📊 Analytics & Calendar 14 core metrics Per-setup breakdown

The metrics that actually predict your next green month.

Most journals hand you a pile of vanity stats — total trades, running P&L, biggest winner. Journali surfaces the metrics that change how you trade: expectancy, R-multiple distribution, and per-setup win rates, all mapped onto a calendar heatmap so you see your month at a glance.

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Calendar heatmap · April 2026 Live
Mon
Tue
Wed
Thu
Fri
Sat
Sun
1+$182
2−$94
3+$612
6+$210
7+$148
8−$520
9−$72
10+$480
13+$96
14+$702
15+$140
16−$48
17+$218
20+$160
21+$540
22+$82
23+$144
Expectancy+$87 / trade
Avg R+1.24 R
Win rate58%
Profit factor2.08
+3R+
6
+1–3R
22
0–1R
12
−1R+
8

Vanity metrics vs decision metrics

Most trading journals overwhelm you with metrics that look impressive on a marketing page and change absolutely nothing about how you trade next week. Your total P&L this year is not a decision metric. Your biggest winner is not a decision metric. Your trade count is not a decision metric.

A decision metric is one where the number, considered alone, suggests a specific adjustment. Your expectancy per trade is a decision metric — because if it's negative, you know with certainty that trading more will lose more. Your per-setup win rate is a decision metric — because the setup with the highest expectancy is the one you should be sizing up. Your R-multiple distribution is a decision metric — because if most of your winners are under 1R, your exits are killing your edge.

✗ Vanity — make you feel things
  • Lifetime P&L
  • Total number of trades
  • Biggest single winner
  • Longest win streak
  • Account balance chart going up-and-to-the-right
✓ Decision — change what you do
  • Expectancy per trade (by setup)
  • R-multiple distribution
  • Win rate × avg R (combined)
  • Drawdown timeline + recovery time
  • Time-of-day P&L heatmap
The core idea

Analytics should tell you what to do differently next week, not just how you did last week. Journali surfaces the narrow set of metrics that reliably correlate with future P&L — and cuts the stats that feel good but don't move the needle.

What's on the analytics page

Calendar heatmap

Every trading day colored green, red, or flat. One glance and you see your month — which days carried you, which days hurt, which weeks you overtraded. Click any day for the trade list.

Expectancy per setup

Your average R per trade, broken down by tagged setup. Your Breakout might be +1.4R expectancy and your Fade might be −0.2R. Size accordingly.

R-multiple distribution

Histogram of your trade outcomes in R. If your winners cluster at +1R and your losers hit −1R cleanly, you need to let winners run. Right-tail distributions win.

Time-of-day P&L

Every trade tagged by session. See exactly which hour of the day you make money in and which hour consistently gives it back. Most traders have a clear answer they'd be shocked by.

Drawdown timeline

Live equity curve with drawdown zones shaded. See your max drawdown, time-to-recover, and whether you're currently above or below your previous high-water mark.

Export to PDF or CSV

One-click export for prop firm payout requests, accountant handoff, or personal record-keeping. PDF includes the calendar and core metrics; CSV gives you the raw trade data.

The 14 core metrics Journali tracks

  1. Net P&L — realized gains minus losses minus fees, any date range.
  2. Win rate — wins / (wins + losses), excluding breakeven trades.
  3. Expectancy — average P&L per trade, the single most predictive metric.
  4. Avg R-multiple — average outcome measured in units of risk.
  5. Profit factor — gross wins / gross losses. Above 1.5 is healthy.
  6. Largest win / largest loss — checked for outlier-driven P&L.
  7. Average win / average loss — the payoff ratio that backs your win rate.
  8. Max drawdown — peak-to-trough decline from any high-water mark.
  9. Drawdown duration — how long it took to recover.
  10. Sharpe-lite — annualized return / annualized volatility of daily P&L.
  11. Per-setup win rate and expectancy — broken down by your tagged setups.
  12. Per-symbol performance — which symbols are edge, which are distractions.
  13. Day-of-week and time-of-day heatmaps — when you actually make money.
  14. Consecutive wins / losses — streak data for position sizing rules.

How Journali's analytics compares

AnalyticJournaliTradeZellaTraderSyncTradervue
Calendar heatmapYesYesYesNo
Per-setup expectancyYesWin rate onlyWin rate onlyWin rate only
R-multiple distributionYesNoLimitedNo
Drawdown timelineYesYesYesPremium only
Export PDF + CSVBothPDF onlyCSV onlyBoth
Included at entry planPro $20$29/mo$29.95$29/mo

Frequently asked

What's R-multiple and why does it matter more than win rate?

R-multiple is your trade outcome expressed in units of risk. A +2R trade means you made twice what you would have lost if stopped out. R normalizes across position size and volatility, which is why it's the standard output unit for professional performance tracking. Win rate alone can't tell you if you're profitable — a 70% win rate with 0.3R avg wins and 1R avg losses is a losing strategy.

Does the calendar pull from my live broker?

Yes — if you connect a broker via Broker Auto-Sync, the calendar updates within 60 seconds of a fill. If you log trades manually, they appear the moment you save. Mixed workflows (some auto-synced, some manual) work fine; Journali deduplicates automatically.

Can I filter the analytics by setup, symbol, or time range?

Yes to all three. Every metric on the analytics page responds to filters at the top — date range, symbol, setup tag, side (long/short), account. So you can isolate "my VWAP Reject setup on NQ during the London session in Q1" and get the full stat breakdown.

Is this the data the AI Coach uses?

Yes. Your trade table and analytics feed directly into the AI Coach on Premier. Cleaner data in means sharper insights out — which is why broker auto-sync and the analytics page work together.

What if my account is tiny — do these metrics still mean anything?

Metrics are scale-invariant. A $1,000 account and a $100,000 account both benefit from knowing their expectancy per trade. R-multiple especially is designed to work across account sizes. The only thing that matters is having enough trades to make the sample meaningful — aim for 50+ before drawing conclusions about per-setup metrics.

Stop guessing. Start measuring the right things.

Decision-grade analytics, built into the same journal you log trades in. No separate dashboard. No setup friction. It just works.

Start Journali Free → 14 core metrics · Calendar heatmap · Per-setup breakdown · PDF/CSV export

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