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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}") |