a bullish / bearish pressure report; S6.6: Statistical calculations are performed based on the bullish and bearish pressure reports to obtain a quantitative analysis report of morphological data; S6.7: Generate a comprehensive analysis report based on quantitative analysis of bullish and bearish forces, support and resistance zone lists, potential reversal point sets, and pattern data; S6.8: Store the comprehensive analysis report in the database and generate an analysis report ready signal to output to the visualization and interactive module.

9. A market data visualization and analysis system based on an adaptive equivalent amplitude benchmark as described in claim 8, characterized in that, Based on a comprehensive analysis report, and using a graphical interface to generate various visual charts, the user-interactive steps include: S7.1: Monitor the readiness signal of the analysis report, retrieve the comprehensive analysis report and chip distribution dataset, and generate the following charts based on the interactive interface: bullish and bearish strength comparison chart, support and resistance area chart, long-term chip distribution display chart, bullish and bearish pressure analysis chart, and trend reversal point annotation chart. S7.2: Based on the interactive interface, it supports user interaction operations and provides data export function; S7.3: Supports users to filter the highlighted area by shape type.

10. A method for visualizing and analyzing market data based on an adaptive equivalent amplitude benchmark, implemented according to any one of claims 1-9, characterized in that, The work includes the following steps: S1: By calling the external data API interface through system commands, financial market-related data is collected in real time, and the data is cleaned and stored to obtain the original market data set; S2: Based on the original market data dataset, adaptively calculate the amplitude of the isoline adjustment for the volatility of the target market to obtain the volatility dataset; S3: Calculate and adjust the equivalent amplitude benchmark value based on the volatility dataset to obtain the benchmark dataset; S4: Based on the benchmark dataset, automatically calculate and update the isoline feature attributes of the multi-amplitude isolines to obtain the isoline sequence dataset; S5: Based on the isoline sequence dataset, and using the decay algorithm to calculate the trading volume distribution for each price range, a chip distribution dataset is obtained; S6: Based on the isovalue line sequence dataset, automatically identify market patterns according to the isovalue lines, generate market pattern sequences, and statistically analyze the market characteristic data of each pattern; Based on the isovalue sequence dataset and the chip distribution dataset, the analysis is conducted on the balance of power between bulls and bears, support and resistance areas in the market, and trend reversal points are identified to obtain a comprehensive analysis report. S7: Based on comprehensive analysis reports, it generates various visualization charts using a graphical interface, while also supporting user interaction.

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