Build a clearer view
of portfolio risk
PortBlend is currently in beta. Upload your strategies, ETFs, or portfolios and help shape the product while exploring drawdown and portfolio-blend analysis.
Beta access is open. Features may evolve as we improve the platform.
Try Drawdown Analysis instantly - no login, no email requiredThe Problem
Most investors and traders analyze assets or strategies in isolation. But true risk happens at the portfolio level.
Hidden drawdown
Two assets or strategies can look fine individually but create devastating drawdowns when combined.
Guesswork allocation
Most allocators and traders struggle to evaluate allocation trade-offs. PortBlend helps analyze the historical impact of different asset weights.
Excel breaks down
Spreadsheets cannot model rebalancing, drawdown interaction, or portfolio-level compounding properly.
Simplify Your Portfolio Analysis
| Date | NAV (Net Asset Value) |
|---|---|
| 2018-05-07 | 200.05 |
| 2020-01-18 | 203.50 |
| 2020-05-31 | 200.25 |
| 2020-05-10 | 203.50 |
| 2024-01-24 | 204.50 |
1. Prepare Data
Format Date and numeric NAV columns sorted chronologically with no duplicate dates.
2. Upload File
Upload CSV, TSV, TXT, or Excel files securely. Processed strictly in memory.
3. Set Parameters
Configure rebalancing modes (calendar or threshold drift) and strategy weights.
4. Generate Report
Examine CAGR, drawdown episodes, recovery duration, and correlation.
Interactive, dynamic line chart
Portfolio Performance: Blended vs. S&P 500 ETF
Jan 2018 - Jan 2024
| Risk Metric | S&P 500 ETF | Blended Portfolio |
|---|---|---|
| CAGR | 10.2% | 11.3% |
| Max Drawdown | -42.0% | -19.9% |
| Max DD Duration | 18 Months | 7 Months |
Supported risk metrics and upcoming modules
Supported Risk Metrics
CAGR
Compound Annual Growth Rate tracking historical strategy compounding.
Max Drawdown
Worst peak-to-trough drop and historical recovery duration tracking.
Drawdown Episodes
Detailed breakdown of all peak-to-trough decline periods and milestones.
Drawdown Duration
Decline and recovery time tracking across all historical episodes.
Future Modules
Correlation Matrix
Deep cross-asset covariance analysis to isolate strategy dependency and overlap risk.
AI-Driven Capital Allocation
Machine learning models to calculate dynamic risk budgets and optimal portfolio weights.
Who It's For
Built for systematic allocators, active traders, and long-term investors
Systematic Traders
Running multiple rule-based strategies and need portfolio-level drawdown control.
ETF Investors
Combining ETFs and want to understand how they interact during drawdown periods.
Portfolio Analysts
Managing across asset classes and seeking deeper insight into portfolio risk and diversification.
Wealth Managers
Analysing historical portfolio behaviour and drawdown characteristics.