JP3573072B2 - Time series data compression device - Google Patents

Description

[0001]
TECHNICAL FIELD OF THE INVENTION
The present invention provides time-series data compression for compressing time-series data of stock prices, foreign exchange, precious metals, indices, futures, semiconductors, materials, and other marketable materials with price fluctuations. Equipment I do.
[0002]
[Prior art]
Conventionally, time-series data, for example, stock price data, has been provided to investors and the like as stock price charts such as candlestick charts on a daily or weekly basis. A database that stores this stock price data over a long period of time can display a large number of records visually when the number of records increases and the number of records exceeds the display resolution of computers, television receivers, portable electronic terminals, etc. It will be difficult. For example, when stock price data of 1000 records is displayed as a chart on a device (display area) of 600 dots in the horizontal direction, the information of a plurality of records converges to one horizontal dot, so that the screen is collapsed and the display becomes difficult to see. .
[0003]
Most market data such as stock price data expresses four prices consisting of “open price”, “high price”, “low price”, and “price discount” in a format called a candle, but it is necessary to represent each record. Since the number of horizontal dots is at least 3 dots, the number of data that can be displayed on the image display device in a limited area is limited.
[0004]
In addition, ordinary investors who do not have a dedicated line take a long time to download a huge amount of stock charts and stock data, so conventional stock data and stock charts are not easy to use for ordinary investors. There was a problem to say.
[0005]
Under such circumstances, various compression methods for compressing stock price data have been proposed.
[0006]
[Problems to be solved by the invention]
As one of the conventional compression methods, there is a method in which data of one month unit is aggregated and displayed on one candle in order to prolong the display period. In the case of this method, there is a problem that an important point becomes difficult to read because the price fluctuation history in one month is simplified.
[0007]
Further, among the conventional display technologies, there are a new price bar, a key bar, and a so-called Point & Figure chart. These display techniques display the price fluctuations in a compressed manner, but are common in that data on the time axis is lost. When the data on the time axis is lost, the following problem occurs. For example, how many months did investors take from the previous high to the next high? Or, the emphasis is on the periodicity of stock price fluctuations, such as how many months it took from the previous adjustment phase to the next adjustment phase. However, the display method of the key feet or the like cannot display the periodicity of the stock price fluctuation.
[0008]
Further, generally, investors determine buying and selling timings with reference to price fluctuation patterns in a stock price chart. As a fluctuation pattern of the stock price chart, for example, a state where the stock price is gradually rising to the bottom of the pot, a state where the vertical amplitude of the stock price is gradually reduced, and a case where the amplitude is up and down, the previous low point ( There is a state where the next low point is gradually rising from the point of change from the time of going down to raising). Such a price fluctuation pattern can be applied to the stock price of each company or the Nikkei Stock Average. Therefore, when compressing time-series data such as stock price data, it is necessary to compress a large amount of stock price data including noise while leaving a fluctuation pattern.
[0009]
The present invention has been made based on the above circumstances, and is capable of compressing time-series data while maintaining the periodicity and fluctuation pattern of the fluctuation of the time-series data. Equipment The purpose is to provide.
[0010]
[Means for Solving the Problems]
In order to achieve the above object, a time-series data compression apparatus according to the present invention sequentially selects necessary target data from time-series data by referring to target data to be processed, and selects other target data. A time-series data compression apparatus for compressing data by discarding data, wherein for each target data, reference is made to the latest N (where N is an integer) past target data including the target data. Preprocessing means for calculating a maximum value, a minimum value, and an average value from the N pieces of target data; and, for the target data, when the target data is smaller than the average value, the maximum value is calculated. A first selection processing means for performing a selection process of a maximum value to be stored and, when the target data is equal to or more than the average value, discarding the target data, and for the target data, the target data is larger than the average value. If the target data is equal to or less than the average value, a second selection processing means for discarding the target data is performed, and a most recent processing is a first selection processing. Storage means for storing whether the maximum value is selected by the means or the minimum value is selected by the second processing selection means, and for each object data, Is the maximum value selection process or the minimum value selection process, and when the latest process is the maximum value selection process, the target data is subjected to the second selection process. And a switching means for causing the first selection processing means to process the target data when the most recent processing is the selection processing of the minimum value.
