Transcript Parallel Genetic Programming and its application to trading model induction Suchitra Goopy

Parallel Genetic Programming
and its application to trading
model induction
Suchitra Goopy
What is a trading model?
• Set of rules that catches the movement of the
market and provides trading recommendations for
financial assets.
• Example of a trading rule:
IF | I | > K Then G = Sign(I) Else G = 0
Trading model……..contd
• Problem with optimizing trading models
- overfitting
• This can be solved by using robust indicators
• Fitness Measure
• No single indicator can represent the whole
picture of the market
Genetic algorithm for trading
models
• Multiple independent subpopulations are
maintained and they interact u sing a ring topology
• A Genetic Program can be regarded as a Lisp
function and is usually represented using a parse
tree
• Each parse tree is created recursively starting from
the root node
• TerminalSet and FunctionSet
Implementation Details
• Has a ring topology
• The code was written in C++ and uses PVM3
message passing library
• The code was implemented on the IBM
SP – 2 machine
• It can be easily ported to other parallel machines
like Cray T3D and on workstation clusters
Crossover
Parent 1
+
a
Parent 2
b
-
Crossover Point
*
/
c
Child 1
y
-
+
x
+
5
Child 2
a
y
z
*
/
b
+
5
z
x
c
Mutation
• Contrary to genetic algorithms, mutation is seldom
used in genetic programming
• The goal of mutation is to introduce diversity in a
population
• This diversity can be obtained using migration and
hence there is no need for mutation
Parallel implementation - Master
• Create initial population
• Distribute the fitness evaluation tasks to processing
nodes
• Execute local and external evaluation tasks
• Receive fitness values sent by other processors or
slaves
• Migration
• Perform selection phase
• Perform reproduction phase
Parallel implementation - Slave
•
•
•
•
Receive a genetic program
Calculate the fitness of the received program
Send the fitness value to the source Master process
Repeat the above steps until reception of
termination signal
Conclusion
• Evolving multiple independent subpopulations
with occasional migration between these
subpopulations helps to deal with the premature
convergence characteristic of genetic algorithms
• This also helps explore the global search space
and find better solutions