Quantitative framework dynamically allocating capital between Bitcoin exposure and a defensive sleeve using systematic signals.
Backtest Period:
January 2015 – February 2026
Data Frequency:
Daily
Strategy Version:
v1.0
Last Updated:
February 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 – February 2026 · Daily mark-to-market simulation · Data source: BTC historical prices
Data & Methodology
Execution Model: The base model assumes a fully funded spot implementation: BTC spot exposure with capital temporarily allocated to a defensive reserve asset when BTC exposure is reduced. No leverage, margin borrowing, or derivative funding costs are 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.
Funding Assumptions: Derivative implementation scenarios use illustrative financing assumptions. Binance perpetual funding is approximated at ~5% annualized and CFD swap financing at ~10% annualized. Actual rates vary over time and are applied daily to BTC exposure through btc_alloc.
Backtest window: January 2015 – February 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 closing prices. Strategy results are shown net of
execution costs as specified in the assumptions section.
Derivatives scenario approximates funding / swap carry (CFD & perpetual futures) using a conservative 5% annual rate applied daily to invested notional (when BTC allocation > 0).
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: February 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 (Non-Disclosive)
Research & validation approach
Fees & Slippage Sensitivity
CAGR sensitivity vs round-trip cost (bps)
Costs are applied per-side (entry and exit). Round-trip cost = 2× per-side.
Implementation Scenarios
Spot (base NAV) vs derivative implementations (Binance Perps funding, CFD swap) using daily btc_alloc.
CAGR sensitivity to annual funding/swap 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: funding/swap (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 execution venues. Scenarios below illustrate how results may vary under typical Spot, Perpetual Futures (funding), and CFD (swap) cost structures.
Funding/swap are modeled as an annualized carry cost 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.
The model is designed as a low-turnover allocation framework rather than a high-frequency trading system.
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 are conservative (15 bps per side).
Primary instruments: BTC spot or derivatives (CFD / perpetual futures)
Turnover: moderate to low compared to high‑frequency crypto strategies
Cost modeling: includes fees, slippage, and optional funding scenario
Capacity expected to scale with market liquidity conditions
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.
RISK DISCLOSURE. The information provided on this website is for informational and educational purposes only and does not constitute investment advice. Past performance does not guarantee future results. Digital asset investments involve significant risk and may not be suitable for all investors.
