Oct-19-2017, 02:13 PM
Dear Python Experts,
What I am doing is that I am running genetic algorithm for prediction of stock prices. I include a sample of code that is in line with http://www.zaneacademy.com/Genetic Algorithms w/. However, I am stuck at the part of the further improvement of code. In particular, the code below provides Generation #0. What I need is that it provides all the possible generations with, so that I can choose the best one. Please, advise me this issue.
What I am doing is that I am running genetic algorithm for prediction of stock prices. I include a sample of code that is in line with http://www.zaneacademy.com/Genetic Algorithms w/. However, I am stuck at the part of the further improvement of code. In particular, the code below provides Generation #0. What I need is that it provides all the possible generations with, so that I can choose the best one. Please, advise me this issue.
# --- IMPORT LIBRARIES --------------------------------------------------------
import pandas_datareader.data as dr # Import pandas asset datareader
import datetime # Import datetime module
import matplotlib.pyplot as plt # Import matplotlib ploting library
import numpy as np # Import numpy
import random # Import random
import scipy as sp # Import scipy
import csv # Import csv
from itertools import zip_longest # Import zip_longest
# --- GOBAL PARAMETERS --------------------------------------------------------
ALLELE_SIZE = 5 # Number of allele + 1
POPULATION_SIZE = 8 # Population size
ELITE_CHROMOSOMES = 1 # Elite chromosome not subjected to evolving(cross-over, mutation) from one generation to the next
SELECTION_SIZE = 4 # Selection size randomly from population
MUTATION_RATE = 0.25 # Probability that chromosome gene does a random mutation
TARGET_FITNESS = 0.9 # Target fitness (change as need to get convergence)
ALPHA = 0.1 # Constant in the fitness test
# Random number generator list (change as needed)
R = [175,280,320,340,360,385]
# Download daily TSLA stock prices for 1 years from Google Finance
symbol = "TSLA" # Ticker symbol
start = datetime.datetime(2016,10,19) # Start date (adjust date for a year)
end = datetime.date.today() # Today's date
data = dr.DataReader(symbol, "google", start, end) # Retrieve data
# Assest closing price and standard deviation
PRICE = data["Close"].values.tolist()
SIGMA0 = np.std(PRICE) # Standard deviation of the entire timeseries
# --- CLASSES -----------------------------------------------------------------
class Chromosome:
"""
Candidate solution.
"""
def __init__(self):
self._genes =
self._fitness = 0
self._genes.append(random.randint(R[0],R[1]))
self._genes.append(random.randint(R[1],R[2]))
self._genes.append(random.randint(R[2],R[3]))
self._genes.append(random.randint(R[3],R[4]))
self._genes.append(random.randint(R[4],R[5]))
def get_genes(self):
return self._genes
def get_fitness(self):
L =
D =
for i in range(len(PRICE)-2):
if self._genes[0] <= PRICE[i]:
if self._genes[1] <= PRICE[i+1] or PRICE[i+1] <= self._genes[2]:
if self._genes[3] <= PRICE[i+2] or PRICE[i+2] <= self._genes[4]:
L.append(PRICE[i+1])
D.append(i)
sigma = np.std(L)
nc = len(L)
self._fitness = -np.log2(sigma/SIGMA0)-ALPHA/nc
return self._fitness
def __str__(self):
return self._genes.__str__()
class Population:
"""
Pupulation of candidate solutions.
"""
def __init__(self, size):
self._chromosomes =
i = 0
while i < size:
self._chromosomes.append(Chromosome())
i += 1
def get_chromosomes(self):
return self._chromosomes
class GeneticAlgorithm:
"""
The genectic algorithm logic for evolving the population via crossover and
mutation.
