Algorithmic Trading Researcher | Market Microstructure Analyst

Employment Type: Full-Time

Level: Mid-Level to Senior

We are seeking a quantitative and research-driven Algorithmic Trading Researcher | Market Microstructure Analyst to develop systematic trading strategies, investigate market behavior, and analyze the mechanisms that influence financial market liquidity, execution quality, and price formation. This role combines quantitative research, statistical modeling, algorithm development, market microstructure analysis, and execution optimization to support data-driven trading decisions across relevant financial markets.

The successful candidate will collaborate with quantitative researchers, software engineers, traders, portfolio managers, and risk professionals to investigate market dynamics, evaluate trading signals, conduct rigorous backtesting, and improve execution performance. The ideal candidate possesses strong mathematical and programming skills, a sound understanding of financial markets, and the ability to translate complex market data into reliable research findings and practical trading strategies.

• Conduct quantitative research to identify potential trading signals, market inefficiencies, and systematic investment opportunities.
• Develop, test, and refine algorithmic trading strategies using statistical analysis, mathematical modeling, and historical market data.
• Analyze market microstructure, including order book dynamics, bid-ask spreads, price discovery, market depth, and liquidity formation.
• Investigate order flow, trade arrivals, transaction costs, and short-term price movements across relevant financial instruments.
• Study limit order books, market orders, limit orders, order cancellations, and queue positioning to understand execution behavior.
• Develop statistical models to evaluate price impact, slippage, spread dynamics, and liquidity-related trading costs.
• Research execution algorithms and order placement techniques designed to improve fill quality and reduce transaction costs.
• Conduct historical backtesting and simulation to assess strategy performance under different market conditions.
• Evaluate trading strategies using metrics such as returns, volatility, drawdowns, Sharpe ratio, turnover, and transaction costs.
• Account for market frictions, execution constraints, fees, slippage, and other realistic assumptions during strategy evaluation.
• Identify and mitigate common research biases, including look-ahead bias, survivorship bias, and data leakage.
• Analyze high-frequency and intraday market data to identify patterns in liquidity, volatility, order flow, and price behavior.
• Develop quantitative models for volatility estimation, short-term forecasting, market impact, and execution performance.
• Build and maintain research pipelines for market data collection, cleaning, normalization, feature engineering, and analysis.
• Implement research methods using Python, C++, or other suitable programming languages and quantitative computing tools.
• Apply statistical techniques, time-series analysis, probability theory, and optimization methods to financial market research.
• Investigate relationships between market conditions, trading signals, order book imbalance, and short-term returns.
• Analyze execution quality across venues, instruments, order types, and market regimes where relevant data is available.
• Collaborate with traders and execution teams to translate research findings into practical trading and order management improvements.
• Design controlled experiments and simulation frameworks to compare alternative strategy designs and execution methods.
• Monitor strategy performance after deployment and investigate changes in signal effectiveness, market impact, and trading costs.
• Develop dashboards, research reports, and analytical summaries to communicate results to technical and investment stakeholders.
• Maintain reproducible research code, experiment records, model documentation, and version-controlled datasets.
• Work with software engineers to implement, test, and optimize research models for production trading systems.
• Support pre-trade and post-trade analysis to evaluate execution decisions, implementation shortfall, and realized transaction costs.
• Investigate market regime changes and assess their implications for strategy robustness and execution performance.
• Apply appropriate risk controls and monitoring methods when evaluating proposed trading strategies.
• Review model assumptions and identify…