Python in Excel: How to understand the random walk with Copilot

As an Excel analyst, you’ve probably had moments where your data seemed to show clear trends,profits rising, sales dropping, or costs climbing steadily. But sometimes, these apparent patterns might actually be random,tricking you into thinking there’s more control and predictability than there really is.

That’s the essence of the “random walk.” It illustrates how purely random processes can look structured, meaningful, and even predictable. Why should you care? Because knowing the difference between true signals and random noise helps you make better, smarter decisions in your analysis, whether it’s forecasting sales, tracking performance, or spotting real trends.

Fortunately, exploring random walks doesn’t require complicated setups or fancy software,just Excel, Python, and Copilot. Open a blank Excel worksheet, launch Copilot, start Advanced Analysis, and you’re ready to follow along.

Simulating a random walk with coin flips

Let’s take a moment to explore randomness using a simple example. Imagine flipping a coin 100 times. Each heads moves you one step forward (+1), each tails one step back (-1). After every flip, you track your position to see how far you’ve drifted from the start.

“Make a Python script that simulates flipping a coin 100 times. Each flip moves you up (+1) or down (–1). Keep a running total so we can see how far we drift. Put the results in a DataFrame with columns Step and Position, and make a simple line plot of the position over time. Explain in comments what’s happening in plain English.”

Copilot responds by creating a neatly organized DataFrame, listing each step alongside your position. It also produces a line chart visualizing your journey through these random flips:

When you examine this chart, you’ll notice the position line moves unpredictably, sometimes drifting upward, other times downward. Each flip was random, yet it’s natural to try to spot trends or meaningful patterns here. Randomness can trick us into seeing patterns that aren’t really there. By practicing simulations like this, you’ll develop intuition about what’s meaningful and what’s simply noise in real-world datasets.

Simulating stock prices with a random walk

Instead of flipping a coin, let’s start our simulation this time with a stock price of 100, then add a small, random increase or decrease each trading day for one year (252 days). This subtle daily randomness resembles the fluctuations you’d see in actual stock data. Here’s exactly what you can ask Copilot to do:

“Now change the random walk so it looks like a stock price. Start at 100 and add a little random up or down each day for 1 year (252 days). Plot the stock price. Add comments explaining why this looks kind of like a real stock price (sometimes up, sometimes down, but trending).”

Notice how, like the coin toss simulation, the stock price doesn’t follow a straight line. Instead, it fluctuates over time, occasionally dipping downward, sometimes climbing higher again. Even though each individual day’s change is random, the result looks strikingly similar to actual stock market behavior.

But here’s the thing: One single forecast can only show one possible future. In real life, many outcomes are possible.

Think of the typical forecasts you see in business like projected revenues, sales goals, or stock prices. They’re often presented as one neat line or a single estimate. But relying solely on one forecast can give a false sense of certainty. It’s easy to overlook that the real world rarely matches that neatly drawn line.

Instead, it’s crucial to embrace uncertainty. By visualizing many different scenarios, you can better understand the full range of possible outcomes. This helps you plan for the unexpected, identify potential risks, and avoid overconfidence in a single prediction.

Creating and visualizing multiple random walk simulations

To truly appreciate this idea, let’s extend our simulation and visualize multiple scenarios at once.

“Simulate 100 random stock price paths instead of just one. Plot them all on the same chart so it looks messy. Add comments explaining why one single forecast isn’t enough — in real life we could end up with any of these.”

At first glance, this chart looks chaotic, and that’s precisely the point. Each line represents one possible scenario that might unfold. Some scenarios end positively, some negatively, and others hover in the middle.

Here’s the key insight for you as an Excel analyst: Relying on a single forecast can be risky, because real-life outcomes can vary widely. When you visualize multiple possible futures, you’re reminded of this uncertainty. This helps you make decisions more cautiously and realistically, preparing you for multiple possibilities instead of placing too much confidence in one narrow prediction.

Visualizing a the “cone of uncertainty”

The previous chart showed us 100 possible outcomes. But realistically, presenting hundreds of scenarios isn’t always practical or helpful. Instead, let’s summarize all these possibilities by showing a clear range of likely outcomes.

Specifically, we’ll calculate the 5th, 50th (median), and 95th percentiles from these 100 scenarios for every day of our simulated year. This creates a visual “uncertainty cone,” clearly communicating not only what’s possible, but also what’s probable.

Here’s how you’d prompt Copilot:

“From the 100 random paths, calculate the 5th, 50th, and 95th percentiles for each day. Plot a shaded cone (5–95%) with the median line in the middle. Add comments explaining how this cone shows a range of likely futures, not just one line.”

Instead of placing too much confidence in a single prediction, this visualization helps you recognize the inherent uncertainty of forecasts. As an analyst, presenting data in this way helps decision-makers understand risk, plan more realistically, and prepare for multiple potential futures—not just the one we hope or expect to see.

Visualizing the distribution of final outcomes

The uncertainty cone showed a broad range of likely outcomes. But let’s focus specifically on the end of our forecast period. If we simulate 500 random stock price paths, where do prices usually end up after one year?

You can prompt Copilot with the following instruction:

“After simulating 500 paths, make a histogram of the final prices after 1 year. Mark the average, the low end (5%), and the high end (95%). Write a short caption in plain English: ‘Most of the time the stock ends up around X, but there’s a chance it could be much lower or higher.’”

This histogram summarizes clearly how our 500 simulations played out. The average final stock price lands around 114, shown by the solid vertical line. But notice the two dashed lines: at the lower end (5%), the stock price could drop to around 87, while at the higher end (95%), it could climb to around 146.

Conclusion

As explored in this post, randomness plays a larger role in your data than you might initially expect. A single forecast can feel clear and precise, but it rarely captures the true uncertainty of real-world outcomes. By visualizing multiple scenarios, using uncertainty cones, and examining distributions of potential outcomes, you communicate clearly about risk and uncertainty, helping decision-makers set more realistic expectations.

These simulations have limitations, however. Real-life data often includes specific trends, seasonal variations, or sudden shocks that our simplified random walk model doesn’t fully address. To extend your analysis further, consider incorporating historical patterns into your simulations or exploring more sophisticated forecasting techniques like Monte Carlo analysis.

Embracing uncertainty makes you a more effective analyst by improving your judgment, clarifying your communication, and better preparing you and your team for a variety of potential futures.

If you’d like to explore these concepts directly, download the complete Excel sample solution file below:

 

The post Python in Excel: How to understand the random walk with Copilot first appeared on Stringfest Analytics.