CN114493662A - Commodity price data analysis method, platform, system and storage medium - Google Patents
Commodity price data analysis method, platform, system and storage medium Download PDFInfo
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Abstract
A commodity price data analysis method is characterized in that a most similar historical interval is anchored according to the current date, price data change in a historical situation is checked, price data change conditions of one month after the past is checked, and the current price data change is predicted. Performing current market industry analysis based on a most similar historical interval, wherein the most similar historical interval is a historical interval under a market environment or a risk event which is quantitatively acquired and is most similar to the current market environment or the risk event, and the analysis comprises backtracking the historical market conditions of the commodity price so as to analyze the change condition of the commodity price in certain market environments or risk events; and collecting price change indexes including fluctuation rate and sharp rate in the most similar historical interval and the backward interval.
Description
Technical Field
The invention belongs to the technical field of industrial data analysis, and particularly relates to a commodity price data analysis method, a platform, a system and a storage medium.
Background
The market history backtracking method in the industry is less in use at present, the market prospect is mainly performed by the market history backtracking method based on the major change of the historical event or the market price trend, the existing market history backtracking method is lack of quantitative analysis on historical data, the qualitative result of market prediction can be obtained mostly, and the market history backtracking method cannot be adopted in real time due to the fact that the backtracked historical interval has the requirement of major risk events or major price changes.
Disclosure of Invention
In one embodiment of the present invention, a method for analyzing commodity price data,
and anchoring the most similar historical interval according to the current date, checking the price data change in the historical situation, checking the price data change condition which is pushed back by one month, and predicting the current price data change.
Performing a current market industry analysis based on a most similar historical interval, the most similar historical interval being a quantitatively obtained historical interval under a market environment or risk event that is most similar to the current one, the analysis comprising,
backtracking the historical market conditions of the commodity price so as to analyze the change condition of the commodity price in certain market environments or risk events;
collecting price change indexes including fluctuation rate and sharp rate in the most similar historical interval and the backward-moving interval;
the most similar historical interval is used as an index of the existing market data, similar market environments are reflected by the similar historical interval, market analysis report data of the similar historical interval is inquired,
and inquiring the price data change in the similar market environment to analyze the current market industry condition and judge the price trend in the current market environment.
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The above and other objects, features and advantages of exemplary embodiments of the present invention will become readily apparent from the following detailed description read in conjunction with the accompanying drawings. Several embodiments of the invention are illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings and in which:
FIG. 1 is a schematic diagram of an example of price data analysis according to one embodiment of the invention.
Detailed Description
In the industry at present, the commodity market history backtracking analysis method is less in use. The method for backtracking the historical market conditions is based on the market condition analysis of major historical risk events or major changes of commodity price trends. Therefore, the existing market backtracking method is lack of quantitative analysis on historical data, mostly only qualitative results of market analysis can be obtained, and the backtracking history interval is limited, can only be used in a special market environment, cannot be adopted in real time, and cannot effectively utilize all historical price data.
The existing method for quantitatively predicting the price trend through the historical price data of the commodities can be divided into two categories. The first category is based on the assumption of commodity market, for example, the distribution of commodity price is assumed to have logarithmic yield obeying normal distribution, investors are assumed to have investors rationality on behavior, the assumption of market friction is ignored, and the like, and a corresponding price prediction model is established for prediction. Such prediction methods are hypothesized to deviate from actual market conditions and are not able to adjust for risk events in a timely manner. The second type is that price data is fitted and predicted by adopting methods such as artificial intelligence and the like based on the price data, and the model cannot investigate the cause of price change and has uncontrollable risk during prediction.
According to the current market theory research, the commodity trading market is not completely an effective market, namely, the price has a time-lag effect on the feedback of information, and investors cannot make timely and rational judgment on certain market changes and risk events, so that the existing price model is difficult to accurately describe the price changes. Similarly, the feedback of the price to the similar information is similar, that is, the time lag effect of the feedback and other factors influencing the price change have similarity, and the investors have similar investment behaviors to similar market changes and risk events, so that the price change in the most similar historical interval can greatly reflect the current price change situation.