[0012]
BEST MODE FOR CARRYING OUT THE INVENTION
Hereinafter, a time-series data compression device according to an embodiment of the present invention will be described with reference to the drawings. In the apparatus of the present embodiment, a case will be described in which stock price data is used as time-series data and weekly data of the stock price data is compressed. The apparatus according to the present embodiment compresses data by judging whether a large number of records in a database is a necessary record for each record, selecting and leaving only necessary records, and discarding other records. I do. In addition, each record of the present embodiment includes at least four fields of “open price”, “high price”, “low price”, and “close price” in addition to “time data”.
[0013]
[Structure of Embodiment] FIG. 1 is a schematic block diagram of a time-series data compression apparatus according to an embodiment of the present invention. The time-series data compression apparatus according to the present embodiment includes a database 10 storing stock price data, a pre-processing unit 11 performing pre-processing before performing a selection process on each record of the database 10, and a first processing unit 11 performing a selection process. A selection processing unit 13 and a second selection processing unit 14, and a storage unit that stores whether the most recent processing was a selection processing by the first selection processing unit 13 or a selection processing by the second processing selection unit 14, and the like. A switching unit 12 for alternately performing a selection process by the first selection processing unit 13 and a selection process by the second selection processing unit 14; a display unit 16 for displaying data after selection and compression; It includes a pointer 17 for storing an address indicating the position of a record in the database 10 and a control unit 20 for controlling the entire apparatus.
[0014]
Before performing the selection process by the first selection processing unit 13 or the second selection processing unit 14 for each record of the database 10, the preprocessing unit 11 refers to the latest three records including the record and refers to the maximum number of records. The value, the minimum value, and the average value are calculated, and the result is stored in the storage unit 15 or the like. When calculating the maximum value, the highest value among the highest values in the past three records is calculated. When calculating the minimum value, the smallest low price is calculated from the low prices in the past three records. When calculating the average value of the past three records, the average value of the closing prices in the past three records is calculated. When calculating the average value, the average value may be calculated using the starting value. Further, all of the maximum value, the minimum value, and the average value may be calculated using the closing price, the opening price, or the volume weighted average. Further, when processing the first record of the database, the first record is used, and when the second record is processed, the first and second two records are used to calculate the maximum value, the minimum value, and the average value. .
[0015]
For each record to be processed, the first selection processing unit 13 compares the closing price of the record with the average value calculated by the preprocessing unit 11, and when the closing price of the record is smaller than the average value, A selection process (selection process of the maximum value) for setting a flag on the record including the maximum value of the high value calculated by 11 is performed, and when the closing price of the record is equal to or more than the average value, the record is discarded.
[0016]
The second selection processing unit 14 compares, for each record to be processed, the closing price of the record with the average value calculated by the preprocessing unit 11, and when the closing price of the record is larger than the average value, Performs a process of setting a flag on a record including the minimum value of the calculated low price (selection process of the minimum value), and when the closing price of the record is equal to or less than the average value, discards the record.
[0017]
Note that the first selection processing unit 13 and the second selection processing unit 14 may store a high value of the maximum value and a low value of the minimum value in a storage unit or the like instead of setting a flag in a record.
[0018]
The pointer 20 indicates a position of a record to be processed when the preprocessing unit 11, the first selection processing unit 13, the second selection processing unit 14, and the like perform processing, and each time the processing of one record ends. Is incremented. Therefore, the pointer 20 is referenced not only by a main program described later but also by a subroutine.
[0019]
For each record, the switching unit 12 refers to the storage unit 15 and determines whether the most recent processing is the maximum value selection processing by the first selection processing unit 13 or the minimum value selection processing by the second selection processing unit 14 It is determined whether the most recent processing is the selection processing of the maximum value. If the latest processing is the selection processing of the minimum value, the target record is processed by the second selection processing unit 14. The target record is processed by the first selection processing unit 13. As a result, after the selection processing is performed by the first selection processing unit 13, the selection processing is always performed by the second selection processing unit 14, and after the selection processing is performed by the second selection processing unit 14, Next, selection processing is always performed by the first selection processing unit 13. That is, in the present embodiment, the process of selecting the maximum value and the process of selecting the minimum value are performed alternately.
[0020]
The control unit 20 controls reading and writing of data in the storage unit 15 in response to a request from the preprocessing unit 11, the first selection processing unit 13, the second selection processing unit 14, and the like, and changes the value of the pointer at the beginning of the processing. A process of setting the value to 1 or incrementing the value of the pointer each time the processing of one record is completed is performed. Further, the control unit 20 displays the compressed data on the display unit 16 by a method described later.
