A-CLUSTER Quantitative Research Lab

All-Weather Macro Regime Portfolio

A systematic multi-asset portfolio designed to remain diversified across changing macroeconomic regimes. The framework combines four distinct economic exposures and compares multiple allocation methods under a common, rolling research process.

Research cutoff: 2026-09-10 252-day estimation window Monthly rebalance yfinance adjusted Close
What is this portfolio?

A portfolio built for regime uncertainty rather than regime prediction

The A-CLUSTER All-Weather Macro Regime Portfolio is a systematic multi-asset framework designed for an environment in which no single macroeconomic scenario can be assumed to persist. Growth, inflation, monetary tightening, recession and geopolitical stress affect asset classes differently. The portfolio therefore combines exposures with different economic functions rather than concentrating on one forecast.

The objective is not to identify the next winning asset. It is to build a portfolio whose components respond differently across regimes and then choose an allocation policy that preserves that diversification with an acceptable level of concentration, turnover and estimation risk.

4economic sleeves
3allocation engines
Monthlyrebalance review
252destimation window
Risk Paritycurrent preferred implementation
European-listedimplementation universe
How the portfolio is constructed

From macroeconomic logic to an investable portfolio

The construction process starts with economic purpose, not with historical return ranking. Each sleeve is selected because it represents a distinct macro function. Statistical tools are then used to test whether those exposures actually behave differently, and allocation engines are compared under identical research conditions.

1. Define the investable universe

Use European exchange-traded instruments suitable for practical implementation, with UCITS funds supplemented where necessary by UCITS-eligible exchange-traded commodity products.

2. Map economic functions

Identify exposures intended to behave differently across slowdown, recovery, inflation, falling real rates and geopolitical stress.

3. Test diversification

Use correlation analysis and hierarchical clustering to verify that the selected assets are not simply different labels for the same underlying risk driver.

4. Compare allocation engines

Apply Equal Weight, Risk Parity and constrained optimization to the same universe, same estimation window and same rebalance dates.

5. Evaluate robustness

Compare realized return and drawdown together with modeled risk contributions, concentration, one-way rebalance turnover, effective N and estimation-window sensitivity.

6. Select the implementation policy

Prefer the method whose assumptions, diversification properties and governance burden best fit the portfolio objective rather than the method with the highest single historical statistic.

Macro regimes
Economic functions
Asset selection
Clustering
Allocation engines
Rolling backtest
Governance checks
Preferred implementation
Research thesis

Diversify economic functions, then test the implementation

The portfolio architecture comes first. Allocation policies are evaluated through modeled risk, realized outcomes, concentration, turnover and estimation sensitivity. No strategy is selected from a single performance statistic.

Equal Weight
Transparent neutral capital benchmark.
Risk Parity
Current preferred implementation, conditional on governance and risk-budget stability.
Optimized
Diagnostic comparator; concentration and sensitivity are explicit costs.
Portfolio architecture

Four economically distinct sleeves

XDEB.DE

Minimum Volatility Equity

Defensive global equity function.

IS3S.DE

Global Value Factor

Cyclical / recovery equity function.

PHPM.MI

Physical Precious Metals

Monetary and geopolitical stress diversifier. ETC/ETP, not a UCITS fund.

LYTR.DE

Energy & Metals Commodities

Inflation and supply-shock function.

Canonical public analysis begins after the January 2023 LYTR benchmark transition. Longer history should be treated as supplementary rather than homogeneous current-strategy history.

Current model state

Allocation and modeled risk

Equal Weight Risk Parity Optimized
Ticker
XDEB.DE 25.0% 27.2% 4.8%
IS3S.DE 25.0% 36.4% 60.0%
PHPM.MI 25.0% 12.6% 0.0%
LYTR.DE 25.0% 23.7% 35.2%
Current capital weights
Current capital allocations across the three policies.
Risk contribution comparison
Capital weight and modeled risk contribution are different quantities.
Correlation matrix
Observed correlation structure across the four economic functions.
Rolling historical evidence

Performance and governance scorecard

Canonical current-strategy evidence: rolling out-of-sample returns from 2024-02-01 to 2026-09-10. The underlying price sample begins after the January 2023 LYTR benchmark transition. The 252-day estimation window delays the first investable out-of-sample observation.
CAGR Volatility Sharpe Sortino Max Drawdown Average Rebalance Turnover Average Effective N Average Largest Weight Maximum Largest Weight Concentration Flag
Equal Weight 22.6% 12.7% 1.51 2.10 -12.6% 1.4% 4.00 25.0% 25.0% OK
Risk Parity 24.2% 12.3% 1.66 2.32 -12.9% 2.4% 3.70 35.9% 42.2% OK
Optimized 28.8% 14.7% 1.66 2.37 -13.7% 10.4% 2.10 58.8% 60.0% HIGH
Metric definitions: CAGR is geometric annualized growth. Sharpe uses annualized arithmetic mean excess daily return divided by annualized volatility. Sortino uses the same annualized mean excess return divided by annualized downside deviation relative to the daily equivalent of the configured risk-free rate.
Turnover definition: average one-way rebalance turnover is calculated against drifted pre-trade weights at each monthly rebalance. Portfolio weights are allowed to drift naturally between rebalance dates.
Growth of 100
Rolling out-of-sample policy paths. Historical performance is evidence, not a forecast.
Drawdowns
Realized drawdown is evaluated separately from covariance-based risk budgets.
Rolling Risk Parity weights
Rolling capital weights document implementation behavior; they do not prove stable realized risk.

