RU2710830C1 - Method for automated provision of recommendations on making investment-relevant decisions on the market of digital assets and device for its implementation - Google Patents

Abstract

FIELD: consulting.SUBSTANCE: invention relates to a method and a device for providing recommendations on making investment-relevant decisions on the digital assets market. Method includes: a step of setting an investment-relevant task, a step of forming a set, or a basket, of n interesting digital assets in accordance with the task set at the previous step, a step for collecting market indicators and news messages, a step for processing market indicator values, a step of processing a set of news data, a step of forming, with a given periodicity, a generalized index Ig (t), characterizing the market, a step for analyzing and interpreting the data obtained at the previous step on changing the generalized index in time, taking into account the task set at the first step and issuing a recommendation on the investment-significant task set at the first step.EFFECT: wider range of technical means.6 cl, 4 tbl, 3 dwg

Description

FIELD OF TECHNOLOGY

The present invention relates to investment management methods and information and analytical systems that implement such a method, and more particularly, to a method for supporting the adoption of investment significant decisions and a system for supporting the adoption of investment significant decisions in the digital asset market, the rate of which is influenced by news.

BACKGROUND

In recent years, actively developing new types of digital payment media - cryptocurrencies. Cryptocurrencies, or tokens, are commonly referred to as units of account that operate on the basis of cryptographic methods of protecting information, and their creation, control and accounting, as a rule, are decentralized.

Further, the concepts of “cryptocurrencies”, “tokens”, “digital assets” are used as synonyms.

The number of cryptocurrencies increases over time due to the possibility of attracting investments for various projects - ICO (Initial Coin Offering) - a procedure in which the project investor is invited to purchase a certain number of tokens. An additional increase in the number of digital assets is expected due to the tokenization process of existing business processes, in particular, well-known social networks, instant messengers, retail chains and catering chains announce the introduction of their own tokens in the near future. Tokens, on the one hand, are the internal currency of the project, and on the other hand, they represent a certain set of rights provided by the project. It is assumed that project investors will be able to use the project tokens in the future to receive the services, goods and services provided by the project, and if the project is listed on the exchange, they can expect to receive income from the sale of such tokens.

The growth rate of the digital asset market encourages investors to look for reliable tools for analyzing and forecasting this market, as well as the means to make investment-relevant decisions regarding the digital asset market.

Indices act as such instruments for the markets of fiat currencies, stocks of companies and commodity assets. Changing indices over time allows you to track market trends, the value of the set of assets of which is taken into account when forming this index. Analysis of trends in stock indices allows you to make investment significant decisions.

However, the digital asset market is significantly different from other markets. First, the digital asset market is open to new entrants, which gives a high degree of token rotation. Secondly, since the volume of this market is still extremely small, the degree of speculativeness in it is also high. Further, the market for digital assets is multidirectional, that is, under the influence of the same factors, the prices of some digital assets may rise, while others may decrease. Not always a change in the growth rate of individual cryptocurrencies / tokens with a relatively small or, conversely, significant capitalization will indicate significant changes in the whole market in one direction or another. In addition, assets with substantial capitalization, but with insignificant trading volume, may be represented on this market. Accordingly, the most traded assets have a greater impact on the market. In addition, an important factor for assessing the suitability of an asset for investment is its liquidity, that is, the property of the asset to be quickly sold at a price close to the market price, which is also determined by the volume of trading, and not the capitalization of the asset.

Secondly, an important distinguishing feature of the digital asset market is the rate of change in the value of market assets depending on changes in investor sentiment. The main group of digital assets currently circulating is not provided with any physical property - a share in the company's property, volume of goods or raw materials, but there are a number of projects that “tie” their tokens to physical assets or existing fiat money. At the same time, a number of states (Singapore, Canada, Japan and a number of other countries), and in addition, international organizations, in particular the ECB, are actively developing the so-called CBCC (Central bank cryptocurrencies) - cryptocurrencies issued by the central bank state. From the point of view of the legislation of many countries, digital assets still do not fall into the legal field. It is also necessary to take into account the fact that, taking into account the speed of information dissemination in the modern world and the availability of information exchange tools for the public (social networks, electronic media), investor reactions to world events, that is, a change in tactics regarding the purchase or sale of certain digital assets, developing very quickly. That is, the digital asset market is highly influenced by the news.

Thus, the traditional tools of analyzing the global financial market prove to be untenable for describing and monitoring the state of the digital asset market. As a result, the task is to develop a tool for analyzing and forecasting an emerging market, taking into account the influence of news on the possible actions of market participants.

Another task that investors must solve to work in the digital asset market is to assess the maturity of the market. In 1980, Michael Porter, in his book Competitive Strategy: Techniques for Analyzing Industries and Competitors, described the industry life cycle model, which is still used in strategic management today. Porter believed that the level of market maturity has a strong influence on the degree and intensity of competition between market participants, and also sets certain rules for competition in the industry. The model describes the development of the industry in the so-called life cycle curve. According to the dynamics of the curve, 4 stages of market maturity can be distinguished: the first is the stage of the emergence of demand, the second is the stage of market growth, the third is the stage of maturity, the fourth is the stage of aging or the stage of market decline. Obviously, all stages except recession can be considered favorable for investment. As the digital asset market is developing very rapidly and in different directions, it can be argued that it is at the initial stage when high profitability exists simultaneously with high risk. Each subsequent stage leads to a reduction in risk and automatically returns. In this regard, it is desirable to develop a tool that would allow us to assess the degree of maturity of the market.

