| 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() |