A transaction data processing method based on multi-transaction variety combination

Abstract

The application discloses a kind of based on the transaction data processing method of multiple transaction variety combination, with transaction as center, through depth mining, cause and come.It takes transaction as main line, and has sent nine different transaction methods in total.These methods, from the second point to the tenth point, are gradually from simple to complicated process.This process, we from the initial two, three or four etc.sum, do to horizontal also do vertical also do, finally just to all ten websites can do to addition and oblique addition.That is the fourth point.Then, we do again, from the fifth point of oblique, to the sixth point of cross, finally, just to the seventh point, that is, all ten websites of addition and oblique addition, such as spot etc.Then, we do again, first is the eighth point of short, medium and long line.Again to short, medium and long line, content is same, and 4 are same ninth point, finally, we arrived the tenth point, that is, the ninth point plus 256 websites.

Description

Technical Field

[0001] This invention belongs to the field of transaction data processing technology, and specifically relates to a transaction data processing method based on a combination of multiple trading instruments. Background Technology

[0002] With the development of financial markets and digital asset markets, the number of trading instruments is constantly increasing. Traders or trading systems typically need to monitor multiple trading instruments simultaneously, including forex, futures, spot, stocks, options, government bonds, and ETFs. Different trading instruments have different pricing methods, trading hours, price fluctuation units, contract multipliers, and market characteristics. Therefore, when conducting trading data analysis, it is often necessary to comprehensively process data from multiple markets, multiple trading platforms, and multiple time periods.

[0003] Current trading data processing methods typically focus on a single trading instrument or platform. For example, trading systems usually generate price charts, technical indicators, or trading signals for a specific currency pair, futures contract, stock, or index. While this approach can reflect price changes for a single trading instrument, it struggles to comprehensively reflect the interrelationships between multiple instruments, and it also fails to capture the combined changes across different markets, trading data sources, and timeframes.

[0004] In actual trading data analysis, there are often certain correlations between multiple trading instruments. For example, multiple foreign exchange pairs related to the same currency may exhibit correlated fluctuations within the same time period; futures, spot, and related stocks of the same commodity may also show price linkages; the same index may also show synchronous or near-synchronous changes on different trading platforms or in different derivatives markets. If only individual trading instruments are observed, it is easy to cause information fragmentation and make it difficult to obtain the comprehensive trends of multiple instruments and markets in a timely manner.

[0005] Furthermore, existing trading software typically displays different trading instruments on separate charts, requiring users to frequently switch between multiple charts, trading platforms, or timeframes. When there are many trading instruments, manual observation and calculation become inefficient, easily overlooking key data changes and making it difficult to generate unified combined analysis results.

[0006] While existing quantitative trading or factor analysis methods can perform data processing on multiple trading instruments to a certain extent, they typically rely on pre-set factors, historical backtesting models, or single-market data structures. For trading data that spans markets, platforms, instruments, and time periods, issues remain such as inconsistent data definitions, insufficient expression of combination relationships, and difficulties in performing unified summation or cross-calculation across different trading instruments.

[0007] Therefore, to address the aforementioned technical issues, it is necessary to provide a trading data processing method based on a combination of multiple trading instruments. Summary of the Invention

[0008] The purpose of this invention is to provide a trading data processing method based on a combination of multiple trading instruments, which can solve the technical problems mentioned in the background art.

[0009] To achieve the above objectives, a specific embodiment of the present invention provides the following technical solution: A method for processing trading data based on a combination of multiple trading instruments includes the following steps: S1, acquiring trading data from multiple trading data sources, wherein the trading data includes market data of multiple trading instruments within at least one time period, and the market data includes at least a trading instrument identifier, a data source identifier, a timestamp, a time period, and price data; S2, grouping the multiple trading instruments according to the trading instrument identifier and / or the attribute information of the trading instruments to obtain at least one trading instrument group, each trading instrument group including one or more trading instruments with a preset correlation; S3, standardizing the market data to obtain the standardized change value of each trading instrument within the corresponding time period; S4, according to the trading instruments... S5. Construct a trading data combination matrix by considering the group, the trading data source, and the time period. Elements in the trading data combination matrix correspond to the standardized change values ​​of a trading instrument, a group of trading instruments, a trading data source, and / or a time period. S6. Perform at least one combination operation on the elements in the trading data combination matrix to obtain a combined trading indicator. The combination operation includes at least one of horizontal aggregation, vertical aggregation, diagonal aggregation, cross aggregation, same-group aggregation, cross-group aggregation, same-source aggregation, and cross-source aggregation. S7. Generate trading data processing results based on the combined trading indicator. The trading data processing results include a combination strength value, direction label, volatility alert, risk alert, ranking results, and / or trading reference information.

