R Distribution Tab Guide

~6 min – the histogram that finally shows whether your edge is a beautiful right-skewed rocket… or a left-tailed death spiral disguised as “consistent small wins”

You’ve sliced time in Temporal Performance. You’ve stress-tested risk-adjusted reality in Performance Ratios. You’ve watched exits confess in PEE Analytics.

Now open R Distribution (fifth tab in CharterElite, Minus icon).

This tab doesn’t care about setups, regimes, or psychology in isolation. It obsesses over one thing: the distribution of your realized R-multiples across every completed trade that has valid R data.

One look at the histogram tells you everything:

• Is your payoff asymmetric (big winners, small losers = green paradise)?
• Or symmetric/negative-skewed (small wins, occasional blow-ups = slow bleed to zero)?
• Are most trades clustering around breakeven (no edge)?
• Do you have fat positive tails (home runs) or fat negative tails (account killers)?

This is the tab that separates “I have an edge” from “I’m just surviving variance.”

Where to Find It

• Sidebar → CharterElite
• Fifth tab → R Distribution (Minus icon)
• Expand tab help accordion (top) for quick benchmarks + pro tips (“more trades >1R than < -1R”, “cut at -1R, let run to +2R+”)

Filters – Same as CharterElite

• Portfolio — All or single
• Setup — Multi-select
• Side — LONG / SHORT / Both
• From / To — Date range (exit_date)

Only completed trades with valid, non-zero R-multiple feed the distribution, stats, and fitting (from canonical realizedR → stored realized_r_multiple → calculated PnL/risk).

No R data? → “No R-Multiple Data Available”

R-Multiple Metrics Cards (Top 6 – Instant Shape Check)

Six large cards (real data):

• Total Trades — Count with valid R (not all completed trades have R)
• Average R-Multiple — Mean realized R
• Median R-Multiple — 50th percentile (less outlier-sensitive)
• Std Deviation — Spread of R values
• % Trades R > 0 — % profitable in R terms
• % Trades R > 1 — % that beat 1:1 risk-reward

Colors: green for positive/healthy, red for negative/dangerous.

R-Multiple Distribution Chart – The Money Shot

Toggle button: Show Bar Chart (default) vs Show Cumulative

• Bar Chart

X: 1R-wide bins (“-2 to -1”, “-1 to 0”, “0 to 1”, “1 to 2”, “≥3”) Y: trade count per bin Colors: red (negative), gray (0–1R), green (1R+) Last bin caps tail (“≥ maxR”) Tooltip: bin, count, % of total Reference lines (dotted): R=0, R=1

• Cumulative

Same X bins

Y: cumulative % of trades ≤ that bin Line chart — shows how quickly % accumulates past breakeven

Ideal shape:

• Heavy green right tail (fat +2R+ winners)
• Thin red left tail (few big losers)
• Gap or low count around 0R (no breakeven clutter)

Distribution Fitting Analysis – The Math Verdict

Shown only if ≥10 valid R values:

• Best Fit Distribution — Normal / Log-Normal / t-Distribution / Heavy-Tailed / Unknown
• Goodness of Fit — % + color (green >70%, amber >50%, red ≤50%)
• Parameters — Mean/std/shape when relevant
• Interpretation — Short paragraph (e.g. “Log-Normal fit: positive skew expected from letting winners run” / “Heavy tails detected: occasional large losses”)

Use this to validate assumptions:

• Normal → symmetric, no edge asymmetry
• Log-Normal → positive skew (good)
• Heavy tails → tail risk (blow-ups)

Correlation Tables – R Sliced by Behavior

Four conditional tables (appear only with enough data):

1. R-Multiple by Entry/Exit Taxonomy

≥5 trades per entry/exit tag → count, avg R, median R, % R ≥1 Sorted avg R descending (top 10)

1. R-Multiple by Exit Quality

High (≥60), Medium (40–59), Low (<40) tiers → same stats

1. R-Multiple by Market Regime

≥3 trades per regime → same stats

1. R-Multiple by Position Management

Scaled In/Out vs Single → same stats

No data? → Message: “Tag trades with [taxonomy/regime/exit quality/position management] to unlock”

Quick Workflow – Distribution Reality Check

1. Open R Distribution → set filters (last 6–12 months, main setups)
2. Read Metrics Cards → avg R >1? % R>1 > % R< -1?
3. Study Bar Chart → right-skewed green tail? Thin red left tail?
4. Toggle Cumulative → how fast does % cross R=1?
5. ≥10 R values? → Read Distribution Fitting → heavy tails? Log-normal skew?
6. Tagged? → Check Correlation Tables → which entry/exit/regime gives best avg R?
7. Action: poor % R>1? → back to PEE/Exit Analysis for trailing sims

Left tail fat? → tighten stops or kill setups with big losers

Quick Reality Checks

• Avg R <0.8 + low % R>1? → Edge is weak—audit setups
• Heavy tails + red left? → Blow-up risk—check Risk Assassin in Situation Room
• All correlations hidden? → Need 5+ trades per taxonomy item, 3+ per regime
• Perfect right skew? → Protect it—check Evolution for decay
• Zero R data? → No valid realizedR/PnL-risk — complete trades properly

Next: Episode 28 – Full Flywheel Mastery: Integrating Weekly Alpha → Calendar Analytics → CharterElite (R Distribution, Temporal, Performance Ratios, PEE) → Council (Kill List, Reality Check, Setup DNA) → Evolution into the self-reinforcing compounding loop that turns monthly audits into weekly micro-kills and quarterly A-grade upgrades—without ever increasing risk % or chasing new shiny setups.

Or open CharterElite → R Distribution right now. Set last 12 months + your main setups.

Look at the Bar Chart.

Is the right tail fat and green… or is the left tail bleeding red?

That’s your edge—or lack of it—in one picture.

Now check % Trades R > 1 vs % Trades R > 0.

If the gap is wide, you’re letting winners run.

If it’s tiny, you’re scalping breakeven.

Fix the asymmetry.

The histogram doesn’t negotiate.

It indicts.

Time to rewrite the shape.

One bin at a time.

Your future PnL depends on it. 😈