Category: 100 Days Challenges

Day 63 of 100 Days Coding Challenge: Python

On a quiet Saturday morning, armed with coffee and optimism, I dove into today’s coding task: building a function that calculates what my portfolio would be worth today if I had invested a certain amount X days ago. In my Excel days, I used to do this sort of thing manually—lots of formulas, lots of scrolling, and the occasional formatting meltdown. But coding it? That felt far more satisfying, like turning a laborious chore into a sleek push-button tool.

In the real investing world, this is a performance measurement exercise: “How much did this lump sum grow or shrink over that period?” It’s also a sobering way to check whether your portfolio is keeping pace with the S&P 500 or whatever benchmark you care about. One moment you’re patting yourself on the back, the next you’re wondering if you should have just bought an index fund and taken the afternoon off.

It’s also fantastic for “what if” scenarios: missed opportunities, potential sector shifts, or just feeding your curiosity. “If I’d put $10,000 into Tesla six months ago, it’d be worth $14,000 today—a 40% gain. How does that compare to the Nasdaq?” You can test those ideas instantly. Today’s code felt like a solid investment itself—an investment in my ability to measure investments.

Today’s Motivation / Challenge

Being able to quickly test “what if” scenarios helps me make more informed choices. Whether it’s checking how a holding performed, comparing it to a benchmark, or seeing how badly I missed the boat on a hot stock, this feature brings insight without the spreadsheet headaches.

Purpose of the Code (Object)

The function takes a starting investment amount and a time frame, then calculates how much that investment—split equally among all tracked stocks—would be worth today. It uses historical price data to find the purchase price and today’s value, so you can see gains, losses, and percentage changes without doing the math yourself.

AI Prompt: 

Create a Python function that simulates the value of an equal-weighted investment across multiple stocks from X days ago to today, using yfinance historical data.

Functions & Features

• Calculate equal-weight portfolio growth over a set number of days
  
• Show per-stock performance in dollars and percentage terms
  
• Handle missing or invalid price data gracefully
  
• Display total portfolio gain/loss and percentage change
  

Requirements / Setup

pip install yfinance pandas matplotlib

Python 3.8+ recommended.

Minimal Code Sample

idx = closes.index.searchsorted(pd.Timestamp(target_date))

first_close = closes.iloc[idx]

last_close = closes.iloc[-1]

Selects the first available close on/after the target date and the last available close—no ambiguous Series logic.

Stock Tracker

Notes / Lessons Learned

When I first ran the code, it hit me with two issues. The first was a friendly FutureWarning:

FutureWarning: YF.download() has changed argument auto_adjust default to True

This is Python’s polite way of saying, “Hey, our defaults changed. You might want to set auto_adjust yourself so you know what you’re getting.” Easy fix: add auto_adjust=False (or True if you prefer adjusted prices) to every yf.download(…) call.

The second was less polite:

The truth value of a Series is ambiguous.

I had code like:

if hist[‘Close’][hist.index >= target_date]:

That hist[‘Close’][…] returns a Pandas Series—a list of prices. Python doesn’t know whether I mean “Is this empty?” or “Are all of these True?” or “Is at least one True?” So it refuses to guess. The fix was to pick a single value with .iloc[0] or check .empty.

My final mistake? Forgetting to restart my Python session after fixing the code. I kept seeing the same error because Python was still running the old function from memory. Lesson learned: restart your environment after major changes, or you’ll be debugging ghosts.

Honestly, I’m glad I chased this bug down—it’s one of those errors I won’t forget, and it’s a reminder that both my code and my process are worth investing in.

Optional Ideas for Expansion

• Add weighted allocation for “X days ago” simulations
  
• Compare portfolio performance to a benchmark like the S&P 500
  
• Save simulation results to a CSV for record-keeping

Day 62 of 100 Days Coding Challenge: Python

Yesterday, I taught my script how to look into the past—specifically, the last 30 days of each tracked stock’s closing prices. Today, we’re talking emotions—well, stock emotions. I added a function in Python to show each stock’s daily percentage change, color-coded like a moody person: green when it’s up, red when it’s down, and default when it’s flat.

