def main(year, month, ga_start_date, ga_end_date, test_start_date,
test_end_date, index):
initiated_time = time.time()
training_file_name = "Kospi_200_portfolio_optimization-" + str(
datetime.datetime.now().strftime("%Y%m%d_%H_%M_%S")) + ".csv"
day_list = scraper.days(KOSPI_TICKER, ga_start_date, ga_end_date)
stock_price_list = scraper.stock_prices(day_list, KOSPI_TICKER,
ga_start_date, ga_end_date)
test_day_list = scraper.days(KOSPI_TICKER, test_start_date, test_end_date)
test_stock_price_list = scraper.stock_prices(test_day_list, KOSPI_TICKER,
test_start_date,
test_end_date)
population = Population(POPULATION_SIZE, day_list, stock_price_list)
population.get_chromosomes().sort(
key=lambda chromosome: chromosome.get_fitness(), reverse=True)
save.save_population(population, 0, year, month, training_file_name)
generation_number = 1
while generation_number < GENERATION_SIZE:
population = GeneticAlgorithm.evolve(population, day_list,
stock_price_list, POPULATION_SIZE)
population.get_chromosomes().sort(
key=lambda chromosome: chromosome.get_fitness(), reverse=True)
save.save_population(population, generation_number, year, month,
training_file_name)
generation_number += 1
if generation_number == GENERATION_SIZE:
completed_time = time.time()
print(index + 1, "th GA GENERATION OF PORTFOLIO OPTIMIZATION FOR ",
test_start_date, ",", test_end_date, "COMPLETED IN",
completed_time - initiated_time, "SECONDS.")
best_chromosome = population.get_chromosomes()[0]
test_fund.test_best_population(best_chromosome, test_day_list,
test_stock_price_list, year, month,
ga_start_date, ga_end_date,
test_start_date, test_end_date)
def main(training_file_name, year, month, ga_start_date, ga_end_date, test_start_date, test_end_date):
day_list = scraper.days(KOSPI_TICKER, ga_start_date, ga_end_date)
stock_price_list = scraper.stock_prices(day_list, KOSPI_TICKER, ga_start_date, ga_end_date)
test_day_list = scraper.days(KOSPI_TICKER, test_start_date, test_end_date)
test_stock_price_list = scraper.stock_prices(test_day_list, KOSPI_TICKER, test_start_date, test_end_date)
population = Population(POPULATION_SIZE, day_list, stock_price_list)
population.get_chromosomes().sort(key=lambda chromosome: chromosome.get_fitness(), reverse=True)
save.save_population(population, 0, training_file_name, year, month)
generation_number = 1
while generation_number < GENERATION_SIZE:
population = GeneticAlgorithm.evolve(population, day_list, stock_price_list, POPULATION_SIZE)
population.get_chromosomes().sort(key=lambda chromosome: chromosome.get_fitness(), reverse=True)
save.save_population(population, generation_number, training_file_name, year, month)
generation_number += 1
best_chromosome = population.get_chromosomes()[0]
save.print_best_population(best_chromosome, year, month, training_file_name)
if generation_number == GENERATION_SIZE:
monthly_best_file_name = "Kospi_200_portfolio_optimization-Monthly Best.csv"
save.save_best_generation(best_chromosome, year, month, monthly_best_file_name)
test_fund.test_best_population(KOSPI_TICKER, best_chromosome, test_day_list, test_stock_price_list, year, month, monthly_best_file_name)