Position: Senior Quantitative Trader – Systematic Alpha & Execution
We are actively seeking a high-calibre Systematic Quant Trader with a strong alpha pedigree, capable of full lifecycle strategy ownership from signal research to execution implementation within a high-throughput, multi-asset environment. The ideal candidate operates at the intersection of alpha signal generation, execution microstructure, and portfolio construction , and brings a demonstrated ability to generate uncorrelated PnL at scale.
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.
- Familiarity with multi-model ensemble frameworks , feature pipelines, and online learning applications.
- 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.
