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Super Strategy – Statistical Arbitrage Strategy Disclosure Document

I. Strategy Overview​

This strategy is designed to identify structural price dislocations that emerge within short time intervals in the cryptocurrency market and to capture the return generated as such dislocations revert toward their equilibrium range. Built upon high-frequency structural monitoring, residual modeling, and multi-layered risk constraints, the strategy forms a trading framework capable of maintaining stable performance across varying market conditions.

The system operates in a fully automated environment. Through a large number of independent, repeatable micro-structure opportunities, the strategy accumulates returns with low drawdowns and a smooth growth pattern.

The strategy does not rely on directional forecasts or trend-driven environments. Its return is derived from the market’s inherent self-correcting dynamics within the time-series structure. By continuously monitoring price trajectories, liquidity states, and order-book depth changes, the model identifies localized inefficiencies and engages in parameterized trading within the correction interval. The execution engine prioritizes robustness, balancing efficiency and stability through strictly defined entry/exit zones, maximum exposure durations, and explicit limits on risk exposure.

II. Investment Philosophy and Model Framework​

The cryptocurrency market, driven by continuous trading and fragmented liquidity distribution, frequently exhibits short-lived price distortions and asymmetric fluctuations. The strategy models the statistical properties of such dislocations, focusing on key dimensions including deviation magnitude, formation speed, reversion probability, and liquidity elasticity. The modeling framework operates simultaneously across asset pairs, time horizons, and depth layers, enabling structural classification of instantaneous price behaviors.

Dislocation signals are derived from changes in residual sequences, such as sudden expansions in price spreads, declines in cross-asset synchronization, shifts in liquidity steps, and breakdown-and-reconstruction cycles of autocorrelation structures. When deviations extend beyond the noise threshold and present clear reversion momentum, the trading engine activates, capturing the core segment of the reversion process. This framework remains functional across diverse market regimes, enabling consistent opportunity density throughout the year.

III. Execution and Operational Mechanism​

All instructions within the strategy are triggered by the automated engine based on model-defined conditions, including zone entry, zone exit, exposure adjustments, and risk-boundary restoration.

The execution framework emphasizes the following operational characteristics:

  • Higher trade density when structural clarity emerges
  • Automatic reduction or suspension of activity during market disorder
  • Explicit maximum exposure duration for all positions
  • Immediate exit once residual correction is completed

Through these mechanisms, the strategy generates returns by accumulating high-frequency, small-scale gains derived from a large number of repeatable opportunities, resulting in a stable and smooth performance curve.

IV. Core Risk-Control Framework​

The strategy employs a rigorous multidimensional risk-control matrix. Real-time computed risk variables determine the permissibility of each trading action. The following indicators constitute the foundational constraints of the system and serve as auditable risk boundaries for custodians and investors.

1. Net Leverage Ratio (NLR)​

NLR = |Net Leverage Notional Value| / AUM Value × 100% Requirement: NLR ≤ 6% This indicator limits the net directional exposure of the portfolio. When the NLR exceeds its threshold, the system adjusts exposures within a predefined remediation window.

2. Gross Leverage Ratio (GLR)​

GLR = |Gross Leverage Notional Value| / AUM Value Requirement: GLR ≤ 2 GLR controls the overall exposure density of the portfolio and prevents concentrated leverage buildup. When GLR approaches the upper bound, the system reduces position size or lowers trade frequency.

3. Maximum Drawdown (MDD%)​

MDD% = (Peak AUM – Trough AUM) / Peak AUM × 100% Requirement: MDD% ≤ 15% MDD% acts as the ultimate risk boundary. Surpassing the limit constitutes a material risk event. Position resizing, volatility filtering, and execution constraints ensure that drawdowns remain within acceptable limits.

4. Underlying Concentration Ratio (UCR)​

UCR = Notional Value of Single Underlying / Total Gross Notional Value × 100% Requirement: UCR ≤ 5% UCR ensures diversification across underlying assets and prevents excessive exposure to any single token, trading pair, or structural path.

5. Maintenance Margin Ratio (MM%)​

MM% = Maintenance Margin / Equity × 100% Requirement: MM% ≤ 30% This metric protects against exchange-level liquidation risk. By adjusting exposures, reducing leverage, and increasing available equity, the system maintains a stable margin structure. Together, these indicators create a complete and self-reinforcing risk-control loop. The strategy operates within a strictly auditable and quantitatively managed framework, ensuring no single event can lead to uncontrolled losses.

V. Return Structure and Performance Characteristics​

Historical performance indicates a distinctive return distribution: Returns accumulate with moderate volatility. Monthly gains generally fall within the 3%–7% range, with certain months reaching double-digit performance when dislocation opportunities are abundant. Loss-bearing months are rare and remain mild in magnitude. This behavior aligns with the characteristics of a structured short-cycle strategy, where returns originate from numerous independent opportunities rather than directional exposure. The overall risk profile is clear, controlled, and transparent.

VI. Investor Suitability​

The strategy is appropriate for investors seeking stable, rules-based, and risk-transparent return generation. It is particularly suitable for:

  • Institutional allocation requiring stable performance trajectories
  • Portfolios prioritizing minimal directional exposure
  • Capital sensitive to drawdown constraints
  • Investors seeking non-directional participation in the cryptocurrency market

By accumulating returns from structural market opportunities, the strategy demonstrates durability, repeatability, and predictability over long-term investment horizons.