import pandas as pd
import matplotlib.pyplot as plt

# Constants
monthly_sip = 100000  # Monthly SIP amount in INR
inflation_rate = 0.15  # 15% annual inflation
tax_rate = 0.125  # LTCG Tax rate (12.5%)
rate_of_return_annual = 0.135  # 13.5% annual return

# Calculating monthly rate of return
rate_of_return_monthly = rate_of_return_annual / 12

# Function to calculate SIP corpus at the end of each year
def calculate_sip_corpus(P, i, n):
    """Calculate Future Value of SIP"""
    return P * (((1 + i) ** n - 1) / i) * (1 + i)

# Function to calculate inflated price of the item
def calculate_inflated_price(initial_price, inflation_rate, years):
    """Calculate the inflated price of the item"""
    return initial_price * ((1 + inflation_rate) ** years)

# Function to calculate taxable profits and LTCG tax
def calculate_taxable_profits_and_tax(total_sip_contributions, corpus, tax_rate):
    """Calculate taxable profits and LTCG tax"""
    profits = corpus - total_sip_contributions
    tax = profits * tax_rate
    post_tax_profits = profits - tax
    return profits, tax, post_tax_profits

# Initial values
initial_item_price = 15000000  # Initial item price in INR (1.5 crore)
total_sip_contributions = 0  # Total SIP contributions, initialized to zero
corpus = 0  # SIP corpus, initialized to zero

# Store results for each 5-year interval
output_data = []

# Simulating the SIP for 30 years
for year in range(5, 31, 5):
    # Number of months till the current 5-year period
    n_months = 12 * year

    # Calculate SIP corpus for this period
    corpus = calculate_sip_corpus(monthly_sip, rate_of_return_monthly, n_months)

    # Total SIP contributions are simply the SIP amount multiplied by the number of months
    total_sip_contributions = monthly_sip * n_months

    # Calculate the inflated price of the item after 'year' years
    inflated_price = calculate_inflated_price(initial_item_price, inflation_rate, year)

    # Calculate taxable profits, tax, and post-tax profits
    profits, tax, post_tax_profits = calculate_taxable_profits_and_tax(total_sip_contributions, corpus, tax_rate)

    # Prepare the output string in the desired format
    output_data.append({
        "Period": f"Year {year - 4} to {year}",
        "Contributed Amount Till Now": f"{total_sip_contributions / 1e7:.2f} Cr",
        "Profits Made Till Now": f"{profits / 1e7:.2f} Cr",
        "Total Taxes Till Now": f"{tax / 1e5:.2f} L",
        "Total Amount Available for Withdrawal": f"{post_tax_profits / 1e7:.2f} Cr",
        "Inflated Value of Asset": f"{inflated_price / 1e7:.2f} Cr"
    })

# Create a DataFrame to display the results
df_output = pd.DataFrame(output_data)

# Display the output for each 5-year period
print("SIP Investment Overview (Every 5 Years):")
for row in df_output.itertuples(index=False, name=None):
    print(f"\n{row[0]} --")
    print(f" - Contributed amount till now: {row[1]}")
    print(f" - Profits made from SIP till now: {row[2]}")
    print(f" - Total taxes paid (12.5% of profits): {row[3]}")
    print(f" - Total amount available for withdrawal: {row[4]}")
    print(f" - Inflated value of the asset at this time: {row[5]}")

# Plotting the data (optional)
plt.figure(figsize=(12, 8))

# Plot SIP Corpus vs Year
plt.plot(df_output['Period'], df_output['Total Amount Available for Withdrawal'], label='Total Amount Available for Withdrawal', marker='o', color='b')

# Adding title and labels
plt.title('Comparison of Total Amount Available for Withdrawal Over 30 Years')
plt.xlabel('Year Period')
plt.ylabel('Amount in INR')

# Show the plot
plt.grid(True)
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()