Correlation Portfolio Tab Guide

~6 min – the diversification X-ray that finally reveals whether your book is a symphony of independent edges… or a choir where every symbol sings the same note and crashes together when the market sneezes

You’ve already watched drawdowns carve deep scars in the Drawdown tab. You’ve quantified every hidden cost in Fee Breakdown. You’ve dissected streak psychology and holding durations.

Now open Correlation & Portfolio (fifteenth tab in CharterElite, Building2 icon).

This tab ignores PnL magnitude, exits, and regimes in isolation. It obsesses over one brutal truth: how your symbols move together — Pearson correlation matrix, covariance, eigenvalue analysis, effective diversification score, and an equal-weight portfolio variance example.

It answers the question most retail traders never ask until a correlated blow-up wipes months of gains: “Are my positions actually diversified… or am I just trading the same idea with different tickers?”

Where to Find It

• Sidebar → CharterElite
• Fifteenth tab → Correlation & Portfolio (Building2 icon)
• Expand tab help accordion (top) for quick benchmarks + pro tips (“low/negative correlations = real diversification”, “effective factors near symbol count = independent sources”)

Filters – Exit-Date Focused

Top card:

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

Only completed trades count. Only symbols with ≥5 trades (after filters) enter the matrices. Need ≥2 such symbols — otherwise “Insufficient Data for Correlation Analysis”

How the Matrices Are Built

• Group filtered completed trades by symbol → drop symbols <5 trades
• Returns: PnL per trade per symbol (pnl / net_pnl / gross_pnl)
• Correlation: Pearson on date-aligned return series (alignByDate: true) → n×n matrix
• Covariance: same aligned returns → n×n matrix (diagonal = variances)
• Eigenvalue analysis: eigenvalues + variance explained % per principal component
• Effective Diversification: number of “independent” factors (derived from eigenvalues) / N symbols
• Portfolio variance: equal weights (1/N) → σ²p = w′Σw → std dev = √σ²p

Correlation Matrix – The Co-Movement Heat Map

Scrollable N×N table:

• Rows/columns = symbols
• Diagonal = 1.00 (grey badge)
• Off-diagonal = Pearson corr (−1 to +1) to 3 decimals
• Color + bold: >0.7 red & bold (strong positive), >0.3 orange, near 0 green, negative blue
• Effective Diversification — “X / N” badge; green ≥70% of N, else orange

Subtitle: “Number of independent factors. Higher = better diversification.”

• Eigenvalue Analysis — List λ1, λ2, … + variance explained % per component

Badge importance (large λ = dominant factor, e.g. market beta)

Interpretation:

• High positive off-diagonals = symbols move together → less diversification
• Near-zero or negative = independent or hedging → real risk reduction

Covariance Matrix – The Raw Co-Risk View

Same symbols as correlation. Scrollable N×N:

• Diagonal = per-symbol variance
• Off-diagonal = covariance (co-movement)
• Values to 2 decimals
• Diagonal grey background + bold

Used for portfolio variance calc.

Portfolio Variance Calculator – Equal-Weight Baseline

• Weights: 1/N for each symbol
• Portfolio Variance (σ²p) — w′Σw
• Portfolio Std Dev — √σ²p
• Values to 4 decimals
• Info alert: “σ²p = w′Σw. Lower variance = lower portfolio risk for same weights. Diversification helps when correlations <1.”

Quick Workflow – Diversification Reality Check

1. Open Correlation & Portfolio → set filters (last 12–24 months, main setups)
2. If “Insufficient Data” → add trades or broaden filters until ≥2 symbols with ≥5 trades each
3. Study Correlation Matrix — high positive reds dominant? → correlated book, vulnerable to regime shocks

Near-zero/greens/blues? → independent sources, real diversification

1. Check Effective Diversification — close to N? → diversified. Much lower? → redundancy
2. Read Eigenvalue Analysis — one large λ? → single dominant factor (market beta?). Multiple similar? → multi-factor edge
3. Review Covariance Matrix — high off-diagonal? → co-risk high
4. See Portfolio Variance — equal-weight std dev low? → book resilient on paper
5. Action: high correlations? → cross to Setup DNA → kill redundant setups

Low effective diversification? → add uncorrelated symbols/strategies

Quick Reality Checks

• “Insufficient Data”? → Need ≥2 symbols ≥5 trades each — tag more or widen range
• All high positive correlations (>0.7)? → Book moves as one — regime shock will hurt
• Effective Diversification << N? → Many symbols but few independent factors — prune redundancy
• One dominant eigenvalue (>50% variance)? → Heavy market/systematic exposure
• Negative correlations? → Rare & valuable — protect those pairs
• Portfolio std dev surprisingly high? → Even equal weights can’t save highly correlated book

Next: Episode 38 – Full Flywheel Mastery: Integrating Weekly Alpha → Calendar Analytics → CharterElite (Correlation & Portfolio, Drawdown, Equity Curve, Fee Breakdown, Consecutive Winners/Losers, Holding Time, Trade Management, 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 → Correlation & Portfolio right now. Set last 24 months + your main setups.

Look at the Correlation Matrix.

Red heat map everywhere? Your book is a single bet in disguise.

Now check Effective Diversification.

Way below symbol count?

That’s your hidden concentration risk.

Prune the correlated setups. Add independent edges. Watch the matrix cool down.

Your portfolio isn’t diversified until the numbers say so.

Make them say it.

One low-correlation pair at a time.

Your future drawdowns are counting on it. 😈