Quant Researcher
Role in brief
Midpoint is a proprietary trading desk focused on cryptocurrency markets, seeking a Quant Researcher to develop and implement trading strategies. Candidates should have experience in mid-frequency trading and a strong background in Python and statistics.
About the role
Midpoint operates as a proprietary trading desk specializing in cryptocurrency derivatives. The team is composed of graduates from top universities, and the firm provides robust infrastructure including historical data and backtesting services. The Quant Researcher will play a crucial role in developing innovative trading strategies for both spot and perpetual futures.
The successful candidate will be responsible for testing hypotheses using historical data, building backtests that reflect true trading conditions, and transitioning strategies from paper trading to live execution. This position also involves leveraging AI agents in the research process, ensuring a collaborative approach to strategy development and performance analysis.
Skills that matter here
- Python: This role requires proficiency in Python for data analysis and backtest development.
- data analysis: Candidates will analyze trading data to inform strategy development.
- statistics: Working-level statistics are essential for hypothesis testing and assessing trading results.
- AI agents: Experience with AI agents is necessary for automating parts of the research cycle.
Who this role suits
- A candidate with a strong quantitative background and experience in mid-frequency trading.
- Someone who enjoys working with data and has a solid understanding of crypto derivatives.
- An individual who is detail-oriented and capable of verifying results from AI-generated outputs.
- A team player comfortable communicating in English within an international environment.
From the employer
Responsibilities
- Develop strategies for spot and perpetual futures: market-neutral and directional.
- Test hypotheses on historical data: trades, candles, funding, open interest, capitalization.
- Build backtests with honest costs, margin considerations, and overfitting control.
- Use AI agents in the research cycle: hypothesis generation, backtest coding, review, report analysis. The agent's results are verified by a human, and mass hypothesis generation is considered in overfitting assessment.
- Transition strategies from paper trading to live and track discrepancies between live results and backtests.
- Participate in the audit of external quant strategies.
Requirements
- Experience in mid-frequency trading and bringing strategies to production: your alphas were traded live — you brought them out yourself or your team did.
- Python for data analysis.
- Working-level statistics: hypothesis testing, multiple comparisons, result stability assessment.
- Understanding of crypto derivatives: perpetual futures, funding, basis, margin, and liquidations.
- Experience with backtests that account for commissions, slippage, and future peeking.
- Experience working with AI agents for coding and data analysis (Claude Code, Codex, or similar): ability to assign tasks to the agent, verify its code, and not accept results without control.
- English at a level sufficient for reading scientific articles and working in an international team.
Conditions
- Fixed salary and bonuses based on the profits of the strategies you brought to live trading.
- Probation period of several weeks, after which the bonus part will be reviewed.
- Ready data and infrastructure: work on strategies, not on loaders.
- Full-time employment.
- Remote work.
Questions about this role
Is this position remote?
Yes, this is a fully remote position.
What skills are required for this role?
Candidates need experience in Python, data analysis, statistics, and AI agents.
How do I apply for this position?
The application process details are not provided in the input.
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