Your edge is in your journal.
Most traders can't answer the only question that matters: what exactly is working, and what isn't? A journal answers it with data instead of memory.
Measure trades in R, not dollars
An R-multiple expresses each trade's result as a multiple of its initial risk: risk $500, make $1,250, that's +2.5R. R-multiples make wins and losses comparable across position sizes and accounts, and a distribution of R-multiples is what Van Tharp's SQN was originally designed to grade — the same statistic the dashboard applies to markets.
What a useful journal records
- The setup and the rule it was traded under — so adherence can be audited, not assumed.
- Initial risk (entry, stop, size) — without it, R can't be computed.
- The regime context — the market type and breadth on the day of entry. A setup that earns +0.8R average in Bullish/Quiet and −0.4R in Volatile regimes isn't one edge, it's two different trades.
- The review — screenshots, thesis, and what you'd do differently.
The feedback loop
Journal → statistics by setup and by regime → rule changes → journal again. That loop is the actual product of journaling; the log is just its raw material. It is also where regime data earns its keep: tagging each trade with the day's market typeturns "this month was bad" into "my breakout setup loses money in Neutral regimes — stop trading it there."
Where NextOrderAlpha is going
A regime-aware trading journal — trade list, calendar, R-multiple summaries, setup tags and rule-adherence review — ships in a later version of the platform, automatically tagged with the day's market type. Until then, the methodology above works in a spreadsheet, and the regime context is already public every day.