The commodity market environment and price change have periodicity and long memory, for example, the trade price of the soybean is highly related to the harvest season of each year, so that the trade price of the soybean is periodic, and the influence of flood disasters in the main production area of the soybean on the price change of the soybean is long-term.
According to one or more embodiments, a product price data analysis system is provided, wherein a backtracking module of the system performs forward rolling segmentation by taking 3 months (one quarter) as a history interval, compares price data of different intervals by adopting a machine learning algorithm, and screens out the history interval with the most similar price trend to the current interval; considering that the price per se has a stable variation trend along with factors such as the inflation of the currency, the backtracking module calculates the most similar historical interval by adopting the relative variation trend of the price, and ignores the absolute price difference of different intervals.
In the historical interval of commodity price change, the price change is influenced by a plurality of types of events, the market environment of similar historical intervals is different from that of the current interval, and the price change data of all similar intervals can be comprehensively used for prediction analysis. If N similar intervals meeting the similarity threshold of the historical interval are obtained, the price trend of one month after each similar interval is analyzed, wherein the price trend comprises the price of rolling, rising and falling, and the probability of the current price trend can be calculated according to the similarity setting weight of each similar interval.
According to one or more embodiments, a commodity price data analysis method is a history market quantitative backtracking tool based on a machine learning algorithm. By means of the tool, a history interval most similar to the current price trend of the commodity can be inquired, and the similar history interval reflects the change condition of the commodity price in a market environment similar to the history. Three targets of historical market reference, market industry analysis and price trend prediction can be achieved by using similar historical intervals.
Firstly, a user can directly use a commodity price data backtracking and display module to anchor a most similar history interval to check the price change in a history situation, check the price change condition which is pushed one month later and make visual guidance on the current price change. For example, if the price changes violently after one month in the similar history interval, it indicates that the price trend of the current commodity has a greater risk; and if the price rises remarkably in one month after the similar history interval, the price of the current commodity also tends to rise in the similar market environment.
Second, the user can perform market industry analysis based on the most similar history intervals. When conducting commodity industry research, it is generally necessary to perform historical market backtracking to analyze the variation of commodity prices in certain market environments or risk incidents. The most similar historical interval can quantitatively obtain the most similar market environment or historical interval under the risk event at present, and further collect price change indexes such as fluctuation rate, sharp ratio and the like in the most similar historical interval and a backward-pushing interval; on the other hand, the most similar history interval can be used as an index of the existing market data, the similar history interval reflects the similar market environment, data such as market analysis reports of the similar history interval is inquired, price variation under the similar market environment can be inquired, the current industry condition can be analyzed more accurately, and price trend under the current market environment can be judged.
And thirdly, the user can directly obtain the rising, falling and rolling probabilities of the future month calculated by the algorithm of the current day based on the commodity price history by using the forecast plate in the commodity price history of the current day, adjust the investment strategy of the user and prevent and control the investment risk.
According to the method, a commodity price market backtracking algorithm is constructed, price time sequence data of certain commodities are utilized, the most similar historical interval when the commodities roll forward for 3 months every day is obtained by utilizing knowledge of metrological economics and an artificial intelligent tool, and the future rise and fall probability of commodity prices is estimated. In the embodiment, the user can obtain the most similar historical interval to the current time for each day, and the investment strategy formulation for the user is intuitively served. Namely, a history interval which is most similar to the current history interval is obtained in real time, and the market trend of the future month in the investment environment is visually and relatively similar; and the information of similar historical intervals is fully used, and reliable estimation of price trend is provided.
According to one or more embodiments, a commodity price data analysis method adopts historical information of a time series of commodity prices, and quantitative comparison and fitting are performed on the price time series of divided sections, so that daily updating is achieved. The backtracking tool updated day by day can fully reflect the periodicity and long memory of the price time sequence to obtain the history interval most similar to the current market, meanwhile, the historical market data of more than one interval can be adopted for the forecast of price fluctuation, the random error generated by backtracking of a single historical time period is smoothed, and more possible and accurate market forecast is obtained. Rolling forward for three months according to the current price to obtain a most similar historical interval to each day, extracting price variation in a market environment similar to the current price in a historical price time sequence, and showing the price trend of one month in the future of the most similar historical interval as a reference of the current price variation; the price prediction based on the most similar historical interval can realize daily real-time updating and visual display. The embodiment solves the problem that the market prospect is performed only for the history interval of the major risk event or the major price change, and the monthly update and even the daily update are difficult to perform in the conventional commodity price data analysis method.
