To new traders, the stock market can seem like utter chaos. They’re not wrong, really. The stock market is often irrational and extremely counterintuitive. 2020 has certainly proven that!

But when you get down to it, there are certain patterns that repeat over and over…

For smart traders, understanding how these patterns play out can lead to ‘aha’ moments that can lead to greater trading consistency.

The keys to understanding stock market patterns? Getting curious and tracking your data. 

For Stephen Johnson, it all started with a question: “What do these gappers all have in common?”

In the episode, you’ll learn how he began obsessively tracking dollar volume data — and what his results reveal! Co-hosts Tim Bohen and Kim Ann Curtin are along for the ride…

Why Does Dollar Volume Matter? 

Every trader looks at volume. And every trader looks at stock prices. But few people look at dollar volume. Is that a mistake?

According to Stephen, dollar volume — a combo of stock price and daily trading volume — is “one of the most overlooked variables in the stock market.”

Psst … wanna know a secret? The point of this episode is less about dollar volume and more about tracking your data. You can actually apply Stephen’s tracking methods and scientific approach to any variable you choose.

Don’t miss our episodes on essential day trading variables:

Calculating Dollar Volume

Not every trader calculates dollar volume the same way. 

For instance, Stephen starts with the opening price and multiplies it by the day’s volume. For example, say the stock opened at $5 and the trading volume was 100,000,000 shares. The dollar volume would be $500,000,000. 

Tim, on the other hand, prefers to start with an average price versus sticking with the opening price. This helps offset the crazy numbers that can come with big premarket moves. 

There isn’t a right or wrong way — it’s all about figuring out which data is most helpful to you. 

What Stephen Learned

Stephen’s tracked hundreds of stocks to evaluate the true meaning of dollar volume, and the results are “quite interesting.”

Price and volume are kind of like dance partners — each needs the other. By tracking dollar volume data, Stephen’s getting a better understanding of what happens when demand outpaces supply … and the crazy price moves that can follow. 

Breaking Down ‘The Billionaire Club’

Stephen’s data was collected in different categories, starting with what he calls ‘the billionaire club.’ These are stocks that trade over a billion dollars in dollar volume per day. 

You’ll learn how he classifies every stock by what it does: 

  • Gap and go: When a stock keeps going higher at the open…
  • Gap and hold: When a stock stays within a 5% range at the open…
  • Gap and crap: When a stock falls apart at the open…

Plus, find out how he uses the dollar volume to anticipate potential “crazy runs.” 

Making Sense of Your Data

No, dollar volume isn’t the secret to stock market success (sorry). It’s just a piece of the puzzle.

Paraphrasing advice he received from fellow trader Jack Kellogg, Stephen suggests that you “use your data to find stocks, but use price action to trade them.”

In the episode, you’ll learn why curiosity is the driving force behind exploring any variable, whether it’s dollar volume, float, or anything else. You’ll also learn why tracking data is one of the keys to slowly improving your trading consistency over time!

Curious About Something?

Wanna know something about the episode … or about trading in general? Hit us up!

Remember: if we select your question or comment as an on-air topic, you could win a SteadyTrade mystery gift box. Leave a comment below or on YouTube!

Thanks for tuning in to the SteadyTrade podcast. Stay tuned for weekly episodes featuring the hottest topics for aspiring traders.

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Meet Our Hosts

Get to know Tim Bohen, Stephen Johnson, and Kim Ann Curtin here.

Follow our hosts on social media…

Tim Bohen

Twitter: @tbohen

Instagram: @tbohen

Stephen Johnson

Twitter: @Jonk87

Kim Ann Curtin

Twitter: @kimanncurtin

Instagram: @kimanncurtin

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