Distributed network for performing complex algorithms
A server computer and a multitude of client computers form a network computing system that is scalable and adapted to continue to evaluate the performance characteristics of a number of genes generated using a software application running on the client computers. Each client computer continues to periodically receive data associated with the genes stored in its memory. Using this data, the client computers evaluate the performance characteristic of their genes by comparing a solution provided by the gene with the periodically received data associated with that gene. Accordingly, the performance characteristic of each gene may be updated and varied with each periodically received data. The performance characteristic of a gene defines its fitness. The genes may be virtual asset traders that recommend trading options, and the data associated with the genes may be historical trading data.
1. A networked computer system comprising a first client computer, the first client computer comprising:
a memory operative to store N genes, each gene characterized by a plurality of conditions and at least one action, wherein N is an integer greater than one;
a communication port through which the first client computer recurrently receives data samples; and
a processor operative to evaluate a performance characteristic of each of the N genes by evaluating each gene against the recurrently received data samples, the performance characteristic of each gene being adjusted with each recurrently received data sample and indicating a fitness of the gene.
2. The networked computer system of claim 1 wherein the data samples comprise historical trading data and wherein an action provided by each gene comprises a trading recommendation made by the gene.
3. The networked computer system of claim 1 wherein said first client computer is configured to discard M of the N genes after evaluating the fitness of the N genes for P of the data samples, each of the discarded genes having a fitness that falls below a first predefined threshold value, each of the remaining N-M genes being a surviving gene, wherein M and P are positive integers and wherein M is smaller than N.
4. The networked computer system of claim 1 wherein the N genes stored in the memory of the client computer are generated in accordance with computer instructions stored in the first client computer and executed by the processor of the first client computer.
5. The networked computer system of claim 1 wherein the data samples comprise historical trading data, each of the data samples comprising historical data for a financial asset taken at a plurality of times within a single trading day.
6. The networked computer system of claim 1 , further comprising a server computer having a server gene pool and configured to transmit to each of a plurality of client computers including the first client computer, one or more genes of the server gene pool for fitness evaluation.
7. A networked computer system comprising a server computer, said server computer configured to:
receive a first set of genes from a first one of a plurality of client computers;
store received genes in a server gene pool; and
transmit to each of at least a first subset of the plurality of client computers one or more genes of the server gene pool for fitness evaluation spanning W trading days, the first set of genes being generated by evaluating a performance characteristic of each of N genes against recurrently received data samples, the performance characteristic of each gene being adjusted with each recurrently received data sample and indicating a fitness of the gene, each gene identifying by a plurality of conditions and at least one action.
8. The networked computer system of claim 7 wherein the data samples comprise historical trading data and wherein an action provided by each gene comprises a trading recommendation made by the gene.
9. The networked computer system of claim 8 wherein said first one of the plurality of client computers is configured to discard M of the N genes after evaluating the fitness of the N genes for P trading days, each of the discarded genes having a fitness that falls below a first predefined threshold value, each of the remaining N-M genes being a surviving gene, wherein M and P are positive integers and wherein M is smaller than N.
10. The networked computer system of claim 7 wherein said server computer accepts only genes whose fitness is higher than the fitness of a least fit gene previously stored in the server gene pool.
11. The networked computer system of claim 10 wherein said server gene pool is configured to store a fixed number of genes.
12. The networked computer system of claim 10 wherein said server computer is further adapted to combine a plurality of fitness values associated with each gene the server computer receives with a corresponding fitness value stored in the server computer for that gene.
13. The networked computer system of claim 7 wherein the data samples comprise historical trading data, each of the data samples comprising historical data for a financial asset taken at a plurality of times within a single trading day.
14. The networked computer system of claim 7 , further comprising said first client computer.
15. A method for solving a computational problem using a plurality of client computers, the method comprising:
storing N genes in a memory accessible to a first one of said client computers, each gene characterized by a plurality of conditions and at least one action, wherein N is an integer greater than one;
through a communications port, recurrently receiving data samples; and
said first client computer evaluating a performance characteristic of each of the N genes against the recurrently received data samples, the performance characteristic of each gene being adjusted with each recurrently received data sample and indicating a fitness of the gene.
16. The method of claim 15 wherein the data samples comprise comprises historical trading data and wherein an action provided by each gene comprises a trading recommendation made by the gene.
17. The method of claim 15 further comprising:
discarding M of the N genes after evaluating the fitness of the N genes for P trading days, each of the discarded genes having a fitness that falls below a first predefined threshold value, each of the remaining N-M genes being a surviving gene, wherein M and P are positive integers and wherein M is smaller than N.
18. The method of claim 15 wherein the N are generated in accordance with computer instructions stored in the first client computer and executed by the first client computer.
19. The method of claim 15 wherein the data samples comprise historical trading data, each of the data samples comprising historical data for a financial asset taken at a plurality of times within a single trading day.
20. A standalone computer comprising:
a processing core configured as a server;
and a plurality of processing cores configured as clients, each of the plurality of clients comprising:
a memory operative to store N genes, each gene characterized by a plurality of conditions and at least one action, wherein N is an integer greater than one;
a port through which the first client processing core recurrently receives data samples, each client processing core configured to evaluate a performance characteristic of each of the N genes against the recurrently received data samples, the performance characteristic of each gene being adjusted with each recurrently received data sample and indicating a fitness of the gene.
21. The standalone computer of claim 20 wherein the data samples comprise historical trading data and wherein the action provided by each gene comprises a trading recommendation made by the gene.
22. The standalone computer of claim 21 wherein a first one of the plurality of client computers is configured to discard M of the N genes after evaluating the fitness of the N genes for P trading days, each of the discarded genes having a fitness that falls below a first predefined threshold value, each of the remaining N-M genes being a surviving gene, wherein M and P are positive integers and wherein M is smaller than N.
23. The standalone computer of claim 21 wherein the N genes stored in each client processing core are generated in accordance with computer instructions stored in and executed by the stand alone computer.
24. The standalone computer of claim 21 wherein the at least one action of each gene is selected from a group consisting of buy, sell, hold, long exit and short exit recommendations.
25. The standalone computer of claim 20 wherein said plurality of conditions are evaluated as a logical expression conjoined by logical AND and/or modified by logical NOT operations.
26. The method of claim 20 wherein the data samples comprise historical trading data, each of the data samples comprising historical data for a financial asset taken at a plurality of times within a single trading day.