Methods for trade decision making

• US 7,496,534 B2
• Filed: 05/14/2001
• Issued: 02/24/2009
• Est. Priority Date: 03/08/2001
• Status: Active Grant

• Current Assignee: OANDA Corp.
• Original Assignee: Olsen Data Limited
• Inventors: Pictet, Olivier V., Dacorogna, Michel M., Jaeger, Lars A., Davé´, Rakhal D., Müller, Ulrich A., Olsen, Richard B.
• Primary Examiner(s): Dass; Harish T.
• Assistant Examiner(s): MILEF, ELDA G
• Application Number:
  US09/855,633
• Publication Number:
  US 20020184134A1
• Time in Patent Office: 2,843 Days
• Field of Search: 705/35, 705/36.R, 705/37
• US Class Current: 705/37
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes
• CPC Class Codes: G06Q 40/04 Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis

Abstract

A method of trading assets on a market including: (1) receiving price data for an asset over one or more computer networks; (2) receiving current system position information; (3) storing the received asset price data and current system position information in a computer-readable medium; (4) calculating trade recommendation information form each of one or more trading sub-models, wherein each sub-model is based on a different time horizon, the calculation based on the received asset price data; and (5) calculating a trade recommendation regarding the asset based on the trade recommendation information from each of the trading sub-models. Each sub-model preferably includes: (1) a price collector component; (2) a price filter component; (3) a price database component; (4) a gearing calculator component; (5) a deal acceptor component; and (6) a book-keeper component.

Claims

1. A method of trading assets on a market, comprising the steps of:

(a) receiving price data for an asset over one or more computer networks;

(b) receiving current system position information;

(c) storing said received asset price data and said current system position information in a computer-readable medium;

(d) calculating trade recommendation information from each of a plurality of trading sub-models;

wherein each sub-model is based on a different time of day, said calculation based on said received asset price data;

(e) calculating a trade recommendation regarding said asset based on said trade recommendation information from each of said trading sub-models.

2. A method as in claim 1, wherein 24 sub-models are used.

3. A method as in claim 1, further comprising the step of evaluating the performance of said sub-models using a risk-sensitive performance measure.

4. A method as in claim 3, wherein said risk-sensitive performance measure is used to optimize the performance of the trading models.

5. A method as in claim 3, wherein said risk-sensitive performance measure is an effective return.

6. A method as in claim 1, wherein each sub-model comprises:

(a) a price collector component;

(b) a price filter component;

(c) a price database component;

(d) a gearing calculator component;

(e) a deal acceptor component; and

(f) a book-keeper component.

7. A method as in claim 1, wherein the step of calculating a trade recommendation regarding said asset based on said trade recommendation information from each of said trading sub-models is performed by summing weighted trade recommendations of the sub-models.

8. A method as in claim 1, wherein the step of calculating a trade recommendation regarding said asset based on said trade recommendation information from each of said trading sub-models is performed by summing weighted trade recommendations of N sub-models within the last T hours, where N and T are positive integers.

9. A method as in claim 1, wherein the step of calculating a trade recommendation regarding said asset based on said trade recommendation information from each of said trading sub-models is based on a ratio obtained by summing weighted trade recommendations of N sub-models within the last T hours, where N and T are positive integers and dividing that sum by the total number of sub-models.

10. A method as in claim 1, wherein each sub-model is based on a triplet comprising price change and volatility data calculated at regular intervals of a basic grid interval.

11. A method as in claim 10, wherein the volatility is measured as a mean of absolute log price change.

12. A method as in claim 11, wherein the mean is taken over the last M consecutive observations of log price change over the basic grid interval, where M is a positive integer.