[0010] In one or more embodiments of the present invention, the plurality of trading instruments include at least two of the following: foreign exchange, domestic futures, foreign futures, spot commodities, domestic stocks, foreign stocks, options, treasury bonds, and ETFs.

[0011] In one or more embodiments of the present invention, the preset association relationship includes at least one of the following: same quotation currency relationship, same base currency relationship, same commodity index relationship, same industry relationship, same market type relationship, same trading direction correlation relationship, same volatility characteristic relationship, and same trading period relationship.

[0012] In one or more embodiments of the present invention, the standardization process includes at least one of the following: converting the price change values ​​of different trading instruments into a unified point value, a unified percentage change value, a unified rate of return, a unified currency unit, a unified contract multiplier caliber, a unified volatility caliber, or a unified risk exposure caliber.

[0013] In one or more embodiments of the present invention, the time period includes at least one of 1 minute, 3 minutes, 5 minutes, 15 minutes, 30 minutes, 1 hour, 4 hours, daily, weekly, and monthly data; and the method supports aggregating data from shorter time periods to longer time periods, and / or splitting or associating data from longer time periods to shorter time periods.

[0014] In one or more embodiments of the present invention, the horizontal aggregation includes summing, weighted summing, or averaging the standardized change values ​​of multiple trading instrument groups in the same trading data source; the vertical aggregation includes summing, weighted summing, or averaging the standardized change values ​​of the same trading instrument group in multiple trading data sources or multiple time periods.

[0015] In one or more embodiments of the present invention, the oblique aggregation includes selecting multiple elements located in different rows and columns from the transaction data combination matrix, and performing summation, weighted summation, or combination function calculation on the multiple elements; the cross aggregation includes selecting at least one element from the first trading instrument group and at least one element from the second trading instrument group for combination calculation to obtain a cross-trading instrument group combined trading indicator.

[0016] In one or more embodiments of the present invention, the combination operation includes at least one of the following: adding elements within group A, adding group A to group B, adding group A to group B and group C, adding any element in group A to any element in group B, adding multiple elements in group A to multiple elements in group B, and multi-level cross-addition between group A to group N.

[0017] In one or more embodiments of the present invention, the plurality of trading data sources include at least one trading platform or market data server; when there are multiple trading data sources, the method further includes: matching market data of the same or similar trading instruments in different trading data sources, and adding the matched market data to the trading data combination matrix.

[0018] In one or more embodiments of the present invention, the number of transaction data sources is determined according to a preset sequence, which includes at least one of an arithmetic sequence, a geometric sequence, a Fibonacci sequence, a square sequence, and a power sequence.

[0019] Compared with existing technologies, the advantages of this invention are as follows: by grouping multiple trading instruments, standardizing market data, constructing a trading data combination matrix, and performing various combination aggregation operations, it achieves unified processing and comprehensive analysis of trading data from multiple instruments, markets, platforms, and time periods. This method can improve the comparability, correlation, stability, and processing efficiency of trading data, and can generate intuitive combined trading indicators and risk warnings, thus possessing good practical and promotional value. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a schematic diagram illustrating the calculation of a combination indicator in a trading data processing method based on a combination of multiple trading instruments, according to an embodiment of the present invention. Figure 1 ; Figure 2 This is a schematic diagram illustrating the calculation of a combination indicator in a trading data processing method based on a combination of multiple trading instruments, according to an embodiment of the present invention. Figure 2 ; Figure 3 This is a schematic diagram illustrating the calculation of a combination indicator in a trading data processing method based on a combination of multiple trading instruments, according to an embodiment of the present invention. Figure 3 . Detailed Implementation

[0022] To enable those skilled in the art to better understand the technical solutions in this disclosure, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments in this disclosure, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this disclosure.

[0023] This invention discloses a trading data processing method based on a combination of multiple trading instruments, primarily addressing the problems of existing trading data analysis methods that only focus on a single trading instrument, trading platform, or time period, resulting in fragmented information, insufficient utilization of correlations, and unclear overall trends. This application combines multiple trading instruments such as foreign exchange, futures, spot markets, stocks, options, government bonds, and ETFs according to certain rules, and performs horizontal, vertical, diagonal, and cross-sectional combination operations on data from multiple trading data sources, multiple time periods, and multiple trading instrument groups. This yields a combined trading indicator that reflects overall market changes, the interrelationships among multiple instruments, and the strength of different time periods.

