Overtrading almost never feels like a mistake while it's happening. It feels like opportunity. A setup shows up, then another, then a third that's "even better," and by the end of the session you've taken twelve trades instead of the three your plan called for. Nobody sits down planning to overtrade. It just piles up, one reasonable-sounding decision at a time.
Experience doesn't fix it either. Paul Tudor Jones, one of the more famous macro traders of the last forty years, has talked openly about an early-career blowup, and traced the cause back to position size relative to his account — he was running far more contracts than his equity justified. The trades themselves weren't the problem. The size he ran them at was.
"The most important rule of trading is to play great defense, not great offense." — Paul Tudor Jones
Every trader learns some version of this eventually: trading is a business, and the businesses that survive are the ones with disciplined cost control, not the ones swinging hardest on every deal.
Why it hides inside a single session
Overtrading is invisible from inside a single session. Each individual trade can look defensible — a real setup, a real reason. What's missing from the in-the-moment view is the aggregate: how many trades you've taken today relative to your usual pace, how your win rate degrades on trade six versus trade one, whether your average size has been creeping up without a matching increase in conviction.
You need enough trades stacked together before any of that becomes visible. One session never tells the story on its own.
Catching it in your own data
CoTrader's Callouts are built to surface patterns like this: a cluster of trades opened right after a loss, or a day where trade count spiked well past your normal average with a matching drop in win rate. The rule doesn't fire because a session "felt busy." It fires because the numbers show the pattern — the same numbers that would have shown it in Jones's own accounts.
His track record isn't required to learn the lesson. Looking at your own data is.