Key Responsibilities
- Lead research on auction dynamics, order book imbalances, ETFconstituent linkages, and related signals.
- Establish and enforce research standards, including data leakage checks, robust validation (cross-sectional & time-series), p-value adjustments, and A/B replay testing.
- Develop and maintain venue-specific cost models, accounting for explicit/implicit costs and rebate structures.
- Build and oversee a unified backtesting pipeline spanning backtest simulation live small-scale strategies.
What We're Looking For
- Proven experience leading a team in taking at least one microstructure strategy from idea through to live trading, with clear understanding of backtest-to-production drift.
- Strong expertise in market microstructure signals (LOB imbalance, depth ratio, queue-length, cancel-to-add, spread slope, interarrival times).
- Mastery of advanced validation techniques such as Newey-West adjustments, resampling, rolling out-of-sample tests, FDR control, and sensitivity heatmaps.
- Deep understanding of simulation frameworks, ensuring queue position, priority matching, and auction clearing integrity.
- Technical stack: Python (NumPy, Pandas, numba, pyarrow), kdb+/q or ClickHouse, Git+CI.
- Prior experience applying deep learning to LOB features, ETFconstituent alignment, or factor orthogonalization will be an added advantage.
Application Process
Interested candidates should submit their resume to Tina Wang at [Confidential Information], quoting the job title and reference number. Only shortlisted candidates will be contacted.
License No: 24S2395
Registration No: R2090553
