Job description
Responsibilities
Conduct research, development, backtesting, deployment, and continuous optimization of quantitative strategies for stock and futures markets, and be responsible for the real-world performance of the strategies;
Independently carry out research in areas such as CTA, trend following, multi-factor, statistical arbitrage, cross-sectional strategies, time series strategies, and cross-commodity strategies, continuously seeking stable, explainable, and scalable sources of Alpha;
Responsible for factor mining and factor engineering, including but not limited to:
• Price-volume factors
• Trend/momentum factors
• Volatility factors
• Liquidity factors
• Fundamental factors
• Event factors
• Sentiment factors
• Alternative data factors
Establish a complete factor research system, including factor construction, IC/IR validation, stratified testing, stability testing, de-correlation, neutralization, and multi-factor combination;
Independently manage the complete lifecycle of strategies from Idea → Data Research → Factor Construction → Backtesting → Portfolio Construction → Real-world Deployment → Monitoring → Optimization/Decommissioning;
Establish a strategy monitoring and attribution system, continuously tracking:
• Returns and drawdowns
• Alpha decay
• Risk exposure
• Factor performance
• Transaction costs
• Slippage
• Market structure changes
Proactively identify, locate, and attribute issues when strategy performance is abnormal, determining whether it is normal drawdown, market regime change, factor failure, execution issues, data issues, or model failure, and develop corresponding adjustment plans;
Participate in multi-strategy portfolios and capital allocation, including strategy capital allocation, risk budgeting, leverage management, strategy correlation control, capital capacity assessment, and dynamic position management;
Continuously optimize trade execution, transaction efficiency, slippage, and impact costs, improving the conversion efficiency of backtest returns to real-world returns;
Establish risk management mechanisms for strategies under extreme market conditions, including stop-loss, position reduction, leverage reduction, halting new positions, strategy downgrading, circuit breakers, and manual intervention;
Collaborate with quantitative development, trading systems, and data teams to promote the engineering implementation of strategy research, real-world trading, risk control, and monitoring systems.
Requirements
Bachelor's degree or above, preferably in mathematics, statistics, financial engineering, computer science, physics, electronic engineering, or related fields;
Over 3 years of experience in quantitative strategy research, with relevant experience in stocks, futures, CTA, quantitative private equity, proprietary trading, or Prop Trading;
Possess real-world trading experience, able to clearly articulate the capital scale, real-world cycle, returns, maximum drawdown, Sharpe ratio, trading frequency, and strategy capacity of the strategies managed;
Experience managing or core participation in large-scale real-world capital;
Familiar with at least one mature strategy direction and have substantial practical research accumulation, such as:
• CTA / Trend Following
• Stock Multi-Factor
• Statistical Arbitrage
• Cross-Sectional Strategies
• Time Series Strategies
• Commodity/Stock Index Futures
• Cross-Commodity Arbitrage
Familiar with the CTA research framework, with a deep understanding of strategy performance under trends, momentum, carry, volatility, cross-section, and different market regimes;
Possess mature factor mining and validation capabilities, able to independently complete factor logic design, data processing, effectiveness validation, stability testing, and portfolio construction;
Able to clearly articulate the strategy's:
• Sources of returns
• Alpha logic
• Applicable market environments
• Failure conditions
• Maximum drawdown characteristics
• Capital capacity
• Risk boundaries
Familiar with capital capacity assessment, transaction cost analysis, slippage control, position management, risk budgeting, and multi-strategy capital allocation;
Strong strategy control capabilities, able to proactively identify, locate, fix, validate, and review issues when strategy performance declines;
Familiar with common biases and risks in quantitative research, including:
• Look-ahead Bias
• Survivorship Bias
• Overfitting
• Data Snooping
• Parameter Mining
Familiar with strategy validation methods such as Out-of-Sample, Walk-Forward, Robustness Test, etc.;
Proficient in Python, familiar with common data analysis and quantitative research tools such as NumPy, Pandas, Scipy, Scikit-learn, Statsmodels;
Clear logic, strong data analysis capabilities, problem decomposition abilities, independent research capabilities, and results-oriented mindset.