Day 69 of 100 Days Coding Challenge: Python

Today’s stop on the Stock Tracker Express was “Sector Classification,” and I decided to pull into that station before the Diversification Warning stop. Why? Because you can’t warn about lopsided allocations until you know where your money is hiding. Sector classification is like labeling your pantry—if you find 14 jars of peanut butter and no pasta, you might reconsider your meal plan.

In investing, this helps spot “unsystematic risk,” which is just the fancy way of saying, “If one company or industry tanks, you don’t want your whole portfolio going down with it.” Sometimes, yes, you might choose to overload in one sector—maybe tech is hot or utilities are shining—but the point is to know when you’re doing it. With the sector view in place, you can pair it with performance metrics, macroeconomic signals, or just your gut to make better decisions. My roadmap had diversification before classification, but order matters—this way, we start with a map before we set up roadblocks.

Today’s Motivation / Challenge

If you’ve ever played a board game without reading the rules first, you’ll understand why knowing your sector exposure before diversifying is important. Classification turns a pile of tickers into an organized story, letting you see not just what you own, but where it lives.

Purpose of the Code (Object)

This function takes your tracked tickers, peeks under the hood, and tells you what sector and industry they belong to. For ETFs and funds, it makes an educated guess—flagging them as multi-sector or fixed income when appropriate. The idea is to give you a quick visual of portfolio balance before you go hunting for weak spots.

AI Prompt

Creates the following functions: Fetch sector and industry, identify ETFs and label them as multi-sector or fixed income, and finally print a tidy classification table and sector summary.

Functions & Features

• Fetch sector and industry data for each tracked stock.
  
• Identify ETFs and label them as multi-sector or fixed income.
  
• Print a tidy classification table and sector summary.
  
• Optionally export results to CSV.
  

Requirements / Setup

• Python 3.10+
  

Install dependencies:

pip install yfinance pandas matplotlib

Minimal Code Sample

import yfinance as yf

def get_sector(symbol):

    info = yf.Ticker(symbol).info

    return info.get(“sector”), info.get(“industry”)

print(get_sector(“MSFT”))  # (‘Technology’, ‘Software—Infrastructure’)

Pulls sector and industry info for a ticker.

Stock Tracker

Notes / Lessons Learned

ETFs can be divas—some announce their sector proudly, others just say “multi-sector” or “bond fund” and walk away. Fixed income ETFs (bond funds) make perfect hedges for my tech-heavy holdings, so I labeled those separately. This classification step also reminded me how ambiguous data can be; without it, I might’ve assumed my ETFs were more focused—or more diversified—than they really are.

Optional Ideas for Expansion

• Show top three sector weights for ETFs when available.
  
• Include historical sector rotation charts.
  
• Add filters to display only certain sectors.

Day 68 of 100 Days Coding Challenge: Python

Yesterday’s three-hour duel with RSI left me a little cross-eyed, so I returned today with a reset brain and a mission: add a Stock Momentum Indicator. I’m not a chart whisperer, but this little gauge helps me decide which stocks deserve a spot on the keep list and which ones can politely see themselves out. Of course, no single indicator is a magic “buy/sell” button—RSI and moving averages still earn their keep, and a quick skim of headlines never hurts. If a name like TSLA suddenly lurches up or down, momentum usually lights up first, and that’s my cue to check the news and sanity-check my long-term plan. I’m investing for the marathon, not the sprint; momentum just helps me keep my shoelaces tied.

Today’s Motivation / Challenge

Prices wiggle. Trends matter. The challenge is turning those squiggles into a simple “up, down, or meh” signal you can read in one glance—something practical enough to use with your existing RSI/MA checks without turning your brain into soup.

Purpose of the Code (Object)

The Momentum Score looks at recent closing prices, fits a straight line through them, and tells you if the general direction is up, down, or flat. It also weighs how “clean” the trend is so wobbly lines don’t shout as loudly as steady climbs. You get a single number plus a label that’s easy to act on.