Therefore, the invention is a research method for analyzing the history interval of a certain commodity which is most similar to the current market. Historical information can be fully reflected on the basis of historical trend prediction and historical interval fitting of the current day, and the user can fully and effectively utilize historical experience and information. When a user adopts a speculative, arbitrage or hedging investment strategy, the user often purchases an investment portfolio of commodities, and the expected income and risk generated by the commodity investment portfolio are important indexes concerned by investors. The commodity trading market is not completely an effective market, namely the feedback of the price to the information is sometimes sluggish, an investor cannot make timely rational feedback to a certain event, and if the investor can master the current price data and simultaneously master the event which does not cause price change, the change trend and risk of the price can be accurately predicted, and the investment target of the investor can be achieved.
The invention can backtrack the similar investment environment at present by fitting the similar history interval, reflects the feedback condition of the price under the similar investment environment, and achieves the effect of simultaneously mastering price data prediction and event prediction. For enterprises, when dealing with risk time such as epidemic situations, disasters or major credit risks, historical information and experience are reference bases for establishing an effective decision making scheme.
To further illustrate the analysis system of the present invention, an example is given below for illustration. The price trend of the deformed steel bar is shown in figure 1.
The optimal historical interval backtracking module acquires an interval which is historically most similar to the current trend, and pre-judges the trend of the future 1 month based on historical subsequent expressions. The upper curve represents the current price sequence of the commodity, taking the commodity price of the deformed steel bar of 10 months and 8 days in 2021 as an example, the value of the rightmost point of the upper curve is 5750, the value of the leftmost point is 5124, which means that the real-time closed-disk price of the deformed steel bar is 5750, the deformed steel bar is cut by rolling forward for 3 months, and the obtained initial point price of the interval is 5124. The leftmost point at the lower level is 3667, and the value of the rightmost point is 4109, which means that the interval from 10/9/2012 to 1/9/2013 is the most similar history interval fitted to the current price interval, and the trend of the predicted current commodity price is similar to the trend of the price from 1/9/2013 to 2/4/2013. The historical interval is divided according to rolling for 3 months, and an interval which is most similar to the current forward 3-month trend is screened out; considering that the average value of the prices tends to rise along with the expansion of the currency and other factors, the relative variation trend of the prices is adopted for obtaining the most similar historical intervals, and the absolute price difference of different historical intervals is ignored.
And (II) the price trend prediction module based on the history interval displays the rise and fall probability distribution of all similar rolling history intervals in the future 1 month. Wherein 3 intervals of pan integration (-10%), rising (> 10%), falling (< 10%) are divided. Still taking the price of the deformed steel bar with the price of 10-8.2021 as an example, after 10-8.8.2021, the price of the deformed steel bar rises by 24.69%, the falling probability is 72.31%, and the coiling probability is 3%, where the falling probability is significantly higher than the coiling probability and the rising probability, and it can be predicted that the price of the deformed steel bar will probably fall in the future 1 month. The optimal threshold value is set to screen out the history intervals with similar quotations, and the artificial intelligence and big data algorithm are adopted to carry out comprehensive calculation on the similar history intervals, so that relatively reliable pre-judgment on the rising and falling probability is obtained.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may also be an electric, mechanical or other form of connection.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention essentially or partially contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (9)
1. A commodity price data analysis method is characterized in that,
and anchoring the most similar historical interval according to the current date, checking the price data change in the historical situation, checking the price data change condition which is pushed back by one month, and predicting the current price data change.
2. The commodity price data analysis method according to claim 1,
performing a current market industry analysis based on a most similar historical interval, the most similar historical interval being a quantitatively obtained historical interval under a market environment or risk event that is most similar to the current one, the analysis comprising,
backtracking the historical market conditions of the commodity price so as to analyze the change condition of the commodity price in certain market environments or risk events;
collecting price change indexes including fluctuation rate and sharp rate in the most similar historical interval and the backward interval;
the most similar historical interval is used as an index of the existing market data, similar market environments are reflected by the similar historical interval, market analysis report data of the similar historical interval is inquired,
and inquiring the price data change in the similar market environment to analyze the current market industry condition and judge the price trend in the current market environment.
