Quantitative Researcher — Macro & Crypto Positioning

Responsibilities

• Research the impact of macro variables on digital assets, including interest rates, Fed expectations, Treasury yields, USD, ETF/stablecoin flows, and cross-asset risk sentiment.
• Analyze crypto derivatives positioning, including Open Interest, Funding, Basis, Liquidations, Options IV/Skew, and related leverage indicators.
• Develop models for market regime detection, crowding, squeeze risk, tail risk, and medium-short horizon directional signals.
• Build daily / medium-frequency cross-sectional crypto factor models combining price action, positioning, flows, and macro conditions.
• Apply LightGBM/XGBoost, HMM/HSMM, time-series and statistical models, with rigorous backtesting, attribution, and live monitoring.
• Work closely with traders to translate research into systematic strategies, position sizing, and risk management.

Requirements

• 2+ years of experience in quantitative research, systematic trading, or crypto derivatives.
• Strong Python skills and solid foundations in statistics, probability, and time-series analysis.
• Good understanding of futures, perpetual swaps, funding, open interest, basis, and options.
• Strong understanding of how rates, USD, liquidity, and macro expectations transmit into risk assets.
• Experience in cross-sectional factor research, regime detection, crypto alpha, or tail-risk modeling is a plus.

职责：

• 研究宏观变量对数字资产的影响，包括利率、Fed 预期、美债收益率、美元、ETF/稳定币资金流及跨资产风险偏好。
• 研究 crypto derivatives positioning，包括 OI、Funding、Basis、Liquidation、Options IV/Skew 等。
• 构建市场 Regime、Crowding、Squeeze、Tail Risk 及中短周期方向模型。
• 开发日频/中频截面多因子选币模型，将价格、持仓结构、资金流和宏观环境结合。
• 使用 LightGBM/XGBoost、HMM/HSMM、时间序列及统计模型，并完成回测、归因及实盘监控。
• 与交易团队合作，将研究信号用于仓位管理、风险控制及系统化策略。

要求：

• 2年以上 Quant Research / Systematic Trading / Crypto Derivatives 相关经验。
• 熟练 Python，扎实的统计、概率、时间序列基础。
• 熟悉 Futures、Perpetual、Funding、OI、Basis、Options 等衍生品机制。
• 对 Fed、利率、美元、流动性如何传导至风险资产有较强理解。
• 有截面多因子、Regime Detection、Crypto Alpha 或 Tail Risk 研究经验优先。