TW201915897A - Financial commodity transaction data processing device and method effectively assists investors to judge the starting point and the ending point of a financial commodity price disc setting trend interval - Google Patents

Abstract

The invention relates to a financial commodity transaction data processing device and a financial commodity transaction data processing method, which comprises a financial information receiving module and a price and volume display module, wherein the price and volume display module displays a plurality of price and volume information of the financial information. The self-customized combination module generates a plurality of information options and provides a user to choose to generate combined information. The featured price volume level module is used for defining the feature price according to the combination information defining the volume level information to generate a plurality of feature price volume level information to the merging generation module, so that a plurality of characteristic prices are combined into a characteristic price trading volume block, and generating characteristic price volume block information to the information display module to generate a comprehensive price volume block diagram. According to the invention, the comprehensive price volume block diagram information can effectively assist investors to judge the starting point and the ending point of a financial commodity price disc setting trend interval and an ascending trend or descending trend.

Description

Financial commodity transaction data processing device and method thereof

The present invention relates to a technique for analyzing price fluctuations of financial commodities, and more particularly to a financial commodity transaction data processing apparatus and method thereof.

Currency, bonds, stocks, futures, options, Exchange Traded Funds (ETFs), hedge funds, etc. can be collectively referred to as financial instruments. The price of financial products is mostly accompanied by fluctuations in the market environment, and price changes are carried out in a volatile manner. Therefore, how to effectively analyze financial products to facilitate the purchase or sale of financial products at an appropriate time is a very important part.

Generally speaking, in the selection screen of the reading information decision system of the conventional financial products, a page with all the function options is usually displayed, and the user is required to select the information to be displayed according to the demand, and the function options such as the index market and the stock price quote. , optional quotation, financial news, basic analysis, chip analysis, technical analysis and other functions, click on the required function button, the disk information decision software can switch views, provide investors to view the information presented by the selected function.

However, in the technical analysis function provided by the conventional financial commodity watch information decision-making system, only the pressure of the one-dimensional consideration or the price of the support feature is provided. The one-dimensional consideration is considered as the historical transaction price, or the historical transaction volume alone. To define the pressure or support feature price. Considering the historical transaction price alone, the high price point that is difficult to break is called the pressure characteristic price; on the contrary, when the price level drops to the low point that is difficult to break through, this price is called the support characteristic price. For details, please refer to the first. On the price chart of the chart, connect "at least 2 waves of the lowest point" and extend a line to the right of the line graph, which is the "support line". Connect "at least 2 waves of the highest point" and extend a line to the right of the line graph, which is the "pressure line". Or the price of the maximum volume defined is the pressure or support price. These conventional techniques, because they only consider the historical transaction price information of financial products or the historical transaction volume information of financial products, except for the need to manually draw pressure lines or support lines, the investment decision information provided by them Slightly weak, such as the price satisfaction point after breaking the pressure line or support line, and the starting point or end point of the price increase or decrease. These prior art technologies are not able to handle the provision, and the investment assistance for investors is limited. The progress of financial commodity prices usually has an upward trend, a downward trend, and a consolidation trend. Financial commodity prices are volatility in various trends, with specific or characteristic price blocks, and characteristic price block boundaries act as the starting or ending point for rising or falling trends. The characteristic price block also shows that the consolidation and chaos in the trend of financial commodity price fluctuations is unclear. It is crucial that the search and discovery of the price block of this financial commodity is important for the importance of investment. It can make investors read more clearly and clearly, and can provide investors with more effective judgments on the current situation of financial products, so as to greatly improve investment efficiency. However, the well-known financial goods watch information decision system has not developed such technology.

In view of the above, the present invention proposes a financial commodity transaction data processing apparatus and method thereof to effectively overcome the above-mentioned problems in view of the above-mentioned shortcomings of the prior art.

