Trireme operates quantitative trading and market-making strategies across digital-asset markets.

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We are building a research-driven trading infrastructure designed to support systematic strategy development across multiple timeframes, instruments and market regimes.

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We are looking for a Quantitative Researcher capable of developing, testing and validating systematic trading strategies from first principles.

You will research market behaviour, build signals and strategies, run rigorous backtests and work closely with engineering and trading teams to move validated research toward production.

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This is not a role for producing attractive backtests.

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We are looking for researchers who understand that most apparent alpha disappears once realistic costs, market regimes, execution and out-of-sample performance are properly considered.

• Research systematic trading strategies across digital-asset markets.
• Develop predictive signals using price, volume, volatility, derivatives, positioning, funding, order-book and other market data.
• Design strategies across multiple time horizons.
• Research market regime detection and strategy selection.
• Develop robust research hypotheses before testing.
• Build and improve backtesting methodologies.
• Perform walk-forward and out-of-sample validation.
• Analyse signal stability and decay.
• Measure exposure to beta, volatility and market regime.
• Model transaction costs, slippage and liquidity constraints.
• Conduct parameter sensitivity and robustness testing.
• Identify overfitting and data leakage.
• Produce clear research documentation.
• Work with engineers to productionise validated strategies.
• Monitor live performance against research expectations.
• Investigate strategy degradation and anomalous behaviour.

Potential areas include:

• Trend and momentum.
• Mean reversion.
• Market microstructure.
• Cross-sectional strategies.
• Volatility.
• Funding and basis.
• Relative value.
• Regime-dependent strategies.
• Machine-learning-based signals.
• Probabilistic forecasting.
• Multi-timeframe trading.
• Portfolio construction and risk allocation.

• Strong quantitative background in mathematics, statistics, physics, computer science, engineering, finance or related discipline.
• Excellent Python.
• Strong understanding of statistics and probability.
• Demonstrable systematic research experience.
• Understanding of financial time series.
• Ability to distinguish genuine signal from noise.
• Strong understanding of backtesting errors and overfitting.
• Intellectual honesty when a strategy does not work.
• Ability to communicate research clearly.

• Professional quantitative trading experience.
• Crypto market experience.
• Experience with futures or perpetual swaps.
• Machine learning experience.
• Regime-modelling experience.
• HMM, clustering or probabilistic-model experience.
• Market microstructure research experience.
• Portfolio optimisation experience.

We care more about your research process than your ability to show us one impressive Sharpe ratio.

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Strong candidates should be able to explain:

• The original hypothesis.
• Why the edge should exist.
• What would invalidate it.
• How transaction costs were modelled.
• How the model behaved out of sample.
• Parameter sensitivity.
• Capacity.
• Expected signal decay.
• Regimes where the strategy should fail.

Success will be judged on:

• Quality of research.
• Number of credible hypotheses tested.
• Validated strategies produced.
• Out-of-sample performance.
• Research reproducibility.
• Production performance versus backtest expectations.
• Risk-adjusted returns.
• Drawdown behaviour.
• Research documentation.
• Contribution to the wider quant research system.