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I Finally Counted My Trades by Day of the Week, and the Results Genuinely Surprised Me

After a long stretch of consistent journaling, I decided to actually break my results down by day of the week instead of just looking at overall monthly numbers.

Mondays, it turned out, were quietly my worst performing day by a meaningful margin — not catastrophic, just noticeably below my average across every other metric I tracked.

I hadn't noticed this pattern just from the day-to-day experience of trading. It only became visible once I actually aggregated the data across a large enough sample and looked at it deliberately.

I still don't fully know why Mondays specifically underperformed for me — some combination of weekend gap volatility and my own re-entry rhythm after two days away, probably. But knowing the pattern exists, regardless of the exact cause, let me adjust — smaller size specifically on Mondays, extra caution about entry quality. The fix didn't require understanding every root cause. It just required actually looking at the aggregated data honestly.

My Win Rate on Trades Under Two Minutes Was Dramatically Worse Than Everything Else

I started tracking trade duration alongside everything else in my journal, mostly out of curiosity rather than expecting to find anything meaningful.

Once I had enough data, a clear pattern emerged. Trades that resolved — win or loss — in under two minutes had a noticeably worse overall win rate than trades that took longer to play out.

My theory, looking at the actual examples, was that these ultra-fast resolutions were disproportionately trades I'd entered too early, before genuine confirmation, catching immediate reversal rather than genuine continuation.

Knowing this specific pattern changed how I evaluate entries now. If a trade is resolving unusually fast in either direction, I've started treating that speed itself as information about entry quality, not just watching the outcome in isolation.

The Setup I Was Most Confident In Had the Worst Actual Numbers

This one stung a little to discover, honestly.

There was a specific setup — a particular combination of structure and momentum confirmation — that I genuinely believed was my strongest, most reliable pattern. I felt good every time I saw it forming.

When I actually filtered my journal by setup type and looked at the real numbers across enough instances, that specific setup had a below-average win rate and a below-average overall expectancy compared to two other, less exciting setups I'd been taking less seriously.

I think the confidence I felt about it was rooted in a handful of particularly memorable wins early on, ones that shaped my gut feeling about the setup long after the actual aggregate data had shifted.

Gut feeling about your own setups is worth checking against real numbers periodically. Mine was confidently, specifically wrong about one of my most-used patterns for longer than I'd like to admit.

I Tracked How I Felt Before Each Trade for Three Months, and One Emotional State Predicted Almost All My Worst Losses

I started adding a simple emotional state note before every entry, nothing fancy, just a word or short phrase describing how I felt in the moment.

Three months in, with enough data to actually see a pattern, one specific state stood out clearly: entries logged as "impatient" had a dramatically worse outcome distribution than any other emotional state I'd recorded, including states I would have guessed were more dangerous, like "frustrated" or "tired."

Impatience specifically, it turned out, was quietly the most expensive emotional state in my entire dataset — more damaging than the more obviously dramatic emotions I'd assumed were the real problem.

Now, if I notice that specific feeling creeping in before an entry, I treat it as a genuine stop sign, not just a vague mood to push through. The data made a mental habit concrete in a way that just "trying to be more patient" as a vague resolution never quite managed.

My Actual Average Win Was Smaller Than My Actual Average Loss, Despite a Win Rate Over 60%

For a long time I felt good about my overall win rate, comfortably above 60% most months, and assumed that alone meant things were working well.

When I finally sat down and calculated actual average win size against actual average loss size — not just the count of wins versus losses — the numbers were uncomfortably close to breakeven overall, despite that seemingly strong win percentage.

The issue turned out to be a subtle habit of cutting winners early out of anxiety about giving back gains, while occasionally letting losers run slightly longer than planned, hoping for a reversal that didn't always come.

Neither habit was dramatic enough to notice day-to-day. Aggregated across hundreds of trades, it was quietly cutting my actual profitability down to almost nothing, hidden behind a win rate number that felt reassuring on its own.

I Discovered My Best Trading Happened in a Two-Hour Window I'd Been Ignoring

I'd always assumed the London-New York overlap was unambiguously my best trading window, since that's what most trading content suggests, and I'd built my whole schedule around it.

Breaking down my actual results by hour, a different, smaller window — a specific two-hour stretch slightly before the overlap officially began — showed up with noticeably better numbers in my own personal data than the overlap period itself.

I don't have a fully satisfying explanation for why this particular window worked especially well for me specifically. It might just be some combination of the specific setups I use and my own personal alertness at that time of day.

The broader lesson mattered more than the specific window, though. General trading wisdom about "best hours" is a reasonable starting point, but your own actual data, once you have enough of it, can reveal something more specific and more personally useful than any generic recommendation.

The Correlation Between My Sleep the Night Before and My Results Was Stronger Than I Expected

I started casually noting roughly how many hours I'd slept before each trading session, mostly out of curiosity about whether it actually mattered as much as people always claim.

It mattered more than I expected, honestly. Sessions following nights with noticeably less sleep than my normal average showed a consistent, meaningful dip in performance across multiple metrics — not a subtle statistical whisper, but a pattern clear enough to see just glancing at the data.

I'd always treated sleep as a nice-to-have wellness suggestion, separate from the "real" work of trading strategy and analysis.

My own data eventually convinced me otherwise. Poor sleep the night before is, for me personally, a legitimate reason to trade smaller size or skip the session entirely — not because some article said so, but because my own numbers made the case undeniably.

I Ran the Numbers on Trades I Took After Breaking My Own Daily Trade Cap, and They Were Genuinely Awful

I had a rule, for a while, capping myself at eight trades per session. For a long stretch, I mostly respected it, but occasionally pushed to nine or ten on days that felt "special" somehow.

Out of curiosity, I went back and specifically isolated every trade that occurred after I'd technically already hit my cap for the day — the ninth, tenth, sometimes eleventh trades on days I'd exceeded the limit.

The numbers on this specific subset were genuinely bad. Meaningfully worse than my overall average, worse even than I expected going into the analysis.

This wasn't a surprising finding in the abstract — of course trades taken past a fatigue threshold would probably underperform. But seeing the actual specific numbers, isolated and undeniable, did more to enforce my own discipline around that cap than years of just generally knowing overtrading was "probably bad" ever managed.

I Ran the Numbers on Trades I Took After Breaking My Own Daily Trade Cap, and They Were Genuinely Awful

I had a rule, for a while, capping myself at eight trades per session. For a long stretch, I mostly respected it, but occasionally pushed to nine or ten on days that felt "special" somehow.

Out of curiosity, I went back and specifically isolated every trade that occurred after I'd technically already hit my cap for the day — the ninth, tenth, sometimes eleventh trades on days I'd exceeded the limit.

The numbers on this specific subset were genuinely bad. Meaningfully worse than my overall average, worse even than I expected going into the analysis.

This wasn't a surprising finding in the abstract — of course trades taken past a fatigue threshold would probably underperform. But seeing the actual specific numbers, isolated and undeniable, did more to enforce my own discipline around that cap than years of just generally knowing overtrading was "probably bad" ever managed.