3. The merchandise price data analysis method of claim 2, wherein the calculated merchandise price rise, fall and price rate for the future month is predicted.
4. A commodity price data analysis platform, characterized in that the platform comprises a server having a memory; and
a processor coupled to the memory, the processor configured to execute instructions stored in the memory, the processor to:
and anchoring the most similar historical interval according to the current date, checking the price data change in the historical situation, checking the price data change condition which is pushed back by one month, and predicting the current price data change.
5. The commodity price data analysis platform of claim 4, wherein the processor performs the following operations:
performing a current market industry analysis based on a most similar historical interval, the most similar historical interval being a quantitatively obtained historical interval under a market environment or risk event that is most similar to the current one, the analysis comprising,
backtracking the historical market conditions of the commodity price so as to analyze the change condition of the commodity price in certain market environments or risk events;
collecting price change indexes including fluctuation rate and sharp rate in the most similar historical interval and the backward-moving interval;
the most similar historical interval is used as an index of the existing market data, similar market environments are reflected by the similar historical interval, market analysis report data of the similar historical interval is inquired,
and inquiring the price data change in the similar market environment to analyze the current market industry condition and judge the price trend in the current market environment.
6. The commodity price data analysis platform of claim 5, wherein the processor performs the following operations:
and predicting the calculated commodity price rising, falling and finishing probability of the future month.
7. An apparatus for analyzing price data of a commodity, comprising a memory; and
a processor coupled to the memory, the processor configured to execute instructions stored in the memory, the processor to:
anchoring the most similar historical interval according to the current date, checking the price data change in the historical situation, checking the price data change condition which is pushed back by one month, and predicting the current price data change;
performing a current market industry analysis based on a most similar historical interval, the most similar historical interval being a quantitatively obtained historical interval under a market environment or risk event that is most similar to the current one, the analysis comprising,
backtracking the historical market of commodity prices to analyze the change condition of commodity prices in certain market environments or risk events;
collecting price change indexes including fluctuation rate and sharp rate in the most similar historical interval and the backward-moving interval;
the most similar historical interval is used as an index of the existing market data, similar market environments are reflected by the similar historical interval, market analysis report data of the similar historical interval is inquired,
inquiring price data change in similar market environment to analyze current market industry condition and judge price trend in current market environment;
and predicting the calculated commodity price rising, falling and finishing probability of the future month.
8. A commodity price data analysis system is characterized by comprising,
the data backtracking module is used for anchoring the most similar historical interval according to the current date, checking the price data change in the historical situation, checking the price data change condition which is pushed back by one month, and predicting the current price data change;
a data analysis module for performing a current market industry analysis based on a most similar historical interval, the most similar historical interval being a quantitatively obtained historical interval under a market environment or risk event that is most similar to the current one, the analysis including,
backtracking the historical market conditions of the commodity price so as to analyze the change condition of the commodity price in certain market environments or risk events;
collecting price change indexes including fluctuation rate and sharp rate in the most similar historical interval and the backward-moving interval;
the most similar historical interval is used as an index of the existing market data, similar market environments are reflected by the similar historical interval, market analysis report data of the similar historical interval is inquired,
inquiring price data change in similar market environment to analyze current market industry condition and judge price trend in current market environment;
and the data prediction module is used for predicting the calculated commodity price rising, falling and finishing probability of the future month.
9. A storage medium on which a computer program is stored which, when executed by a processor, carries out the method of any one of claims 1 to 3.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115170166A (en) * | 2022-09-06 | 2022-10-11 | 山东省市场监管监测中心 | Big data sensing method and system for judging monopoly behavior |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115170166A (en) * | 2022-09-06 | 2022-10-11 | 山东省市场监管监测中心 | Big data sensing method and system for judging monopoly behavior |
| CN115170166B (en) * | 2022-09-06 | 2023-04-11 | 山东省市场监管监测中心 | Big data sensing method and system for judging monopoly behavior |
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