Supermarket Mishap: The Hidden Flaw in Your Trading Strategy
And How to Fix It With Normalization
A few weeks ago, on a Thursday just 45 minutes before the market opened—my wife called.
“We need groceries. There’s nothing to cook for the kids,” she said in the most casual tone, as if announcing a last-minute culinary crisis was the most normal thing in the world.
“You’re telling me this now?!” I screamed internally, while somehow muttering, “I won’t make it back in time for the market.”
“Well, I forgot we were out of everything. It’s just a few small things—you’ll be fine,” she said.
I hung up and bolted to the supermarket.
Forty-five minutes. I had a neat little list in WhatsApp and knew exactly where everything was—except for one absurd item: beet powder. Why on earth does she need beet powder?
I raced through the aisles, checking off items like a machine. The only thing left was that powder.
I went to the health section, which, naturally, was a jungle of powders with names like “Spirulina,” “Ashwagandha,” and “Moringa.” Amid the chaos, I spotted it: the last jar of beet powder. But just as I reached for it, I saw her—a woman with pink hair and a nose ring—heading straight for the same jar.
I swear she picked up speed when she saw me. I did the same, breaking into a half-walk, half-run, pretending to browse. But we both knew the truth: this was war.
I got there first. I snatched the jar and walked away without looking back. Did she really want it? No clue, and I wasn’t sticking around to find out.
I made it to the checkout line with 25 minutes to spare. I scanned the lanes like a radar system and found the perfect spot: Lane 3. Just one person ahead of me with a half-full cart.
“Jackpot!” I thought. I got in line, smug with my choice—until I saw the real horror.
This wasn’t just any cart. It was a produce cart. A mountain of apples, untagged bags of lettuce, exotic fruits without codes, and an unreasonable amount of parsley dangling off the sides.
The cashier, bless her patience, began manually entering each item: “Broccoli… 4060. Cucumber… wait, is this organic or regular? Which bin did you get this from, sir?”
The man had no idea. By now, I was too invested in this lane to leave — it would feel like defeat.
Then, disaster struck. The guy realized he’d forgotten something.
“Oh! I need cilantro!” he announced casually and walked off, leaving me stuck with a growing line behind me.
I glanced at the guy behind me, holding just a Coke and a bag of chips, his face full of despair.
When the cilantro man finally returned, the cashier rang him up: “That’ll be $54.46.” And then it happened. He pulled out… coins. Not a credit card, not even cash. Actual coins.
At this point, I gave up. The market had already opened. It was Thursday—my best day for shorting small caps—but my chance was gone.
So, why am I telling you this story? Because it’s the perfect analogy (I think) for a ticking time bomb in your trading strategy.
All of this happened because I didn’t consider all the variables when choosing my line.
You can’t just compare the lengths of different lines and automatically pick the shortest one.
It’s not just the length of the line that matters—it’s also the type of person standing in it and what’s in their cart.
The same principle applies to trading strategies: absolute numbers aren’t enough; you need to normalize your data.
Here’s Why Normalization Is Key
When backtesting a strategy, you might find a pattern that looks promising.
For example: “Short every gap over 30% with a 10% stop loss, and you’ll have a positive expected value.”
But who says this will work in the future?
If the market changes—say volatility increases like it did in 2020—a 30% gap might no longer be significant, and a 10% stop loss could lead to constant losses.
Another example: let’s say a stock typically starts dropping after trading a million shares.
If more money flows into the market, traders might be able to buy even more shares before the stock starts to fall, rendering that number irrelevant.
On top of that, comparing absolute volume between stocks ignores their price—and therefore the dollar amount traded. To make an apples-to-apples comparison, you need to use dollar volume.
A fixed percentage stop loss is another issue. It ignores the unique volatility of each stock, causing you to exit too quickly on volatile ones and too late on stable ones.
Without normalizing your data, it becomes much harder to predict the future, and your strategy is at risk of failing.
Just like picking the wrong checkout line, where you have to normalize all the variables by comparing them to those of the other lines.
How to Normalize Key Parameters for a More Reliable Strategy
Gap Thresholds (Minimum/Maximum):
For non-small caps: Normalize gap size by dividing it by the stock’s ADR% (average daily range). For example, a 5% gap on a low-volatility stock is equivalent to a 10% gap on a highly volatile stock. This allows for relative, rather than absolute, comparisons.
For small caps: Because gaps are often huge, the stock’s prior movement becomes negligible in comparison. Instead, divide the gap size by the average gap size of similar small caps over the past X days. This helps you understand how unusual the gap is compared to recent activity.
(Personal note: Gap thresholds are the one parameter I don’t normalize. I monitor them closely, as they can change quickly with market conditions.)
Stop Loss Adjusted for Volatility:
Use tools like ATR (average true range) or ADR to align your stop loss with the stock’s volatility.
Alternatively, use the distance to the last resistance level as a natural stop, which inherently accounts for the stock’s volatility.
Volume – Dollar Volume & Relative Comparison:
Start with dollar volume: Multiply the traded shares by the stock’s price to get a clearer picture than raw share volume.
Compare against peers: Measure the stock’s dollar volume relative to similar stocks over the past X days.
Example: If the average pre-market dollar volume for all small-cap gaps over the past 30 days is $10M, and your stock has $40M, that’s 4x the average. This could signal a unique edge worth exploring.
Final Thoughts
Normalizing parameters like gaps, stop loss, and volume is essential to building a strategy that withstands market changes. By relying on relative data instead of absolutes, you can adapt to shifting conditions and maximize your strategy’s survival chance.
Of course, not every parameter needs to be normalized.
Some metrics often provide valuable insights in their raw form. Each parameter requires careful thought and context-specific evaluation.
If you’re unsure whether to normalize a specific metric, feel free to ask me in the comments—I’m happy to help!