Quantitative Trading Associate (Systematic Strategies)

About Us

Cross Light Capital is a Malaysian SC-regulated fund manager at the forefront of alternative investments, licensed since 2020 and currently running wholesale unit trust funds across hedge funds, digital assets, and private markets.

Our edge in systematic strategies comes from a disciplined research pipeline. We ingest a high volume of candidate strategies from the academic and practitioner literature, code them up against a proprietary Python backtesting framework, and apply rigorous out-of-sample and portfolio-contribution filters before anything is allocated capital. We run a flat team and expect every researcher to own ideas end-to-end.

Job Scope

You will sit on the research desk and be responsible for moving strategies from concept to live-ready candidate. A typical week might include:

• Idea sourcing and triage — reading new academic papers, practitioner blogs, and conference work; deciding what is worth coding up and what isn't.

• Strategy implementation — translating research write-ups into clean, parameterised Python code that plugs into our backtesting framework.

• Robustness testing — parameter sweeps, walk-forward analysis, in-sample / out-of-sample splits, sensitivity to filters and sizing choices, and portfolio-level contribution metrics.

• Documentation and review — preparing each candidate for analyst review with reproducible notebooks, clear rationale, and honest reporting of weaknesses.

• Iteration on bench / live strategies — diagnosing degradation, proposing enhancements, and re-validating before changes are pushed to production.

• Building and maintaining monitoring frameworks as well as models where necessary.

• To work alongside and provide assistance and guidance to juniors.

What We're Looking For

Must-have:

• Strong general programming ability — along with familiarity in Python and Jupyter.

• Solid grounding in statistics and time-series — you understand why an in-sample Sharpe is a starting point, not a result.

• A genuine interest in systematic trading and a healthy skepticism toward published "edges."

• Ability to communicate research findings clearly in writing.

• Understanding and interest in financial markets.

• Self-motivated individuals who can thrive under minimal guidance.

• 3 to 6 years of relevant experience.

Nice-to-have:

• Experience implementing systematic strategies.

• Experience in asset management.

• Brief understanding of how LLMs work, and how and where to use AI to improve your productivity.

• Familiarity with regime detection, walk-forward validation, or portfolio construction techniques.

• Background in finance, mathematics, physics, engineering, computer science, or a related quantitative discipline.

• As a small firm, the willingness to help in other areas of the business as required.

What You'll Be Working On

Our current bread-and-butter is multi-day systematic strategies on liquid US-listed instruments, spanning trend, mean-reversion, seasonality, volatility, and cross-asset themes. We trade all asset classes.

The role is hands-on: you'd be the person actually building, breaking, and validating these strategies.

The role will also eventually cover single-name equity strategies — extending our universe beyond ETFs into individual stocks, with the additional modelling considerations that brings (liquidity, borrow, single-name risk, factor exposure) and intraday strategies — exploring shorter holding periods on the same underlyings, initially as a research stream while we evaluate the execution infrastructure required to take it live.

What We Offer

• A flat team and a short feedback loop — your work goes live (or doesn't) based on its own merit.

• Ownership of a research pipeline rather than a narrow slice of one.

• Competitive base plus performance-linked component, calibrated to seniority and contribution.

• Direct working relationship with the firm's co-founders.

If Interested

Please send a CV and a short note to [email protected] describing:

1\. A systematic strategy you have built or studied in depth, and what you concluded about it.

2\. One thing you think the published quant literature consistently gets wrong.

Shortlisted candidates will be invited to the interview process which follows.