Day 64 of 100 Days Coding Challenge: Python
For the last few days, I’ve been steadily upgrading my stock tracker with little nuggets of market intelligence—kind of like giving a hamster a GPS and a Fitbit. Today’s upgrade: a Volatility Checker, which ranks your stocks by how wildly they’ve been swinging around lately.
One detail worth noting is the setting auto_adjust=False. Normally, when you download stock prices, finance APIs like to quietly “fix” the numbers for dividends and stock splits. Nice in theory, but I wanted raw, unpolished numbers—like getting your coffee without sugar or cream—so that my results match every other function in my program and avoid those overly polite warning messages.
I also baked in the formula: annualized volatility = daily_std * sqrt(252). Here, daily_std is the standard deviation of daily returns—basically, how jittery a stock has been day-to-day. Multiplying by the square root of 252 assumes there are about 252 trading days in a year. It’s like saying, “If this stock keeps bouncing around at the same pace all year, here’s how dramatic it will look.”
But, markets don’t always stick to 252 trading days. So I went a step further—adding a flag that lets the program annualize based on the actual number of trading days in my dataset. It’s like tailoring a suit instead of buying it off the rack—more accurate and much better fitting.
Today’s Motivation / Challenge
Some investors watch price charts like hawks. Others, like me, enjoy knowing which stocks are acting like calm lakes and which ones are more like caffeinated squirrels. By ranking volatility, you can spot potential opportunities—or avoid heartburn—before making your next move.
Purpose of the Code (Object)
This function checks recent price swings for your tracked stocks, calculates how volatile they’ve been, and ranks them from wildest to tamest. It works for short windows like 10 days or longer periods like 90, depending on how much market drama you want to see. The output includes both daily volatility and an annualized version.
AI Prompt
Please add the following function to the script. “Volatility Checker: Calculate and rank stocks by recent price volatility.”
Functions & Features
- Tracks and ranks stocks by recent volatility
- Calculates both daily and annualized volatility
- Works with a user-selected date range
- Optionally annualizes based on actual trading days in your dataset
Requirements / Setup
pip install yfinance
pip install matplotlib
pip install pandas
Python 3.9+ recommended.
Minimal Code Sample
returns = closes.pct_change().dropna()
daily_vol = returns.std()
annual_vol = daily_vol * math.sqrt(len(closes)) # based on actual days
Calculates daily volatility, then annualizes it based on your dataset length.
Notes / Lessons Learned
Finance isn’t new to me—I’ve studied it and used it in real life—but coding it is a different story. You can know the theory (like “252 trading days in a year”) without realizing the programming implications until you see the raw code. AI acts like the friend who not only reminds you of the theory but also shows you how to plug it into your function, keeping you from stepping into common traps.
By now—day 64—I’m convinced that AI’s real power isn’t just spitting out answers; it’s stitching together knowledge from different worlds and handing you a well-fitted solution. Still, the joy comes from building something from scratch. With AI as your co-pilot, you’re suddenly flying first class.
Optional Ideas for Expansion
- Allow filtering for “Top 5 most volatile stocks” only
- Save volatility rankings to a CSV for record-keeping
- Add color-coded output for quick scanning (green for calm, red for wild)