AI Prompt

“Make this function cleaner. Keep inputs/outputs simple, follow PEP 8, handle edge cases (short/NaN data), add concise docstrings, and avoid side effects. Return a small dict with last_close, ann_pct, r2, score, and direction. Prefer readable names over cleverness.”

Functions & Features

• Momentum Score: Up/Down/Flat with a strength-weighted score.
  
• RSI status: Quick read on overbought/oversold.
  
• Moving Averages: Short vs. long for context.
  
• Fetch recent closes: Pulls the last N days for each ticker.
  

Requirements / Setup

• Python 3.10+
  

Install:

pip install yfinance pandas numpy matplotlib

Minimal Code Sample

def momentum_score(closes, lookback=60):

    s = pd.Series(closes).dropna().astype(float).tail(lookback)

    if len(s) < 20:

        return {“direction”: “flat”, “score”: 0.0, “r2”: 0.0, “ann_pct”: 0.0, “last_close”: float(‘nan’)}

    y = s.values

    x = np.arange(len(y), dtype=float)

    slope, intercept = np.polyfit(x, y, 1)

    y_hat = intercept + slope * x

    ss_res = np.sum((y – y_hat) ** 2)

    ss_tot = np.sum((y – y.mean()) ** 2)

    r2 = 0.0 if ss_tot == 0 else max(0.0, 1.0 – ss_res / ss_tot)

    ann_pct = (slope / np.mean(y)) * 252.0 * 100.0

    score = ann_pct * r2

    direction = “up” if score > 1 else “down” if score < -1 else “flat”

    return {“direction”: direction, “score”: score, “r2”: r2, “ann_pct”: ann_pct, “last_close”: float(s.iloc[-1])}

Fits a straight line through recent prices, scales the slope to annualized %, weights by trend quality (R²), and labels the direction.

Stock Tracker

Notes / Lessons Learned

Yesterday taught me a simple truth: sometimes the best fix is a fresh chat and a tidier workspace. I kept stuffing more context into the same thread and the AI started juggling chainsaws. Uploading the project folder and the roadmap calmed everything down—clean inputs, clean outputs, faster help. People get overwhelmed by messy instructions; AI does too (just less visibly).

Under the hood, momentum is pleasantly un-mystical. Grab the last N closes (I like 60), draw a line, scale the slope to an annualized percentage, weight it by how straight that line is, and call it: up, down, or flat. It runs in seconds, which is delightful—just remember fast ≠ infallible. Spiky data, low volume, or surprise news can throw elbows, so pair this with RSI, moving averages, and a quick look at headlines before you make grown-up decisions.

Optional Ideas for Expansion

• Add a tiny sparkline plot with the regression line for each ticker.
  
• Auto-flag “momentum flips” (e.g., score crosses ±1) and show the last few related headlines.
  
• Let each symbol have its own lookback window so slow movers and high fliers get fair treatment.

Day 67 of 100 Days Coding Challenge: Python

Today’s experiment was all about giving my stock tracker a sixth sense—adding the Relative Strength Index (RSI), RSI Stock Indicator . Think of RSI as the mood ring of the trading world: if it reads over 70, your stock might be partying too hard and due for a hangover (price pullback). Under 30? That’s the sulking, underappreciated stock that might be ready to rally.

Now, RSI isn’t the crystal ball some traders wish it was, but it’s a handy companion—especially when paired with the moving average I added yesterday. It’s popular for a reason: it’s simple, visual, and just a little bit addictive. The fun part is seeing it tell a different story than the price chart itself, which feels a bit like watching a behind-the-scenes documentary instead of just the movie.

Today’s Motivation / Challenge

I wanted to give my tracker more “personality” by adding an indicator that can hint when prices might reverse. Also, part of me just wanted to prove to myself that I could wrestle with the math, the formatting, and Yahoo Finance’s occasional mood swings—and win.

Purpose of the Code (Object)

This code calculates the RSI for each stock I’m tracking, then tells me whether each one might be overbought, oversold, or just sitting in the middle lane. It takes the raw price data, does a little statistical gymnastics, and spits out a number that’s easy to read, even if you’ve never taken a finance class.