The main object of the present invention is to provide a financial commodity transaction data processing device and a method thereof, which provide a feature price of a one-dimensional consideration in the technical analysis function provided by the conventional financial commodity watch information decision-making system, the one-dimensional Consider the historical transaction price to define the pressure or support characteristic price. Or define the pressure or support characteristic price for the historical maximum volume consideration. The invention simultaneously combines the transaction price of the past history of the financial commodity with the historical transaction volume information, and changes it into two-dimensional data, and converts into the characteristic price volume block information by two quantization processes, and the characteristic price volume block information Representing the consolidation trend of financial commodity price fluctuations, the price changes are unclear and the chaos is unclear. When the financial commodity price fluctuations deviate from the characteristic price trading volume block, it can be regarded as the beginning of the financial commodity price fluctuation regardless of the rising trend or the downward trend. Point or end point can effectively assist investors and enhance the efficiency of financial commodity investment.

Another object of the present invention is to provide a financial commodity transaction data processing apparatus and method thereof, which can be weighted according to different properties of different financial commodity transaction objects, and provide more accurate information to enhance the investment efficiency of financial commodities.

To achieve the above objective, the present invention provides a financial commodity transaction data processing apparatus for use in a computer having a display, and the financial commodity transaction data processing apparatus includes: an information receiving module receiving financial information of at least one financial product externally The one-price volume display module displays the multi-price transaction information of the financial product according to the financial information; a custom combination module generates a plurality of display information options, and displays a plurality of display information options on the display of the computer to provide a user selection At least one display information option generates a combined information, the combined information includes a defined volume level information; a feature price volume level module receives the complex price volume information and the combined information generated by the customized group and the module, the characteristic price The volume level module sets the price information of the price level greater than the preset value of the volume condition as the characteristic price level according to the volume information of the combined information, so as to generate the characteristic price level information to a merge generation module. Merging and generating modules and then multiple feature price points And a feature price volume block to generate at least one combined feature price volume block information; finally, the financial information, the feature price volume level information and the combined feature price volume block information are transmitted to the information display mode. The group generates a comprehensive price volume block map to display according to the characteristic price level information and the characteristic price volume block information.

In addition, the present invention also provides a method for processing financial commodity transaction data. First, an activation signal is sent to start receiving financial information of at least one financial commodity according to the activation signal, and displaying the volume price information of the financial commodity according to the financial information; Then generating a plurality of display information options to select at least one display information option to generate a combined information, the combined information includes a defined volume level information; according to the definition, the volume level information will be greater than a volume quantity condition preset value price volume information The feature price is set to generate a feature price volume level information; the plurality of feature price points are combined into a feature price volume block to generate at least one combined feature price volume block information; finally, according to financial information, characteristics The price level class information and the combined feature price volume block information generate a block graph of the comprehensive price volume.

The purpose, technical content, features and effects achieved by the present invention will be more readily understood by the detailed description of the embodiments.

Referring to the second figure, as shown in the figure, the financial commodity transaction data processing apparatus of the present invention is applied to a computer 1. The computer 1 includes a processor 10 and a display 12, and the financial commodity transaction data processing apparatus of the present invention. The information receiving module 14 can be a wireless or wired signal receiver for receiving wired or wireless signals, and the financial product is Stock, currency, bond, option, futures, index trade investment fund (ETF) or interest rate, etc.; information receiving module 14 is electrically connected to a price level display module 15 to enable the information receiving module 14 The financial information is transmitted to the price volume display module 15, which then displays the transaction price of the plural price according to the multi-price transaction volume information of the financial information, wherein the displayed price volume is as shown in the third figure, according to each of the financial products. The price level and the volume of the transaction show a bar-shaped distribution pattern. Each bar-shaped figure in the figure represents different price range, the length of the bar-shaped figure. A custom combination module 16 is electrically connected to the display 12 to generate a plurality of display information options, and displays information options in the display of the display 12, providing the user with at least one display information option, a custom combination mode. The group 16 then generates a combined information according to the display information option selected by the user, wherein the combined information includes a defined volume level information, a weighted module information, a feature price volume block comparison information, and a class to block ratio. A combination of information, a long-term average price display information, and a time-area display information, any kind of information and information.