"""
@staticmethod
def evolve(pop):
return GeneticAlgorithm._mutate_population(GeneticAlgorithm._crossover_population(pop))
@staticmethod
def _crossover_population(pop):
crossover_pop = Population(0)
for i in range(ELITE_CHROMOSOMES):
crossover_pop.get_chromosomes().append(pop.get_chromosomes()[i])
i = ELITE_CHROMOSOMES
while i < POPULATION_SIZE:
chromosome1 = GeneticAlgorithm._select_population(pop).get_chromosomes()[0]
chromosome2 = GeneticAlgorithm._select_population(pop).get_chromosomes()[0]
crossover_pop.get_chromosomes().append(GeneticAlgorithm._crossover_chromosomes(chromosome1, chromosome2))
i += 1
return crossover_pop
@staticmethod
def _mutate_population(pop):
for i in range(ELITE_CHROMOSOMES, POPULATION_SIZE):
GeneticAlgorithm._mutate_chromosomes(pop.get_chromosomes()[i])
return pop
@staticmethod
#does random gene selection from each of parent chromosomes
def _crossover_chromosomes(chromosome1, chromosome2):
crossover_chrom = Chromosome()
for i in range(ALLELE_SIZE):
if random.random() < MUTATION_RATE:
if random.random() < 0.5:
crossover_chrom.get_genes()[i] = chromosome1.get_genes()
[i] else:
crossover_chrom.get_genes()[i] = chromosome2.get_genes()
[i] return crossover_chrom
@staticmethod
#For each gene in the past in chromosome if random number < mutation rate, perform mutation
#Gene takes randomly values from R (random values generator list)
def _mutate_chromosomes(chromosome):
if random.random() < MUTATION_RATE:
chromosome.get_genes()[0] = random.randint(R[0],R[1])
elif random.random() < MUTATION_RATE:
chromosome.get_genes()[1] = random.randint(R[1],R[2])
elif random.random() < MUTATION_RATE:
chromosome.get_genes()[2] = random.randint(R[2],R[3])
elif random.random() < MUTATION_RATE:
chromosome.get_genes()[3] = random.randint(R[3],R[4])
elif random.random() < MUTATION_RATE:
chromosome.get_genes()[4] = random.randint(R[4],R[5])
@staticmethod
#Select the one with the highest fitness
def _select_population(pop):
select_pop = Population(0)
i = 0
while i < SELECTION_SIZE:
select_pop.get_chromosomes().append(pop.get_chromosomes()[random.randrange(0,POPULATION_SIZE)])
i += 1
#Sort by fitness, highest is the first
select_pop.get_chromosomes().sort(key=lambda x: x.get_fitness(), reverse=True)
return select_pop
# --- FUNCTIONS ---------------------------------------------------------------
def print_population(pop, gen_number):
"""
Print evolving generation results.
"""
print("\n-----------------------------------------------------------")
print("Generation #", gen_number, ": Fittest chromosome fitness: %3.2f" % pop.get_chromosomes()[0].get_fitness())
print("-----------------------------------------------------------")
i = 0
for x in pop.get_chromosomes():
print("Chromosome #", i, " :", x, "| Fitness: %3.2f" % x.get_fitness())
i += 1
# --- MAIN --------------------------------------------------------------------
# Generation 0
population = Population(POPULATION_SIZE)
population.get_chromosomes().sort(key=lambda x: x.get_fitness(), reverse=True) #Population with the highest fitness is the first
print_population(population, 0)
# Future generations
#While the fitness of the fittest chromosome in each population is smaller than target fitness
# evolve the population and sort the evolved the population, print it and go to another population
gen = 1
while population.get_chromosomes()[0].get_fitness() < TARGET_FITNESS:
population = GeneticAlgorithm.evolve(population)
population.get_chromosomes().sort(key=lambda x: x.get_fitness(), reverse=True)
print_population(population, gen)
gen += 1
# Price list that met the criteria with the heigest fitness
genes =
for i in range(5):
genes.append(population.get_chromosomes()[0].get_genes()[i])
L = # List of price that met cretria with the target fitness
D = # List of numbered days that met the criteria (starting form day 0)
for i in range(len(PRICE)-2):
if genes[0] <= PRICE[i]:
if genes[1] <= PRICE[i+1] or PRICE[i+1] <= genes[2]:
if genes[3] <= PRICE[i+2] or PRICE[i+2] <= genes[4]:
L.append(PRICE[i+1])
D.append(i)[/i][/i][/i][/i][/i][/i][/i][/i][/i]