AI Prompt

Add an RSI tracker to my existing Code

Functions & Features

• Calculate the 14-day RSI for each tracked stock.
  
• Flag stocks as overbought (≥70), oversold (≤30), or neutral.
  
• Handle messy or missing data without breaking the whole program.
  

Requirements / Setup

• Python 3.x
  
• pip install yfinance pandas numpy matplotlib
  

Minimal Code Sample

def _compute_rsi(series, period=14):

    delta = series.diff()

    gain = delta.clip(lower=0)

    loss = -delta.clip(upper=0)

    avg_gain = gain.ewm(alpha=1/period, adjust=False).mean()

    avg_loss = loss.ewm(alpha=1/period, adjust=False).mean()

    rs = avg_gain / avg_loss

    return 100 – (100 / (1 + rs))  # RSI formula

Calculates the RSI from closing prices using Wilder’s smoothing.

Stock Tracker

Notes / Lessons Learned

If there’s one thing RSI taught me, it’s that the market isn’t the only thing that can get moody—code can too. I ran into a storm of “err arg must be…” errors when pandas didn’t like the format of the data coming from Yahoo Finance. It wasn’t just the low-volume ETFs either; even TSLA decided to throw a tantrum. I tried to fix it, and in the process, managed to break other parts of my program.

After a frustrating 150 minutes of debugging, I threw in the towel and started fresh. One hour later—miracle!—it worked. The fixes? Making value conversions safer, checking Series length before indexing, and printing clear error messages. Then came the “not 1-D” ambush—some tickers return MultiIndex columns, which threw my code into existential crisis. The solution was to always extract a clean 1-D float series before running calculations. Now it runs without cryptic complaints, and I finally feel like I’ve tamed it… for now.

Optional Ideas for Expansion

• Plot RSI alongside the price chart for a quick visual check.
  
• Let the user choose the RSI period length (not just 14 days).
  
• Add alerts for when a stock crosses the overbought/oversold thresholds.

Day 66 of 100 Days Coding Challenge: Python

I’ve added a new trick to my stock tracker: the moving average. Think of it as the “Zen master” of stock analysis—calm, collected, and uninterested in daily drama. I set it up so I can see both the short-term (7-day) moving average, which catches the mood swings, and the long-term (30-day) moving average, which reveals the bigger picture. Short-term trends tell you if a stock is suddenly sprinting, while long-term averages show if it’s been strolling uphill—or slowly rolling downhill—over time.

In finance, we do this on all sorts of timelines: monthly, quarterly, yearly. I’m not a day trader, so this isn’t about chasing every market twitch. Instead, this helps me filter out noise and focus on the broader trend—much like ignoring the gossip at the office and paying attention to the quarterly earnings report. In the future, I’d like to use this to spot “support” and “resistance” levels—that is, the invisible floor and ceiling where prices tend to bounce.

Today’s Motivation / Challenge

Daily stock prices are a roller coaster. Fun if you like screaming every 3 seconds, exhausting if you actually want to get somewhere. Moving averages smooth out the ride, letting you see if the track is going up, down, or looping sideways.

Purpose of the Code (Object)

This feature takes the recent stock prices for each symbol, calculates the 7-day and 30-day averages, and compares them to the most recent closing price. The result: a quick glance at short-term momentum versus long-term direction, without getting blinded by daily price noise.

AI Prompt

“Add a function to track the 7-day and 30-day moving averages of listed stocks, comparing each to the last closing price, and display the results clearly.”

Functions & Features

• Calculates 7-day and 30-day simple moving averages (SMA).
  
• Compares latest price to SMA values.
  
• Displays trends for quick decision-making.
  

Requirements / Setup

pip install yfinance pandas

Minimal Code Sample

closes = hist[‘Close’]

last = float(closes.iloc[-1])  # Get most recent closing price

sma7 = closes.rolling(window=7).mean().iloc[-1]

sma30 = closes.rolling(window=30).mean().iloc[-1]

Calculates the last closing price and both moving averages.