[0021]
[Operation of Embodiment] Next, a processing procedure of the time-series data compression device of the present embodiment will be described with reference to FIGS. 2 to 5. FIG. 2 is a diagram showing a processing procedure of the main program, and FIGS. 3 to 5 are diagrams showing a processing procedure of a subroutine (function) called from the main program. In FIGS. 2 to 5, a record is also referred to as a block.
[0022]
After performing the predetermined initialization, in step S1, a block length (TheBlock) is defined. In the case of stock price data, the block length (N) is set by selecting an appropriate numerical value from 3 to 10. In the present embodiment, as described above, the pre-processing is performed with reference to the records for three weeks, so the block length is set to 3 here.
[0023]
In step S2, since the selection processing by the first selection processing unit or the second selection processing unit has not been performed, the final detection data variable (LastLabel) indicating the result of the previous selection processing is initially initialized to null.
[0024]
In step S3, the value of the pointer 20 is set to a value "1" indicating the first record of the database. In step S4, it is determined whether or not the record being referred to is the last record in the database 10. If it is not the last record, the process proceeds to step S5. In step S5, three subroutines shown in FIGS. 3 to 5 are executed. Note that the position of the record of the database referred to in each subroutine shown in FIGS. 3 to 5 is the same position (the same pointer 20) as the position set in the main program.
[0025]
In the processing procedure of the subroutine shown in FIG. 3, after performing predetermined initialization, in step S21, it is determined whether or not the reference position P (the value of the pointer 20) of the record of the current database is larger than the block length (BlockSize) = 3. Judge. In this case, since the position P of the pointer 20 is 1, the process proceeds to step S23, and the high value from the first record to the record at the position of P (in this case, only the first record) is temporarily set as a return value. Remember. Note that when P is 2, the highest value among the two records from the beginning and among the three records from the beginning when P is 3 are calculated, and the maximum value is calculated. It is temporarily stored as a return value. On the other hand, if the reference position P is larger than the block length (BlockSize) = 3, the process proceeds to step S22, and the block length (BlockSize) = 3 including P in the past direction from the current reference position P is traced back. The maximum value of the high value is calculated from the records in the range (three records), and the maximum value is temporarily stored as a return value.
[0026]
In the processing procedure of the subroutine shown in FIG. 4, after performing predetermined initialization, in step S31, it is determined whether or not the reference position P (the value of the pointer 20) of the record of the current database is larger than the block length (BlockSize) = 3. Judge. In this case, since the position P of the pointer 20 is 1, the processing shifts to step S33 to temporarily store the low value from the first record to the record at the position of P (in this case, the first record) as a return value. I do. When P is 2, the lowest value is calculated from the first two records, and when P is 3, the lowest value is calculated among the first three records, and the minimum value is returned. As a temporary storage. On the other hand, if the reference position P is larger than the block length (BlockSize) = 3, the process proceeds to step S22, and the block length (BlockSize) = 3 including P in the past direction from the current reference position P is traced back. The minimum value of the low price is calculated from the records in the range (three records), and the minimum value is temporarily stored as a return value.
[0027]
In the processing procedure of the subroutine shown in FIG. 5, after performing a predetermined initial setting, in step S41, it is determined whether or not the reference position P (the value of the pointer 20) of the record of the current database is larger than the block length (BlockSize) = 3. Judge. In this case, since the position P of the pointer 20 is 1, the processing shifts to step S43 to temporarily store the end value from the first record to the record at the position of P (in this case, the first record) as a return value. I do. When P is 2, the average value of the closing prices is calculated in the two records from the beginning, and when P is 3, the average value of the closing prices is calculated in the three records from the beginning, and the average value is used as the return value. Store temporarily. On the other hand, if the reference position P is larger than the block length (BlockSize) = 3, the process proceeds to step S22, and the block length (BlockSize) = 3 including P in the past direction from the current reference position P is traced back. The average value of the closing price is calculated from the records in the range (three records), and the average value is temporarily stored as a return value.
[0028]
When the processing of the subroutine shown in FIGS. 3 to 5 ends, the processing of step S6 in FIG. 1 is performed. In step S6, it is determined whether the final detection data variable (LastLabel) indicating the result of the previous selection processing is the initial value set in step S3 or "Min" indicating that the minimum value selection processing has been performed. I do. Since the current value is the initial value, the process proceeds to step S7. If it is not the initial value or “Min”, the process proceeds to step S9.