Now, to be clear, I’m not a day trader glued to five monitors with live tickers whizzing by. I have a full-time job, thank you very much, and no one’s got time for that kind of circus. I check in weekly, adjust when needed, and focus on consistency over drama. Still, this function gives me a quick visual snapshot of how things are moving—and more importantly, how they might move. It’s like giving your portfolio a morning coffee and asking, “How are we feeling today?”

Patterns are the name of the game. If I can start spotting them, maybe one day I’ll teach this program to make some educated guesses—and eventually, add a bit of risk management to keep me from playing financial roulette. The next few days are all about adding the kind of analytical firepower that helps with smarter decisions down the road. And honestly? I’m kind of pumped.

Today’s Motivation / Challenge

You know that moment when you look at your bank account and squint, wondering, “What happened yesterday?” That’s what this feature is for. It’s like a digital mood chart for your stocks—without the therapy bills. Adding daily change percentages, especially in color, helps me scan for drama or calm in seconds. It’s a tiny step toward building a truly useful tool for hands-off investors like me, who want the intel without the stress.

Purpose of the Code (Object)

This code adds a quick visual check-in for each stock in my portfolio. It tells me whether a stock’s price went up or down today—and by how much—without needing a spreadsheet or third-party app. It’s lightweight, readable, and tailored to my investment rhythm.

AI Prompt (used to create today’s function):

“Please add the following function to the script.
Daily Change (%): Show today’s % change for each stock with green/red highlight.“

Functions & Features

• Calculates daily percent change for each stock.
  
• Highlights gains in green and losses in red.
  
• Leaves unchanged stocks in default color.
  
• Makes my console look like it actually cares.
  

Requirements / Setup

pip install yfinance

Minimal Code Sample

if change_percent > 0:

    color = “\033[92m”  # Green

elif change_percent < 0:

    color = “\033[91m”  # Red

else:

    color = “\033[0m”   # Reset

This assigns a text color based on the day’s percent change in stock price.

Stock Tracker

Notes / Lessons Learned

I’m starting to feel like a feature-adding machine—well-oiled, slightly caffeinated, and surprisingly organized. Thanks to the roadmap I drafted early on, it’s easier to stack new functions like LEGO bricks (minus the foot pain).

Today, I learned how to add color using ANSI escape codes. Think of them as secret signals you whisper to your terminal to make it look fancier. So now, green means the stock had a good day, red means it might need a hug, and the reset command keeps the rest of the terminal from turning into Christmas.

What I love about this feature? It’s subtle but powerful. Visual clarity goes a long way when you don’t have time to deep dive. And hey—who knew the terminal had a flair for drama?

Optional Ideas for Expansion

• Add arrows (↑/↓) next to the percent for more flair.
  
• Let users set custom alert colors (purple for panic? Why not).
  
• Create a summary line showing how many stocks gained vs. lost today.

Day 61 of 100 Days Coding Challenge: Python

The last five days were all about laying the foundation—think of it as assembling IKEA furniture, but with Python and fewer Allen wrenches. I got the stock tracker’s bones in place, installing essentials like the yfinance library. Now, it’s time to dress it up and make it do something more alive—like track real stock movement over time.

I’m finally stepping into the exciting part: creating a portfolio visualization that doesn’t require squinting at 14 browser tabs.

We’re starting simple. One historical price chart per stock. No more, no less. Why? Because trying to cram five charts into one screen is like watching five soap operas at once—dramatic, confusing, and probably bad for your blood pressure.

Back in university, my finance professors swore by stock observation like it was a sacred rite. Watch the markets, they said. Understand the impact of events—unemployment numbers, interest rate announcements, or when Jerome Powell so much as blinks. These things shake the market, and now, thanks to a bit of Python magic, I can watch those ripples without hopping from one website to another.

