AZIMUTH QUANT

Quantitative Strategies & Market Research

Azimuth BTC Strategy

Systematic Bitcoin Allocation Strategy

Strategy Factsheet

Proprietary quantitative Bitcoin-focused allocation framework. Public material describes historical performance, risk, validation and implementation assumptions while withholding the production portfolio-construction and decision logic.

Backtest Period:

January 2015 – August 2026

Data Frequency:

Daily

Strategy Version:

Public Investor Version

Governance:

Systematic · tested · audited · review protocol

Last Updated:

August 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 – August 2026 · Daily mark-to-market simulation · Data source: validated historical market data

Data & Methodology

Execution Model: The base model assumes a fully funded spot implementation with no leverage or margin borrowing. The complete instrument set, allocation limits, transition rules and internal safeguards are proprietary and are intentionally omitted from the public factsheet.

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 Measure: Spread and slippage stress tests use a consistent modeled portfolio-turnover reference. The exact allocation series and internal portfolio construction fields are not disclosed publicly.

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 – August 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 market reference 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 / Method	Daily VaR 95%	Expected Shortfall 95%	Worst Observed Day
Strategy Net — historical	-2.60%	-4.51%	-13.41%
Strategy Net — parametric approximation	-3.57%	-4.47%	Not applicable
BTC Hold — historical	-5.36%	-8.28%	-38.97%
BTC Hold — parametric approximation	-5.82%	-7.30%	Not applicable

VaR is a loss threshold associated with a confidence level, not a worst-case loss. Historical expected shortfall is the average loss beyond historical VaR. Parametric figures use a zero-drift normal approximation and can understate non-normal tail events. Exchange, liquidity, custody, issuer, de-pegging and model failures are outside these estimates.

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–August 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.

Rolling-window summary

Window	Worst	Median	Mean	Positive windows
6M	-20.84%	45.19%	80.45%	94.9%
1Y	10.34%	128.23%	251.29%	100.0%
2Y	68.79%	489.56%	1069.95%	100.0%
3Y	290.49%	1526.45%	3246.63%	100.0%

Windows start on every available date and overlap. They are entry-date sensitivity diagnostics, not thousands of independent market histories.

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

Base portfolio compared with generic implementation-cost stress scenarios. Exact allocation logic is not disclosed.

CAGR sensitivity to generic annual implementation-cost drag.

Same comparison in log scale for readability across regimes.

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

Cost Robustness Frontier

Joint implementation-cost stress test. Heatmap shows the strategy’s CAGR advantage versus passive Bitcoin under progressively harsher cost assumptions.

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 on the portfolio.

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.

The audit improves confidence but cannot prove future profitability. Production portfolio-construction and decision logic are intentionally omitted from the public factsheet.

Test family	Result
Nearby-parameter sensitivity	304 of 304 tested neighboring configurations remained above BTC Hold and the strategy remained inside a broad viable region.
Independent starts	All tested annual start dates remained positive versus BTC Hold over the available endpoint.
Leave-cycle-out	The advantage remained after excluding each major cycle block.
Asset failure / unavailability	Anonymized component-removal and modeled disruption scenarios remained within the documented robustness range; component identities and exposure limits are confidential.
Order failure / missing signals	Random missed implementation events and retry delays generally preserved the historical edge; extreme repeated failures can materially reduce it.
Adverse path clustering	The main structural weakness in this diagnostic is a high concentration of severe adverse moves immediately after portfolio changes; sufficiently extreme clustering can materially reduce the historical edge.

Operational robustness summary

Anonymized implementation scenario	CAGR	Max DD	Calmar
Scenario A	158.33%	-27.02%	5.86
Scenario B	139.92%	-29.26%	4.78
Scenario C	136.03%	-29.92%	4.55
Scenario D	124.93%	-31.42%	3.98
Scenario E	110.53%	-31.09%	3.55
Scenario F	102.63%	-36.26%	2.83

Operational implementation sensitivity

Disciplined execution: the live process includes private safeguards intended to reduce uneconomic micro-adjustments and tracking noise. Exact thresholds, batching rules and synchronization logic are confidential.

Execution & Structural Robustness

Robustness & Risk

Underwater curve — Strategy (Net)

Underwater curve — BTC Hold

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

Public process overview — portfolio components, weights and signal logic intentionally omitted.

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 improve long-horizon risk-adjusted outcomes relative to passive Bitcoin exposure through a proprietary systematic portfolio process.

Portfolio changes are systematic and relatively infrequent. The production decision logic is confidential.

The objective is not to maximize trading frequency, but to improve risk-adjusted performance relative to passive Bitcoin exposure through disciplined systematic portfolio 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 a private approved instrument set; no leverage or short selling
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: rolling performance and risk, data quality, implementation quality, turnover, modeled costs and return concentration.

Every two years: re-run the complete backtest and refresh rolling, stress, Monte Carlo, resampling and implementation-sensitivity studies before considering any model change.

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 require material evidence from data quality, implementation conditions and validated performance diagnostics.

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.

Directional exposure policy

The public implementation is fully funded and non-leveraged. Exploratory research alternatives are not part of the production strategy and do not reveal the private portfolio-construction logic.

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.