[0029]
In step S7, it is determined whether or not the closing price of the record to be processed (the record indicated by the pointer 20) is smaller than the average value (dBBlockAve) obtained in step S5. If it is smaller than the average value, the process proceeds to step S8, and if it is not smaller than the average value, the process proceeds to step S9.
[0030]
In step S8, a record having a high value equal to the maximum value (dBLockMax) obtained in step S5 is searched, and data including time data of the record is selected as display data (a flag is set on the record). At this time, when there are a plurality of records having the same high value as the corresponding maximum value, a predetermined record, for example, a later record in time, is selected as the display data. Further, in order to indicate that the selection process is a process of selecting the maximum value, “Max” is substituted for a last detection data variable (LastLabel) indicating the result of the previous selection process. By setting the final detection data variable to “Max” in this way, when processing the next and subsequent records, the second selection processing unit always executes the selection processing (steps S9 to S11) for searching for the minimum value. You can try to look for.
[0031]
In step S9, it is determined whether the final detection data variable (LastLabel) indicating the result of the previous selection process is an initial value or “Max” indicating that the maximum value has been selected. If it is the initial value or “Max”, the process proceeds to step S10. If it is not the initial value or “Max”, the process proceeds to step S12.
[0032]
In step S10, it is determined whether or not the closing price of the record to be processed (the record indicated by the pointer 20) is larger than the average value (dBBlockAve) obtained in step S5. If it is larger than the average value, the process proceeds to step S11. If it is smaller than the average value, the process proceeds to step S12.
[0033]
In step S11, a record having a low value equal to the minimum value (dBBlockMin) obtained in step S5 is searched, and data including the time data of the record is selected as display data (a flag is set on the record). At this time, if there are a plurality of records having the same low price as the corresponding minimum value, a predetermined record, for example, a later record in time, is selected as the display data. In addition, “Min” is substituted for the last detection data variable (LastLabel) to indicate that the selection process is a process of selecting the minimum value. By setting the last detected data variable to “Min” in this manner, when processing the next and subsequent records, the first selection processing unit always executes the selection processing (steps S6 to S8) for searching for the maximum value. You can try to look for.
[0034]
By performing the processing from step S6 to step S11, when the closing price of each record to be processed slightly falls below the average value of the past three records, the highest value among the past three records is obtained. Find the maximum value and process the selection. Further, when the closing price of the record to be processed is slightly higher than the average value of the past three records, the minimum value of the low price is found and selected from the past three records. Further, the data is compressed by alternately selecting the maximum value of the high value and the minimum value of the low value.
[0035]
In step S12, the value of the pointer 20 is incremented in order to change the record to be processed to the next record. After this processing, the process returns to step S4. Hereinafter, the number of records is reduced by repeating the processing from step S4 to step S12 until the processing of the last record of the database is completed, that is, the same number of times as the number of records of the target database. I do.
[0036]
If it is determined in step S4 that the record to be processed is the last record, the process proceeds to step S13, where the closing price of the last record is selected as display data, and the process ends.
[0037]
According to the above-described processing procedure, for the third and subsequent records, the maximum value, the minimum value, and the average value are calculated and held in advance for each record based on the data of the past three records. The maximum value selection processing by the selection processing unit 13 or the minimum value selection processing by the second selection processing unit 14 is performed. The first selection processing unit 13 compares the closing price of the record to be processed with the average value, selects the maximum value when the closing price is smaller than the average value, and discards the record when the closing price is equal to or more than the average value. To process. The second selection processing unit 14 compares the closing price of the record to be processed with the average value, and if the closing price is larger than the average value, performs the selection processing of the minimum value. If the closing price is less than the average value, discards the record. To process. The switching unit 12 controls the maximum value selection processing and the minimum value selection processing to be performed alternately. By compressing records by such selection processing and discarding processing, data can be compressed while leaving periodicity and fluctuation patterns of stock prices.
[0038]
FIG. 6 is a graph displayed based on the data after the actual stock price data of Konaka Co., Ltd. is compressed by the apparatus of the present embodiment. In the figure, data is compressed from February 1998 to June 2000 based on weekly data. The minimum value (low price) and the maximum value (high price) are sequentially connected with a straight line from the high price to the low price or from the low price to the high price, an arrow is attached to the tip, and the actual price is displayed at the position of each low price and the high price. In FIG. 6, the price of the closing price of the last record is not displayed, but this price may be displayed.
