Intelligent agents for predictive modeling

• US 20050091147A1
• Filed: 10/21/2004
• Published: 04/28/2005
• Est. Priority Date: 10/23/2003
• Status: Abandoned Application

• Current Assignee: Robert Levinson, Rosario Michael Ingargiola
• Original Assignee: Robert Levinson, Rosario Michael Ingargiola
• Inventors: Ingargiola, Rosario Michael, Levinson, Robert
• Application Number:
  US10/971,954
• Publication Number:
  US 20050091147A1
• Time in Patent Office: Days
• US Class Current: 705/37
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• 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

An apparatus and method employing intelligent agents for predictive modeling is described and illustrated. In one embodiment, the invention is a system-of-systems for nonparametric, multifactor financial time-series modeling. The base system is not itself a model, but rather an environment for creating and dynamically managing a user'"'"'s or other proprietary predictive model(s), which could be comprised of any number of user specified factors, indicators and trading systems (proprietary models) of other systems.

Claims

1. A method for predicting securities prices and other data types comprising:

a) Pre-processing securities price data and other data types using mathematical indicators to produce indicator output signals;

b) Entering the indicator output signals into a database;

c) Processing with advisors the indicator output signals to produce advisor output signals;

d) Entering the advisor output signals into a database;

e) Inputting the advisor output signals into a neural network to produce a prediction of a securities price or other data types;

f) Entering the neural network prediction into the database;

g) Processing output signal data with overlay advisors to produce overlay advisor output signals;

h) Entering the overlay advisor output signals into a database;

i) Inputting the overlay advisor output signals and lower-level neural network output signals into a second high-level neural network to produce a final prediction of securities price or other data types; and

j) Iteratively updating the neural network weights for all securities and other data types and system components upon receipt of new data.

2. ) The method of claim 1, wherein a machine learning based system for predicting securities prices and other data types is modularized to provide insertion points for use by a non-technical user to fully configure the system for use;

3. ) The method of claim 2, comprising:

an insertion point for securities instruments and factor data;

an insertion point for mathematical indicators;

an insertion point for advisors; and

an insertion point for overlay advisors;

4. ) The method of claim 2, wherein the insertion points can accept any number of additions from the user;

5. ) The method of claim 1, wherein the indicators can be any form of signal generating algorithm or output device;

6. ) The method of claim 1, wherein the advisors can be any form of signal generating algorithm or output device;

7. ) The method of claim 1, wherein the overlay advisors can be any form of signal generating algorithm or output device;

8. ) The method of claim 1, wherein the system is employing a base set of advisors that comprise machine learning components and a short-term trend advisor that can be re-specified or removed by the user, together with user inserted signal generating advisors;

9. ) The method of claim 8, wherein the machine learning based advisors comprise nearest neighbor and decision tree algorithms;

10. ) The method of claim 1, wherein the system can employ spectrum processing of signal generating indicators, advisors and overlay advisors, where an array of the variables used to produce the output signal (such as the number of data points to use in the processing) can be specified with the best variable set being selected for each predictive task (e.g., a 10-20 day moving average would cause each of the 1I1 different moving averages will be processed and the best selected);

11. ) The method of claim 1, wherein the system employs a second processing layer where user selected or specified overlay advisors are processing input data to produce output signals that will be combined with the lower neural network outputs;

12. ) The method of claim 11, wherein the overlay advisors comprise:

a Surprise overlay advisor which evaluates the difference between the actual close and the predicted close (EMA1 of close-predicted close);

Momentum overlay advisor that reviews the total change in the last Average Trend Length period (close-close ATL periods previous);

Pattern Analysis Prediction overlay advisor that reviews signals from pattern analysis (retracement) advisors to approximate the populations of traders correlated with mimicing (following) them or fading (leaving off following) them;

Buying Pressure overlay advisor that adjusts for trending or chopping market movements; and

Pivot Point overlay advisor which uses 3 day pivot points; and

Balance overlay advisor which estimated bulls and bears as determined by a review of the pattern analysis advisor outputs.

13. ) The method of claim 11, wherein the overlay advisor outputs and lower neural network outputs are combined using a second neural network, producing a final prediction;

14. ) The method of claim 1, wherein the system'"'"'s final prediction is produced using three processing layers that could be used independently, in any combination.

15. ) The method of claim 13, wherein the second neural network combining process is optional.

16. ) The method of claim 1, whereas the neural networks are perceptrons.

17. ) A machine-readable medium having instructions for:

a) Pre-processing securities price data and other data types using mathematical indicators to produce indicator output signals;

b) Processing with advisors the indicator output signals to produce advisor output signals;

c) Inputting the advisor output signals into a neural network to produce a prediction of a securities price or other data types;

d) Processing output signal data with overlay advisors to produce overlay advisor output signals;

e) Inputting the overlay advisor output signals and lower-level neural network output signals into a second high-level neural network to produce a final prediction of securities price or other data types; and

f) Iteratively updating the neural network weights for all securities and other data types and system components upon receipt of new data.

18. An apparatus for predicting securities prices and other data types comprising:

means for processing securities price data and other data types using mathematical indicators to produce indicator output signals;

means for processing with advisors the indicator output signals to produce advisor output signals;

means for processing output signal data with overlay advisors to produce overlay advisor output signals; and

means for iteratively updating neural network weights for all securities and other data types and system components upon receipt of new data.