Spreadsheets are great for tracking what already happened. But they’re terrible at predicting what’s gonna happen.
And if you’re serious about financial freedom, you need to stop just tracking the past and start modeling the future.
That’s where Python comes in.
Why your spreadsheet is holding you back #
Financial independence means your passive income covers your living expenses. Simple concept but not that easy to execute.
You’re planning for 30+ years of retirement. You’ve got inflation, market volatility and sequence of returns risk (the danger that the market crashes right when you retire). Unknown healthcare costs. Life not turning the way you expect.
A static spreadsheet with some formulas? That’s not gonna cut it.
You need to model volatility and not assume stability. Worst case scenario!
Critical: Always model volatility and plan for worst-case scenarios. Your financial future depends on it!
Python lets you move from just reporting on your money to actually simulating and optimizing your future. It’s the difference between looking in the rear mirror and having a GPS that shows you every possible route.
The four ways Python changes your financial game #
Python and its libraries turn personal finance into a legit analytical project. Where YOU are the analyst.
1. Automated budgeting #
First step to FI? Knowing exactly where your money goes.
Here’s what Python can do:
| Library | What It Does | Why It’s Powerful |
|---|---|---|
| Pandas | Organizes your data | Imports transactions from all your accounts into clean, organized tables you can actually work with |
| Plaid/APIs | Connects to your banks | Pulls data automatically from over 11,000 financial institutions - no more manual downloads |
| Matplotlib/Seaborn | Creates charts | Generates spending heatmaps and savings trends that show you patterns you’d never spot otherwise |
My daily routine: I run Python scripts daily that track my spending and email me a report before I’ve had my morning coffee. I perfected that routine to my own liking.
No manual work. No forgetting to log stuff. Just automated tracking that runs in the background.
Automated tracking isn’t just convenient; it ensures accuracy and consistency, freeing you from manual errors and forgotten entries. Embrace automation to gain a true, real-time picture of your financial flows. Make it a habit!
2. Portfolio optimization #
This is where Python separates amateurs from pros.
Monte Carlo simulations #
Instead of assuming “the market returns 7% every year” (which never happens), you can run 10,000+ simulations of different possible futures.
Each simulation represents a different way the market could play out. Some years are up 30%. Some are down 20%. Some are flat.
What you get:
- Actual probability your money lasts 30+ years
- Real success rates (like “95% chance of success”)
- Quantified sequence of returns risk
This is what financial advisors charge thousands for. You can do it yourself.
Building better portfolios #
Python lets you use Modern Portfolio Theory to find the optimal mix of assets for YOUR risk tolerance.
Pull historical data using free libraries like yfinance. Test different allocations. Customize everything based on when you want to retire and how much risk you can take.
Not some generic “60/40” portfolio everyone recommends. YOUR optimal mix.
3. Automated rebalancing #
Your target is 80% stocks, 20% bonds. Market moves and now you’re at 75/25.
Manually calculating what to buy or sell? A pain in the ass.
Python script? Tells you exactly how many shares to buy or sell to get back to your target. Accounts for trading costs. Optimizes which accounts to trade in for tax efficiency.
One click and you’re done. Check out my portfolio rebalancing calculator.
Automated rebalancing isn’t just about maintaining your target asset allocation; it can also be configured to optimize for tax efficiency by making trades in the most advantageous accounts.
4. Back-testing your plan #
The biggest barrier to pulling the trigger on early retirement is Fear.
“What if I run out of money?” “What if the market crashes?” “What if I’m wrong about my safe withdrawal rate rule?”
Python lets you stress-test your exact plan against decades of real market history.
Want to know what would’ve happened if you retired in 2008? Run it.
Curious if 3.5% withdrawal is safer than 4%? Test both.
Wondering if a dynamic withdrawal strategy beats a static one? Back-test it.
This turns “hopeful guessing” into data-driven conviction.
You’ll know if your plan would’ve survived the Great Depression, the dot-com crash, 2008, COVID. All of it.
How to actually start (my own framework) #
You don’t need to be a developer. You just need a plan.
Here’s my PLOUTOS 4.0 framework:
| Phase | What You’re Doing | Tools | First Action |
|---|---|---|---|
| Phase 1: Data Gathering | Automate tracking of transactions and balances | Pandas, yfinance, API calls | Write a script to download and categorize 12 months of spending into five buckets: Housing, Food, Transport, Fun, Investing |
| Phase 2: Prediction | Figure out if your plan will actually work | NumPy, SciPy | Build a Monte Carlo simulator to test the 4% rule against your portfolio over 30 years - see your actual success rate |
| Phase 3: Optimization | Make your portfolio better | riskfolio-lib, Pandas | Create a function that shows your current allocation and tells you exactly what trades to make to hit your targets |
| Phase 4: Monitoring | Track everything in real-time | Streamlit, Plotly | Build a simple dashboard showing your FI status, withdrawal safety score, and spending vs budget |
Pro tip: Check out my calculators on Finfr.ee for back-testing and simulations. I’ve already built a bunch of this stuff.
Why this actually matters #
Financial independence is about maximizing freedom in your life.
Python is the tool that lets you:
1. Reduce anxiety by putting numbers on your risks instead of just worrying about them
2. Save time by automating tedious tasks that eat up hours every month
3. Gain confidence by stress-testing your plan against history’s worst scenarios
You will have to put in the time to learn Python. There are some great learning platforms for python. For example Udemy.
But with AI tools now? It’s easier than ever. AI tools can be a great coding companion.
The power you get over your financial future is worth the effort.
Your next steps #
Stop reading. Start doing.
This week:
- Install Python
- Download your transaction history from your bank (CSV file)
- Run a basic Pandas script to categorize your spending
This month: 4. Build a simple Monte Carlo simulation for retirement 5. Test your current portfolio allocation 6. Set up automated data pulls from your accounts
This quarter: 7. Create your first dashboard 8. Run back-tests on different withdrawal strategies 9. Optimize your portfolio based on actual data
The difference between people who talk about FI and people who achieve it? The ones who achieve it measure everything, test everything, and optimize relentlessly.
Python is how you do that without spending 40 hours a week on spreadsheets. Don’t get me wrong. Spreadsheets still have their place, but not as a standalone tool. They are far more powerful when combined with Python.
Start coding. Start automating. Accelerate your path to freedom.
Your future self will thank you.
Got questions about getting started with Python for finance? Drop them in the comments. I’ve been doing this for years and I’m happy to help.