AZIMUTH QUANT

Quantitative Strategies & Market Research

Azimuth BTC Strategy

Systematic BTC/PAXG/USDT Allocation Strategy

Strategy Factsheet

Proprietary quantitative framework dynamically allocating capital between Bitcoin exposure, a PAXG defensive sleeve and a USDT cash bridge using proprietary market-regime, on-chain, recovery, risk-control and USDT/PAXG defensive overlay signals. It is presented as a systematic, rules-based, tested and audited allocation strategy, governed by a formal review and optimization protocol.

Backtest Period:

January 2015 – June 2026

Data Frequency:

Daily

Strategy Version:

Public Investor Version

Governance:

Systematic · tested · audited · review protocol

Last Updated:

June 2026

Executive Summary (Net Base Case)

Equity Curve (Net Base Case)

Y-axis shown as Index (Base 100) with a secondary Multiple (×) scale for readability.

Equity shown in logarithmic scale.

Initial capital: $10,000 · Period: January 2015 – June 2026 · Daily mark-to-market simulation · Data source: BTC and PAXG/gold proxy historical prices

Data & Methodology

Execution Model: The base model assumes a fully funded spot implementation: BTC spot exposure with capital temporarily allocated to PAXG/gold and USDT/cash bridge sleeves when BTC exposure is reduced. The current version includes the USDT/PAXG Defense V2 R90/F75 overlay: when the engine already requires a meaningful USDT sleeve and PAXG/gold is trend-weak, PAXG exposure can be temporarily shifted to USDT, with staged partial PAXG restoration after prolonged defensive periods. No leverage or margin borrowing is included in the base NAV.

BTC Benchmark: BTC benchmark represents a buy-and-hold spot BTC position normalized to the same initial capital as the strategy, with no trading costs applied.

Turnover Proxy: Spread and slippage stress tests use an allocation turnover proxy defined as the absolute day-to-day change in btc_alloc (|Δ btc_alloc|). This provides a conservative approximation of capital rotation when a full trade ledger is unavailable.

Carry Assumptions: Additional carry scenarios are shown only as generic implementation-cost diagnostics. They are not part of the public base implementation, which is fully funded and spot-only.

Backtest window: January 2015 – June 2026. The starting date was selected to ensure consistent data integrity, sufficient market liquidity, and reliable trading infrastructure conditions.

Performance is calculated using daily mark-to-market equity based on historical BTC and PAXG/gold proxy closing prices. Strategy results are shown net of execution costs as specified in the assumptions section.

Key Metrics (Strategy vs BTC Hold)

Daily Tail Risk: VaR & Expected Shortfall

Series	Daily Volatility Used	Daily VaR 95%	Daily CVaR / Expected Shortfall 95%	Method
Strategy Net Base Case	1.93%	-3.17%	-3.98%	Parametric approximation from realized annual volatility; 365-day convention.
BTC Hold	3.55%	-5.83%	-7.32%	Same methodology for a like-for-like benchmark comparison.

Methodology: one-day VaR 95% estimates the loss threshold exceeded by roughly the worst 5% of modeled daily observations. CVaR / Expected Shortfall 95% estimates the average loss inside that worst 5% tail. For this public BTC factsheet, the calculation is a parametric approximation using annualized realized volatility shown above, converted to daily volatility with √365 and a zero-drift normal assumption. VaR is not a worst-case loss and does not capture exchange outages, liquidity gaps, model error, tax effects or future regime shifts.

Implementation Scenarios & Generic Carry Stress

Carry-stress scenario uses a conservative 5% annual generic carry assumption applied daily to invested BTC allocation. The public base implementation remains spot-only and fully funded.

Out-of-Sample Validation (Time-Based Holdout)

This is a time-based holdout split designed to evaluate robustness across distinct market regimes. It is not walk-forward re-optimization. The split reduces hindsight bias while keeping proprietary signal logic undisclosed.

Development: 2015–2019 · Out-of-sample: 2020–June 2026 (net base case)

Rolling Stability (1Y)

Rolling max drawdown (1Y)

Rolling volatility (1Y, annualized)

Rolling metrics help validate consistency across regimes without revealing model internals.

Methodology & Governance

Investor summary: This factsheet presents the strategy universe, assumptions, historical simulation, risk metrics, stress tests and governance process in a format designed for investor review. Approved clients receive the live execution instructions required to follow the strategy.

Research & validation approach

Fees & Slippage Sensitivity

CAGR sensitivity vs round-trip cost (bps)

Costs are applied per leg/side. Round-trip cost = 2× per-side.

Implementation Scenarios

Spot base NAV compared with generic implementation-cost stress scenarios using daily btc_alloc.

CAGR sensitivity to annual carry rate (applied daily on btc_alloc).

Same comparison in log scale for readability across regimes.

Relative outperformance vs BTC (equity-normalized): Strategy / BTC - 1.

Cost Robustness Frontier

Joint stress test: carry (annual %) on BTC exposure and round-trip spread+slippage (bps) applied on an allocation-turnover proxy. Heatmap shows CAGR advantage vs BTC.

