About EigenDex
EigenDex is an independent platform for ETF analysis. We build tools that help investors understand what they actually own — and how their portfolio behaves in the real world.
Our Mission
The ETF industry has democratized investing — but it's also created a new problem. With thousands of funds available, it's easy to build a portfolio that looks diversified but is actually 80% the same companies across multiple funds. Hidden overlap, correlated exposures, and compounding expense ratios quietly erode returns without most investors realizing it.
We built EigenDex to make that hidden structure visible. Our tools analyze real fund holdings (sourced from public SEC filings), actual price history, and mathematical portfolio theory — and present the results in a way that's immediately useful without requiring a finance degree.
Most tools on EigenDex are free and require no account. Advanced features — including portfolio backtesting, multi-ETF overlap analysis, and portfolio optimization — are available through EigenDex Pro. We do not provide investment advice and are not affiliated with any fund provider, broker, or financial institution.
What We Build
ETF Overlap Analyzer
See exactly how much two ETFs share in common — weighted by position size, not just ticker presence. Compare over 248 ETFs.
Correlation Finder
Discover which ETFs move together and which don't. Rolling correlation windows from 7 to 60 days across 90+ ETFs.
Portfolio Backtester
Backtest any ETF portfolio with historical price data going back 30 years. Supports DCA contributions, market timing signals, and XIRR returns.
Portfolio Optimizer
Find the optimal allocation for any set of ETFs using mean-variance optimization. Risk-adjusted return maximization.
Expense Ratio Calculator
A 0.50% vs 0.03% expense ratio difference can cost $45,000+ over 30 years on a $100,000 portfolio. See the real numbers.
Sector X-Ray
Reveal your true sector concentration across a multi-ETF portfolio. See what you actually own beyond the fund labels.
Dividend Optimizer
Analyze overlapping holdings across multiple dividend ETFs. Optimize yield without accidentally doubling down on the same stocks.
ETF Rankings
Rank ETFs by expense ratio, AUM, performance, dividend yield, and more. Updated weekly.
Our Data
Holdings data is sourced from public SEC filings (Form N-PORT and 13F). We analyze complete fund portfolios — typically 100 to 500+ individual positions per ETF — to calculate overlap using a normalized min-weight methodology that accurately reflects how much economic exposure is shared, not just which tickers appear in both funds.
Price data covers up to 30 years of daily closing prices for 90+ ETFs and major indices, cached on our servers and refreshed regularly.
All data carries inherent limitations: holdings reflect point-in-time SEC filings (typically 30–60 day lag), and historical prices may contain gaps or adjustments. Always verify important data with official fund provider sources.
Methodology
Overlap Calculation
Portfolio overlap between two ETFs is calculated using the weighted overlap formula: for each shared holding, we take the minimum weight from each fund, sum these minimums, and express the result as a percentage. This method reflects true economic exposure overlap, not just holding count.
Correlation Analysis
Pearson correlation coefficients are computed on daily log returns over rolling windows (7, 14, 30, and 60 calendar days). Correlations closer to +1.0 indicate ETFs that move together; correlations near 0 suggest independence; negative correlations indicate inverse movement.
Backtesting Engine
The backtester simulates portfolio performance using actual daily closing prices. Returns are computed on a daily basis with optional periodic rebalancing. When DCA (periodic contributions) are enabled, we report both time-weighted return (TWR) and money-weighted return (XIRR/IRR) to accurately reflect the impact of contribution timing. Risk metrics (Sharpe, Sortino, Calmar) use a 5% annualized risk-free rate.
Portfolio Optimization
The optimizer uses mean-variance optimization (Markowitz) to find the portfolio allocation that maximizes the Sharpe ratio given a set of ETFs. Inputs include historical covariance and expected return estimates. Results should be treated as illustrative, not prescriptive.
Contact
We welcome feedback, bug reports, and feature suggestions. EigenDex is an independent project and we read every message.
Email:contact@eigendex.com
Website:eigendex.com