Quant Research / Developer

[robertococca]

Quant Research Collaborator (Python) — Real Path to Becoming a Quant
Status: Part-time/Contract (remote, CET-friendly)

Focus: Testing user-provided strategies across market regimes, risk/return assessment, and go-to-market for live trading

Why this role
You’ll work directly with a hands-on strategist who provides the ideas. Your mission is to pressure-test those strategies in Python, quantify risk and edge, and help select the ones that are robust enough to go live. If you love clean data, thoughtful experiments, and turning research into execution, this is a genuine apprenticeship-to-quant track.

What you’ll do

• Translate strategy briefs into code: Build tidy, reproducible research notebooks and modules in Python.
• Test across regimes: Evaluate performance in bull/bear, high/low vol, crisis windows, and regime shifts.
• Measure the right things: Sharpe/Sortino, Calmar, drawdowns, tail risk, hit rate, payoff asymmetry, exposure, turnover.
• Make it realistic: Include slippage/fees, borrow/assignment risk for options, liquidity constraints, and execution frictions.
• Robustness checks: Walk-forward, out-of-sample/forward tests, CPCV/PBO, Monte Carlo/bootstrap, sensitivity and stress tests.
• Risk first: Scenario analysis, position sizing, max loss breakers, capital allocation rules.
• Weekly collaboration: Join a short, structured review to discuss results, tradeoffs, and next steps.
• Go-to-market: Prepare selected strategies for paper and then live trading.

What you’ll bring

• Solid Python skills: pandas, NumPy, SciPy, scikit-learn, matplotlib/plotly; Jupyter and Git discipline.
• Experience with event-driven backtesting (e.g., vectorbt, Backtrader, custom engines).
• Comfort with market data wrangling and careful handling of survivorship/Look-Ahead bias.
• Clear, concise research writing: charts that matter, conclusions with caveats.
• Curiosity, integrity, and a practical mindset.

Nice to have

• Options know-how (credit spreads, condors, skew/IV rank, assignment/early exercise).
• Interactive Brokers + ib_insync experience for paper/live connectivity.
• Basic Linux/Docker and cloud familiarity.

What you’ll get

• Direct mentorship and weekly feedback loops focused on turning you into a practicing quant.
• Ownership of real research problems with a clean route to paper-then-live deployment.
• A high bar for code quality and methodology that will level up your craft.
• Flexible engagement structure; performance-based upside possible on live strategies.

Tools & Stack
Python 3.x • pandas/NumPy/SciPy • scikit-learn • Jupyter • Git • vectorbt/Backtrader (or your own engine) • ib_insync (nice to have) • Linux environments welcome

How we work

• Weekly check-in (60–90 min): review results, discuss risks, plan experiments.
• Asynchronous research: ship notebooks, reports, and PRs with clear documentation.
• Evidence over opinion: decisions driven by data, realism, and risk controls.

How to apply
Email a short note with:

• a link to your GitHub or a sample research notebook/backtest you’re proud of,
• a brief description of a strategy you tested and how you validated it,
• your timezone and weekly availability,
• your preferred engagement model (hourly or project-based).

We welcome applicants from all backgrounds. What matters most is rigor, curiosity, and the ability to turn ideas into robust, testable systems.

Ready to grow into a real quant? Let’s build, test, and ship strategies—together.