Global Top-Tier Hedge Fund | MFT Strategy Team
Join a world-renowned hedge fund's elite Mid-Frequency Trading (MFT) team, specializing in Futures and Equities markets. As a Quantitative Researcher, you will lead the design, implementation, and optimization of advanced trading strategies that drive portfolio diversification and risk-adjusted returns. Collaborating with a team of PhD-level researchers and senior engineers, you will shape innovation in systematic trading through data-driven modeling and cutting-edge technology.
Strategy Development & Innovation
- Design, backtest, and deploy mid-frequency algorithmic strategies for Futures (e.g., index, commodity, FX) and Equities markets, focusing on alpha generation and risk management.
- Explore novel trading products (e.g., derivative structures, alternative data integrations) to diversify portfolio revenue streams.
Quantitative Modeling & Optimization
- Build statistical models and machine learning frameworks to enhance signal generation, position sizing, and trade execution.
- Continuously refine strategies through rigorous performance analysis, adapting to market microstructure changes and liquidity dynamics.
Cross-Functional Collaboration
- Partner with quantitative developers to implement strategies in low-latency trading systems (Python/C++).
- Collaborate with risk teams to integrate multi-factor risk models (e.g., Barra, Axioma) and optimize portfolio constraints.
Market Intelligence & Research Leadership
- Monitor global market trends, academic literature, and technological advancements to inform strategy evolution.
- Lead research initiatives, presenting findings and driving data-driven decisions within the team.
- Academic Excellence: Master's/PhD in Mathematics, Physics, Statistics, Computer Science, or Quantitative Finance-with a focus on applied research in financial modeling.
- Professional Expertise:
* 1+ years of experience in mid-frequency Futures/Equities strategy development at a hedge fund, prop trading firm, or systematic trading organization.
* Verifiable track record in alpha generation, the development of high-performing signals, and PnL contribution. - Technical Proficiency:
* Expertise in Python/C++ for quantitative research, backtesting (e.g., Zipline, Backtrader), and algorithmic implementation.
* Deep knowledge of machine learning frameworks (TensorFlow/PyTorch) and applied techniques (random forests, LSTM, gradient boosting) for signal enhancement. - Domain Knowledge:
* In-depth understanding of Futures market microstructure, contract specifications, and arbitrage opportunities (e.g., cash-futures basis, inter-commodity spreads).
* Familiarity with Equities order flow analysis and execution algorithms (VWAP, TWAP, POV). - Soft Skills:
* Exceptional problem-solving under high-pressure environments, with the ability to translate complex research into scalable trading strategies.
* Strong communication skills for collaborating with cross-functional teams and presenting research to senior stakeholders.
