Quantitative Researcher Spot Futures Market Making
What You ll Do
- Design advanced market making algorithms Adaptive Pricing Hedging: Design pricing and hedging algorithms for Spot and Futures markets, specifically managing basis and funding-rate risk across the two. Liquidation Logic: Research and optimise intelligent liquidation models to minimise market impact and slippage in high-leverage Futures environments. Quoting Inventory: Develop models for optimal quoting, spread setting, and inventory management that hold up across wide-ranging market conditions.
- Research crypto microstructure signals Order Book Intelligence: Analyse limit order book (LOB) dynamics to identify toxic flow from sophisticated actors, and aggregate liquidity across fragmented CEX/DEX venues. Regime Detection: Build models to detect shifts in market volatility and liquidity so our bots can widen or tighten spreads as regimes change.
- Build predictive risk strategy monitoring Risk Prediction: Build predictive models for inventory risk management,
ensuring optimal position sizing across volatile crypto assets. Strategy Health: Design monitoring that detects strategy decay or anomalous P and L behaviour on live bots before it becomes critical leveraging automated, always-on watchdog agents for 24/7 coverage.
- Deep-dive RD tech liaison Emerging Venues: Conduct long-term research on liquidity provision in emerging crypto sub-sectors (e.g. L2s, Perpetual DEXs). Technical Blueprints: Author BRDs (Business Requirement Documents) to guide the Quant dev team in implementing your strategy and agent architectures.
- Backtesting AI-driven research pipelines Robust Backtesting: Develop rigorous backtesting environments including adversarial stress scenarios such as flash crashes and exchange outages to validate MM strategies before deployment. Research Automation: Use AI-driven pipelines to automate feature engineering and scale backtesting across thousands of crypto pairs, accelerating the research loop.