Playbook Analysis Tab Guide

~6 min – the taxonomy-powered autopsy that turns your own tags into a ranked leaderboard of what actually prints money… and what you keep doing even though it bleeds R

You’ve already sliced exits in Exit Analysis. You’ve watched R distributions reveal asymmetry (or lack thereof). You’ve seen time-of-day killers in Temporal Performance.

Now open Playbook Analysis (seventh tab in CharterElite, BookOpen icon).

This tab does one thing ruthlessly well: it takes every taxonomy tag (playbook item) you’ve ever applied to a trade and ranks them by real performance—Net PnL, Win Rate, Average R-Multiple, or Trade Count. It answers the question most traders never dare ask: “Which of the things I tag as ‘good’ or ‘bad’ actually correlate with making or losing money?”

No vibes. Just your own data, grouped by item or category, telling you exactly which behaviors deserve more capital and which deserve the death penalty.

Where to Find It

• Sidebar → CharterElite
• Seventh tab → Playbook Analysis (BookOpen icon)
• Expand tab help accordion (top) for quick tips (“tag consistently”, “group by item vs category”, “filter by source type”)

Analysis Controls – Your Ranking Knobs

Top controls (real-time recompute):

• Group by
• Taxonomy item — one bar per specific playbook tag (e.g. “PDH Rejection”, “Early Fear Exit”, “FOMC Avoidance”)
• Category (source type) — aggregated by high-level bucket (entry / execution / exit / planning / context / market regime)

• Metric (what the bars measure)
• Net PnL — sum of net/gross PnL
• Win Rate — % positive PnL
• Average R-Multiple — mean realized R
• Trade Count — number of trades tagged with that item/category

• Category filter (optional)

When grouping by item → restricts to one source type (e.g. only “exit” tags) When grouping by category → shows only that category

Chart title updates dynamically (e.g. “By taxonomy item — Average R-Multiple”)

Filters – Same CharterElite Precision

• Side — LONG / SHORT / Both
• Portfolio — All or single
• From / To — Date range (exit_date / date)

Only completed trades count. No Setup filter here (inherited from parent if passed).

The Chart – Your Playbook Leaderboard

• Bars: one per item (or category) with ≥2 trades
• When grouping by item → top 25 only (sorted descending by chosen metric)
• Colors: blue for non-negative (PnL/R/Win Rate/Count), red for negative (PnL/R)
• Tooltip: label, metric value (formatted: $/%/R/count), trade count
• Y-axis: auto-scaled by metric

Empty chart? → “No taxonomy data with at least 2 trades. Tag more trades with playbook items.”

Trade List – The Raw Evidence

Below chart: paginated table of all filtered completed trades (not just tagged ones):

• ID | Symbol | Side | Net PnL (green/red) | R-Mult | Entry (time) | Exit (time)
• Pagination: 10/25/50 rows; X–Y of Z label

Use this to drill into any bar: Bad item high on chart? → paginate list → open worst trades → autopsy why.

How Taxonomy IDs Are Collected

From `getTradeTaxonomyIds(trade)`:

• `taxonomy_item_ids` array (numeric IDs)
• `trade_comment_ids` array → parse strings like `"taxonomy_42"` → extract 42

IDs merged + deduplicated. A single trade can contribute to multiple items (and thus multiple bars when grouping by item). When grouping by category → each trade counted once per unique source_type it touches.

Quick Workflow – Playbook Murder Ritual

1. Open Playbook Analysis → set filters (last 6–12 months, main side/portfolio)
2. Choose Group by: Taxonomy item + Metric: Average R-Multiple
3. Read chart → top items green/high R? → double down (add to pre-trade checklist)

Bottom items red/low R? → kill or tag as anti-pattern

1. Switch Metric: Net PnL → same ranking? → confirms edge
2. Switch Group by: Category → which bucket bleeds most (exit? execution?)
3. Set Category filter to “exit” → see worst exit behaviors
4. Scroll Trade List → open worst trades for that item → tag deeper or fix rule
5. Action: top negative item? → add opposite positive tag (“Avoided FOMO Entry”) → enforce next week

Quick Reality Checks

• Chart empty? → No taxonomy tags (or <2 trades per item) → tag entry/execution/exit consistently
• All negative avg R? → Your playbook is anti-correlated with profit—rewrite tags
• Exit category dominates red? → Cross to Exit Analysis/PEE for deeper autopsy
• One item 80% of trades? → Over-tagging or narrow playbook—diversify tags
• No category filter? → Add source_type to taxonomy items in Settings

Next: Episode 30 – Full Flywheel Mastery: Integrating Weekly Alpha → Calendar Analytics → CharterElite (Playbook Analysis, Exit Analysis, 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 → Playbook Analysis right now. Set Group by: Taxonomy item + Metric: Average R-Multiple + last 12 months.

Look at the top bar (highest avg R).

That’s your real edge named.

Now look at the bottom red bars.

Those are your self-inflicted wounds.

Tag them harder. Avoid them. Kill them.

Your own tags just indicted you.

Now execute the sentence.

One tag at a time.

The market doesn’t care what you call it. It cares what R it delivers.

Make the green bars taller.

Your future audits are counting on it. 😈