| import pandas as pd import numpy as np def f(z, c, n): return c * abs(z)**n * (np.cos(n * np.angle(z)) + 1j * np.sin(n * np.angle(z))) * (1 + np.log(abs(z))) def buy_signal(data, c, n): if f(data['Close'].iloc[-1], c, n) > 0: return True else: return False def sell_signal(data, c, n): if f(data['Close'].iloc[-1], c, n) < 0: return True else: return False def trade(data, c, n): bought = False for i in range(len(data)): if buy_signal(data.iloc[i:i+1], c, n): if not bought: bought = True data.loc[i, 'Action'] = 'Buy' elif sell_signal(data.iloc[i:i+1], c, n): if bought: bought = False data.loc[i, 'Action'] = 'Sell' def backtest(data, c, n): trade(data, c, n) data['Return'] = data['Close'].pct_change() data['Strategy Return'] = data['Action'].shift(1) * data['Return'] strategy_return = data['Strategy Return'].sum() return strategy_return def genetic_algorithm(population_size, num_generations): population = [] for i in range(population_size): c = np.random.uniform(0.01, 0.2) n = np.random.randint(2, 10) individual = (c, n) population.append(individual) for generation in range(num_generations): offspring = [] for i in range(int(population_size / 2)): parent1 = population[np.random.randint(0, population_size)] parent2 = population[np.random.randint(0, population_size)] offspring1 = parent1 offspring2 = parent2 if np.random.rand() < 0.5: offspring1 = (offspring1[0] + offspring2[0]) / 2, offspring1[1] else: offspring1 = offspring1[0], (offspring1[1] + offspring2[1]) / 2 offspring.append(offspring1) offspring.append(offspring2) population = offspring for i in range(len(population)): c = population[i][0] n = population[i][1] population[i] = (c, n) best_individual = population[0] for individual in population: data = pd.read_csv('data.csv', index_col='Date', parse_dates=True) strategy_return = backtest(data.iloc[i:i+1], individual[0], individual[1]) if strategy_return > best_individual[2]: best_individual = individual + (strategy_return,) print(f"Generation {generation + 1} Best Individual: {best_individual}") return best_individual best_individual = genetic_algorithm(population_size=100, num_generations=100) print(f"Overall Best Individual: {best_individual}") |