Job Description
Quantitative Strategy Researcher | CTA & Factor Research Direction
This role involves the research, development, backtesting, deployment, and continuous optimization of quantitative trading strategies for stocks, futures, and Crypto markets. The candidate will be responsible for the live performance of these strategies.
Key Responsibilities
- Conduct independent research on CTA, trend-following, multi-factor, statistical arbitrage, cross-sectional strategies, time-series strategies, and cross-asset strategies to identify stable, explainable, and scalable Alpha sources.
- Perform factor mining and factor engineering, including but not limited to: price-volume factors, trend/momentum factors, volatility factors, liquidity factors, fundamental factors, event-driven factors, sentiment factors, and alternative data factors.
- Establish a comprehensive factor research framework, covering factor construction, IC/IR validation, stratified testing, stability checks, decorrelation, neutralization, and multi-factor portfolio construction.
- Manage the full lifecycle of strategies from idea generation, data research, factor construction, backtesting, portfolio building, live deployment, monitoring, to optimization/decommissioning.
- Develop strategy monitoring and attribution systems to track performance metrics such as returns, drawdowns, Alpha decay, risk exposure, factor performance, transaction costs, slippage, and market regime shifts.
- Diagnose and resolve performance anomalies, distinguishing between normal drawdowns, market regime changes, factor decay, execution issues, data problems, or model failures, and implement corrective measures.
- Participate in multi-strategy portfolio construction and capital allocation, including risk budgeting, leverage management, correlation control, capacity assessment, and dynamic position sizing.
- Optimize trade execution, fill rates, slippage, and market impact to enhance the conversion efficiency from backtested returns to live trading performance.
- Establish risk management protocols for extreme market conditions, including stop-loss mechanisms, position reduction, leverage cuts, trading halts, strategy downgrades, circuit breakers, and manual intervention.
- Collaborate with quant developers, trading systems, and data teams to ensure the engineering implementation of strategy research, live trading, risk controls, and monitoring frameworks.
Job Requirements
- Bachelor's degree or higher in Mathematics, Statistics, Financial Engineering, Computer Science, Physics, or related fields preferred.
- 1-3 years of experience in quantitative strategy research, with exposure to stocks, futures, CTA, hedge funds, proprietary trading, or similar domains.
- Proven live trading experience with ability to articulate strategy details including capital size, live duration, returns, max drawdown, Sharpe ratio, trading frequency, and capacity.
- Experience managing or contributing to live portfolios exceeding $2M equivalent (200万U+) is advantageous.
- Deep expertise in at least one established strategy domain such as CTA/trend-following, equity multi-factor, statistical arbitrage, cross-sectional strategies, time-series strategies, commodity/equity index futures, or cross-asset arbitrage.
- Thorough understanding of CTA frameworks including trend, momentum, carry, volatility, cross-sectional dynamics, and regime-dependent performance.
- Strong factor research capabilities encompassing logical design, data processing, validation, stability testing, and portfolio integration.
- Ability to clearly explain strategy attributes: return drivers, Alpha logic, applicable market conditions, failure modes, drawdown characteristics, capacity limits, and risk boundaries.
- Proficiency in capacity assessment, transaction cost analysis, slippage control, position management, risk budgeting, and multi-strategy capital allocation.
- Strong diagnostic skills to identify, troubleshoot, and rectify underperforming strategies through rigorous validation and post-mortem analysis.
- Familiarity with quantitative research pitfalls including look-ahead bias, survivorship bias, overfitting, data snooping, and parameter mining.
- Experience with validation methodologies like out-of-sample testing, walk-forward analysis, and robustness checks.
- Advanced Python skills with libraries including NumPy, Pandas, SciPy, Scikit-learn, and Statsmodels.
- Exceptional analytical reasoning, problem decomposition skills, independent research ability, and results-driven mindset.
Benefits
- Remote work arrangement
- Single-day weekend (6-day work week)
- Compensation in USDT