Today’s Motivation / Challenge

Have you ever tried checking your stocks manually, one by one? It’s like chasing toddlers in different directions. Today’s goal: automate that madness. With a single function, I want to see the historical price trends of each stock in my portfolio—visually. Because numbers are fine, but a chart? A chart speaks volumes.

Purpose of the Code (Object)

This little script fetches and plots the past 30 days of closing prices for each stock in your portfolio. It doesn’t try to impress anyone with fancy calculations. It just shows you what your stock has been up to, in a nice, simple line graph—so you can stop opening 27 browser tabs.

AI Prompt

Add the function to plot past 30-day price history for a single stock

Functions & Features

• Plots past 30-day price history for a single stock
  
• Automatically fetches historical data from Yahoo Finance.
  
• Easy to reuse for each stock in your portfolio
  

Requirements / Setup

pip install yfinance matplotlib

Minimal Code Sample

import yfinance as yf

import matplotlib.pyplot as plt

def plot_stock_history(ticker):

    stock = yf.Ticker(ticker)

    hist = stock.history(period=”30d”)

    hist[‘Close’].plot(title=f”{ticker} – Last 30 Days”)

    plt.xlabel(“Date”)

    plt.ylabel(“Price ($)”)

    plt.grid(True)

    plt.show()

→ Fetches and plots 30-day closing price for a given stock symbol.

Stock Tracker

Notes / Lessons Learned

To get this feature working, I first installed yfinance and matplotlib. Easy enough—until I noticed the pip version nagging me to update (again). I was on 25.1.1, and suddenly it wants 25.2. It’s like that one friend who insists on “just one more update” before going out.

This is the reality of programming: libraries get updated more often than your favorite streaming shows, and if you’re not careful, your working code might just throw a tantrum the next day. I used to roll my eyes when game servers went down for “maintenance.” Now I know—those devs were heroes.

Also, pro tip: stock movements don’t just come from earnings reports. Tariff announcements? Those hit hard. I saw a few of my stocks tumble right after one went into effect. Should’ve bought the dip… but hey, now I can at least see it happening, in style.

Optional Ideas for Expansion

• Add a dropdown menu or an input box to select stock instead of typing it
  
• Color-code gains vs losses on the chart
  
• Combine charts into one interactive dashboard (when you’re feeling brave)

Day 60 of 100 Days Coding Challenge: Python

When it comes to investing, I tend to lean toward the systematic approach. I like a plan. Not a “buy high, panic low” kind of plan, but a “how can I make $100 go as far as possible without needing a spreadsheet and a prayer” plan. Many stocks—especially ETFs—allow fractional share purchases, which is a blessing for anyone whose investing budget is less Wall Street and more wallet lint. That’s the beauty of modern investing: even if you don’t have a mountain of capital, you can still climb the hill.

So today I asked myself, what if my program could simulate that for me? What if I could say, “Here’s my $100,” and the program could allocate it automatically? Then I thought: what if I want to invest 25% in TSLA and only 5% in that random biotech company I’m not sure I trust? That’s where the idea of weighted allocation came in. A little money invested consistently can become something big over time—and the code now knows how to treat your dollars like they matter. Because they do.

Today’s Motivation / Challenge

You’ve probably heard some version of the advice “invest consistently, even with small amounts.” But how do you split that $100 in a smart, intentional way? Today’s challenge was about helping the program act like a friendly robo-advisor. Instead of dividing your money equally, it now lets you decide how much each stock gets. More TSLA, less risk. Or more risk, less cash. Either way, it’s your call—and the math is on your side.

Purpose of the Code (Object)

This new feature lets you simulate how much stock you could buy with a specific amount of money based on custom weight percentages. You choose the amount to invest, set the allocation weights for each stock, and the script calculates how many shares you could get, what it would cost, and how much remains. It’s budgeting, but for your imaginary portfolio.

AI Prompt: 

Create a Python function that takes a total investment amount and a custom percentage for each stock, then calculates how many shares can be purchased and the cost per stock using yfinance.