Primary equity index benchmark

The primary equity-market reference used in this run is S&P 500 Total Return Index (^SP500TR). The preferred specification is the S&P 500 Total Return Index because it includes reinvested distributions and is therefore more comparable with adjusted ETF return series. If the total-return series is unavailable, the page labels the S&P 500 price-index fallback explicitly.

CAGR Volatility Sharpe Sortino Max Drawdown
S&P 500 Total Return Index 19.9% 15.4% 1.12 1.65 -18.7%

Investable comparators

For implementation-level comparison, Risk Parity is also compared with adjusted-price investable alternatives: SPPW.DE and a monthly rebalanced 60/40 SPPW.DE / SXRM.DE portfolio.

CAGR Volatility Sharpe Sortino Max Drawdown
Risk Parity 24.2% 12.3% 1.66 2.32 -12.9%
Investable Global Equity — SPPW.DE 16.3% 13.6% 1.04 1.45 -21.6%
Investable 60/40 — SPPW.DE / SXRM.DE 10.5% 8.4% 0.99 1.41 -12.7%
All metrics use the same canonical calendar start and end dates, but each series retains its own trading calendar. The chart aligns normalized wealth levels with forward-filling between market holidays; strategy returns are never deleted merely because another market was closed.
Risk Parity, S&P 500 and investable comparators
Canonical out-of-sample comparison. The S&P 500 benchmark is the primary equity-market reference; SPPW.DE and the 60/40 portfolio are investable comparators.
Supplementary historical context

Extended evidence — context, not current-strategy history

The same rolling methodology is applied to the longest common four-instrument yfinance history, producing out-of-sample returns from 2020-03-02 to 2026-09-10. These results provide context only because LYTR's benchmark changed in January 2023. Pre-2023 observations are therefore not a homogeneous history of the current benchmark specification.

Do not merge these metrics with the canonical current-strategy evidence and do not use the extended window to claim a longer live-equivalent track record.
CAGR Volatility Sharpe Sortino Max Drawdown
Equal Weight 13.2% 13.5% 0.84 1.14 -21.7%
Risk Parity 13.9% 13.1% 0.91 1.24 -21.2%
Optimized 16.8% 15.0% 0.98 1.36 -19.8%
Supplementary extended growth of 100
Supplementary extended rolling evidence. Pre-2023 LYTR history reflects an earlier benchmark specification and is shown only for context.
Scenario analysis

Monte Carlo as a robustness lens

A moving-block bootstrap resamples observed rolling Risk Parity returns. The distribution is conditional on the historical sample and should not be read as a probability forecast.

Metric Value
Median terminal value 125.51
5th percentile terminal value 101.94
95th percentile terminal value 150.31
5th percentile return +1.94%
Probability of loss 3.5%
Expected Shortfall — worst 5% mean return -3.83%
Interpretation
Lower-tail statistics are expressed as returns to avoid ambiguous sign conventions. Expected Shortfall is the mean return of the worst 5% of simulated terminal outcomes. Regime changes, liquidity shocks and structural breaks can still produce outcomes outside the simulated distribution.
Monte Carlo distribution
One-year moving-block bootstrap from initial portfolio value 100.
Current monitoring

YTD realized strategy performance and current-weight attribution

Two different questions are shown separately. Realized YTD strategy performance comes from the rolling monthly out-of-sample Risk Parity return series, using the weights that applied through time. Current-weight YTD attribution is a diagnostic that applies today's weights to each asset's YTD return; it is not a realized strategy track record.

Realized YTD strategy vs benchmarks

YTD Return
Realized Risk Parity Strategy 23.8%
Dow Jones 7.6%
S&P 500 10.7%
DAX 3.3%

Current-weight attribution summary

Diagnostic Value
Current-weight YTD attribution — risky sleeve 23.9%
Current-weight YTD attribution — total portfolio (90% risky overlay) 21.5%
Realized YTD Risk Parity monitoring
Benchmark comparison uses the realized rolling Risk Parity YTD return, not a backward application of today's portfolio weights.

Current-weight YTD attribution by sleeve

Current Risky Weight Current Total Portfolio Weight Asset YTD Return Current-Weight Attribution (Risky Sleeve) Current-Weight Attribution (Total Portfolio)
XDEB.DE 27.2% 24.5% 6.8% 1.9% 1.7%
IS3S.DE 36.4% 32.8% 36.6% 13.3% 12.0%
PHPM.MI 12.6% 11.4% -5.1% -0.6% -0.6%
LYTR.DE 23.7% 21.3% 39.5% 9.4% 8.4%
Current-weight attribution is an explanatory decomposition only. It answers: “What would today's weights imply when applied to YTD asset returns?” It should not be described as realized portfolio performance.
Research conclusion

Preferred implementation: Risk Parity — conditional, not doctrinal

Why Risk Parity is preferred here
It seeks to distribute modeled portfolio risk across the four economic functions while preserving broader participation than the optimized comparator.
Why optimization remains in the research
Diagnostic comparator. Historical efficiency must be weighed against concentration, turnover and estimation sensitivity.

No automatic winner from return, Sharpe or a composite score. A change in covariance structure, concentration, turnover, sensitivity, instrument design or portfolio purpose can change the preferred implementation.

Disclosures

Limits of the evidence

This material is quantitative research and educational analysis, not individualized investment advice. Historical returns, risk contributions, correlation estimates and simulated distributions are sample-dependent. Transaction costs, taxes, liquidity, currency exposure, tracking difference and implementation constraints can materially alter realized outcomes.