Stock Tracker

Notes / Lessons Learned

The first time I ran this, Pandas wagged its finger at me with a “FutureWarning” about float(series). Apparently, in Pandas 2.2+, turning a whole Series into a single float is too vague. Imagine handing a librarian a 300-page book and saying, “Turn this into a number.” Which page, line, or word? Pandas now demands you be explicit:

• .iloc[0] → picks the first element.
  
• .item() → converts it directly to a Python number.
  

Once I cleaned up the code and restarted, it ran like a charm. And honestly, seeing my own program spit out these averages feels much better than refreshing some finance site.

Optional Ideas for Expansion

• Add a chart to visualize SMA crossovers.
  
• Let the user set custom time windows for moving averages.
  
• Include alerts when the short-term average crosses the long-term average.

Day 65 of the 100 Days Coding Challenge: Python

Today’s project was all about giving my stock tracker a voice—or at least the Python equivalent of tapping me on the shoulder. I added a suite of functions to build a simple price alert system. Now, I can set an alert for any stock symbol, pick a target price (whether I expect it to go soaring or take a nosedive), and check those alerts whenever I feel like it. The alerts live in alerts.json, which means they survive beyond the current run of the program, unlike my patience for earnings season.

Given the market’s recent mood swings—some days it’s caffeine-powered optimism, other days it’s “time to move into a cabin in the woods”—this feature feels like a smart way to keep tabs on a few key stocks. I also tossed in the ability to list all active alerts and remove them once they’re no longer relevant.

I’m not a day trader, so I won’t be glued to this alert system like it’s the final episode of a drama series. But I’m already thinking about how this little foundation could be built into something bigger—perhaps even a full-blown, automated “call me when it matters” system.

Today’s Motivation / Challenge

Sometimes you don’t want to watch the market all day—you just want it to tap you politely when something interesting happens. This project scratches that itch. It’s like hiring a butler who only bothers you when your investments hit the numbers you care about.

Purpose of the Code (Object)

The code lets you set stock price alerts for any symbol, save them, and check them against live prices. You can remove old alerts, list current ones, and trust they’ll be there the next time you run the program. It’s a neat way to track specific opportunities without manually refreshing a stock chart every five minutes.

AI Prompt

Please add a Price Alert system. Let the user set a target price and alert if the current price crosses it. The system needs the following functions: set a price alert, check price alerts now, list alerts, and remove an alert.

Functions & Features

• Set a Price Alert – Choose a stock, pick a price, and decide if you want to be notified when it rises above or falls below that level.
  
• Check Alerts – Compare live prices against saved alerts.
  
• List Alerts – See what you’re tracking at a glance.
  
• Remove Alerts – Delete alerts you no longer need.
  

Requirements / Setup

pip install yfinance

Python 3.8+ recommended.

Minimal Code Sample

def check_price_alerts():

    alerts = load_alerts()

    for alert in alerts:

        price = get_price(alert[“symbol”])

        if (alert[“type”] == “above” and price > alert[“target”]) or \

           (alert[“type”] == “below” and price < alert[“target”]):

            print(f”ALERT: {alert[‘symbol’]} hit {price:.2f}”)

Checks each saved alert and prints a message if the condition is met.

Stock Tracker

Notes / Lessons Learned

Yes, I was a bit tired today, so when I created my shiny new alerts.json file, I somehow misspelled it. Naturally, the program refused to recognize it. That was my first mistake. The second? Entering an alert incorrectly—forgetting to choose “above” or “below” before typing in the target price. This left the file corrupted and the program more confused than I was during my first economics class.

The fix was simple but satisfying: I added a load_alerts() function that returns [] if the file is empty and quietly backs up a corrupt JSON before starting fresh. I also made save_alerts() write to a temporary file first, then replace the original in one move. No more half-written files, no more unexpected crashes.

The real lesson? Creating new errors isn’t a bad thing—it’s a great way to see where the cracks are before the whole thing falls apart. It’s a bit like stress-testing your code, except the stress is mostly on you.

Optional Ideas for Expansion

• Add email or SMS notifications when an alert triggers.
  
• Integrate with a dashboard to show alert history.
  
• Let alerts expire after a set number of days.