A feature price volume level module 18 is electrically connected to the custom combination module 16 to receive the defined volume level information of the combined information and the plural price transaction information of the financial product, as defined in the defined volume level information. The information, the level of the information on the volume of the financial product, the price level of the transaction, the characteristic level trading volume information, wherein the volume level information includes at least one volume condition preset value, to define the plural price through the volume condition preset value Whether the volume information is a characteristic price, wherein the preset value of the volume condition can be defined by the user, or according to the information receiving module 14, the historical data of a plurality of financial commodity transactions can be continuously received, and the neural network and the machine learn and Recursively back to the test to determine the best volume condition preset value for the user's reference. Please refer to the fourth figure, which is a schematic diagram of the feature price position generated by the feature price level module 18 of the present invention after determining the feature price level. As shown in the figure, each bar graph represents different price range and bar graph. The length of the system represents the transaction volume. When the user customizes the combination module 16, the price is set to the characteristic price according to the preset value of the volume of the user. In this embodiment, the default value of the volume condition of the price range of 18-40 is 50, and the volume of the preset value of the price range of 50-64 is 25, so the volume of the price of 18-40 exceeds 50 and the price is 50- The transaction volume information of 64 with a volume of more than 25 will be regarded as the characteristic price. As shown in the fourth figure, the bar graph of the diagonal line represents the characteristic price. After the feature price is obtained, the feature price volume level module 18 can be integrated. Characteristic price, the first time quantization of the characteristic price and the non-character price bar graph, that is, the transaction volume of the feature price is quantized to 100, and the transaction volume of the non-characteristic price is quantized to 0, to generate Zheng daily price class information, characterized in daily price information i.e. as shown in the fifth hierarchy in FIG.

Please refer to the second figure. The processor 10 further includes a merge generation module 20 electrically connecting the feature price volume level module 18 and the custom combination module 16 to perform the second time according to the characteristic price level information. Quantization, that is, combining a plurality of characteristic price points into at least one feature price volume block, and generating at least one characteristic price volume block information, please refer to the sixth figure. This embodiment has two feature price volume blocks, that is, In the sixth figure, the slanted block is combined with the non-characteristic price volume block to form the feature price volume block information of the sixth figure. In this embodiment, the adjacent two feature price points can be merged into the comprehensive feature price. In addition to the quantity block, it is also possible to set the price information of at least one non-characteristic price position between the two characteristic price points, or merge into a comprehensive feature price level volume block, for example, there are two between the two characteristic price points. And when the price information of the two non-characteristic price points is the same, the price of the same characteristic price is also the same, and the non-characteristic price of the two characteristic price points is Volume information annexation bit, to produce a feature-priced volume block information. And an information display module 21 is electrically connected to the combination generation module 20 and the display 12 to generate a comprehensive price volume block map to the display according to the financial information, the feature price volume level information and the characteristic price volume block information. 12 shows.

Referring to the second figure, the processor 10 further includes a weighting module 22 electrically connected to the custom combination module 16 and the feature price level module 18, and the price level can be defined in the feature price level module 18. Before the characteristic price of the information, the action of weighting the price of different trading object conditions, that is, the transaction volume of each transaction has a large number of transaction orders (large orders) and small transaction orders (small orders), the user can The large single order is defined, and the large single order is given different weights. When the custom combination module 16 generates the weighted module information to the weighting module 22, the weighting module 22 performs weight adjustment on the volume of the price volume information. When adjusting, the weighted value can be modified according to the current state of financial commodity system rising or falling. When the financial commodity system is in the falling stage, a negative weighted set value is added to the trading volume of the financial commodity small order, and the financial commodity is large. The volume is added with a positive weighted set value, and then enters the characteristic price. The volume level module 18 defines whether the weighted plural price transaction information is the characteristic price level; when the financial product system is on In the upswing phase, the small order volume of the financial commodity is added with a positive weighted set value, and the large single volume of the financial commodity is added with a negative weighted set value, and then enters the characteristic price volume level module 18 to define the weighted plural price level. Whether the volume information is a characteristic price. The weighted price information will be lengthened or shortened to increase the volume of the price to be weighted, as shown in the seventh figure, to enter the characteristic price level module 18 to define the weighted level of the complex price information. Both the positive weighted set value and the negative weighted set value are set by the user.