Another challenge facing investors in the digital asset market is to increase the speed of making the right investment-relevant decisions. The abundance of conflicting sources of information about the current situation in the markets for digital assets, the global access to these markets and the high speed of transactions make it difficult to build the right strategy for working in such markets at the right time. The proposed method and system for the automated provision of recommendations allows investors to save time on making the right investment-relevant decisions.

The group of authors whose work is taken as a prototype of the proposed invention is Young Bin Kim, Jun Gi Kim, Wook Kim, Jae Ho Im, Tae Hyeong Kim, Shin Jin Kang, Chang Hun Kim in the article “Predicting Fluctuations in Cryptocurrency Transactions Based on User Comments and Replies ”(published on 08.17.2016 in the journal“ PLOS ONE ”, doi: 10.1371 / journal.pone.0161197) proposed a method for predicting price fluctuations of three cryptocurrencies - Bitcoin, Ethereum, Ripple based on sentiment analysis of information from social networks. The authors suggested that if cryptocurrencies are mentioned in comments and messages on social networks, and if the tonality of such messages and comments can be defined as negative or positive, then after a while, characteristic changes in the cost of three cryptocurrencies can be observed. At the first stage, comments and messages from social networks were collected, which contained a mention of one of the three cryptocurrencies: Bitcoin, Ethereum, Ripple. After that, text messages via sentiment analysis were divided into groups depending on their degree of positivity or negativity - messages were assigned ratings from -1 to 1: Very Positive [0.6; 1], Positive [0.2; 0.6), Neutral [-0.2; -0.2), Negative [-0.6; -0.2), Very Negative [-1; -0.6). The number of published messages, the number of comments and views were normalized taking into account their values for a certain specified number of previous days. The fluctuations in the price of crypto assets and the number of relevant transactions were subjected to the same normalization. Then, normalized indicators of the number of messages broken down by tone (very negative, negative, neutral, positive, very positive) and normalized indicators of prices and transactions subjected the Granger causality test. The Granger causality test is based on the assumption that if the variable X causes a change in the variable Y, then the changes in X will systematically occur before the change in Y. The data obtained were used for machine learning in order to create a forecasting model. It was possible to establish that the fluctuations in the price of BTC (Bitcoin) cryptocurrency were mainly caused by positive and very positive messages, and the fluctuations in the price of Ethereum and Ripple cryptocurrencies were more dependent on negative and very negative messages.

However, the known method has several significant disadvantages.

Firstly, messages on social networks were taken as a basis for sentiment analysis, which are essentially a reaction to some kind of informational reason. That is, they represent a secondary product in relation to the event itself, which served as a source for the reaction. In addition, the known method did not take into account that a certain proportion of messages and comments could be formed artificially and not reflect the real mood of the audience.

Secondly, in the prototype, digital assets for analysis were selected only and only by the criterion of maximum capitalization, excluding trading volume or other indicators. However, capitalization alone cannot serve as a reliable criterion for selecting an asset for subsequent analysis. The fact is that in the absence of established regulatory practice, the digital asset market is open to new entrants, which leads to a very high degree of cryptocurrency / token rotation. For example, the BNB coin that appeared in the summer of 2017 in February 2019 entered the top by capitalization, although before that it was only an internal coin of the Binance exchange. Coin XRP (Ripple) entered the top in terms of capitalization at the time of its appearance, since it initially issued almost one hundred billion coins. Thus, for any new project introducing its tokens into circulation, it is enough to issue, conditionally, one trillion coins to immediately become the leaders in capitalization.

Thirdly, the prototype analyzed the influence of news on the value of each of the selected digital assets individually, that is, there was no suggestion of a tool to predict the behavior of the digital assets market as a whole, or the behavior of digital assets associated with a particular industry, for example, digital assets related to power distribution systems.

Thus, the prototype has a number of obvious disadvantages, which are intended to eliminate the invention.

SUMMARY OF THE INVENTION

The task to which the invention is directed is to develop a method and system for analyzing and predicting changes in digital asset markets in order to obtain correct investment-relevant decisions and provide recommendations based on analysis and forecast, given that the actions of market participants are influenced by news.

The technical results achieved by the application of the present invention include expanding the arsenal of technical tools for analyzing and forecasting the market for digital assets, increasing the accuracy of forecasting changes in the markets, taking into account the exposure of market participants to the news, determining the maturity of markets formed by new financial instruments, and providing decision support tools for investing in one or another set of digital assets, reducing the amount of resources (first of all, times n) necessary for the investor to make the right investment-significant decision.

The problem is solved, and the technical results are achieved by the fact that in accordance with the task at a given frequency, they obtain the values of the generalized index, analyze and interpret data on the change in the generalized index over time and recommend that one or another investment-significant decision be made.

The proposed method essentially includes the main steps: the step of setting an investment-significant task; the step of forming a set, or basket, of n digital assets of interest in accordance with the task set in the previous step; a step of collecting market indicators and news messages, at which, with a given frequency for a given period of time, for each i-th asset, n assets form a set of market indicators, including the price P _i (t) of the digital asset of the current period, the price P _i (t -1) a digital asset of the previous period, the trading volume V _i of the i-th digital asset of the current period; and also with a given frequency over a given period of time form a set of news that can affect the rate of selected assets; step of processing the values of market indicators; the step of processing a news set, including the implementation, by machine learning, of sentiment analysis of a news set with the distribution of news in at least five groups, depending on the expected strong positive, positive, neutral, negative or very negative impact of this news on the digital asset market; the step of forming, with a given periodicity, a general index characterizing the market represented by a selected basket of assets, taking into account the influence of news on the rate of the specified basket of assets, taking into account the formula

,

,

where R _i (t) is the correction factor of the current period of the i-th digital asset, S (t) is the sum of the products of the number of news items assigned to each group by the weight of the corresponding group, and the constants C1, C2 and C3 are such that at S (t) = 0

;

;

a step of analysis and interpretation of the data obtained in the previous step on the change in the generalized index over time, taking into account the problem posed in the first step. The final step may optionally include issuing a recommendation for investment-relevant decisions.

