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HLS Trading · 1 day ago

Quantitative Developer

Greenwich, CT

Full-time

Onsite

Mid Level

3+ years exp

HLS Trading is a systematic investment manager looking for a talented Quantitative Developer to join their trading team. The role involves building and maintaining the market data infrastructure while supporting quantitative research for systematic trading strategies.

Financial Services

Responsibilities

Market Data Infrastructure: Design, develop, and maintain high-performance, resilient systems for capturing, processing, and validating live (real-time) and historical tick-by-tick market data for Futures and FX

Quantitative Research Support: Serve as the technical bridge to the quant research team, ensuring their data requirements are met. This includes creating and optimizing the research environment (data access APIs, backtesting frameworks) and ensuring data consistency

Systematic Strategy Development: Actively participate in the research and development of systematic trading strategies by analyzing large market data sets, identifying alpha signals, and implementing models for testing and production deployment

Data Acquisition & Vendor Integration: Implement connectors and APIs to ingest data from various sources, including direct exchange feeds (e.g., CME, ICE) and third-party data vendor APIs (e.g., Bloomberg, Refinitiv)

Data Modeling & Storage: Define, implement, and optimize data models in time-series databases to ensure ultra-fast querying and analysis by the research team

Code Optimization & Tooling: Improve the performance and efficiency of research and production code, and develop internal tools for data visualization, quality control, and pipeline monitoring

Qualification

PythonC++Time-series databasesAPIs integrationQuantitative librariesQuantitative financeSoft skills

Required

Minimum of 3+ years of professional experience in quantitative finance, market data engineering, or systematic trading

Expert proficiency in Python (for quantitative modeling, backtesting, and data analysis) and strong proficiency in a compiled language like C++ or Java (for high-performance data processing/feed handlers)

Mandatory, hands-on experience with time-series databases (e.g., InfluxDB, TimescaleDB) for storing and efficiently querying high-frequency market data

Direct experience integrating with commercial vendor APIs (e.g., Bloomberg, Refinitiv) and/or exchange data feeds

Strong familiarity with Python's quantitative and data science ecosystem (e.g., Polars, Pandas, NumPy, Scikit-learn)

Strong understanding of Futures and Foreign Exchange (FX) market data conventions, microstructure, and the unique challenges associated with their data (e.g., order book reconstruction, roll dates)

Solid understanding of systematic trading concepts, statistical analysis, time-series modeling, and the methodology behind quantitative strategy backtesting and simulation