Cost Sensitivity

Same strategy, different implementation-cost assumptions. Scenarios below illustrate how results may vary under the spot base case, higher fee/slippage assumptions and generic annual carry stress. Carry stress is modeled as an annualized implementation-cost drag applied to the strategy's average BTC allocation.

Probability of Ruin & Recovery After Shock

“Ruin” is defined here as an ≥80% capital impairment (equity falling below 20% of initial capital). Probabilities are estimated from bootstrap resampling and rolling-window diagnostics.

Synthetic Stress Scenarios

Transparent transformations of the daily return series (net base case).

Stress Comparison vs BTC

Stress scenarios are synthetic transformations of returns and do not represent forecasts. The table highlights whether the strategy improves outcomes vs passive BTC under adverse conditions.

Robustness & Risk

Underwater curve — Strategy (Net)

Underwater curve — BTC Hold

Rolling Sharpe (1Y, rf=0) — Net vs BTC Hold

BTC allocation — Strategy (Net)

Volatility-matched BTC benchmark (log)

Rolling CAGR (1Y) — Strategy (Net)

Rolling CAGR (3Y) — Strategy (Net)

Monte Carlo (Bootstrap)

Equity fan (P5–P95, log)

CAGR distribution

Max drawdown distribution

Monte Carlo Summary

Regime Analysis (Bull / Bear / Sideways) — Strategy vs BTC

Strategy Profile

The model is designed as a systematic, rules-based and low-turnover allocation framework rather than a high-frequency trading system.

The strategy has been backtested, stress-tested, audited against the internal engine package, and placed under a defined review protocol so that future parameter changes require evidence rather than discretionary adjustment.

Its objective is to capture major Bitcoin market cycles while reducing downside volatility through defensive allocation during adverse market regimes.

Portfolio exposure changes only when the underlying regime model shifts, resulting in relatively infrequent allocation adjustments. Entry and exit decisions are driven by systematic regime signals designed to identify structural changes in market conditions, rather than short-term trading activity.

The objective is not to maximize trading frequency, but to improve risk-adjusted performance relative to passive Bitcoin exposure through disciplined exposure management.

The strategy is therefore structurally different from leveraged or high-frequency crypto trading systems.

Capacity & Liquidity Considerations

The strategy is designed for liquid Bitcoin markets with low turnover relative to typical crypto trading systems. Execution assumptions use the base execution cost of 10 bps per leg, with additional sensitivity tests.

Primary implementation: fully funded spot allocation across BTC, PAXG and USDT
Turnover: moderate to low compared to high‑frequency crypto strategies
Cost modeling: includes fees, slippage, and generic carry stress scenarios
Capacity expected to scale with market liquidity conditions

Review & Optimization Protocol

Recommended control framework

Governance objective: preserve the strategy as a systematic, tested and auditable process. The review protocol exists to monitor degradation, execution risk, data quality and structural market changes without turning the model into discretionary or reactive optimization.

Recommendation: this should not be treated as a single automatic optimization every two years. The safer approach is a full biennial research review plus ongoing monitoring. Parameters remain frozen unless the evidence shows a real structural change and a candidate passes robustness gates.

Monthly / quarterly monitoring: BTC realized volatility 365d/730d, rolling 1Y/2Y/3Y returns, rolling drawdown, signal-health monitoring, latency watch, data quality, average exposure drift, turnover, cost/carry sensitivity and return concentration.

Every two years: re-run the complete backtest with the same warm-up logic, refresh rolling/stress/Monte Carlo/bootstrap/leave-one-cycle-out/delay/cost studies, and only then test whether volatility-dependent risk-control settings should become adaptive.

Change gates: any candidate must improve robustness without materially worsening Max DD, Ulcer, worst 1Y/2Y, average drawdown, trade concentration or execution burden. No parameter should be changed only because a new two-year date arrived.

Review decision: keep the strategy logic frozen and use this protocol as surveillance. Research changes only if BTC structural volatility, on-chain data quality, cost regime or rolling behavior materially deteriorates.

Institutional Notes (Methodology & Interpretations)

Why “Shock −80” can show ~83.70 Max DD

The “Shock −80” scenario is a deliberately severe tail-risk diagnostic built by injecting an extreme crash into the daily return series (net base case). This stress exceeds typical historical conditions and is intended to probe structural resilience rather than represent a baseline expectation. Under such a discontinuous shock, peak-to-trough drawdowns can approach total impairment even for robust strategies.

Interpretation is based on comparative behavior (strategy vs BTC), recovery characteristics, and probability metrics—not the single worst point estimate.

This factsheet therefore also reports: recovery time after shock, probability of ruin, and stress comparisons vs BTC.

Short module

Short exposure was evaluated during research but was not incorporated, as it did not improve risk-adjusted efficiency under realistic execution assumptions. The framework therefore focuses on dynamic long exposure with defensive allocation.

Parameter sensitivity

Parameter sensitivity checks were performed manually across reasonable ranges to confirm the model is not dependent on a narrow “knife-edge” configuration. Results remained stable within practical parameter bands, supporting robustness and reducing overfitting concerns.