[0039]
FIG. 7 is a graph showing the stock price data of Konaka Co., Ltd. before being compressed by the apparatus of the present embodiment as a weekly candle. The graph of FIG. 7 shows data from February 1999 to June 2000. Comparing the corresponding periods in FIG. 6 and FIG. 7, the period from February 1999 to June 2000 displayed in FIG. 7 is compressed to approximately half or less in FIG. . While the actual total number of records of Konaka Co., Ltd. in this period is 155, the number of records after compression by the apparatus of the present embodiment is 29, and the compression ratio is about 1/3. Further, when the actual stock price data of Sony Corporation was compressed in the same manner by the apparatus of the present embodiment, weekly data of 280 records was compressed to 60 records. As described above, according to the apparatus of the present embodiment, the stock price data can be compressed to at least 1/2 to 1/10.
[0040]
Also, looking at the graph of FIG. 6, it can be easily recognized that the fluctuation pattern of this stock chart is pennant. Further, it can be easily recognized from the graph of FIG. 6 that the period of the price fluctuation is gradually shortened. On the other hand, if you look closely at FIG. 7, you can see that there is a variation pattern and periodicity similar to FIG. As described above, according to the apparatus of the present embodiment, the data can be compressed while keeping the fluctuation pattern and periodicity of the stock price chart, and the fluctuation pattern and periodicity can be easily recognized as compared with the conventional chart. Become. Therefore, the investor can easily read the date and price that should be used as a guide for the next price change from the past trend shown in FIG. In the above embodiment, the case where the stock price data of one company is compressed has been described. However, the same compression can be performed by compressing the data of the Nikkei Stock Average. Further, according to the present embodiment, it is possible to easily and surely find a price fluctuation pattern other than the pennant, such as a double top or a double bottom.
[0041]
Further, it is not easy to find a resistance line or an instruction line from a large amount of data by using a computer. However, according to the above-described embodiment, since the number of data is small, as shown in FIG. In addition, the resistance line A and the instruction line B can be easily displayed. It is not easy to search for a fluctuation pattern or periodicity from a large number of data even by using a computer. However, finding a fluctuation pattern or periodicity using data compressed according to the present embodiment requires only a small amount of calculation, and can be easily found even by a weak computer. Further, it is difficult for a general investor to find a resistance line or an instruction line from the conventional complicated stock price chart shown in FIG. 7, but according to the present embodiment, it is not necessary to draw the resistance line A or the instruction line B. Also, ordinary investors can easily recognize the indicating line and the resistance line.
[0042]
In the case of stock price data, if the value of N is 2 or less, it is difficult to extract a feature point, and if the value of N is 10 or more, it becomes too rough. Therefore, in the case of stock price data, the value of N is preferably between 3 and 10. When the data amount to be processed is large or the display device is small, the value of N can be set to 11 or more.
[Effects of Embodiment]
According to the above-described embodiment, when the market price keeps rising, the past minimum value (low price) is found, the data while the market price keeps rising is discarded, and the next data is acquired by the market price. It is time to go down. Then, while the market price continues to fall, the data during that period is discarded. As a result, the data can be compressed and the data can be selected by focusing only on the past highs and lows. The average investor is paying attention to the high price when the market is raising. In addition, when prices are lower, we focus on low prices. However, investment experts are paying attention to the lows of the markets during the uptrend. In the case of a downtrend, he pays attention to the high price of the market. According to the present embodiment, it can be said that the market data is compressed from the viewpoint of the expert.
[0043]
Further, according to the present embodiment, a huge amount of stock price data can be compressed to a required size so that the price fluctuation pattern and periodicity remain, so that it has been impossible in the past for portable terminals. It is possible to transfer a chart of stock price data according to the display capability via the Internet or the like. That is, since the transfer amount of conventional stock price data is several tens of gigabytes, transferring this huge amount of stock price data via the Internet increases transfer costs. However, by compressing the data with the apparatus of the present embodiment, even when the stock price data is transferred to the portable terminal using the Internet, the transfer fee becomes low. Therefore, according to the present embodiment, it is also possible to provide a service of transferring stock price data to a portable terminal as a business.