[0024] First, transaction data is acquired from multiple transaction data sources. These data sources can be a single trading platform, multiple trading platforms, market data websites, market data servers, trading software interfaces, exchange interfaces, brokerage servers, data service provider interfaces, or local historical databases. Each transaction data source can provide market data for one or more trading instruments. This market data may include data such as trading instrument identifier, data source identifier, timestamp, time period, opening price, highest price, lowest price, closing price, trading volume, open interest, bid price, ask price, spread, price change, and yield.

[0025] The trading instruments can include, but are not limited to, foreign exchange, domestic futures, foreign futures, spot commodities, domestic stocks, foreign stocks, options, government bonds, ETFs, etc. In other words, this application is not limited to a single market, but can combine data from different markets, trading platforms, and trading instruments within the same processing framework for combined calculations. For example, it can simultaneously acquire data for trading instruments such as USD / JPY, EUR / USD, GBP / USD, gold, silver, crude oil, Hang Seng Index, Nasdaq Index, domestic stock index futures, and foreign stock index futures.

[0026] The trading instruments are not treated in isolation, but rather grouped according to the relationships between them. Specifically, multiple trading instruments can be grouped based on their identifiers and / or attribute information, resulting in at least one trading instrument group. Each trading instrument group may include one trading instrument or multiple trading instruments with pre-defined relationships. These pre-defined relationships may include: the same quoting currency, the same base currency, the same underlying commodity, the same underlying index, the same industry, the same market type, the same trading direction, the same volatility characteristics, or the same trading session.

[0027] For example, in foreign exchange trading, pairs of instruments related to the US dollar can be grouped together, such as EUR / USD, GBP / USD, USD / JPY, USD / CHF, AUD / USD, and USD / CAD; similarly, pairs of instruments related to the Japanese yen can be grouped together, such as USD / JPY, EUR / JPY, GBP / JPY, AUD / JPY, and CAD / JPY. In the commodities market, commodity pairs such as gold, silver, copper, crude oil, and natural gas can be grouped separately, or gold spot, gold futures, gold ETFs, and gold-related stocks can be combined into a gold-related pairing. Furthermore, in the stock or index market, stocks within the same industry can be grouped together, or a specific index, its futures, its ETF, and its options can be combined into an index-related pairing.

[0028] The aforementioned grouping method organizes previously scattered trading instruments according to market or trading logic, transforming trading data from "single-instrument observation" to "multi-instrument combined observation." Its advantage lies in its ability to more comprehensively reflect the overall changes in a particular asset class, currency class, market sector, or trading direction. For example, observing the rise or fall of EUR / USD alone might only indicate the change between the euro and the dollar; however, observing multiple dollar-related instruments simultaneously and performing combined calculations provides a more intuitive assessment of the overall strength of the dollar.

[0029] After acquiring market data and grouping trading instruments, the market data is standardized to obtain the standardized change value of each trading instrument within the corresponding time period. Since different trading instruments may differ in price units, quotation methods, contract multipliers, volatility, and minimum increments, directly adding their price change values ​​can easily distort the calculation results. For example, changes in gold prices, foreign exchange pairs, stock prices, and stock index futures points differ significantly in numerical scale and cannot be simply added together.

[0030] Therefore, price changes across different trading instruments can be converted into standardized point values, percentage changes, yields, currency units, contract multipliers, volatility, or risk exposure. For example, standardized changes can be calculated as follows: The standardized change value of a trading instrument over a time period can be calculated based on the difference between the closing price and the opening price, the difference between the current price and the closing price of the previous period, or a combination of the highest price, lowest price, and closing price. Specifically, it can be expressed as: Standardized change value = Closing price of the current period - Opening price of the current period; or, Standardized change value = Current period closing price / Previous period closing price - 1; or, Standardized change value = Price change value / Minimum unit of change; or, Standardized change value = Price change value × Contract multiplier / Preset capital unit.

[0031] Different standardization methods can be used for different types of trading instruments. For example, for forex trading instruments, price changes can be converted into points; for stocks, price changes can be converted into percentage changes; and for futures, point changes can be converted into cash changes based on the contract multiplier. Through standardization, different trading instruments can be combined and calculated using a relatively uniform numerical standard, thereby improving the comparability and accuracy of the combined calculation results.

[0032] After standardization, a trading data combination matrix is ​​constructed based on the trading instrument groups, trading data sources, and time periods. This trading data combination matrix can be understood as a data structure formed by arranging multiple trading instruments, multiple trading instrument groups, multiple trading platforms, and multiple time periods according to predetermined rules. Each element in the matrix can correspond to the standardized change value of a trading instrument within a certain time period, the combined value of a trading instrument group from a certain data source, or the comprehensive value of a certain trading data source within a certain time period.