So, analyzing the obtained dependence of the change in the generalized index over time, determine the stage of maturity of the digital assets market and its development trends. In particular, if it is determined that, over a given period of time, an increase in the I _g index is detected, a conclusion is drawn about the gradual uniform development of the market represented by this basket of digital assets. If it is determined that, over a given period of time, a drop in the I _g index is detected, a conclusion is drawn about the degradation of the market represented by this basket of digital assets. If it is determined that, over a given period of time, a change in the direction of change in the index I _{g is} revealed (it is revealed that the growth of the index was replaced by a drop, or the decline of the index was replaced by an increase), a conclusion is drawn about a short-term strengthening or weakening of a long-term trend.

In addition, if it is determined that for a given number of days the ratio U (t) = I _g (t) / I _g (t-1), where I _g (t) is the index of the current day, and I _g (t-1 ) - the index of the past day, does not exceed a certain threshold value, or the value of U (t) for each subsequent day is less than the value U (t) of the previous day for at least a specified number of days, they decide to change the investment portfolio of assets.

In addition, in order to determine the most attractive group of digital assets for investment, it is possible to build an index in this way for several different sets (baskets) of digital assets. For example, one of the baskets of such assets may be a basket of cryptocurrencies, another basket may be represented by tokens of energy supply projects, a third basket may be represented by tokens of projects in the field of product authenticity, and so on.

BRIEF DESCRIPTION OF THE DRAWINGS

In FIG. Figure 1 shows some options for news sources that may affect digital asset markets.

In FIG. 2 shows a logical block diagram of the claimed device for automated provision of recommendations for making investment significant decisions.

In FIG. 3 shows a generalized diagram of a hardware-software complex, which includes the claimed system.

DETAILED DESCRIPTION OF THE INVENTION

Hereinafter, investment management solutions will be called significant. Such solutions, in particular, include solving the problem of selecting assets for the formation of an investment portfolio, solving the problem of changing the assets of an investment portfolio, solving the problem of determining the maturity of the market; solving the problem of choosing the moment of acquisition or sale of digital assets; qualitative assessment of the news background and others like that.

Studying the dynamics of the generalized index provides an opportunity for investors to understand the general trend in the movement of a segment of the digital assets market without the need to evaluate the positions of many disparate digital assets. An analysis of various sectors of the economy makes it possible to determine the groups of the most promising projects and currencies, which means to form an investment portfolio with the highest profitability. Evaluation of the index over a long time allows investors to get a complete picture of the state of the market.

One of the investment significant tasks may be the task of identifying trends in the digital assets market over a given period of time in relation to assets with the highest trading volume in all sectors of digital assets; statement of the problem includes setting the criteria for selecting digital assets and the time interval after which the audit of digital assets is carried out.

For example, it can be specified that, to solve the problem, M digital assets with the highest trading volume for the past P days are selected, while the indicated M assets are selected again every P days, where M and P are integers. Revision of the basket is carried out every P days. The composition of the basket is determined on the basis of the analysis of the “trading volume” indicator for the past period - when calculating the index, market indicators of P digital assets will be taken into account, which were included in the first P digital assets by trading volume the most times in the previous period in P days.

Another investment significant task may be the task of identifying trends in a separate basket of digital assets, formed on the basis of compliance with any criterion.

Such a criterion may be a platform, or, in other words, a technological base, a type of blockchain that has its own unique characteristics), and assets released on a given platform, for example, tokens issued on the Ethereum platform or on the Waves platform, will be selected in the basket.

Also, the criterion may be the presence of a correlation between assets. For example, in the basket of Monero, BinanceCoin, IOTA, VeChain, Pura, the assets of Monero and BinanceCoin are closely linked through the Binance exchange; VeChain and Pura are widely used trading pairs with Monero. IOTA is one of the possible Monero mining rewards. In the basket of Siacoin, AdEx, Regalcoin, 0x, Wings, the assets of Siacoin and AdEx are the main pair when analyzing crypto market experts, and the assets of Regalcoin, 0x, Wings are often used as trading pairs with Regalcoin. As part of the Litecoin, Zcash, Dash, Qtum, Byteball Bytes baskets, Litecoin and Zcash assets share a common gateway on the Waves platform; Dash uses Genesis Mining, like Litecoin to mine its coin, and Qtum, Byteball Bytes are the most commonly used trading pairs with Litecoin.

Also, the criterion may be the belonging of an asset to a particular sector of the economy. For example, a basket of the financial sector (OmiseGo, TenX, Veritaseum, Kyber Network), a basket of infrastructure projects (Ardor, EOS, Gas, Walton, ChainLink), a basket of media platforms (TRON, FunFair, ATMChain, Edgeless, MobileGo), etc.

The criterion may also be the territorial affiliation of the product or service in respect of which tokens were issued. For example, baskets of tokens of infrastructure projects implemented in Russia (EncryptoTel, SONM, Wings, etc.), baskets of tokens of Hong Kong cryptocurrency exchanges (OKCoin, BTCC, etc.) or baskets of tokens of Estonian projects providing financial services (Exscudo, Polybios).

Obviously, other criteria for selecting an asset basket may be proposed.

Another investment significant task may be the task of creating a portfolio of digital assets, for which, according to user-defined criteria, a set of several baskets of assets is formed to further compare trends for several baskets and select the most profitable asset baskets for investment.

