SYSTEMS AND METHODS FOR IDENTIFYING INDICATORS OF CRYPTOCURRENCY PRICE REVERSALS LEVERAGING DATA FROM THE DARK/DEEP WEB

• US 20200134579A1
• Filed: 10/29/2019
• Published: 04/30/2020
• Est. Priority Date: 10/30/2018
• Status: Abandoned Application

• Current Assignee: Cyber Security Works LLC (Zuggand, Inc.)
• Original Assignee: Cyber Reconnaissance, Inc.
• Inventors: Almukaynizi, Mohammed, Shakarian, Paulo, Shah, Malay
• Application Number:
  US16/667,253
• Publication Number:
  US 20200134579A1
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/2379 Updates performed during online database operations; commit processing
• CPC Class Codes: G06F 16/2474 Sequence data queries, e.g. querying versioned data
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/025 Extracting rules from data
• CPC Class Codes: G06N 5/048 Fuzzy inferencing
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06Q 20/065 using e-cash
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 30/0206 Price or cost determination based on market factors
• CPC Class Codes: G06Q 40/04 Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange

Abstract

Computer-implemented systems and methods are disclosed for learning correlations between D2web activity and historical cryptocurrency trend reversals. The learned correlations are leveraged to generate rules executable by a computing device. When satisfied, the rules are utilized to predict a cryptocurrency price reversal.

Claims

1. A method performed by a computing device for generating logic-based rules executable by the computing device for predicting cryptocurrency price reversals, comprising:

accessing a first dataset including textual information from the dark or deep web over a predetermined time period;

generating a plurality of tags from the textual information, each of the plurality of tags defining an entity identified from the textual information mapped to a time point from the predetermined time period associated with a mention of the entity;

defining a portion of the plurality of tags as spiking tags, each of the spiking tags defining by an entity mentioned over the predetermined time period in a frequency that satisfies a predefined threshold as determined by applying a statistical measurement that compares mentions of the entity on a given day with mentions to the entity over a predetermined number of days preceding the given day;

storing the spiking tags within a database as features along with historical cryptocurrency price movement data defining a list of dates and known cryptocurrency price reversals; and

applying annotated probabilistic temporal logic rule learning to learn temporal correlations between the features and the historical cryptocurrency price movement data and output a set of rules, each of the set of rules defining a probability of a cryptocurrency price reversal based on mentions of a given entity defined by the spiking tags.

2. The method of claim 1, further comprising assigning to each of the set of rules a significance value which corresponds to a measure of a percentage increase of the probability of each of the set of rules over a probability of a null rule having a same consequence.

3. The method of claim 2, wherein the significance value of each of the set of rules is high in value when the probability of each of the set of rules is greater than the probability of the null rule.

4. The method of claim 1, wherein the probability of a cryptocurrency price reversal is equal to a ratio of a number of mentions of the entity followed by known cryptocurrency price reversals within the predetermined number of days and a total number of times that the entity was mentioned.

5. The method of claim 4, wherein the given day is determined to be preceding the cryptocurrency price reversal when 1.5 times a sum of an average value for a predetermined number of days preceding the given day of a difference between a maximum and a minimum closing price for a predetermined number of proceeding days from the given day, and a standard deviation corresponding to the difference between the maximum and the minimum closing price for the predetermined number of proceeding days from the given day is less than or equal to the difference between the maximum and the minimum closing price for the predetermined number of proceeding days from the given day.

6. The method of claim 1, further comprising generating the plurality of tags using by implementing named entity recognition that classifies entities of the textual information into pre-defined categories.

7. The method of claim 1, wherein the database comprises a time series database optimized for time-stamped data or time series data and configured for computation by a processor of a percentile increase in references to particular keywords from the first dataset over a predetermined period of time.

8. The method of claim 1, wherein a cryptocurrency price reversal occurs if there is a fall in price of a predetermined percent after rising a predetermined number of days.

9. The method of claim 1, wherein a cryptocurrency price reversal is deemed to occur where a maximum difference in a set of closing prices over a predetermined number of days is greater than a threshold, and based on identification of multiple consecutive days of failing prices and of rising prices.

10. A system for generating rules executable by a computing device for predicting cryptocurrency price reversals, comprising:

a computing device, including;

a processor,

a database in operable communication with the processor, the database storing a first dataset defining textual information associated with cryptocurrency activities and a second dataset defining historical price reversals of cryptocurrency information, and

a memory storing a set of instructions executable by the processor, the set of instructions, when executed by the processor, operable to;

access the first dataset and the second dataset from the database,

identify a plurality of indicators of a cryptocurrency reversal from the first dataset, and

learn temporal correlations between the plurality of indicators of the first dataset and the historical price reversals of cryptocurrency information from the second dataset.

11. The system of claim 10, wherein the database comprises a time series database optimized for time-stamped data or time series data and configured for computation by the processor of a percentile increase in references to particular keywords from the first dataset over a predetermined period of time.

12. The system of claim 10, further comprising:

a remote computing device in operable communication with the computing device, the remote computing device configured for extracting the first dataset from the deep or dark web.

13. The system of claim 12, wherein the computing device accesses the first dataset from the remote computing device by way of an application programming interface provided by the remote computing device.

14. The system of claim 10, wherein the computing device is configured to execute a crawler to obtain the first dataset from the deep or dark web.

15. A tangible, non-transitory, computer-readable media having instructions encoded thereon, the instructions, when executed by a processor, are operable to:

access a first dataset associated with cryptocurrency activities;

map a set of predicates defined by the cryptocurrency activities from the first dataset to a plurality of time points;

access a second dataset including identifications of historical cryptocurrency price reversals; and

learn a set of rules based on temporal correlations between the set of predicates of the first dataset and information associated with the historical cryptocurrency price reversals from the second dataset.

16. The tangible, non-transitory, computer-readable media of claim 15, wherein the set of rules are learned based on the set of predicates that evaluate to a true value.

17. The tangible, non-transitory, computer-readable media of claim 15, wherein the instructions, when executed by the processor, are further operable to:

apply machine learning or knowledge representation and reasoning to the first dataset to derive the set of predicates from the cryptocurrency activities.

18. The tangible, non-transitory, computer-readable media of claim 15, wherein the set of predicates are based on activities, abnormalities, or a measured increase in references to predefined keywords within the first dataset.

19. The tangible, non-transitory, computer-readable media of claim 15, wherein the instructions, when executed by the processor, are further operable to:

determine a significance of a given one of the set of rules based on a percentage increase of a probability of the given one of the set of rules being found true over a probability of the given one of the set of rules being found null with a same consequence.

20. The tangible, non-transitory, computer-readable media of claim 15, wherein the set of rules define a logic program executable by the processor that predicts, for each rule, a trend reversal associated with cryptocurrency within t days following a spike in a predetermined activity with a probability p.