Functions & Features

• Add or remove stock symbols from your portfolio
  
• View real-time prices of all tracked stocks
  
• Simulate investment allocation based on equal weights
  
• Simulate investment allocation based on custom weight percentages
  
• Check the price of a specific stock
  
• Exit the program (option 99)
  

Requirements / Setup

pip install yfinance

Works with Python 3.8+. Use a virtual environment for clean setup.

Minimal Code Sample

def simulate_weighted_allocation():

    total = float(input(“How much to invest? $”))

    for symbol in stocks:

        weight = float(input(f”{symbol} weight (%): “))

        allocated = (weight / 100) * total

        shares = allocated / get_price(symbol)

        print(f”{symbol}: {shares:.4f} shares for ${allocated:.2f}”)

Takes your total investment and simulates purchasing based on custom weights.

Stock Tracker App

Notes / Lessons Learned

Initially, my simulation just split the money evenly—simple and safe. With 13 stocks in my portfolio, that reminded me of those index funds that allocate 1% to everything, like some kind of financial democracy. And while that’s a solid strategy (Vanguard would approve), I wanted control. I wanted to give TSLA a bigger slice of my $100 pie. So I added a custom weight input. Now the script lets me play mini portfolio manager.

One catch: if your percentages don’t add up to exactly 100, the program will throw a gentle tantrum. So you do have to plan ahead and know what you’re allocating. Otherwise, it’s like hosting a potluck and forgetting who brings dessert—awkward and disappointing. Still, once it worked, I thought: This is cool. It’s like mock investing with real math—and zero risk.

Optional Ideas for Expansion

• Save your weight presets for recurring investment styles
  
• Add error handling to automatically rebalance weights to 100%
  
• Display a pie chart of your allocation with matplotlib
  

This marks the point where the stock tracker grows from a utility to a personal investing playground. And we’re only 60 days in.

Day 59 of 100 Days Coding Challenge: Python

Yesterday, I caught myself on the edge of a rookie mistake: copying the same chunk of “fetch price” code into every place that needed it. It was like déjà vu with worse indentation. So I took a breath, cleaned it up, and extracted that logic into a shiny, single-purpose function: get_price(). Now, anytime I need the current price of a stock, I just call one clean little line. And if I ever want to swap out Yahoo Finance for Alpha Vantage—or carrier pigeons, who knows—I only have to update it in one place.

Unlike my previous project (the slightly chaotic book tracker that looked like it was written in a storm), this time I’ve actually thought about structure. I outlined a rough roadmap and started slicing features into small, logical pieces. Turns out, coding for your future self is a real kindness. It’s already making the script easier to manage, and I haven’t even spilled coffee on it yet.

I also added an extra feature that turned out to be more useful than expected: the ability to check the current price of a single stock on demand. It’s perfect for those moments when I randomly wonder, “How’s TSLA doing today?” or “Is MSFT still climbing?” Now I don’t have to scroll through my entire portfolio—I just type, fetch, and boom. Market curiosity satisfied.

Today’s Motivation / Challenge

 This project is starting to feel like a real tool, not just a list of stock symbols collecting digital dust. But with functionality growing, it was time to tidy up the code before it turned into a spaghetti bowl. By modularizing the price-fetching logic, I gave myself room to grow—because adding new features is so much easier when your code isn’t held together with duct tape and denial.

Purpose of the Code (Object)
 

The script now includes a reusable function, get_price(), that handles all price-fetching tasks. It simplifies the process of displaying current prices and opens the door to adding new features without clutter. I also added a quick price-check tool for any stock—not just ones in your portfolio.

AI Prompt: 

Refactor price-fetching logic into a reusable Python function using yfinance. Add an option to check the real-time price of any single stock.

Functions & Features

• Add or remove stock symbols
  
• View your portfolio with current prices
  
• Fetch real-time price for any single stock
  
• Show stock symbol list
  
• Exit the program (option 99, always last)
  

Requirements / Setup

pip install yfinance

Python 3.8+ recommended. Use a virtual environment if possible for clean installs.

