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}")