The processor 10 further includes a time zone display module 28 electrically connected to the custom combination module 16 and the information display module 21 to define information of a certain time zone of the financial product, and display it when the user is When the display time zone is selected in the binding combination module 16, the custom combination module 16 generates time zone display information to the time zone display module 28, which generates a time zone according to the time zone set by the time zone display information, and It is transmitted to the information display module 21 to combine with the feature price volume level information and the feature price volume block information to generate a comprehensive price volume block map.

The processor 10 further includes a feature price volume block comparison module 32 electrically connecting the custom combination module 16 and the information display module 21 to compare the feature price of the same financial product or different financial products. Block information, when the user selects the display block similarity value in the custom combination module 16 to compare the similarity of the feature price volume block information of different financial products, the custom combination module 16 generates the block. The similarity value is transmitted to the information display module 21 to combine with the characteristic price volume level information and the plurality of characteristic price volume block information to generate a price level block map. The method for calculating the feature price volume block comparison module calculates the similarity value by cosine similarity calculation.

The processor 10 further includes a hierarchical and block comparison module 26 electrically connected to the custom combination module 16 and the information display module 21, and the hierarchical and block comparison module 26 can compare the same commodity feature price level. The similarity between the information and the combined feature price volume block information, when the user selects the similarity of the feature price level information and the combined feature price volume block information in the custom combination module 16 The subscription module 16 generates a hierarchical and block comparison information to the hierarchical and block comparison module 26, and the hierarchical and block comparison module 26 begins to compare different volume information and feature price volume block information. Similarity to generate class and block similarity values, and pass them to the information display module 21 to combine with the feature price volume class information and the feature price volume information to generate a comprehensive price volume block. Figure. Investors can determine the similarity between the merged feature price volume block information and the unconsolidated feature price volume class information according to the class and block similarity value, so as to modify the rationalization merger condition. The method for calculating the similarity value is calculated by cosine similarity.

The processor 10 further includes a long-term average price display module 30 electrically connected to the information receiving module 14, the customized combination module 16, and the information display module 21, and generates a long-term average when the user operates the customized combination module 16. When the price is displayed to the long-term average price display module 30, the long-term average price display module 30 can generate a long-term average price information according to the financial information of the financial products collected in the information receiving module 14, and transmit the information to the information. The display module 21 is combined with the feature price volume block information to generate a comprehensive price volume block map, wherein the long-term average price information can be an annual line, a half-year line or a seasonal line. The long-term average price information is displayed in the manner of the eighth figure, and the long-term average price is displayed in a bar shape with different colors or patterns at the characteristic price. This embodiment is presented in a black bar shape. In addition, the long-term average price display module 30 can further transmit the long-term average price information to the merge generation module 20 after generating the long-term average price information, and provide the merge generation module 20 to treat the long-term average price information as a feature. The price level is combined with the characteristic price level as the characteristic price volume block.

The integrated price volume block diagram generated by the information display module 21 is formed by the combination generation module 20, the hierarchical and block comparison module 26, the time zone display module 28, and the long-term average price display module 30. The characteristic price volume block is combined with any information generated by the module 32 and the like, and is generated according to the combination selected by the user.

After the description of the structure of the present invention, please refer to the flow chart of the method of the second figure and the ninth figure. As shown in the figure, a financial commodity transaction data processing method first proceeds to step S10, and the computer 1 is operated to issue a start signal. The information receiving module 14 starts to receive financial information of at least one financial product according to the start signal, and displays the plural price transaction information of the financial product according to the financial information; then proceeds to step S12, and the custom combination module 16 generates a plurality of display information options. Displayed in the display 12, wherein the manner in which the plurality of display information options are displayed on the display 12 is as shown in FIG. 10, to provide a user to select at least one display information option to generate a combined information to generate a definition according to the selected information option. The volume level information, the weighted module information, the characteristic price volume block comparison information, the class and block comparison information or the long-term average price display information; the example definition manner in the embodiment is in step S12, firstly defined 1. Time interval 2014/09 to 2016/03, select 2. Display the price range of financial products is between 10- 66 is divided into 30 equal parts, and 3 is selected for large orders or small orders of financial products. Users can define large single orders and give different weights to large orders. 4. Define a certain price range. Among them, the preset value of the condition greater than one volume is set as the characteristic price. In this embodiment, the display price of the preset value of the volume condition 18-40 is defined as the display characteristic price of 50, and the characteristic price is presented in the pattern 1 And the volume of the 50-64 volume condition preset value is the display characteristic price of 25, and the characteristic price position is represented by the pattern 1, and the combination is 5. When the combination is added to the lower five characteristic price points and two non-characteristic price points, Merged into the same block (multiple numbers can be set), and the seasonal, semi-annual or annual line is regarded as the characteristic price, and the merger rule is combined for consolidation, and the selection is 7. Displaying the long-term average price information (annual line, semi-annual line) , the season line), and marked with different colors to get the pattern (pattern 1, pattern 2 ... or pattern n), the long-term average price display module 30 can generate a long-term average price information based on the financial information of financial products.