Here, the criteria listed above may serve as criteria for selecting asset baskets. In addition, it is necessary to introduce criteria on the basis of which a recommendation will be formed for the formation of an asset portfolio. Criteria for preparing a recommendation may include, for example, the period of time during which trends for several baskets are identified, and a quantitative criterion that allows you to rank the trends of these several baskets. For example, it can be given that three baskets of digital assets belonging to three sectors of the economy are subject to comparison; the comparison should be carried out within 7 days; the criterion for including the basket in the investment portfolio is the non-decreasing performance of the basket for the specified 7 days.

A similar task is the task of updating the portfolio of digital assets, when, based on the results of comparing the trends of several baskets of digital assets, a decision is made to exclude or add some baskets to the existing portfolio of assets.

Another investment significant task may be the task of determining the stage of maturity of the digital asset market.

At the step of forming a set, or basket, from n digital assets of interest, a data set is generated that includes data on at least one basket of digital assets, including n digital assets, each i-th digital asset (i = 1, 2, ... n, where n is an integer) are selected in accordance with the selection criteria defined in the previous step. Generally speaking, the minimum number of digital assets in the basket is two digital assets.

At the step of collecting market indicators and news messages with a given frequency for a given period of time, market indicators related to the selected digital assets are collected and an appropriate array of market indicators data is generated. As a rule, market indicators are collected once a day, namely, market indicators at the end of the corresponding trading session of the current day are taken into account as market indicators of the current day. However, the frequency may be different, which is determined by both the task and the general expediency. Frequency does not necessarily represent a fixed value. So, if it is assumed that market indicators will change smoothly over a period of time, the frequency of collection of market indicators may be reduced; if it is assumed that market indicators will actively change during even one trading session, the frequency of collection of relevant indicators may be increased. To market indicators include P _i (t) - i-th value of the digital asset current period (e.g., the current day), P _i (t-1) - i-th value of the digital asset of the previous period (e.g., the previous day); V _i - trading volume of the i-th digital asset of the current period (for example, the current day).

In particular, digital asset exchanges, as well as market data aggregators, such as OKEx, Binance, HitBTC, etc. can act as a source of market indicators.

In addition, with a given periodicity over a given period of time, news is collected, or, otherwise, news data that can affect the price of each of the digital assets of each basket, and form a set of news data. As a rule, the frequency of news gathering is also 1 day, however, as in the case of market indicators, the frequency of gathering can be increased or decreased as needed. As a rule, for each period at least one news item is included in the set. Areas of activity in which news can influence the digital asset market are presented in FIG. 1. These areas include legislative changes, finance, the economy, the banking sector, politics, business, industry, the government sector, the corporate sector, and technology. News sources can be electronic media, news aggregator sites, social networks, blog platforms, legislative databases, government portals of draft regulatory acts, sites of enterprises and organizations, sites of specialized network communities (for example, cryptocurrency holder forums) and others.

The criterion for selecting news in a news dataset can be, in particular, the mention in the news of at least one term related to the digital asset market. These are terms such as “cryptocurrency”, “digital asset”, “mining”, “bitcoin”, including slang “cue ball”, “crypt”, etc. Additionally, in the case when an investment-significant decision should be made in relation to a basket of assets, representing a certain sector of the economy, the selection of news, in addition, is based on the terms characterizing the sector. For example, the terms of the financial services sector tokens will be “p2p lending”, “cryptocurrency wallet”, etc. Thus, news items containing at least terms related to the topic of digital assets can be included in the news set, but it’s obvious that for a more objective analysis is desirable to include in the set of news and news related to a particular sector of the economy.

The choice of language of the news source is also determined by the current task. In particular, English-language news sources, Russian-language news sources, news sources in Chinese, Arabic, Hindi and any other language can be analyzed. In addition, news in one language, as well as news in several languages, published in information sources of different countries, can be included in the news set. In addition, the choice of the language of the news source and the choice of the country of the news source can be determined by whether the task of making an investment significant decision is territorial. For example, when an investor is interested in the situation in the market of digital assets related to projects implemented in a certain region, or when it is assumed that market participants who are able to influence the rate of digital assets of a given sector of the economy are mainly located in a certain region. Thus, news in at least one language can be included in the news set, but it is obvious that for more objective accounting, the news set should include news at least taking into account the language of the region of interest of investment and the language of the region where the investor is a resident. Obviously, upon further analysis of the impact of news on the digital asset market, if the impact of news in different languages is compared, territorial trends in the digital asset market can be identified. The specialist will understand that further analysis of the news can be carried out by first translating all the selected news into one language, convenient for analysis.

The list of news sources can remain constant over a given time interval. Alternatively, the list of news sources may change after a specified interval or in any other way. Changing the sources of news messages allows you to adjust the areas of the impact.

Next, they form a generalized index characterizing the market, represented by a selected basket of assets, taking into account the influence of news on the rate of this basket of assets.

The generalized index includes the following main components: basket price index of n digital assets I _b (t), basic index I ₀ (t), news coefficient N (t). Each of the components is formed with a given periodicity, while the frequency of formation of the components must coincide. For example, all three components can form daily; or all three components can form once every few days.

The basket price index of n digital assets is defined as the ratio of the sum of prices of the current period to the sum of prices of the previous period of all assets included in this basket of digital assets and gives a general idea of the market change represented by a given basket of assets:

where P _i (t) is the price of the i-th digital asset of the current period, P _i (t-1) is the price of the i-th digital asset of the past period.

The basic index I ₀ (t) of the current period allows, along with a general idea of market changes, to take into account the trading volume of each of n digital basket assets for the current period:

where V _i (t) is the trading volume for the current period for the i-th digital asset, R _i (t) is the correction coefficient of the current period of the i-th digital asset.

