System and method for predicting stock prices

• US 20050091146A1
• Filed: 10/21/2004
• Published: 04/28/2005
• Est. Priority Date: 10/23/2003
• Status: Abandoned Application

• Current Assignee: Robert Levinson
• Original Assignee: Robert Levinson
• Inventors: Levinson, Robert
• Application Number:
  US10/970,892
• Publication Number:
  US 20050091146A1
• 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
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis

Abstract

An apparatus and method for a stock investment method with intelligent agents is described and illustrated. In one embodiment, the invention is a stock prediction system that through experience learns to make money based on short-term stock predictions and due to inherent flexibility continues to be profitable in virtually all market environments.

Claims

1. A method for predicting a stock price comprising:

1.1. Pre-processing stock data from a large set of mathematical indicators to produce indicator output signals;

1.2. Entering the indicator output signals into a database;

1.3. Processing with advisors the indicator output signals to produce advisor output signals;

1.4. Enter the advisor output signals into a database; and

1.5. Inputting the advisor output signals into a neural network to produce a prediction of a stock price;

1.6. Entering the neural network prediction into the database; and

1.7. Iteratively updating the neural network weights for all stocks and system components upon receipt of new data.

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

3. The method of claim 1, wherein the minimum default indicator is the calculated change of a data value over the prior data value in the series.

4. The method of claim 1, wherein machine learning based advisors process the indicator output signals.

5. The method of claim 4, wherein the machine learning algorithms are nearest neighbor and decision tree algorithms.

6. The method of claim 5, wherein the nearest neighbor and decision tree algorithms operate in parallel with other advisors, the method further comprising:

static mathematical advisors and hybrid mathematical advisors with embedded learning mechanisms.

7. The method of claim 6, wherein the learning mechanism embedded in the otherwise static advisor is a neural network.

8. The method of claim 1, wherein nearest neighbor, decision tree and neural network algorithms are used together in a single system.

9. The method of claim 1, wherein the raw data is normalized so that disparate data types can be used for reasoning by analogy.

10. The method of claim 1, wherein indicator output signals and features which are functions of indicator output signals, are themselves predicted by the system and correlated with stock price predictions.

11. The method of claim 1, wherein simulated annealing is implemented within the neural network.

12. The method of claim 11, wherein simulated annealing is a process comprising:

a mechanism for adjusting the learning rate to be higher (hotter) or lower (cooler) by increasing or decreasing, respectively, the historical time period covered by output signals used by the system to make predictions.

13. The method of claim 12, wherein the simulated annealing process is implemented in the neural network combiner and operates on advisor output signals.

14. The method of claim 5, where the use of the machine learning algorithm based advisors'"'"' signal outputs are dynamically changing as opposed to being locked based upon a backtested system.

15. The method of claim 1, wherein the indicators are not related to particular instruments or specified for a particular purpose, allowing their output signals to be used in any way by the system, including contrary to their traditional use.

16. The method of claim 1, wherein an apparatus determines the average trend length dynamically, comprising:

17. The method of claim 6, wherein the advisors comprise:

a mutual find and stock scoring system based upon the human assessment of the individual value of a large set of indicators;

a trading system based upon Joe DiNapoli'"'"'s published retracement system; and

a trading system based upon traditional Fibonacci ratios with an embedded neural network.

18. A method of claim 1, wherein the advisor output histories are normalized based upon a set of recent periods, based upon the number of standard deviations from the mean, so that when the number of standard deviations from the mean is negative, the advisor output, although positive is treated as a negative output by the system.

19. A method of claim 18, wherein any output signal prediction including the neural net'"'"'s final prediction can be output in a contrarian way.

20. A method for predicting a stock price comprising:

19.1. processing stock data from a set of mathematical indicators to produce indicator output signals;

19.2. entering the indicator output signals into a database;

19.3. processing with advisors the indicator output signals to produce advisor output signals;

19.4. entering the advisor output signals into a database; and

19.5. entering the advisor output signals into a neural network to produce a prediction of a stock price.

21. The method of claim 20 additionally comprising

entering the neural network prediction into the database;

and

iteratively updating neural network weights for all stocks and system components upon receipt of data.

22. The method of claim 20, wherein the indicators comprise any form of signal generating algorithm or output device.

23. The method of claim 20, wherein the minimum default indicator comprises the calculated change of a data value over the prior data value in the series.

24. The method of claim 20, wherein machine learning based advisors process the indicator output signals.

25. The method of claim 24, wherein the machine learning algorithms comprise nearest neighbor and decision tree algorithms.

26. The method of claim 25 wherein the nearest neighbor and decision tree algorithms operate in parallel with other advisors,.

27. The method of claim 25 additionally comprising:

static mathematical advisors and hybrid mathematical advisors with embedded learning mechanisms;

the learning mechanism embedded in the otherwise static advisor is a neural network;

the nearest neighbor, decision tree and neural network algorithms are used together in a single system;

the raw data is normalized so that disparate data types can be used for reasoning by analogy;

the indicator output signals and features which are functions of indicator output signals, are themselves predicted by the system and correlated with stock price predictions; and

the simulated annealing is implemented within the neural network.

28. The method of claim 27 wherein simulated annealing is a process comprising:

a mechanism for adjusting the learning rate to be higher (hotter) or lower (cooler) by increasing or decreasing, respectively, the historical time period covered by output signals used by the system to make predictions.

29. The method of claim 28, wherein the simulated annealing process is implemented in the neural network combiner and operates on advisor output signals.

30. The method of claim 25, wherein the use of the machine learning algorithm based advisors'"'"' signal outputs are dynamically changing as opposed to being locked based upon a backtested system;

the indicators are not related to particular instruments or specified for a particular purpose, allowing their output signals to be used in any way by the system, including contrary to their traditional use; and

the advisors comprise a mutual fund and stock scoring system based upon the human assessment of the individual value of a large set of indicators;

a trading system based upon a (Joe DiNapoli'"'"'s published) retracement system; and

a trading system based upon Fibonacci ratios with an embedded neural network.

31. The method of claim 20 wherein the advisor output histories are normalized based upon a set of recent periods, based upon the number of standard deviations from the mean, so that when the number of standard deviations from the mean is negative, the advisor output, although positive is treated as a negative output by the system;

and any output signal prediction including the neural net'"'"'s final prediction can be output in a contrarian way.

32. A machine-readable medium having stored thereon instructions for:

processing stock data from a set of mathematical indicators to produce indicator output signals;

entering the indicator output signals into a database;

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

entering the advisor output signals into a database; and

entering the advisor output signals into a neural network to produce a prediction of a stock price.

33. The machine-readable medium of claim 32 additionally comprising instructions for:

entering the neural network prediction into the database; and

iteratively updating neural network weights for all stocks and system components upon receipt of data.

34. An apparatus for predicting a stock price comprising:

means for processing stock data from a set of mathematical indicators to produce indicator output signals;

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

means for producing a prediction of a stock price from entering the advisor output signals into a neural network.