Minimal Code Sample

python

CopyEdit

def get_price(symbol):

    ticker = yf.Ticker(symbol)

    return ticker.info.get(“regularMarketPrice”)

A reusable function to get the latest market price for a stock symbol.

Stock Tracker

Notes / Lessons Learned
 

The key to keeping a growing script from turning into chaos? Organization. Even with AI in your corner, you still need to think ahead. During my book tracker project, I just tacked on features as they came to mind—resulting in a menu that looked like a word jumble and an exit function mysteriously wedged in the middle of everything.

This time, it’s different. Each function has a purpose, and I’m building with intention. Adding the “check a specific stock” feature was fast and satisfying. I tested it with TSLA and MSFT—worked like a charm. It’s amazing what a little structure (and a lot less clutter) can do.

Optional Ideas for Expansion

• Add error handling for invalid or misspelled stock symbols
  
• Include the timestamp of last price update
  
• Build a search history so you can revisit recently checked stocks
  

This project is starting to feel less like a toy and more like a tool. Clean code, clear menus, and real data—what’s not to love?

Day 58 of 100 Days Coding Challenge: Python

Today, I finally unlocked the magic of seeing live stock prices for the companies I’ve been tracking. I activated the virtual environment I had so carefully crafted for this project—like slipping into a freshly pressed lab coat—and copied my previous file to create the next version. This has become my ritual. I don’t trust myself enough to overwrite yesterday’s work. I’ve learned the hard way: one misstep, and suddenly you’re debugging into the void at midnight.

But this time, everything felt smoother. More methodical. Like my stock tracker finally got itself a LinkedIn profile and a morning routine. I added the new function and, for the first time, watched real prices pop up for the stocks I entered. It was like watching your kid take their first steps… except your kid is Apple and it’s walking straight into your fantasy portfolio.

Today’s Motivation / Challenge

Sure, it’s fun to say you’re tracking stocks—but it’s even better to see what they’re actually worth. Today’s goal was to bring those tickers to life with live price data. It’s like turning a guest list into a party: you know who’s there, but now you see what they’re wearing. A clean interface that pulls prices makes the tracker feel real—and a lot more useful than a plain list of letters.

Purpose of the Code (Object)

This update adds a function that displays all currently tracked stocks alongside their latest prices pulled from Yahoo Finance. It keeps things simple and readable, so users can check their “portfolio” at a glance without needing to log into a brokerage or scroll through a financial website.

AI Prompt:

Add a function to a Python script that displays all stock symbols in a list along with their current market prices using the yfinance library.

Functions & Features

• Add stock symbols
  
• Remove stock symbols
  
• Show tracked symbols
  
• View current stock prices
  
• Exit the program (option 99, always last)
  

Requirements / Setup

pip install yfinance

You’ll also need Python 3.8+ and a virtual environment (optional but recommended).

Minimal Code Sample

import yfinance as yf

def view_portfolio():

    for symbol in load_stocks():

        price = yf.Ticker(symbol).info.get(‘regularMarketPrice’)

        print(f”{symbol}: ${price:.2f}” if price else f”{symbol}: Price unavailable”)

Fetches and displays each stock’s latest price using Yahoo Finance.

Stock Tracker

Notes / Lessons Learned

For this feature, I added the yfinance library and made sure to import it up top with the rest of the code crew. I also handled the situation where your portfolio might be empty—no one likes being told they have nothing, but it’s better than a crash. Thankfully, all the stocks I added yesterday were valid, so prices rolled in smoothly like a stock market ticker parade.

The function pulls from regularMarketPrice, which seems to be the most consistent for daily price checks. There were no errors, no false alarms, and no imaginary companies to spoil the moment. Just sweet, sweet confirmation that this tracker is starting to walk, maybe even jog.

Optional Ideas for Expansion

• Show daily percentage change or price movement alongside current price
  
• Highlight gains and losses in green/red
  
• Let users refresh prices without restarting the script
  

Day 58 marks the moment this project stopped being theoretical. It’s starting to feel real—and I’m here for it.