import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# Read the S&P 500 data
df = pd.read_csv('/path/to/your/csv/file.csv')

# Filtering the dataset to only include data from January 1, 1990, onwards
df_1990 = df[df['Date'] >= '1990-01-01'].reset_index(drop=True)

# Number of data points in the dataset starting from 1990
n_1990 = len(df_1990)

# Calculate the daily returns for the actual S&P 500 data starting from 1990
s_actual_1990 = df_1990['Close'].values
daily_returns_1990 = (s_actual_1990[1:] / s_actual_1990[:-1]) - 1

# Define the annual fee rate for 2x, 3x, and 4x leveraged products (1% annual fee)
annual_fee_rate = 0.01
daily_fee_multiplier = 1 - (annual_fee_rate / 252)  # Assuming 252 trading days in a year

# Initialize arrays for 2x, 3x, and 4x leveraged products with daily rebalancing and fees starting from 1990
s_2x_leverage_daily_rebalance_fee_1990 = np.zeros(n_1990)
s_3x_leverage_daily_rebalance_fee_1990 = np.zeros(n_1990)
s_4x_leverage_daily_rebalance_fee_1990 = np.zeros(n_1990)

s_2x_leverage_daily_rebalance_fee_1990[0] = s_actual_1990[0]
s_3x_leverage_daily_rebalance_fee_1990[0] = s_actual_1990[0]
s_4x_leverage_daily_rebalance_fee_1990[0] = s_actual_1990[0]

# Perform the simulation with daily rebalancing and fees
for t in range(1, n_1990):
    daily_return = (s_actual_1990[t] / s_actual_1990[t-1]) - 1
    s_2x_leverage_daily_rebalance_fee_1990[t] = s_2x_leverage_daily_rebalance_fee_1990[t-1] * (1 + 2 * daily_return) * daily_fee_multiplier
    s_3x_leverage_daily_rebalance_fee_1990[t] = s_3x_leverage_daily_rebalance_fee_1990[t-1] * (1 + 3 * daily_return) * daily_fee_multiplier
    s_4x_leverage_daily_rebalance_fee_1990[t] = s_4x_leverage_daily_rebalance_fee_1990[t-1] * (1 + 4 * daily_return) * daily_fee_multiplier

# Normalize the leveraged products and actual S&P 500 to start from 100
normalized_s_actual_1990 = s_actual_1990 / s_actual_1990[0] * 100
normalized_s_2x_leverage_daily_rebalance_fee_1990 = s_2x_leverage_daily_rebalance_fee_1990 / s_2x_leverage_daily_rebalance_fee_1990[0] * 100
normalized_s_3x_leverage_daily_rebalance_fee_1990 = s_3x_leverage_daily_rebalance_fee_1990 / s_3x_leverage_daily_rebalance_fee_1990[0] * 100
normalized_s_4x_leverage_daily_rebalance_fee_1990 = s_4x_leverage_daily_rebalance_fee_1990 / s_4x_leverage_daily_rebalance_fee_1990[0] * 100

# Plotting the graph
plt.figure(figsize=(15, 6))
plt.plot(df_1990['Date'], normalized_s_actual_1990, label='Normalized Actual S&P 500', color='r')
plt.plot(df_1990['Date'], normalized_s_2x_leverage_daily_rebalance_fee_1990, label='Normalized 2x Leveraged with Fee (Daily Rebalance)', color='g')
plt.plot(df_1990['Date'], normalized_s_3x_leverage_daily_rebalance_fee_1990, label='Normalized 3x Leveraged with Fee (Daily Rebalance)', color='b')
plt.plot(df_1990['Date'], normalized_s_4x_leverage_daily_rebalance_fee_1990, label='Normalized 4x Leveraged with Fee (Daily Rebalance)', color='m')
plt.title('Normalized Actual S&P 500, 2x, 3x, and 4x Leveraged with Daily Rebalancing and Fees (Base Value = 100, Starting from 1990)')
plt.xlabel('Date')
plt.ylabel('Normalized Value')
plt.legend()
plt.grid(True)
plt.show()