Then, as shown in step S14, the feature price level module 18 determines whether the price information is a characteristic price based on the preset value of the volume condition of the volume information in the combination information, so as to generate the characteristic price level information. Since the example of the embodiment has been selected to perform weighting, the graph is as shown in the seventh figure. Therefore, the feature price volume level module 18 judges according to the weighted price information, and the present embodiment sets the defined price. The transaction volume of the 18-40 volume is preset to 50, and the volume of the 50-64 volume condition preset value is 25. After determining the characteristic price, as shown in the eleventh figure, it will be a feature. The bar graph of the price level is covered with the slash pattern 1, and the feature price level module 18 integrates and quantifies the feature price level to generate the feature price volume level information, and the feature price volume level information will be as shown in the twelfth figure. As shown, the first quantization process is completed.

Then, the process proceeds to step S16, and the second quantization process is performed. The merge generation module 20 combines the complex feature price points into at least one feature price bit volume block according to the above conditions. The example definition mode is selected in step S12. 5. When combining the two characteristic price points and two non-characteristic price points, merge them into the same block (multiple numbers can be set), and treat the seasonal line and the half-year line as the characteristic price points, and implement the merger rule together. Merging, so the characteristics of the feature price volume block information will be as shown in the thirteenth figure, that is, when the sum of the feature price points of the non-characteristic price points is 5, 2 and 2 or less. The non-characteristic price points are merged. In the process of processing, first, a non-characteristic price point between the feature price points adjacent to the non-characteristic price points is first merged into a feature price bit volume block, and then the non-characteristic price points are adjacent to each other. The two non-characteristic price points between the feature price points are merged into the feature price bit volume block to generate the merged feature price bit volume block information. In addition, the number of non-characteristic price points between the upper and lower adjacent feature price points can be judged by the merger generation module 20 according to the quantity of the price information of the financial commodity, and the quantity of the volume transaction volume information is merged. The more the non-characteristic price points, the more the sum of the feature price points of the merged non-characteristic price points. For example, when the number of trading positions of financial products is 100, the combined non-characteristic price points are merged. The non-characteristic price points below 5 and 5, however, the sum of the feature price points of the adjacent non-characteristic price points must be greater than or equal to 7.

Then, proceeding to step S18, corresponding information is generated according to the instruction of the combined information generated by the display information option selected in the previous step S12. In addition, if the similarity between the feature price volume level information and the feature price volume block information is selected in the interval defined in step S12, the feature price volume area is generated after step S14 and step S16. Block, in step S18, the similarity between the feature price level information and the feature price volume block information is generated by the class and block comparison module 26, and the class and block similarity values are generated, wherein the comparison is performed. The method has been described in the above structure, and therefore the description will not be repeated. If the interval is defined in step S12, the selection of the similar graphic product is displayed and displayed in a classified manner, that is, the similarity of the feature price volume block information of the different financial products is displayed, which is generated after the step S14 and the step S16. The characteristic price volume block, in step S18, can generate the block similarity value through the feature price volume block comparison module 34, to compare the similarity of the block price information of the characteristic price of different financial products, wherein The comparison method has been described in the above configuration, and therefore the description will not be repeated.