The correction coefficient allows you to reduce the dependence of the index fluctuations on changes in the exchange rates of the leading digital assets basket. At the moment, there are a number of the most popular digital assets (for example, BTC and Ethereum cryptocurrencies), the share of the trading volume of which in any basket of digital assets will be more than 60%. In the absence of adjustment, when calculating the index, the shares of other digital assets will be practically not taken into account, which means that the final results will not be able to reflect the direction of movement of the entire digital assets market. The introduction of an amendment - the inverse coefficient - eliminates this drawback.

In one embodiment, the correction factor Ri may be defined as the proportion of the basket’s least traded asset in relation to the basket’s most actively traded asset. To determine the inverse ratio for each of the digital assets included in the basket, proceed as follows. Based on the market indicators obtained in the third step, for each of the n assets, the trading share of each i-th asset is determined in relation to the total trading volume of the entire basket of assets for the current period. Then all the basket assets are ranked according to the share in the total trading volume, from highest to lowest. Then, the shares are ranked in the reverse order - from the smallest to the largest, and these values are assigned to the corresponding i-th correction factor. The methodology for the formation of the correction factor is illustrated in table 1.

Table 1. The first version of the formation of the correction factor

Asset and its share in the trading volume A1 A2 A3 A4 A5 0.1 0.6 0.2 0.04 0.06 Ranking by share from maximum to minimum A2 A3 A1 A5 A4 0.6 0.2 0.1 0.06 0.04 Reverse ranking (shares) 0.04 0.06 0.1 0.2 0.6 Inverse coefficient R2 R3 R1 R5 R4 0.04 0.06 0.1 0.2 0.6

In yet another embodiment of the invention, the correction coefficient can be determined as follows: n digital basket assets are ranked by trading volume for the current period and x is determined from n digital assets. For these x assets, the correction coefficient is defined as the share of the least traded asset of x basket assets in relation to the most actively traded asset of x basket assets, and for all other (n-x) basket assets, the correction coefficient is determined as the average value of the remaining shares in the trading volume. An example of calculating the correction coefficient Ri in this case is illustrated in table 2.

Table 2. The second option for the formation of the correction factor

Assets Vi Share
in trading volume Ri Vi · ri 1 17,000 0.0960452 0.05649718 960.451977 2 16,000 0,09039548 0,06214689 994,350282 3 15,000 0.08474576 0.06779661 1016,94915 4 14,000 0,07909605 0,07344633 1028,24859 5 13,000 0,07344633 0,07909605 1028,24859 6 14,000 0,07909605 0,07344633 1028,24859 7 13,000 0,07344633 0,07909605 1028,24859 8 12,000 0.06779661 0.08474576 1016,94915 9 11,000 0,06214689 0,09039548 994,350282 10 10,000 0.05649718 0.0960452 960.451977 eleven 9,000 0,05084746 0,03389831 305.084746 12 8,000 0,04519774 0,03389831 271.186441 thirteen 7,000 0,03954802 0,03389831 237,288136 14 6,000 0,03389831 0,03389831 203,389831 fifteen 5,000 0.02824859 0,03389831 169.491525 16 4,000 0.02259887 0,03389831 135.59322 17 3,000 0.01694915 0,03389831 101.694915 Amount 177,000 1 1 11 480

Here, for a basket of 17 assets, 10 assets with the largest share of trading volume were selected.

In yet another embodiment of the invention, the correction coefficient can be determined differently: for those k of n digital basket assets whose trading volume for the current period exceeds a predetermined value, the correction coefficient is defined as the share of the least traded asset of k basket assets in relation to the most actively traded an asset of k basket assets, and for all other (nk) basket assets, the correction coefficient is determined as the average value of the remaining shares in the trading volume.

The basket price index of n digital assets I _b (t) and the base index I ₀ (t) are formed at the stage of processing market indicators.

The influence of news is quantitatively taken into account in the news processing step by introducing a news coefficient. News coefficient is formed as follows. All news selected in the current period as a news item in the third step is sentimentally analyzed and distributed according to news groups depending on the degree of possible influence on the digital asset market participants and, as a consequence, on the digital asset market rate. The minimum number of groups is three: “negative influence”, “neutral influence”, “positive influence”. However, it is desirable that there are at least five groups (“strong negative influence”, “negative influence”, “lack of influence (neutral)”, “positive influence”, “strong positive influence”). Preferably, the news is distributed into seven or more groups: “extremely strong negative influence”, “strong negative influence”, “negative influence”, “lack of influence (neutral)”, “positive influence”, “strong positive influence”, “extremely strong positive influence. ” Generally speaking, the number of news groups l is determined both by the task and the resources available to the user to isolate a given number of news gradations.

An example of the distribution of news into five groups is shown in the following table 3.

Table 3. An example of the distribution of news in groups

News Post Example I
(strong negative impact) “China announces a ban on the BTC exchange”
"Congressional Amendment Against Money Laundering"
The Danger of Bitcoin Splitting II
(Negative influence) “Cryptocurrency Ban Plans in Malaysia”
Rogoff: Bitcoin will crash
“SEC comments on cryptocurrency as an obvious bubble”
“The ECB's statement about blockchain technology as not mature enough and potentially dangerous when used illegally” III
(neutral) “BlackRock’s chief investment strategist, Richard Torull, says cryptocurrencies are in bubbles, but blockchain technology is promising.” IV
(positive impact) “Three major Asian banks and regulator in Singapore come together for a new blockchain trial”
“Christine Lagarde, head of the IMF, warned that central banks and financial services need to pay more attention to cryptocurrencies”
"BTC rate reached a new high" V
(strong positive effect) “Michael Novogratz said in an interview that, in his opinion, the value of bitcoin will reach $ 10,000 in six to ten months.”
“Wall Street analyst Bernstein calls bitcoin a sustainable asset.”

