Market structure determines how derivatives strategies behave in practice. In crypto futures, the structure is defined by perpetual funding, liquidation mechanisms, and a diverse participant mix. These elements influence everything from price discovery to volatility. A strategy that ignores structure…

Performance attribution in futures trading is not just about returns; it is about understanding where returns come from relative to risk. A professional attribution framework decomposes PnL into strategy effects, execution effects, and market conditions. This allows a desk to…

Microstructure variables such as tick size, spread, and depth determine the true cost of trading perpetual futures. Tick size influences how quickly prices can move and how granular the order book is. Smaller tick sizes allow tighter spreads but can…

Position sizing is often treated as a pure risk‑return calculation, but in futures markets, execution constraints can dominate. A position size that looks optimal on a risk model can be untradeable if the order book cannot absorb it without significant…

Index construction and mark price integrity are foundational to the stability of perpetual futures. The mark price determines liquidation triggers and unrealized PnL calculations, so inaccuracies can cause unfair liquidations. A professional analysis begins with the index methodology: which spot…

Volatility targeting is a portfolio‑level technique that scales exposure according to market conditions. In crypto futures, where volatility regimes can shift rapidly, targeting a stable volatility band can improve risk‑adjusted performance. The core idea is to reduce leverage when volatility…

Cross‑venue basis arbitrage exploits price discrepancies between futures venues trading the same contract. While the idea appears straightforward, the execution is complex due to latency, fees, and differing funding mechanics. A professional approach begins by quantifying persistent basis gaps after…

Liquidation clusters are one of the most actionable structural signals in crypto futures. They indicate price zones where forced closures are likely, creating feedback loops that can amplify volatility. A professional approach to cluster mapping begins with estimating the distribution…

Funding rates are a structural feature of perpetual futures and provide a unique signal environment. When funding becomes extreme, it often indicates crowded positioning, creating conditions for mean reversion. A research‑grade signal must quantify how funding extremes translate into price…

A practical entry model for perpetual futures must begin with the order book, not the chart. Thin books amplify execution costs, making otherwise sound signals untradeable. The research objective is to quantify how depth, spread, and refill speed shape the…