Quant Researcher

Responsibilities

1. Conduct quantitative research on alpha signals, trading factors, market anomalies, and systematic trading strategies across multiple asset classes.
2. Develop and test quantitative factors, including momentum, reversal, volatility, liquidity, carry, positioning, order flow, macro, and cross-asset signals.
3. Perform statistical analysis, feature engineering, signal evaluation, and predictive modeling using historical market data.
4. Build and improve backtesting frameworks for evaluating strategy performance, including PnL, Sharpe ratio, drawdown, turnover,capacity, transaction costs, and slippage.
5. Assist in portfolio construction, including signal combination, position sizing, risk budgeting, factor neutralization, beta hedging, and exposure control.
6. Analyze strategy performance and attribution, including alpha decay, regime dependency, market exposure, execution impact, and risk-adjusted return.
7. Research trading strategies across global markets, including equities, futures, commodities, ETFs, options, FX, and digital assets.
8. Work with researchers and traders to convert research ideas into robust, testable, and potentially production-ready trading strategies.
9. Support data validation and research data preparation when necessary, including cleaning market data, checking data quality, and understanding data limitations.

Preferred Market Coverage

Candidates with familiarity in one or more of the following markets are preferred:

1. U.S. equities / A-shares
2. Equity index futures and ETFs
3. Commodities
4. Precious metals
5. FX and rates
6. Options and volatility markets
7. Digital assets / cryptocurrencies

Preferred Research Areas

Candidates with experience or strong interest in one or more of the following areas are preferred:

1. Cross-sectional equity factor research
2. Futures / CTA / trend-following strategies
3. Statistical arbitrage and relative value strategies
4. Options, volatility, Greeks, and derivatives strategies
5. Macro and cross-asset research
6. Crypto market microstructure, funding rate, basis, and on-chain / exchange data
7. Portfolio optimization and risk management
8. Machine learning or statistical learning applied to financial markets

Requirments

1. Strong academic background in mathematics, statistics, computer science, engineering, economics, finance, physics, operations research, or related quantitative fields.
2. Strong Python programming skills, especially in data analysis, numerical computation, research scripting, and backtesting.
3. Solid understanding of probability, statistics, linear regression, hypothesis testing, time series analysis, and basic machine learning concepts.
4. Ability to conduct independent research, form hypotheses, design tests, interpret results, and clearly explain conclusions.
5. Familiarity with financial markets, trading strategies, asset pricing, or portfolio theory.
6. Strong attention to detail, especially in backtesting assumptions, data quality, look ahead bias, survivorship bias, transaction costs, and risk controls.
7. Comfortable working in a Linux environment and using Git for version control.
8. Strong self-learning ability and ability to work independently in a remote environment.
9. Proficiency in using AI-assisted research and coding tools such as Claude Code, Codex, Cursor, or similar tools is preferred.