[0033] For example, the rows of the matrix can be set as the trading instrument group, and the columns of the matrix can be set as the time period; the rows of the matrix can be set as the trading data source, and the columns of the matrix can be set as the trading instrument group; the matrix can also be designed as a multi-dimensional matrix, with one dimension representing the trading instrument group, one dimension representing the trading data source, one dimension representing the time period, and one dimension representing the trading direction or trading type.

[0034] Specifically, Group A can be represented as the first type of trading instrument group, Group B as the second type of trading instrument group, and Group C as the third type of trading instrument group. Group A can contain multiple trading instruments such as A1, A2, A3, and A4; Group B can contain multiple trading instruments such as B1, B2, B3, and B4; and Group C can contain multiple trading instruments such as C1, C2, C3, and C4. Each trading instrument can form a standardized value under different time periods. For example, A1 forms A1-1m in a 1-minute timeframe, A1-5m in a 5-minute timeframe, A1-1h in a 1-hour timeframe, and A1-D in a daily timeframe.

[0035] Horizontal aggregation can be performed within the aforementioned matrix. Horizontal aggregation can be understood as summing, weighted summing, or averaging the standardized change values ​​of multiple trading instrument groups from the same trading data source. For example, on the same trading platform, the combined values ​​of groups A, B, and C within the same time period can be added together to obtain a horizontal combination trading indicator for that trading platform within that time period. Horizontal aggregation can be used to observe the overall direction among different trading instrument groups on the same platform and within the same period. If the standardized change values ​​of multiple trading instrument groups move in the same direction, it indicates a strong resonance trend in the market within that time period; if different trading instrument groups move in opposite directions, it indicates a possible market divergence.

[0036] Horizontal aggregation can be represented as: Horizontal combination value = standardized change value of group A + standardized change value of group B + standardized change value of group C; or, Horizontal combination value = w1 × standardized change value of group A + w2 × standardized change value of group B + w3 × standardized change value of group C; Here, w1, w2, and w3 represent the weights for different trading instrument groups. These weights can be determined based on the importance of the trading instrument, trading volume, volatility, historical correlation, user settings, or risk exposure.

[0037] Vertical aggregation can also be performed within the matrix described above. Vertical aggregation can be understood as summing, weighted summing, or averaging the standardized change values ​​of the same trading instrument group across multiple trading data sources or time periods. For example, the standardized change values ​​of group A across multiple time periods such as 1 minute, 5 minutes, 15 minutes, 1 hour, 4 hours, and daily charts can be summed to obtain a multi-period composite indicator for group A. Similarly, data from the same trading instrument group across different trading platforms or different market data servers can be summed or averaged to obtain a cross-platform composite indicator.

[0038] The advantage of vertical aggregation lies in its ability to integrate short-term, medium-term, and long-term data into a unified analysis. For example, 1-minute, 3-minute, and 5-minute charts can be used as short-term timeframes; 15-minute, 30-minute, and 1-hour charts as medium-term timeframes; and 4-hour, daily, weekly, and monthly charts as long-term timeframes. When the combined short-term, medium-term, and long-term indicators move in the same direction, it suggests a strong trend consistency within the trading instrument group. Conversely, when short-term and long-term indicators move in opposite directions, it indicates that the market may be in a short-term rebound, pullback, or cyclical conflict. This avoids the problem of biased judgments caused by relying on only one timeframe.

[0039] Diagonal aggregation can also be performed. Diagonal aggregation can be understood as selecting multiple elements from different rows and columns in the transaction data combination matrix and performing combined calculations. For example, you can select data from A1 in a 1-minute timeframe, A2 in a 5-minute timeframe, B1 in a 15-minute timeframe, B2 in a 1-hour timeframe, and C1 in a 4-hour timeframe and add them together or perform weighted summation. This method is equivalent to selecting data along a diagonal line in the matrix, hence it can be called diagonal addition or diagonal aggregation.

[0040] The advantage of diagonal aggregation is that it is not limited to the same group of trading instruments or the same time period, but can connect the changes of different instruments, different time periods, and different data sources according to a certain path. For example, some trading instruments may first change in a short time period, and then gradually transmit to other instruments or other time periods; through diagonal aggregation, this cross-instrument and cross-time period change path can be captured. This method is particularly suitable for observing market linkages, capital transmission, sector rotation, and cross-market resonance.

[0041] Cross-aggregation can also be performed. Cross-aggregation can be understood as selecting at least one element from the first trading instrument group and at least one element from the second trading instrument group for combined calculation to obtain a combined trading indicator across trading instrument groups. For example, any trading instrument in group A can be added to any trading instrument in group B, or multiple trading instruments in group A can be added to multiple trading instruments in group B. Furthermore, multi-level cross-additions can be performed between groups A, B, C, D, and up to group N.

