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Investment thesis

Tree-based ML — economic intuition. Supervised non-linear models exploit conditional relationships between engineered features (volatility regime, cross-asset spreads, calendar effects) and short-horizon return distributions. The edge comes from noisy but persistent micro-structure in FX and metals where linear factor models under-fit interaction terms.

Risk-adjusted performance — live track record

Forward-tested daily against live market data. Metrics derived from end-of-day portfolio marks; methodology documented on the Due Diligence and About pages.

ReturnValueRisk-adjustedValue
Current portfolio worth$10918.07Sharpe ratio1.27
Total return9.18%Sortino ratio0.65
CAGR16.85%Calmar ratio3.72
Volatility (annualised)13.28%Profit factor2.18
Days live139Maximum drawdown-4.52%

Process consistency

Positive months28.6%
Best month5.73%
Worst month0.00%
Recovery from max drawdown1 days

Market independence

Correlation and beta versus passive benchmarks, computed over the full live series.

BenchmarkCorrelation90-day rolling correlationBeta
S&P 500 (SPY)0.150.000.19
Bitcoin (BTC-USD)0.140.170.06

A correlation materially below 1.0 to both benchmarks indicates the strategy’s returns are not a simple re-expression of long equity or long crypto beta.

Equity curve

Live track record — forward-tested performance from the strategy's production start date.

XAUZenbotTreeBot live equity curve

Drawdown profile

Underwater curve — percentage below the running high-water mark. Institutional allocators read this before the equity curve.

XAUZenbotTreeBot drawdown profile

Current holdings

SymbolQuantity
USD10918.0692

Research & documentation

🛡️ Skin in the game: Our principals and founders deploy their own capital alongside our clients using these exact quantitative models. We are aligned with your downside.

Other strategies in the Tree-based ML family:


For professional investors

Request the investor deck, DDQ, and extended analytics. Firm-gated and reviewed manually.

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