import pandas as pd import numpy as np import matplotlib.pyplot as plt  def f(z):     c = 0.1     n = 2     return c * abs(z)**n * (np.cos(n * np.angle(z)) + 1j * np.sin(n * np.angle(z)))  def buy_signal(data):     if f(data['Close'].iloc[-1]) > 0:         return True     else:         return False  def sell_signal(data):     if f(data['Close'].iloc[-1]) < 0:         return True     else:         return False  def trade(data):     bought = False     for i in range(len(data)):         if buy_signal(data.iloc[i:i+1]):             if not bought:                 bought = True                 data.loc[i, 'Action'] = 'Buy'         elif sell_signal(data.iloc[i:i+1]):             if bought:                 bought = False                 data.loc[i, 'Action'] = 'Sell'  def backtest(data):     trade(data)     data['Return'] = data['Close'].pct_change()     data['Strategy Return'] = data['Action'].shift(1) * data['Return']     strategy_return = data['Strategy Return'].sum()     print(f"Strategy Return: {strategy_return}")  # Load data data = pd.read_csv('data.csv', index_col='Date', parse_dates=True)  # Backtest strategy backtest(data)  # Plot results plt.plot(data['Close']) plt.plot(data['Strategy Return'] + data['Close']) plt.show()