Systematic Trading Across Digital Asset Markets

Models & Signal Generation

We maintain a library of models that capture relationships between instruments at varying horizons — cross-asset relationships, spot–perpetual basis dynamics, calendar spreads across futures maturities, and relationships between derivatives and underlying spot markets. Each relationship is parameterised using historical data but continuously updated to reflect prevailing market conditions. Signal generation is based on deviations from estimated fair value relationships, normalised by volatility and adjusted for liquidity conditions. Entry thresholds are defined such that expected convergence exceeds total execution cost, including fees, slippage, and model uncertainty.

Trade Construction & Execution

Trade construction is explicitly hedged. Positions are structured to isolate the relative value component while minimising exposure to outright market direction — typically involving simultaneous long and short positions across correlated instruments, sized according to estimated hedge ratios and adjusted for liquidity constraints. Execution is coordinated across venues to minimise legging risk. Where possible, trades are executed atomically or with tight sequencing logic to prevent partial fills from creating unintended directional exposure. In less liquid markets, execution strategies prioritise completion probability over immediacy.

Risk Management & Regime Detection

Risk management is embedded at both the position and portfolio levels. Individual trades are monitored for divergence beyond expected bounds, with stop-loss and de-risking mechanisms triggered when relationships break down or regime shifts are detected. Portfolio-level constraints ensure diversification across signals, instruments, and time horizons. Our models incorporate regime detection components that adjust signal weighting, thresholds, and capital allocation in response to changing volatility, correlation structures, and liquidity conditions.

Carry, Funding & Model Maintenance

Funding rates, carry, and financing costs are explicitly incorporated into expected return calculations. In perpetual futures, the cost or benefit of funding can materially impact profitability and is treated as an integral component of the signal rather than an external adjustment. Model decay and crowding are continuously monitored — signals that exhibit persistent degradation are either recalibrated or retired. We assume that any sufficiently profitable and observable relationship will attract competition, compressing returns over time.

Execution & Latency

Execution is designed to minimise latency risk and legging exposure. Where both sides of a trade can be executed simultaneously, orders are submitted in parallel. Where this is not possible, sequencing logic prioritises the leg with higher execution certainty while managing the risk of incomplete fills. Inventory is pre-positioned across venues to enable rapid execution without reliance on transfers, which introduce latency and settlement risk. Capital allocation is dynamically adjusted based on observed opportunity frequency, execution quality, and counterparty risk. Latency asymmetry is a central consideration — faster data feeds can provide a predictive edge, but also expose the strategy to adverse selection if slower venues update with delay. Our models distinguish between actionable arbitrage and transient noise that cannot be captured reliably.

Operational Risk & Capacity

Operational risk is non-trivial in this domain. Exchange outages, API instability, and withdrawal delays can all impact execution and capital availability. Venue-specific risk assessments are incorporated into capital allocation decisions, with redundancy maintained where feasible. Decentralised venues introduce additional complexity — block confirmation times, gas costs, and smart contract risk — and are evaluated only when expected returns justify the additional friction. The strategy is inherently capacity-constrained: as capital increases, the ability to deploy it efficiently into available spreads diminishes. Cross-venue arbitrage is, in practice, less about identifying price differences and more about executing reliably within tight time constraints. The edge lies in infrastructure, execution discipline, and risk control rather than in the visibility of the opportunity itself.

Execution & Queue Management

Execution behaviour adapts continuously to market conditions. In balanced markets with low volatility, strategies may provide liquidity passively, capturing spread through high fill rates. During periods of directional flow or imbalance, strategies switch to more aggressive execution or withdraw liquidity entirely to avoid adverse selection. Queue management is a key component — given price-time priority, the value of an order depends on its position in the queue. Our systems estimate expected fill probability based on queue length, cancellation rates, and trade flow, adjusting order placement accordingly. Signals indicating informed flow, such as rapid price movement or sustained aggressive trading in one direction, trigger adjustments in quoting behaviour: widening spreads, reducing size, or cancelling orders to avoid execution at unfavourable prices.

Risk Controls & Performance

Transaction costs, including fees and rebates, are incorporated into all decision-making. Strategies are calibrated to ensure that gross edge exceeds these costs on a consistent basis. Risk controls operate at high frequency — position limits, loss thresholds, and exposure constraints are enforced in real time, with automated mechanisms to reduce or halt trading if limits are breached. The performance of high-frequency strategies is characterised by a large number of small trades, each with modest expected value. Profitability emerges from consistency and scale rather than from isolated large gains. Stability of execution and robustness of models are prioritised over short-term optimisation.

Position Management & Hedging

Positions are actively managed rather than passively held. As prices move, liquidity ranges are adjusted to remain relevant to current market conditions — involving removal and redeployment of capital to new ranges, with consideration given to gas costs and execution timing. Exposure resulting from on-chain positions is hedged using centralised markets, allowing us to isolate fee income while reducing directional risk. Hedging strategies are calibrated to account for the discrete nature of AMM liquidity and the non-linear exposure profile associated with concentrated positions.

Costs, Risks & Constraints

Transaction costs on-chain — including gas fees and slippage — are explicitly incorporated into decision-making, with strategies designed to ensure expected fee income exceeds these costs over the relevant time horizon. Smart contract and protocol-specific risks are assessed, with capital allocation across protocols reflecting both expected return and risk, diversified to mitigate exposure to any single system. Block times and transaction confirmation introduce delays that limit the ability to react instantaneously to market changes. Our models account for these constraints, favouring strategies that are robust to slower execution and integrating on-chain activity with our broader trading operations rather than treating it as a standalone strategy.

Trade Construction & Hedging

Trades are constructed to isolate volatility exposure while minimising directional risk, typically involving combinations of options positions dynamically hedged using the underlying asset to manage delta. Realised volatility is continuously monitored against implied levels — when implied is elevated relative to expected realised, strategies may involve selling volatility; when implied is low, buying volatility may be warranted. These decisions are made within a risk-controlled framework that accounts for uncertainty and tail risk. Greeks — including delta, gamma, vega, and theta — are actively managed, with hedging performed dynamically to maintain desired exposure profiles, particularly in response to large price movements or volatility shifts.

Execution, Risk & Performance

Liquidity in options markets can be uneven, and execution requires careful consideration of bid-ask spreads and market depth. Trades are structured to minimise impact and transaction cost. Risk management is central, particularly given the non-linear nature of options — stress scenarios including large price moves and volatility spikes are incorporated into position sizing and portfolio construction. The strategy is evaluated on its ability to generate consistent returns from volatility risk premia and relative value opportunities, with emphasis on disciplined hedging and risk control.