Quant Trader

**Position: Senior Quantitative Trader - Systematic Alpha & Execution** Role Overview: The trader will be responsible for deploying **research-driven, fully automated trading strategies**across equities, futures, or liquid macro products. You will manage **real-time signal ingestion, risk-normalised portfolio weights, and execution logic under latency constraints**, with direct access to infrastructure, capital, and bespoke research tooling. You are expected to manage the **entire research-to-production pipeline**, including alpha mining, regime modelling, transaction cost estimation, and performance attribution. **Core Responsibilities**: - Design and deploy **alpha-generating strategies**across stat arb, medium-frequency, and short-horizon signals using advanced statistical and ML techniques (e.g. Bayesian optimisation, tree-based models, PCA, feature orthogonalisation). - Conduct high-resolution **tick-level market microstructure analysis**, including order book dynamics, spread capture, adverse selection models, and queue position management. - Implement **execution frameworks**leveraging smart order routing (SOR), schedule-based execution (VWAP/TWAP), and custom execution algos sensitive to real-time volatility and liquidity. - Manage and monitor **risk-adjusted capital allocation**via volatility targeting, signal de-correlation, turnover optimisation, and capacity-aware constraints. - Interface with quant researchers and low-latency engineers to productionise models, calibrate execution engines, and deploy code into live environments under strict performance SLAs. - Backtest and stress test strategies using **multi-threaded simulation engines**across multiple data regimes (pre/post-fee, post-TCA, slippage-aware). - Proactively identify signal decay, latency arbitrage windows, execution drag, or regime shifts through ongoing analytics and internal tooling. **Required Expertise**: - **5-10+ years**of live trading experience in systematic alpha trading, ideally within a prop, HFT, or multi-manager hedge fund model. - Demonstrated track record of **persistent alpha**, ideally with Sharpe > 1.5 over multiple market regimes and statistically significant out-of-sample PnL. - Proficient in **Python, C++ (or Rust), KDB/Q**, with experience in distributed computing environments and event-driven architecture (e.g. Kafka, Redis, custom OMS/EMS). - Expertise in **real-time signal execution integration**, from model inference to order routing under millisecond-level latencies. - Strong grasp of **execution cost models**(Almgren-Chriss, propagator models), and working knowledge of **optimal execution theory**. - Advanced quantitative training — MSc/PhD in **applied mathematics, statistics, CS, or financial engineering**from a top-tier institution. **Preferred Edge**: - Experience running **delta-neutral, cross-sectional, or market-neutral books**, across APAC, US, or global hours. - Demonstrated ability to manage **drawdown and regime-specific tail risk**using real-time diagnostics and alpha/risk overlays. - Understanding of **exchange microstructure**in major venues (CME, Eurex, HKEX, SGX, Nasdaq, LSE). **Strategic capital allocation based on signal quality, strategy orthogonality, and turnover constraints.**: - Performance-aligned payout structure with potential for **P&L share, team lift-outs, or principal platform structures**. - True autonomy in research and execution, with collaborative support from engineering, quant dev, and TCA teams.