[0044]
Further, according to the present embodiment, it is possible to efficiently remove the disturbing data (noise) that makes it difficult to recognize a characteristic portion such as a periodicity or a fluctuation pattern of a stock price, and perform compression while retaining the characteristic portion. Therefore, the characteristic portion can be easily grasped by using the compressed data, and therefore, it is possible to quickly make an accurate judgment on the stock price trading. For example, according to the present embodiment, it is possible to check the price pattern of several tens of brands per minute, but if the same reading operation is performed by the conventional method shown in FIG. It is necessary, and eyes are tired because I look at the screen.
[0045]
[Other Embodiments] The present invention is not limited to the above embodiment, and various modifications can be made within the scope of the gist. For example, in the above embodiment, the case where N is 3 has been described. However, by changing the value of N, the compression ratio and compression accuracy can be freely changed. That is, by setting the value of N in accordance with the amount of data to be compressed and the capability of the display device such as a portable terminal, it is possible to display the data on a small display device while leaving as much of the feature portion of the data as possible. Becomes possible.
[0046]
Further, in the above embodiment, the case where the stock price data is compressed has been described. However, the present invention is not limited to this. Forex, precious metals, indices, futures, semiconductors, materials, and other price fluctuations are involved. The time series data of the market material may be compressed.
[0047]
Furthermore, the present invention is not limited to the above-described embodiment, and has a case where time series data having no opening price or high price field, that is, so-called single value is compressed, It can be used when compressing.
[0048]
【The invention's effect】
As described above, according to the present invention, by setting an appropriate value of N, the time series data can be changed in time series and the fluctuation pattern in accordance with the data amount and the capability of the display device. Time-series data compression that can compress data Equipment Can be offered.
[Brief description of the drawings]
FIG. 1 is a schematic block diagram of a time-series data compression device according to an embodiment of the present invention.
FIG. 2 is a diagram showing a processing procedure of a main program.
FIG. 3 is a diagram showing a processing procedure of a subroutine (function) called from the main program of FIG. 2;
FIG. 4 is a diagram showing a processing procedure of a subroutine (function) called from the main program of FIG. 2;
FIG. 5 is a diagram illustrating a processing procedure of a subroutine (function) called from the main program of FIG. 2;
FIG. 6 is a graph displayed based on data obtained by compressing actual stock price data of Konaka Co., Ltd. using the apparatus of the present embodiment.
FIG. 7 is a graph showing stock price data of Konaka Co., Ltd. before compression by the apparatus of the present embodiment as a weekly candle.
[Explanation of symbols]
10: Database, 11: Pre-processing unit, 12: Switching unit,
13: first selection processing unit, 14: second selection processing unit, 15: storage unit,
16: display unit, 17: pointer, 20: control unit

Claims (5)

A time-series data compression apparatus for sequentially selecting target data required by referring to target data to be processed from time-series data and compressing the data by discarding other target data, ,
For each target data, the latest N (where N is an integer) target data including the target data is referred to, and the maximum value and the minimum value are determined from among the N target data. , An average value and a pre-processing means,
For the target data, when the target data is smaller than the average value, a maximum value storing process for storing the maximum value is performed. When the target data is equal to or more than the average value, a first selection process for discarding the target data is performed. Processing means;
For the target data, when the target data is larger than the average value, a minimum value storing process for storing the minimum value is performed, and when the target data is equal to or less than the average value, a second selection process for discarding the target data is performed. Processing means;
Storage means for storing whether the most recent processing was the maximum value selection processing by the first selection processing means or the minimum value selection processing by the second processing selection means;
For each target data, it is determined whether the most recent processing was the maximum value selection processing or the minimum value selection processing with reference to the storage unit, and the most recent processing was the maximum value selection processing. A switching means for causing the target data to be processed by the second selection processing means when the processing is processing, and a processing means for processing the target data by the first selection processing means when the most recent processing is processing for selecting the minimum value. ,
A time-series data compression device comprising: The time-series data is market data, and the target data is a record having at least a plurality of fields including an opening price, a high price, a low price, and a closing price in addition to the time data. Time series data compression device. The time-series data compression device according to claim 2, wherein the maximum value is a high value, the minimum value is a low value, and the average value is an average value of closing prices. 4. The time-series data compression device according to claim 1, wherein the value of N is any one of 3 to 10. A display control means for sequentially displaying the maximum value and the minimum value selected by the maximum value selection processing and the minimum value selection processing on a display means as a line graph connected by a straight line. 5. The time-series data compression device according to 1, 2, 3, or 4.

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