Early Retirement - Achieving Financial Independence with Python 💸🔮

Introduction

In this article, I want to share with you my journey into the world of early retirement planning and how I used a Python script to calculate investment scenarios. Together, we'll explore the good, the bad, and the ugly aspects of early retirement. So, let's dive in! 🚀

The Idea 💡

Imagine being able to retire in your 40s simply by investing a small amount of money every month. That's the essence of the F.I.R.E. (Financial Independence Retire Early) movement. Intrigued by this idea, I set out to test its efficacy and determine how I could optimize it for my future self.

The 4% Rule 📊

One key concept in early retirement planning is the 4% rule. It helps us determine the Safe Withdrawal Rate (SWR) - the amount of money we can withdraw annually while our portfolio continues to grow. By setting our SWR at 4%, we can calculate the net worth (FireNumber) required to sustain our desired expenses.


#Basic example 
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E = FireNumber * 0.04
FireNumber = E / 0.04
FireNumber = E * 25

Implementing Compound Interest 📈

To better understand the power of compound interest in growing our portfolio, I developed a Python script. The script leverages the formula:


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NWi_plus_1 = NWi * (1 + interest_rate) ** years

Considering Fees and Inflation 💸💣

Unfortunately, investing in financial assets comes with fees. These fees can have a significant impact on our portfolio growth. To account for this, I deducted the fees from the portfolio value each year in my Python script. Additionally, I factored in inflation, as our expenses and FireNumber increase over time.


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while i < years:
    portfolio_value = portfolio_value * (1 + interest_rate)
    portfolio_value = portfolio_value * (1 - fees)
    deposit = (monthly_deposit * 12)
    portfolio_value += deposit
    i += 1

Retirement Modeling 🌴📊

Retirement planning isn't just about accumulation; it's also about managing our portfolio during retirement. In my Python script, I simulated the retirement period. By withdrawing our monthly/yearly spending from the growing portfolio, I assessed its sustainability over time. The script considered factors like inflation, fees, and market returns to provide a comprehensive retirement modeling experience.


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while years < 80:
    retirement_fund -= (monthly_expenses * 12) * (1.3) * ((1 + inflation_rate) ** (years - starting_age))
    retirement_fund = retirement_fund * (1 + float(growth[year]) / 100)
    retirement_fund = retirement_fund * (1 - fees)
    years += 1
    year += 1
    if retirement_fund <= 0:
        bad_ending += 1
        break

The Ugly - Taxes 💰💔

Ah, taxes - a certainty in life. To make our calculations more accurate, I had to factor in taxes on capital gains. The specific tax rates vary by country, and in my case, I considered a 26% tax rate based on Italian regulations. I adjusted the withdrawal amounts to ensure we achieve our desired net value after taxes.


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net_withdrawal = gross_withdrawal / (1 - tax)

Simulation Results 📊

Running multiple simulations using real-world stock market data, I gained insights into the success rates of early retirement strategies. While the average of the simulations aligned with expectations, it's important to note that not all simulations had a happy ending. Approximately 15% of the simulations depleted the net worth before reaching the age of 80, highlighting the need for careful

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