Computer Implemented Method and System of Trading Indicators Based on Price and Volume

• US 20040225592A1
• Filed: 05/08/2003
• Published: 11/11/2004
• Est. Priority Date: 05/08/2003
• Status: Abandoned Application

• Current Assignee: Eduardo Enrique Churquina
• Original Assignee: Eduardo Enrique Churquina
• Inventors: Churquina, Eduardo Enrique
• Application Number:
  US10/249,802
• Publication Number:
  US 20040225592A1
• Time in Patent Office: Days
• US Class Current: 705/37
• CPC Class Codes: G06Q 40/04 Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange

Abstract

A method and system for providing trading indicators for selected instruments traded in a market such as stocks, currency contracts, bonds, commodities contracts, options contracts, and futures contracts. The method and system create trading indicators using Time and Sales data as provide by exchanges or financial data providers. The method comprise parsing time, price and volume of individual transactions into a collection of volume per price bracket per time interval quantities, wherein each quantity is an aggregate volume of transactions executed during one of a set of sequential time intervals and executed at prices within one of a set of price brackets. The method generate trading indicators by using mathematical algorithms to score individual volume per price bracket per time interval quantities corresponding to an evaluation time interval against a population of individual volume per price bracket per time interval quantities corresponding to a set of previous time intervals. The system generates trading indicators in real time, without the time lag associated to traditional technical analysis indicators. The method and system can also generate trend indicators based on analysis of volume accumulation, and defines trading indicators based on maximum volume prices.

Claims

1. A computer implemented method for creating trading indicators for a financial instrument traded in a market comprising:

a) having a set of sequential time intervals, and

b) having a set of price brackets, wherein each price bracket is narrower than ⅕

of the estimated difference between highest and lowest transaction prices of the total time span of said set of sequential time intervals, and

c) computing a set of VPPB quantities, wherein each VPPB quantity is an aggregate of said financial instrument volume of transactions executed during one time interval of said set of sequential time intervals and executed at prices within one price bracket of said set of price brackets, and

d) selecting an evaluation time interval from said set of sequential time intervals, and

e) selecting one or more population subsets of said set of VPPB quantities by applying predetermined data filtering and preprocessing means, and at least one of said population subsets comprising VPPB quantities corresponding to a plurality of time intervals preceding said evaluation time interval, and

f) selecting one or more evaluation VPPB quantities, at least one evaluation VPPB quantity corresponding to said evaluation time interval, and

g) applying mathematical algorithms to obtain one or more scores for each said evaluation VPPB quantity with respect to one or more of said population subsets, and

h) creating a trading indicator when said scores meet predetermined criteria.

2. The method of claim 1 wherein said data filtering and preprocessing means restrict one or more of said population subsets using mathematical algorithms comprising said transaction time of said financial instrument.

3. The method of claim 1 wherein said data filtering and preprocessing means restrict one or more of said population subsets using mathematical algorithms comprising said transaction volume of said financial instrument.

4. The method of claim 1 wherein said data filtering and preprocessing means restrict one or more of said population subsets using mathematical algorithms comprising said transaction price of said financial instrument.

5. The method of claim 1 wherein said data filtering and preprocessing means restrict one or more of said population subsets using mathematical algorithms comprising a market index.

6. The method of claim 1 wherein said data filtering and preprocessing means merge said VPPB quantities corresponding to a single said time interval and corresponding to adjacent or near adjacent price brackets.

7. The method of claim 1 wherein said data filtering and preprocessing means merge said VPPB quantities corresponding to adjacent or near adjacent time intervals and corresponding to adjacent or near adjacent price brackets.

8. The method of claim 1 wherein said time intervals span is determined by mathematical algorithms comprising transaction volume of said financial instrument.

9. The method of claim 1 wherein said time intervals span is determined by mathematical algorithms comprising transaction prices of said financial instrument.

10. The method of claim 1 wherein said price brackets amplitude is determined by mathematical algorithms comprising transaction volume of said financial instrument.

11. The method of claim 1 wherein said price brackets amplitude is determined by mathematical algorithms comprising transaction prices of said financial instrument.

12. The method of claim 1 wherein said scores are statistical deviation scores between said evaluation VPPB quantity and a measure of central tendency of said subset.

13. The method of claim 1 wherein said scores are statistical z-Scores of said evaluation VPPB quantity with respect to one or more said population subsets.

14. The method of claim 1 further including the steps of:

a) Obtaining a trend indicator by applying a trend evaluation model to one or more evaluation VPPB.

15. A computer implemented method for creating trading indicators for a financial instrument traded in a market using a computer system receiving data for said financial instrument in real time comprising:

a) having a set of sequential time intervals, wherein said set of sequential time intervals comprises a time interval including the current time and a plurality of prior time intervals, and

b) having a set of price brackets, wherein each price bracket is narrower than ⅕

of the expected difference between highest and lowest transaction prices of said financial instrument for the total time span of said set of sequential time intervals, and

c) computing a set of VPPB quantities, wherein each VPPB quantity is an aggregate of said financial instrument volume of transactions executed during one time interval of said set of sequential time intervals and executed at prices within one price bracket of said set of price brackets, and

d) selecting as evaluation time interval the time interval including current time, and

e) selecting one or more population subsets of said set of VPPB quantities by applying predetermined data filtering and preprocessing means, and one or more said population subsets comprising VPPB quantities corresponding to a plurality of said prior time intervals, and

t) selecting one or more evaluation VPPB quantities, at least one evaluation VPPB quantity corresponding to said evaluation time interval, and

g) applying mathematical algorithms to obtain one or more scores for each said evaluation VPPB quantity with respect to one or more of said population subsets, and

h) creating a trading indicator when said scores meet predetermined criteria.

16. A computer implemented method for creating trading indicators based on a set of maximum volume prices of a financial instrument traded in a market comprising:

a) having a set of sequential time intervals, and

b) having a set of price brackets, wherein each price bracket is narrower than ⅕

of the estimated difference between highest and lowest transaction prices of the total time span of said set of sequential time intervals, and

c) computing a set of VPPB quantities, wherein each VPPB quantity is an aggregate of said financial instrument volume of transactions executed during one time interval of said set of sequential time intervals and executed at prices within one price bracket of said set of price brackets, and

d) compiling a set of maximum volume prices wherein each maximum volume price is a price within the price bracket with largest VPPB of all price brackets corresponding to a single time interval, and said set of maximum volume prices includes VPPB quantities corresponding to a plurality of time intervals, and

e) applying mathematical algorithms to said set of maximum volume prices.