Real-time adaptive moduluar risk management trading system for professional equity traders

• US 20060080212A1
• Filed: 11/22/2004
• Published: 04/13/2006
• Est. Priority Date: 08/26/2004
• Status: Abandoned Application

• Current Assignee: Mark Mooney, Timothy Anderson
• Original Assignee: Mark Mooney, Timothy Anderson
• Inventors: Anderson, Timothy, Mooney, Mark
• Application Number:
  US10/995,059
• Publication Number:
  US 20060080212A1
• Time in Patent Office: Days
• US Class Current: 705/37
• CPC Class Codes: G06Q 30/00 Commerce
• CPC Class Codes: G06Q 40/04 Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange

Abstract

The present invention provides traders with a real-time fed computer-based system for trading commodities based on a traders risk profile, particularly equities, by providing a careful selection of the data to analyze and selecting the correct manipulation of that data. The invention uses the initially selected data components or factors, by manipulating them with operators, or asset specific mathematical functions, a fuzzy or Baeysian advisors helps to assist in the genetic learning of the system by being rewards and punished based on the correlation to success and failure, and overlay advisors, or meta-advisors as they are implemented in the present invention. The invention provides several control or monitoring layers which can exit and recommend immediate action or adjust the neural-based computational processes, such as iteration, based on criteria in the interpreted “multiplexed” real-time data or a discovered neural relationship.

Claims

1. A computer-implemented method for assisting in a commodity transaction in which a processor is executing instructions that perform the following acts:

selecting from a group of mathematical operators to transform a set of arrays located in data storage;

performing said mathematical operations of a set of arrays, such that preliminary data is produced;

analyzing said preliminary data with a first set of Baeysian-logic functions, each with a corresponding adjustable weights; and

determining a recommendation for said equity based on said Baesyian logic analysis, and reporting said recommendation to a user as output; and

comparing an actual result for said equity to said recommendation and adjusting at least one of said Bayesian logic function corresponding weights for any future recommendation, wherein said improvement includes the acts of;

setting a target interval for said analysis step;

providing a real-time data feed to said processor, said real-time data feed providing information for at least one of said set of arrays; and

performing said analysis step at each target interval.

2. The method as recited in claim 1, wherein said target interval is set manually.

3. The method as recited in claim 1, wherein said target interval is set automatically based on a trader-chosen factor.

4. The method as recited in claim 1, wherein said target interval is adjusted by shortening the interval.

5. The method as recited in claim 1, wherein said target interval is adjusted by shortening or lengthening said interval based on computational constraints.

6. The method as recited in claim 5, wherein said computational constraints are monitored.

7. The method as recited in claim 5, wherein said real-time feed is also fed to a commodity trading computer.

8. The method as recited in claim 7, wherein said target interval is shortened based on information flagged from said real-time data feed to said commodity trading computer, said commodity trading computer instructing said processor to shorten said target interval.

9. The method as recited in claim 1 assisting in an equity trade in which a processor further including wherein the improvement includes setting an adjustable risk profile for said equity trade.

10. A computer-implemented method for assisting in an equity trade in which a processor is executing instructions that perform the following acts:

selecting from a group of mathematical operators to transform a set of arrays located in data storage;

performing said mathematical operations of a set of arrays, such that preliminary data is produced;

analyzing said preliminary data with a first set of baeysian-logic functions, each with a corresponding adjustable weights; and

determining a recommendation for said equity based on said Baesyian logic analysis, and reporting said recommendation to a user as output; and

comparing an actual result for said equity to said recommendation and adjusting at least one of said Bayesian logic function corresponding weights for any future recommendation, wherein the improvement includes setting an adjustable risk profile for at least one equity trader and publishing stop loss and take profit levels generated by executable instructions, prior to performing any of said mathematical operations wherein;

a step is performed prior to said determining step to analyze real-time data in order to determine whether a set of one or more contingency conditions have been met.

11. The method as recited in claim 10 wherein said contingency conditions include data that shows whether a triggered stop-loss or take-profit has been met.

12. The method as recited in claim 11, wherein suggested take profit and/or stop loss recommendations are provided immediately if said contingency condition have been met.

13. The method as recited in claim 10, in which at least one of said set of contingency conditions is configured to be triggered with the price movement of at least particular stock.

14. The method as recited in claim 10, in which at least one of said set of contingency conditions is configured to be triggered with the movement of at least one of said set of indicators.

15. The method as recited in claim 10, in which at least one of said set of contingency conditions is configured to be triggered by a discovered correlation.

16. The method as recited in claim 15, wherein said discovered correlation is between two or more of said set of indicators.

17. The method as recited in claim 15, wherein said discovered correlation is between one of said advisors and a second piece of data.