[0042] For example, in one implementation, the following combination can be made: A1+B1; A1+B2; A2+B1; A2+B2; A1+A2+B1+B2; Total value of Group A + Total value of Group B; Total value of Group A + Total value of Group B + Total value of Group C; Any element from group A + any element from group B + any element from group C.

[0043] The above methods can generate a large number of combined trading indicators. Each combined trading indicator can reflect a certain combination relationship between different trading instruments. Compared with observing a single trading instrument, cross-aggregation can provide more dimensions of information. For example, when group A represents dollar-related instruments, group B represents gold-related instruments, and group C represents stock index-related instruments, the cross-combination of groups A, B, and C can be used to observe the correlation between the dollar, gold, and stock indices. When multiple combined indicators point in the same direction, the reliability of trading data analysis results can be enhanced; when multiple combined indicators diverge, it can serve as a warning of market risk or unclear direction.

[0044] Preferably, the combination operations in this application can include adding elements within group A, adding group A to group B, adding group A to group B and group C, adding any element in group A to any element in group B, adding multiple elements in group A to multiple elements in group B, and multi-level cross-addition between group A to group N. This progressively expanding combination method allows calculations to be performed starting with a small number of trading instruments or extended to a large number of trading instruments and data sources.

[0045] In addition, the number of transaction data sources can be expanded according to a preset sequence. The preset sequence may include an arithmetic sequence, a geometric sequence, a Fibonacci sequence, a square sequence, or a power sequence. For example, the number of transaction data sources can be increased incrementally in the order of 1, 2, 4, 8, 16, 32, 64, 128, 256.

[0046] When there is only one trading data source, multiple trading instruments within that data source can be combined for calculation. When there are two trading data sources, identical or similar trading instruments from the two data sources can be matched and then added together. When the number of trading data sources increases to four, eight, sixteen, or more, cross-source aggregation can be performed according to the same rules. In this way, this application can uniformly incorporate data from multiple trading platforms, multiple market data websites, and multiple servers into the calculation, avoiding the impact of delays, abnormal quotes, or missing data from a single data source on the analysis results.

[0047] For example, when the data source for transactions includes a first website, a second website, a third website, and a fourth website, the combined transaction indicators within each website can be calculated separately. Then, the combined transaction indicators from all four websites can be added together or averaged to obtain a cross-website combined indicator. Alternatively, data from group A in the first website can be cross-added with data from group B in the second website, and data from group C in the third website can be diagonally added with data from group D in the fourth website, resulting in more complex combined results. This not only improves data utilization but also enhances the stability and resistance to interference in transaction data analysis.

[0048] In the case of multiple trading data sources, this application can also match market data of the same or similar trading instruments from different trading data sources. After matching, the data of these trading instruments can be added to a trading data combination matrix and subjected to unified standardization and combination calculations.

[0049] To ensure the reasonableness of the combined calculation results, sign correction can be applied to trading pairs with opposite directions. For example, in forex currency pairs, although EUR / USD and USD / JPY are both related to the US dollar, the US dollar serves as the quote currency in the former and the base currency in the latter. Therefore, the direction of their price changes may have opposite implications for the strength of the US dollar. Thus, when adding multiple US dollar-related pairs, the trading pairs with opposite directions can be reversed to ensure that the sum more accurately reflects the overall strength of the US dollar. This process can include taking the opposite number, absolute value processing, sign correction, or separate labeling.

[0050] For example, when calculating the USD / USD strength / weakness index, for pairs like USD / JPY, USD / CHF, and USD / CAD where the USD is the base currency, an increase in these pairs can be considered a strengthening of the USD; conversely, for pairs like EUR / USD, GBP / USD, and AUD / USD where the USD is the quote currency, a decrease in these pairs can be considered a strengthening of the USD. Therefore, in a unified calculation, the standardized changes in EUR / USD, GBP / USD, and AUD / USD can be reversed. This method allows the index to more accurately represent the true direction of change for a particular currency, asset, or market factor.

[0051] Regarding timeframes, this application supports multiple timeframes including 1 minute, 3 minutes, 5 minutes, 15 minutes, 30 minutes, 1 hour, 4 hours, daily, weekly, and monthly charts. For short-term trading, 1-minute, 3-minute, and 5-minute timeframes can be used in combination for calculations; for medium-term trading, 15-minute, 30-minute, and 1-hour timeframes can be used; and for long-term trading, 4-hour, daily, weekly, or monthly timeframes can be used. Data from different timeframes can be calculated separately or combined from short-term to long-term data.

