| 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}") |