Multi-Asset Compare & AI Optimizer
Constructing a high-conviction portfolio requires testing asset interactions under real market conditions. The Crypto Compare and ETF Compare & AI Optimizer tools provide an institutional-grade sandbox to benchmark historical performance, stress-test current allocations, and mathematically optimize asset weights using AI-driven Modern Portfolio Theory.
Figure 2.1: Compare & AI Optimization Terminal Architecture
1. Dual Engine Architecture: Crypto vs. ETF Compare
Because digital assets and traditional equities exhibit fundamentally different trading hours and volatility regimes, two dedicated comparison modules are provided:
- Crypto Compare: Continuous 24/7 price feeds covering Bitcoin, Ethereum, layer-1s, DeFi, and altcoins with asymmetric upside tracking.
- ETF Compare: Regulated multi-asset indexation covering global equity indices (S&P 500, MSCI World), thematic sectors, fixed-income Treasuries, and commodities with dividend total-return compounding.
2. The Core Execution Engines: Analyze vs. AI Optimize
At the center of the comparison interface, you configure asset weights and choose between two primary computational engines:
Figure 2.2: Interactive Weight Sliders, Flash Performance Table, Analyze (Orange) & AI Optimize (Purple) Engines
1. Analyze Current Portfolio (Orange Engine)
Evaluates your current manually assigned asset weights:
- Computes your portfolio cumulative return over the selected historical period.
- Measures historical Max Drawdown, Annualized Volatility, Sharpe, and Sortino ratios.
- Serves as your quantitative baseline to evaluate whether active management beat the benchmark.
2. AI Optimize Portfolio (Purple Engine)
Mathematically recalculates the optimal asset weights:
- Applies Modern Portfolio Theory and covariance analysis across your selected assets.
- Finds the asset distribution that maximizes return while minimizing downside risk and volatility.
- Allows setting custom constraints (Target Return, Max Drawdown threshold, Min/Max asset weights).
3. Starting Conditions & Optimized Portfolio Analysis
Launching the AI Optimization generates an immediate compliance check of your constraints along with the mathematical allocation result:
Figure 2.3: Starting Conditions Constraint Verification, Optimal Weight Donut & Backtested Performance Curves
- Starting Conditions Status: Cards verify if your specified Target Return (e.g. >20%), Max Drawdown (e.g. >-5%), Asset Count, and Weight Bounds (10%–60%) were achieved or flagged.
- Optimal Assets Allocation Donut: Visual breakdown displaying exact rebalanced weight percentages per asset (e.g. 60% LCUA.DE, 10% HLTW.PA, 10% CBRS.DE).
- Portfolio Performance Curve: Multi-line equity chart comparing the Optimal Combination (black curve) against individual asset trajectories over the active backtesting period.
- Optimized Risk Metrics: Instant summary of Portfolio Return (+35.13%), Max Drawdown (-11.09%), Annualized Volatility (15.82%), and Sharpe Ratio (1.39).
4. Deep Dive into the 6 Analytical Tabs
Once calculated, the platform provides 6 specialized analytical views:
| Tab Name | Analytical Focus | Strategic Utility for Allocators |
|---|---|---|
| 1. 📁 Design & Compare | Asset selection, percentage sliders, Base-100 relative price curves, and Flash Performance table. | Build the basket, assign initial allocations, and visually compare individual asset momentum over multiple timeframes. |
| 2. 🤖 AI Allocation | Starting constraint verification (Target Return, Max Drawdown, Min/Max Weight) & Optimal Weight Donut Chart. | Compare your initial weights against the AI-proposed weights to eliminate risk-heavy concentration bottlenecks. |
| 3. 📈 AI Returns | Cumulative capital growth curves and period return comparisons over customizable backtesting horizons. | Evaluate whether the optimized strategy smoothed out market pullbacks compared to holding individual high-volatility assets. |
| 4. 📊 AI Daily Stats | Intraday win/loss distributions, positive vs. negative session ratios, and average daily PnL swings. | Assess psychological stress and return consistency: a strategy with fewer extreme down days provides smoother compounding. |
| 5. 📉 Volatility & Drawdown | Annualized standard deviation (σ), Value at Risk (VaR), and historical peak-to-trough Drawdown depth. | Ensure that the worst-case historical capital drop fits within your personalized risk tolerance and liquidity needs. |
| 6. 🔀 Correlation & Risk | Interactive Pearson Correlation Heatmap Matrix ($r$) across all portfolio holdings. | Identify hidden correlation clusters: eliminate duplicate assets that move in lockstep to build genuine multi-asset resilience. |
5. Customizing Optimization Constraints (The Gear Settings)
Clicking the gear icon () on the AI Optimize button opens the Constraint Configuration Modal:
- Target Return Constraint: Specify the minimum annualized percentage return you require from the allocation.
- Max Acceptable Drawdown: Set a hard cap on maximum tolerable portfolio drawdown (e.g., limit capital loss to -25%).
- Min / Max Asset Weights: Prevent over-concentration (e.g., cap any single crypto at 25% and set a minimum holding threshold of 5%).
Pro Tip: If your initial constraints are overly restrictive (e.g., demanding 100% annual return with only -5% drawdown), the solver will highlight which constraint created a feasibility bottleneck in the Starting Conditions cards.
6. Start Analyzing & Optimizing Your Basket
Select your assets, test historical performance, and let mathematical optimization guide your position sizing.
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