To automate the process of calculating the news coefficient, it is preferable to distribute the news into groups using machine learning techniques (conducting preliminary lexical processing - selecting stop words, spam words; presenting words in the form of vectors to identify concepts used in one context; creating training and test samples based on cross-validation; classifier selection and training on a training set; model operation testing on a test sample; model adjustment to ensure the required go, a predetermined level of correspondence of the results of sorting news; sorting the entire array of selected news through a trained model). In general, standard techniques of machine learning are well known to the specialist and will not be disclosed in detail.

After sorting the entire array of news into groups, depending on their expected impact on the digital assets market, each group is assigned a weight coefficient f that reflects the expected degree of influence.

In this case, the weight coefficient of each group is determined in such a way that the time change in the sum of the news group weights for each given time period correlates as accurately as possible with the change in time of the basket price index I _b over a period of time of the same duration, but shifted forward by a predetermined interval d. In other words, at this stage, a correlation is sought between the value of the basket price index I _b for the current period t (for example, on a given day) and the value of the sum of the weights f of newsgroups C for the past period td (here d can be equal to for example, 3 days, or another number of periods, which is determined experimentally or in accordance with a given value). The search for such a correlation is based on the fact that the appearance of news precedes their influence on the digital asset market, that is, there will be a time delay between the appearance of the news and the change in the price of the basket of digital assets. The procedure for searching for weight coefficients is carried out by the gradient descent method.

The interval d can be fixed or change in a predetermined way. However, the authors found that, as a rule, the influence of news affects the market performance after 2-3 days and lasts some time, about 10-17 days, after which it is necessary to review the weighting factors in this way. That is, the weighting coefficients of news groups after their determination are taken constant for a certain period of time, conditionally, "time constant weighting factors", and then recounted.

Thanks to the search for correlation with a shift of 2-3 days, it is possible to ensure the predictive nature of the weight coefficients.

Once for each news group its weight coefficient f has been established, the procedure for determining the total indicator of the current period S (t), which is the sum of the products of the number of news z of this group by the weight coefficient f of this group, is carried out:

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.

Further, the total indicator S (t) of the current period is taken into account when determining the news coefficient of the current period N (t):

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.

Here C1, C2, C3 are constants, the ratio of which is chosen so that for S (t) = 0 the news coefficient N (t) = 1 - this will mean a situation where there is no news that could affect the course of the market under study , or their total effect is leveled. The constant C4 actually serves to scale the share of the news coefficient in the value of the generalized index and, in fact, serves to facilitate the perception and interpretation of the data. The choice of the constant C4 is thus determined by the extent to which the user wants to highlight the contribution of the influence of news to the value of the generalized index.

Table 4 below shows an example of calculating the news coefficient of the current period N (t) for two consecutive days with the constants C1 = 0.9, C2 = 5, C3 = 1.6, C4 = 9.

Table 4. Example of calculating the news ratio

Day 1 Group I II III IV V Weight coefficient -7 -3.3 1 2 5 Number of News
in Group 2 3 5 4 1 f _i * g _i -14 -9.9 5 8 5 S (t) -5.9 N (t) 0.9688 Day 2 Group I II III IV V Weight coefficient -7 -3.3 1 2 5 Number of News
in Group 0 1 4 4 1 f _i * g _i 0 -3.3 4 8 5 S (t) 13.7 N (t) 1.0644

The values of the generalized index for each given period of time are obtained, for example, daily, and form the dependence of its change in time. The generalized index is the product of the base index by the news coefficient and is calculated by the formula:

In certain cases, namely, in order to more accurately display the market situation at intervals of more than two to three weeks, it is necessary to adjust the composition of the basket of digital assets. In order to avoid significant fluctuations in the index value after the audit, a correction factor D ("divisor") is used, which is defined as the ratio of the generalized index of the current period calculated for the new set of assets to the generalized index of the current period calculated for the previous set of assets:

,

,

where I _{g_new} (t) is the value of the generalized index of the new asset basket for the current period, I _{g_old} (t) is the value of the generalized index of the old asset basket for the current period.

Then the values of the adjusted generalized index for the periods following the audit of the basket of digital assets are determined as follows:

.

.

The authors found that at present, the optimal interval for adjusting the basket is the interval of 14-20 days, more preferred is the interval of 17 days. At the same time, those assets from the asset group of interest that showed the highest trading volumes for the indicated interval are introduced into the new basket.

In the analysis and interpretation step, in accordance with the task set in the first step, the time dependence of the generalized index I _g obtained in the previous step is analyzed and recommendations are made regarding investment-relevant decisions. In the event that recommendations are expected in relation to the results of market analysis for a time interval exceeding the recommended basket audit interval, the time dependence of the adjusted generalized index I _{g_new_corr} obtained in the previous step is _analyzed and recommendations for investment-significant decisions are received.

The claimed method for the automated provision of recommendations for making investment-significant decisions in the digital asset market can be implemented using a device that includes essentially the following blocks.