[0052] For example, you can first calculate short-term combination indicators: Short-term combination indicator = 1-minute combination value + 3-minute combination value + 5-minute combination value; Recalculate the medium-term combination indicator: Medium-term combination indicator = 15-minute combination value + 30-minute combination value + 1-hour combination value; Then calculate the long-term combined indicator: Long-term combination indicator = 4-hour combination value + daily combination value + weekly combination value.

[0053] Finally, the short-term, medium-term, and long-term combined indicators can be weighted and calculated to obtain the overall combined trading indicator. In this way, this application can not only determine the current short-term fluctuation direction but also the medium- to long-term trend background, thereby improving the reference value of the trading data processing results.

[0054] Combined trading indicators can be used to generate trading data processing results. These results may include combined strength values, direction labels, volatility alerts, risk warnings, ranking results, or trading reference information. Specifically, when the combined trading indicator is greater than a preset positive threshold, an upward direction label or long position reference information can be generated; when the combined trading indicator is less than a preset negative threshold, a downward direction label or short position reference information can be generated; when the combined trading indicator is within a preset range, a sideways status label or a wait-and-see alert can be generated; and when the absolute value of the combined trading indicator suddenly increases, a volatility amplification alert or risk warning can be generated.

[0055] For example, a first threshold T1 and a second threshold T2 can be set, where T1 is a positive threshold and T2 is a negative threshold. When the combined trading indicator is greater than T1, it indicates that the overall changes of multiple trading instruments, multiple trading data sources, or multiple time periods are biased upward, and the result can be output as "strong combination" or "upward trend". When the combined trading indicator is less than T2, it indicates that the overall changes are biased downward, and the result can be output as "weak combination" or "downward trend". When the combined trading indicator is between T2 and T1, the result can be output as "oscillating", "unclear direction", or "awaiting confirmation".

[0056] Multiple trading instruments or groups of trading instruments can also be sorted based on combined trading indicators. For example, the combined strength values ​​of each trading instrument group can be arranged from largest to smallest to obtain strong and weak instrument groups. Traders or trading systems can then select strong, weak, or strong-weak hedging combinations based on the sorting results. This sorting method reduces the workload of manually searching and comparing multiple charts, improving the efficiency of trading data analysis.

[0057] By combining data from multiple trading instruments, the randomness of signals from a single instrument can be avoided. For example, the price changes of a particular instrument may be affected by short-term news, insufficient liquidity, abnormal pricing, or individual large orders. Judging the direction solely based on that instrument can easily lead to misjudgments. By combining data from multiple trading instruments, multiple trading data sources, and multiple time periods, when multiple related instruments show consistent changes, the combined indicator can better reflect the overall market direction. When an instrument experiences abnormal fluctuations, its impact can be smoothed out by data from other instruments, thereby improving the stability of the processing results.

[0058] Another benefit is the ability to transform complex multi-market data into one or more intuitive combined indicators. Traditionally, users need to simultaneously view multiple trading software programs, websites, charts, and timeframes, manually determining the relationships between different instruments. This application, however, automates operations such as horizontal, vertical, diagonal, and cross-additions through matrix construction and combination calculations, allowing the system to directly output combined strength values, direction labels, volatility alerts, risk warnings, and ranking results. This reduces the difficulty of manual analysis and improves data processing speed.

[0059] Since the number of trading data sources can be expanded according to a preset sequence, such as from 1 to 2, 4, 8, 16, 32, 64, 128, or 256, this application is applicable to data processing scenarios of different scales. When there are few data sources, it can be used for personal trading terminals or small quantitative systems; when there are many data sources, it can be used for large-scale trading data analysis platforms, multi-market monitoring systems, or cross-platform market analysis systems. The number of trading instruments, data sources, and time periods can all be dynamically increased according to actual needs.

[0060] Furthermore, this solution enhances cross-market analysis capabilities. For example, there are often correlations between the foreign exchange market, commodity market, stock index market, bond market, and digital asset market. A stronger US dollar may affect gold, crude oil, and other currencies; interest rate changes may affect government bonds, stock indices, and exchange rates. Traditional single-asset analysis methods often fail to detect these cross-market relationships in a timely manner. This application, through cross-group aggregation, oblique aggregation, and cross-aggregation, can incorporate data from different markets into the same matrix for calculation, making it easier to identify resonances or divergences between multiple markets.

[0061] In a specific application scenario, users can select multiple USD-related instruments in the foreign exchange market as Group A, commodities such as gold, silver, and crude oil as Group B, and stock index futures and index ETFs as Group C. The system acquires 1-minute, 5-minute, 15-minute, 1-hour, and daily data for these instruments across multiple trading platforms and performs standardization processing. Subsequently, the system constructs a trading data combination matrix and calculates the internal combination values ​​for Group A, Group B, and Group C, and further calculates combination indicators such as A+B, A+C, B+C, and A+B+C. Finally, the system outputs a strength ranking, directional label, and risk warning based on the magnitude and direction of each combination indicator.

