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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$10245.12Sharpe ratio0.32
Total return2.45%Sortino ratio0.23
CAGR4.39%Calmar ratio0.36
Volatility (annualised)19.46%Profit factor1.14
Days live139Maximum drawdown-12.25%

Process consistency

Positive months42.9%
Best month5.68%
Worst month-9.19%
Recovery from max drawdownstill underwater

Market independence

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

BenchmarkCorrelation90-day rolling correlationBeta
S&P 500 (SPY)0.370.000.71
Bitcoin (BTC-USD)0.190.230.12

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.

XAUSyntheticMetalTreeBot live equity curve

Drawdown profile

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

XAUSyntheticMetalTreeBot drawdown profile

Current holdings

SymbolQuantity
USD0.0000
^XAU31.1948

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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