Block (1) determining the input criteria, made with the ability to determine the input criteria depending on the task. In accordance with a certain block (1) of the input criteria, further collection, processing, interpretation and, optionally, analysis of the relevant data are carried out. In addition, the block (1) for determining the input criteria is configured to exchange data with external sources of information, for example, with websites of exchange indicators, as well as with news sources, geographical maps, etc. At the first input of the block (1) for determining the input criteria, data are presented that characterize the assigned investment-significant task. For example, it can be data containing a command to select for further analysis a given number of digital assets in a given industry with a given threshold value of the trading volume that was observed over a given time interval. This can be data containing a command to select for further analysis the total number of digital assets that are traded on a given digital exchange. This can be data containing a team for selection for further analysis of the total number of digital assets related to a given industry. This can be data containing a command to select for further analysis only those digital assets that represent a given geographical region. This can be data containing a team for selection for further analysis of digital assets under any other conditions that are understandable to a specialist. After processing the data received at the first input and, optionally, after interacting with external sources of information by means of the second data input, the input criteria determination unit (1) outputs the quantity n and a list of digital assets to be further analyzed; list of news sources; frequency, that is, the time interval, the collection of market indicators; the frequency, that is, the time interval, news gathering; frequency, that is, the time interval, adjustments to the basket of assets; periodicity, that is, the time interval for revising news sources; the interval d, after which a correlation will be searched between the sum of the weights f of the newsgroups and the basket price index I _b ; time interval for the constancy of weighting factors; possibly other input criteria. Data from the output of the block (1) for determining the input criteria is fed to the input of the block (2) of automatic data collection and to the input of the block (3) of calculations.

Block (2) of automatic data collection is essentially designed to complete a set of news, as well as a set of market indicators in relation to a basket of n assets defined by block (1) with a given frequency and consists of two modules, the first (2a) of which is configured to receive news feed from various information sites, the second (2b) of which is made with the possibility of receiving market indicators at the entrance, in particular, from cryptoexchanges and market indicators aggregators. The first one is connected to the sentiment analysis module (3) of the sentiment analysis module (3), and the second to the module (3b) of generating the basket price index I _b .

The calculation block (3) is configured to calculate, with a given frequency, the following indicators: basket price index I _b using module (3b) for generating the basket price price index I _b , news coefficient using sentiment analysis modules (3a), calculation of weighting factors of news groups (3c ), the formation of the news coefficient (3g), the basic index I ₀ through the module for calculating the basic index (3d), the generalized index I _g through the module for calculating and storing the values of the generalized index (3e). In addition, the calculation unit (3) is _configured to calculate the adjusted generalized index I _{g_new} through the module for calculating and storing the values of the adjusted generalized index I _{g_new} (3g).

The values of the generalized index and / or the adjusted generalized index obtained by block (3) are transmitted to the input of block (4) of data interpretation and analysis. The data interpretation and analysis unit (4) is configured to present the obtained values in a form suitable for human perception (for example, in the form of tables, in the form of graphs, in the form of text data, in the form of numerical data or in the form of any combination of the above) by means of the presentation module data (4a). The data interpretation and analysis unit (4) is configured to, in addition, generate statistical data by means of the statistical data generation module (4b). This data includes, in particular, the values of the generalized and / or adjusted generalized index, basket price index, base index, as well as market indicators in the context of one (current or any other given) day. The data interpretation and analysis unit (4) is made with the possibility, in addition, of analyzing the data obtained and preparing recommendations for making investment-significant decisions through the analysis and recommendations module (4c).

In addition, the device contains a synchronization block (5), the input of which is supplied with temporary input criteria of the block (1) for determining the input criteria, and the outputs of which transmit data according to the corresponding time criteria to the inputs of the block (2) of automatic data collection and the input of the calculation block (3 )

In addition, the device comprises a communication unit (6) (not shown in the figures), which enables the exchange of data between the system and external data sources. Also, said communication unit (6) provides the ability to exchange data between the device and users in case of remote location.

Each of the blocks (1) - (5) and their modules, in addition, is associated with the device’s memory (not shown in the figures), which is designed to store all collected and calculated values and can be functionally implemented as a database, and in practice it can be standard means of storing information that are widely known in the art and are not the subject of the present invention. Data stored in the device’s memory can later be retrieved and subjected to appropriate processing and interpretation.

Each of the blocks (1) - (5), in addition, can be equipped with its own data processor (processor) (not shown in the figure), which allows the necessary manipulations with the data, in particular, to identify, sort, calculate, etc. Alternatively , each of the blocks (1) - (5) can be associated with a common data processor (processor) (not shown in the figure) associated with the claimed device.

For convenient user work, this device can be included in the hardware and software complex, including the server part (A) and the client part (B). The functionality of the server part is implemented by units (1) - (6) of the device. The client part includes a user authorization block (7), a task statement block (8), configured to transmit data to the server part of the input part definition block (1), a communication block (9) to enable data exchange between the client and server parts, the block ( 10) receiving and displaying data, configured to receive and display data received from the data interpretation and analysis unit (4) of the server part.

Claims (41)

1. A method for the automated provision of recommendations for making investment significant decisions in the digital asset market, including: step of setting an investment significant task, the step of forming a set, or basket, of n digital assets of interest in accordance with the task set in the previous step, a step of collecting market indicators and news messages, at which, with a given periodicity over a given period of time, for each i-th asset, n assets form a set of market indicators, including the price P _i (t) of the i-th digital asset of the current period, the price P _i (t-1) of a digital asset of the previous period, the trading volume Vi of the i-th digital asset of the current period, as well as with a given frequency over a given period of time, form a set of news data that can affect the rate of selected assets, step of processing the values of market indicators, step of processing the news data set, including the implementation, by machine learning, of sentiment analysis of the news data set with the distribution of news in J groups, where J is at least five, depending on the expected impact of the news on the digital asset market, selected from a very positive, positive the neutral, negative or severely negative impact of the news, the step of formation, with a given periodicity, of a generalized index I _g (t) characterizing the market represented by a selected basket of assets, taking into account the influence of news on the rate of the specified basket of assets, taking into account the formula

where n is the number of assets in the basket; Ri (t) is the correction coefficient of the current period of the i-th digital asset; S (t) is the sum of the products of the number of news items assigned to each of the J groups by the weight coefficient f _{j of the} corresponding group; constants C1, C2 and C3 are such that for S (t) = 0