Finally, proceeding to step S20, generating a combination according to the information of the user selection condition to generate a comprehensive price volume block map, and according to the content defined in the above embodiment, the overall price level finally presented on the display 12 in the embodiment. The block diagram is as shown in FIG. 14, and of course, the weighting module 22, the class and block comparison module 26, the time zone display module 28, the long-term average price display module 30, and the feature price volume area. The block comparison module 32 generates any kind of information and the like, and is not limited thereto.

In summary, the present invention can simultaneously combine the transaction price information of the past history of the financial commodity with the historical transaction volume information, and convert the information into the characteristic price volume block information by two quantization processes, and the characteristic price volume block information representative In the consolidation trend of financial commodity price fluctuations, the price changes and chaos are unknown. When the financial commodity price fluctuations deviate from the characteristic price volume block, it is out of the consolidation state. It can also effectively judge the starting point or end point of each financial commodity price fluctuation when it is in an upward or downward trend, so that investors can effectively assist investors and enhance the efficiency of financial commodity investment.

The above is only the preferred embodiment of the present invention and is not intended to limit the scope of the present invention. Therefore, any changes or modifications of the features and spirits of the present invention should be included in the scope of the present invention.

1‧‧‧ computer

10‧‧‧ processor

12‧‧‧ display

14‧‧‧Information receiving module

15‧‧‧ Price Volume Display Module

16‧‧‧Custom Combination Module

18‧‧‧Characteristic price level module

20‧‧‧Combined production module

21‧‧‧Information Display Module

22‧‧‧ Weighting module

26‧‧‧Class and block comparison module

28‧‧‧Time zone display module

30‧‧‧Long-term average price display module

32‧‧‧Price Volume Block Comparison Module

The first figure is a schematic diagram of the price of conventional pressure and support features. The second figure is a block diagram of the system of the present invention. The third figure is a schematic diagram of the price transaction information of the present invention. The fourth figure is a schematic diagram of the characteristic price mark of the present invention. The fifth figure is a schematic diagram of the characteristic price level of the present invention. The sixth figure is a schematic diagram of the combined feature price volume block of the present invention. The seventh figure is a schematic diagram of the weighted post-price trading volume information of the present invention. The eighth figure is a schematic diagram of the long-term average price information of the present invention. The ninth diagram is a flow chart of the method of the present invention. The tenth figure is a schematic diagram of the display information options of the present invention. The eleventh figure is a schematic diagram of the weighted post-feature price mark of the present invention. The twelfth figure is a schematic diagram of the information of the weighted post-featured price level of the present invention. The thirteenth figure is a schematic diagram of the information of the feature price volume block of the weighted and merged invention of the present invention. The fourteenth figure is a schematic diagram of the block diagram of the comprehensive price volume of the present invention.

Claims (19)