[0062] In another specific application scenario, users can combine market data for the same trading instrument from different websites or trading platforms. For example, a user can select gold spot data from four trading platforms, obtaining gold price change values ​​from the first, second, third, and fourth platforms respectively. The system matches, standardizes, and adds the gold price change values ​​from the four platforms to obtain a cross-platform gold composite indicator. If any platform experiences abnormal or delayed quotes, the outlier values ​​from that platform can be identified, removed, or have their weight reduced, thus making the final composite indicator more stable.

[0063] like Figures 1-3 As shown, in another specific application scenario, users can calculate combined indicators based on short-term, medium-term, and long-term trends. Short-term indicators reflect immediate changes, medium-term indicators reflect phased trends, and long-term indicators reflect the overall direction. When short-term, medium-term, and long-term indicators all show the same direction, the system can output a strong directional label; when short-term and long-term indicators are opposite, the system can output prompts such as "Short-term reversal," "Trend conflict," or "Increased risk." In this way, this application can directly represent the relationship between different timeframes, helping users reduce errors caused by relying solely on a single timeframe.

[0064] In the event of missing or abnormal data, this application can also perform fault tolerance processing. For example, when a certain trading data source is temporarily unable to return market data, the corresponding matrix element can be marked as null, or it can be filled with data from the previous period, or replaced with the average of other trading instruments in the same group. When the price change of a certain trading instrument exceeds a preset abnormal threshold, its weight can be temporarily reduced or it can be removed from the combined calculation. In this way, this application can improve the stability of the system in a real trading environment.

[0065] This solution can also display the results of transaction data processing through a graphical interface. The graphical interface uses nodes to represent trading instruments, groups of trading instruments, trading data sources, or time periods; connecting lines to represent combined calculation relationships; different colors to represent upward, downward, or oscillating states; and different thicknesses to represent the strength of combined indicators. The results of horizontal, vertical, diagonal, and cross-additions can also be displayed in different areas. In this way, users can intuitively see the relationships between different trading instruments, different trading data sources, and different time periods.

[0066] For example, the interface can display the combined strength values ​​of groups A, B, and C, and show cross-combination results such as A+B, B+C, A+C, and A+B+C. When a combination indicator exceeds a preset threshold, the user can be alerted with color, arrows, sound, or text prompts. For scenarios with a large number of trading platforms, the interface can display combined results from different data sources in hierarchical levels (1, 2, 4, 8, 16, 32, 64, 128, 256), allowing users to view the comprehensive changes from a single data source to multiple data sources layer by layer.

[0067] In summary, this application acquires market data for multiple trading instruments from multiple trading data sources, groups the trading instruments, standardizes the different market data, and further constructs a trading data combination matrix. Then, it generates combined trading indicators through horizontal aggregation, vertical aggregation, diagonal aggregation, cross aggregation, same-group aggregation, cross-group aggregation, same-source aggregation, and cross-source aggregation, ultimately outputting combination strength values, direction labels, volatility alerts, risk alerts, ranking results, or trading reference information. Compared with existing technologies, this application has at least the following beneficial effects: It can integrate multiple trading instruments into the same data processing framework, avoiding the lack of information caused by analyzing only a single trading instrument.

[0068] It can standardize and process data from different markets such as foreign exchange, futures, spot, stocks, options, government bonds, and ETFs, making them comparable across different products.

[0069] It can fully utilize the correlation between different trading instruments through various combinations such as horizontal addition, vertical addition, diagonal addition, and cross addition.

[0070] It can process market data from multiple trading data sources simultaneously, and the number of data sources can be expanded in the form of sequences such as 1, 2, 4, 8, 16, 32, 64, 128, 256, etc., demonstrating strong scalability.

[0071] It can combine and analyze data from different time periods, such as short-term, medium-term, and long-term, to more comprehensively reflect the market's immediate changes, phased trends, and long-term direction.

[0072] By combining trading indicators to generate directional labels, strength rankings, volatility alerts, and risk warnings, the efficiency of trading data processing is improved, and the workload of manually switching and comparing multiple platforms, websites, and charts is reduced.

[0073] By combining data from multiple varieties, platforms, and periods, the impact of anomalies in a single data source, abnormal fluctuations in a single trading variety, or misjudgments in a single period on the analysis results can be reduced, thereby improving the stability and reliability of the trading data processing results.