, and the constant C4 is determined by the user by choice; a step of analyzing and interpreting the data obtained in the previous step on the change in the generalized index over time taking into account the problem posed in the first step and issuing recommendations regarding the investment significant task posed in the first step. 2. The method according to p. 1, characterized in that the weight coefficient f _{j of} each of the groups is determined in such a way that the change in time of the sum of the weights of the newsgroups in time t correlates with the change in time of the basket price index

in a period of time t + d, where d is a predetermined period of time, each of the weights being determined by the gradient descent method. 3. The method according to p. 2, characterized in that the procedure for determining the weighting coefficients of each of the news groups is carried out again after a predetermined amount of time, and the values of the weighting coefficients of each of the newsgroups are not changed until a new determination of the weighting coefficients. 4. The device for the automated provision of recommendations for making investment-significant decisions in the digital asset market, in particular for implementing the method according to claim 1, including: unit (1) for determining input criteria, configured to receive data on its first input characterizing the assigned investment task and store them in memory, and based on the entered data definitions, using a data processor, input criteria, store them in memory, and also transmitting certain input criteria by outputting to the input of the block (2) automatic data collection and to the input of the block (3) of calculations, an automatic data collection unit (2) configured to automatically collect data in accordance with the input criteria received at the input of the unit (2) from the output of the input criteria determination unit (1), wherein said automatic data collection unit (2) includes: a news gathering module (2a) configured to receive news messages from external data sources at its input, store them in memory and, through its output, transmit the collected news messages to the input of sentiment analysis module (3a) of the calculation block (3), and module (2b) collecting market indicators, arranged to receive at its input the market indicators from external data sources, including Pi rates (t) of the digital asset current period, the price P _i (t-1) digital asset of the previous period and the volume Vi Bid i of the digital asset of the current period, storing them in memory and transmitting the collected market indicators through its output to the input of the basket price index module (3b) I _b and to the second input of the base index calculation module (3d), calculation unit (3), including: sentiment analysis module (3a), configured to perform a sentiment analysis of a set of news data with the help of machine learning tools for sentiment analysis using a data processor with the distribution of news data into J groups, where J is at least five, depending on the intended effect of the news on the analyzed a set of n digital assets, as well as with the ability to save newsgroups sorted by groups in memory, and the output of sentiment analysis module (3a) is connected to the first input of calculation module (3c) in cial data coefficients news groups, a module (3b) for generating a basket price index I _b configured to determine, by means of a data processor, the value of the basket price index

and storing the obtained values in memory, as well as transferring the obtained values to the second output of the module (3c) for calculating the weighting coefficients of newsgroups and to the first input of the module (3d) for calculating the base index, a module (3c) for generating weights of newsgroups, configured to determine, by means of a data processor, weights f _j for each of the J news data groups by finding a correlation between the time change in the sum of the weights of the news data groups in time t and the time change in the basket price index I _b (t) in time t + d, where d is a predetermined period of time, and each of the weighting coefficients is determined by the gradient descent method, storing the obtained values of the weighting coefficients of newsgroups in memory and transmitting values of the weighting coefficients news groups of data into the module (3a) forming the news coefficient a news coefficient generating module (3 g) configured to determine, by means of a data handler, the value of the news coefficient of the current period taking into account the formula

where S (t) is the sum of the products of the number of news items assigned to each of the J groups by the weight coefficient f _{j of the} corresponding group, and the constants C1, C2 and C3 are chosen so that when S (t) = 0 the news coefficient N (t ) = 1, and the constant C4 is determined by the user by choice; moreover, the module (3G) is made with the possibility of storing the obtained values of the news coefficient in memory and transferring the obtained values of the news coefficient to the first input of the module (3e) for calculating the generalized index I _g , a module (3d) for generating the values of the basic index, configured to determine the values of the basic index through a data processor

where R _i (t) is the correction coefficient of the current period of the i-th digital asset, as well as with the possibility of storing the obtained values in memory and transferring the obtained values to the second input of the module (3e) for generating the generalized index I _g , and module (3e) for the formation of a generalized index I _g , made with the ability to determine, using a data processor, the values of the generalized index I _g (t) = I ₀ (t) · N (t), store the obtained values in memory and transfer the obtained values to the input of the data interpretation and analysis block (4); a data interpretation and analysis unit (4) configured to, using a data processor, present, in a human-readable form, the obtained values of the generalized index by the data presentation module (4a), generate, by the data processor, statistical data, the statistical data generation module (4b), analysis of the obtained values and issuing recommendations for making investment-significant decisions, through a data processor, analysis and recommendations module (4c), synchronization block (5), configured to receive at its input temporary input criteria from block (1) to determine input criteria and to transmit from its outputs the values of the corresponding temporal criteria to the inputs of block (2) of automatic data collection and input of the calculation block (3) for ensuring the timely collection, processing and transmission of data, and a communication unit (6) configured to provide data exchange with external data sources. 5. The device according to claim 4, characterized in that said communication unit (6) is also configured to provide data exchange with a user device in case of remote location of the user. 6. The device according to claim 4, characterized in that it further includes a user authorization unit (7), a task setting unit (8) configured to transmit data to the input criteria determination unit (1), and data reception and display unit (10) configured to receive and display data received from the data interpretation and analysis unit (4).

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