A financial commodity transaction data processing device is applied to a computer having a display, the financial commodity transaction data processing device comprising: an information receiving module, receiving financial information of at least one external financial product; a price level display module, according to The financial information displays the multi-price transaction volume information of the financial product; a custom combination module generates a plurality of display information options, and displays the display information options on the display to provide a user to select at least one of the display information options. Generating a combination of information, including a defined volume level information; a feature price volume level module, receiving the price information of the price level, and based on the combination information, the volume level information is greater than a volume condition The price information of the preset price is defined as a feature price level to generate a feature price volume level information; a merge generation module, the feature price points in the feature price volume level information are combined into at least one feature price transaction Volume block to generate a feature price volume block information And an information display module, based on the financial information, wherein the daily price class daily price information and feature information block, generating a block diagram of an integrated volume price to the display to display. The financial commodity transaction data processing device of claim 1, wherein the combined information further comprises a weighted module information, a feature price volume block comparison information, a hierarchical and block comparison information, and a long-term average price. Display information or display information in a time zone. The financial product transaction data processing device of claim 2, further comprising a weighting module, when the customized combination module generates the weighting module information to the weighting module, the weighting module deals the price of the price The trading volume of the information is weighted. When the price of the financial product falls, a negative weighted setting value is added to the trading volume of the small sheet of the financial product, and the trading volume of the financial commodity is added to the large amount. Positively weighting the set value, and then entering the feature price level module to define the weighted volume information; when the price of the financial product rises, the volume of the financial item is added to the volume The weighted set value is added to the volume of the large order of the financial commodity plus the negative weighted set value, and then enters the characteristic price level module to define the weighted volume information. The financial product transaction data processing device of claim 2, further comprising a time zone display module, when the custom combination module generates the time zone display information to the time zone display module, according to the time zone The time zone set by the information is displayed, and the time zone is generated and transmitted to the information display module. The financial commodity transaction data processing device of claim 2, further comprising a feature price volume block comparison module, wherein the customized combination module generates the feature price volume block comparison information to the feature price level When the volume block compares the modules, the similarity of the block price information of the feature price is compared, and the block similarity value is generated and transmitted to the information display module. The financial commodity transaction data processing device of claim 2, further comprising a hierarchical and block comparison module, wherein the custom combination module generates the hierarchical and block comparison information to the hierarchical comparison with the block In the module, the similarity between the price level information of the feature price and the volume information of the feature price is compared, and the similarity value of the layer and the block is generated and transmitted to the information display module. The financial commodity transaction data processing device of claim 2, further comprising a long-term average price display module, when the customized combination module generates the long-term average price display information to the long-term average price display module, the long-term The average price display module generates a long-term average price information and transmits it to the information display module display. The financial product transaction data processing device of claim 7, wherein the long-term average price display module generates the long-term average price information, and further transmits the long-term average price information to the merge generation module to The long-term average price information and the characteristic price level are combined into the feature price volume block. The financial commodity transaction data processing device of claim 1, wherein the combination generation module merges the feature price points into the feature price volume block, and the adjacent price points of the features are merged, or the interval is at least A non-characteristic price point is combined to generate a feature price volume block information. The financial commodity transaction data processing device of claim 1, wherein the financial product is a stock, currency, bond, option, futures, index trade investment fund (ETF) or interest rate. A method for processing financial commodity transaction data, the steps comprising: (a) issuing an activation signal, starting to receive at least one external financial information according to the activation signal, and displaying the plural price transaction information of the financial product according to the financial information; (b) generating Plural display information option to select at least one of the display information options to generate a combined information, including a defined volume level information; (c) according to the definition, the volume level information will be greater than a volume condition preset value The volume information is defined as a characteristic price level to generate a characteristic price volume level information; (d) combining the characteristic price points into at least one characteristic price volume block to generate at least one characteristic price volume block information; (e) Generate a block graph of the comprehensive price volume based on the financial information, the price level information of the characteristic price level, and the block price information of the characteristic price. The method for processing financial commodity transaction data according to claim 11, wherein before step (c), the method further comprises weighting the volume of the volume transaction information, and when the price of the financial product falls, the financial product is The volume of the small order plus a negative weighted set value, plus a positive weighted set value for the volume of the financial item's large order; if the price of the financial item rises, the small list of the financial item The volume is added to the positive weighted set value, and the negative weighted set value is added to the volume of the large order of the financial item. The financial commodity transaction data processing method of claim 11, wherein after step (d), further comprising setting a time zone to display the time zone. The method for processing financial commodity transaction data according to claim 11, wherein after step (d), the method further comprises comparing the similarity of the feature price block information of different financial products, and generating a block map similarity value, The block similarity value is displayed. The method for processing financial commodity transaction data according to claim 11, wherein after step (d), the degree of similarity between the feature price level information and the feature price volume block information is generated to generate a hierarchical map and a region. The block graph similarity value is used to display the similarity value between the hierarchical graph and the block graph. The method for processing financial commodity transaction data according to claim 11, wherein before step (d), further comprising generating a long-term average price information, and then proceeding to step (d) to merge the long-term average price information with the characteristic price The feature price position volume block information. The financial commodity transaction data processing method of claim 11, wherein after step (d), further comprising generating a long-term average price information to display the long-term average price information. The method for processing financial commodity transaction data according to claim 11, wherein in the step (d), the feature price points are merged into at least one feature price volume block, the adjacent price points of the features are merged, or At least one non-characteristic price point is combined to generate a feature price volume block information. The financial commodity transaction data processing method according to claim 11, wherein the financial commodity is a stock, currency, bond, option, futures, index trade investment fund (ETF) or interest rate.