[0074] Therefore, the trading data processing method based on multiple trading instruments provided by this invention can realize unified combination analysis of multiple instruments, multiple markets, multiple platforms, and multiple periods, and is applicable to scenarios such as trading data monitoring, market analysis, quantitative trading assistance, risk warning, strength ranking, and cross-market linkage analysis.

[0075] It will be apparent to those skilled in the art that this disclosure is not limited to the details of the exemplary embodiments described above, and that this disclosure can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of this disclosure is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within this disclosure. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0076] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A method for processing trading data based on a combination of multiple trading instruments, characterized in that, Includes the following steps: S1. Obtain transaction data from multiple transaction data sources. The transaction data includes market data for multiple trading instruments within at least one time period. The market data includes at least the trading instrument identifier, data source identifier, timestamp, time period, and price data. S2. Based on the trading instrument identifier and / or the attribute information of the trading instrument, group the multiple trading instruments to obtain at least one trading instrument group, and each trading instrument group includes one or more trading instruments with a preset association relationship. S3. Standardize the market data to obtain the standardized change value of each trading instrument in the corresponding time period; S4. Construct a trading data combination matrix based on the trading instrument group, the trading data source, and the time period. The elements in the trading data combination matrix correspond to the standardized change values ​​of a trading instrument, a trading instrument group, a trading data source, and / or a time period. S5. Perform at least one combination operation on the elements in the transaction data combination matrix to obtain a combined transaction indicator. The combination operation includes at least one of horizontal aggregation, vertical aggregation, diagonal aggregation, cross aggregation, same group aggregation, cross group aggregation, same source aggregation, and cross source aggregation. S6. Generate trading data processing results based on the combined trading indicators. The trading data processing results include combined strength value, direction label, volatility prompt, risk prompt, sorting results and / or trading reference information.

2. The transaction data processing method based on a combination of multiple trading instruments according to claim 1, characterized in that, The multiple trading instruments include at least two of the following: foreign exchange, domestic futures, foreign futures, spot commodities, domestic stocks, foreign stocks, options, treasury bonds, and ETFs.

3. The transaction data processing method based on a combination of multiple trading instruments according to claim 1, characterized in that, The preset association relationship includes at least one of the following: Relationships based on the same quoted currency, the same base currency, the same commodity, the same index, the same industry, the same market type, the same trading direction, the same volatility characteristics, and the same trading session.

4. The transaction data processing method based on a combination of multiple trading instruments according to claim 1, characterized in that, The standardization process includes at least one of the following: Convert the price changes of different trading instruments into a unified point value, a unified percentage change value, a unified rate of return, a unified currency unit, a unified contract multiplier, a unified volatility, or a unified risk exposure.

5. The transaction data processing method based on a combination of multiple trading instruments according to claim 1, characterized in that, The time period includes at least one of the following: 1 minute, 3 minutes, 5 minutes, 15 minutes, 30 minutes, 1 hour, 4 hours, daily, weekly, and monthly. Furthermore, the method supports aggregating data from shorter time periods to longer time periods, and / or splitting or associating data from longer time periods to shorter time periods.

6. The transaction data processing method based on a combination of multiple trading instruments according to claim 1, characterized in that, The horizontal aggregation includes summing, weighted summing, or averaging the standardized change values ​​of multiple trading instrument groups from the same trading data source. The vertical aggregation includes summing, weighted summing, or averaging the standardized changes of the same trading instrument group across multiple trading data sources or multiple time periods.

7. The transaction data processing method based on a combination of multiple trading instruments according to claim 1, characterized in that, The oblique aggregation includes selecting multiple elements located in different rows and columns from the transaction data combination matrix, and performing summation, weighted summation, or combination function calculation on the multiple elements; The cross-aggregation includes selecting at least one element from the first trading instrument group and at least one element from the second trading instrument group for combined calculation to obtain a combined trading indicator across trading instrument groups.

8. The transaction data processing method based on a combination of multiple trading instruments according to claim 1, characterized in that, The combined operation includes at least one of the following: Adding elements within group A, adding group A to group B, adding group A to group B and group C, adding any element in group A to any element in group B, adding multiple elements in group A to multiple elements in group B, and multi-level cross-addition between group A and group N.

9. The transaction data processing method based on a combination of multiple trading instruments according to claim 1, characterized in that, The multiple transaction data sources include at least one trading platform or market data server; When there are multiple transaction data sources, the method further includes: Market data for the same or similar trading instruments from different trading data sources are matched, and the matched market data is added to the trading data combination matrix.

10. The transaction data processing method based on a combination of multiple trading instruments according to claim 9, characterized in that, The number of transaction data sources is determined according to a preset sequence, which includes at least one of the following: arithmetic sequence, geometric sequence, Fibonacci sequence, square sequence, and power sequence.

Images