Job description
Responsibilities
- Responsible for the research, development, backtesting, launch, and continuous optimization of quantitative strategies in the stock, futures, and Crypto markets, accountable for the real-world performance of the strategies;
- Independently conduct 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 verification, stratified testing, stability testing, de-correlation, neutralization, and multi-factor combinations;
Independently complete the full lifecycle management of strategies from Idea → Data Research → Factor Construction → Backtesting → Portfolio Construction → Real-world Launch → Monitoring → Optimization/Decommissioning;
- Establish a strategy monitoring and attribution system, continuously tracking:
returns and drawdowns, Alpha decay, risk exposure, factor performance, trading costs, slippage, market structure changes
- In case of abnormal strategy performance, proactively complete problem discovery, localization, and attribution, determining whether it is a normal drawdown, market regime change, factor failure, execution issue, data issue, or model failure, and formulate 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 trading execution, transaction efficiency, slippage, and impact costs, improving the conversion efficiency of backtest returns to real-world returns;
- Establish a strategy risk management mechanism under extreme market conditions, including stop-loss, position reduction, leverage reduction, pausing 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 explain 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, with a preference for experience with capital scales of over 2 million USD equivalent;
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/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 explain 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, trading cost analysis, slippage control, position management, risk budgeting, and multi-strategy capital allocation;
- Strong strategy control ability, able to proactively complete problem discovery, localization, repair, validation, and review 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 ability, problem decomposition ability, independent research ability, and results-oriented awareness.
Bonus Points
- Background in stock quantitative private equity, brokerage proprietary trading, futures CTA, or Prop Trading;
- Managed or core participated in real-world capital of over 2 million USD equivalent;
- Experience in both stock multi-factor and CTA research;
- Experience in building mature factor libraries, Alpha Libraries, or Strategy Libraries;
- Experience in cross-commodity, cross-market, statistical arbitrage, or multi-asset strategy research;
- Trading research experience in multiple markets such as stocks, futures, Crypto;
- Experience in on-chain data, Funding Rate, Open Interest, Liquidation, Basis, Exchange Flow, or Crypto alternative data research;
- Experienced extreme market conditions or significant strategy drawdowns, and personally led risk disposal, strategy adjustments, and post-event reviews;
- Experience in strategy monitoring, PnL Attribution, Factor Attribution, or automated attribution system construction;
- Relatively stable real-world strategy performance in bull, bear, and volatile market environments;
- Familiar with SQL, Linux, C++, or high-performance computing frameworks.