Position Overview

We are seeking an Algorithmic Trading Developer to build and maintain software tools that support quantitative strategy research, historical simulation, and the transition of validated trading ideas into operational systems. The role combines programming, market-data analysis, backtesting, and collaboration with quantitative researchers.

You will help develop research infrastructure that enables the team to evaluate strategy logic, assess performance under different market conditions, and identify implementation issues before deployment. The position requires a disciplined approach to data quality, reproducible experiments, transaction-cost modelling, and the separation of historical results from expected live performance.

The ideal candidate is comfortable working with financial datasets, writing analytical code, and improving the reliability and efficiency of quantitative research workflows.

Key Responsibilities

• Develop and maintain backtesting frameworks for systematic foreign exchange and other supported financial-market strategies.

• Build data-processing pipelines for historical prices, market events, trading signals, and execution records.

• Implement strategy logic, position-sizing rules, entry and exit conditions, and parameter configurations based on approved research specifications.

• Work with quantitative researchers to test hypotheses and translate research prototypes into robust, maintainable software.

• Incorporate realistic assumptions for transaction costs, spreads, slippage, latency, and execution constraints where supported by available data.

• Identify and address common research problems such as look-ahead bias, survivorship bias, data leakage, and inappropriate parameter fitting.

• Develop tools for evaluating performance metrics, drawdowns, exposure, turnover, and risk-adjusted returns.

• Compare simulated results with paper-trading or live-system observations to identify implementation differences.

• Improve the reproducibility, automation, and computational efficiency of research experiments.

• Validate historical market data and investigate missing observations, duplicate records, timestamp inconsistencies, and other data-quality problems.

• Maintain version-controlled strategy code, experiment records, configuration files, and technical documentation.

• Collaborate with engineering colleagues to prepare validated strategies for controlled integration into production infrastructure.

• Communicate findings clearly, including limitations, assumptions, and cases where results do not support the original hypothesis.

Qualifications and Technical Skills

• Bachelor’s or master’s degree in Computer Science, Mathematics, Statistics, Physics, Finance, Engineering, or a related discipline.

• Strong programming experience in Python, with practical use of libraries such as NumPy, pandas, and SciPy.

• Understanding of algorithms, data structures, numerical computation, and software testing.

• Familiarity with time-series data, statistical analysis, probability, and quantitative research methods.

• Experience working with historical financial data, trading signals, portfolio metrics, or simulation frameworks.

• Understanding of financial-market concepts such as bid-ask spreads, volatility, transaction costs, and order execution.

• Knowledge of SQL and experience managing structured datasets.

• Familiarity with Git, notebooks, automated testing, and reproducible research workflows.

• Strong analytical reasoning and the ability to explain technical findings to quantitative and non-technical colleagues.

Experience with event-driven backtesting engines, vectorised simulation, market microstructure, or systematic trading research is an advantage. Knowledge of C++ or another compiled language may be beneficial when performance optimisation is required.

Research and Development Environment

The role may involve Python, Jupyter, pandas, NumPy, SciPy, SQL databases, historical market-data systems, visualisation tools, and internal simulation frameworks. The successful candidate will help improve the consistency of research processes and the reliability of strategy evaluation.

Ideal Candidate

The ideal applicant combines curiosity with methodological discipline. They should be willing to challenge assumptions, investigate unexpected results, and document experiments carefully. A strong candidate recognises that a profitable historical simulation does not guarantee future returns and that research quality depends on realistic assumptions, reliable data, and appropriate validation.

Career Opportunity

This position provides a pathway to deeper specialisation in quantitative development, systematic trading research, financial data engineering, or algorithmic execution technology. It is suitable for a developer who wants to apply software engineering skills to rigorous financial-market research and the development of systematic trading systems.

Are you interested in this position?

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