Quant Developer Backtesting & Execution

Were hiring a hands-on Quant Developer who can create trading edges understand our existing trading infrastructure and write clean efficient Python code. Youll work directly on strategy research backtests and live execution pipelines.

• Strategy R&D: Research ideas test hypotheses evaluate indicators/market structure build rule-based statistical edges.
  

• Backtesting: Convert ideas into reproducible backtests using our existing engine data loaders calendars and portfolio modules.
  

• Execution: Understand and extend broker adapters (REST/WebSocket) and integrate strategies into live/paper pipelines.
  

• Data: Work with our ready-made loaders caches and roll logic; add transformations and metrics as needed.
  

• Code Quality: Write clean modules vectorized logic tests logs configs and performance-optimized code.
  

• Market Awareness: Apply knowledge of options futures IV Greeks technical patterns and intraday behavior.
  

• DevOps workflow: Git branches PRs meaningful commits; Docker for local runs; AI-assisted development documented in PRs.
  

• Take-home assignment (mandatory): Estimated effort 1218 hours with a 72-hour calendar window to submit. The task will align with the key responsibilities of this role.
  

• Review & presentation: 1520 minute demo of your solution code walkthrough and a small live interview discussion on testing debugging risk/edge cases and collaboration.
  

• Team interview: Discussion of testing debugging approach risk/edge cases collaboration and trade-offs.
  

• AI usage: Allowed and encouraged (ChatGPT/Copilot/etc.) but you must cite key prompts and verify all outputs. Keep commits clean and ensure the project runs from the youre not able to commit to the assignment and presentation please do not apply.
  

Apply only if you can:

• Complete a 1218 hour assignment within 3 days
  

• Present your own code confidently (demo brief walkthrough).
  

• Use Git and run a Docker/WSL/venv setup. (Linux users may skip Docker if a native setup works reliably).
  

Requirements

Core Technical Requirements

• Python 3.x proficiency with OOP typing NumPy/Pandas.
  

• Ability to read and extend existing large codebases (backtester loader execution pipelines).
  

• API skills: REST WebSocket fundamentals; requests/httpx familiarity.
  

• Testing & debugging: pytest fixtures log-driven troubleshooting.
  

• Data & SQL: Joins indices Postgres/MySQL basics.
  

• Time handling: Timezones trading calendars intraday timestamps.
  

• Git & Docker: Branch/PR workflow; Docker Compose basics.
  

• AI fluency: Use ChatGPT/Copilot to scaffold modules/tests; clearly validate and separate AI-generated vs hand-written logic.
  

Quant Market Knowledge

• Understanding of order types futures/options basics margins/fees.
  

• Familiarity with technical analysis indicators chart patterns trend vs. range regimes.
  

• Comfortable analyzing options IV premiums Greeks and basic volatility behavior.
  

• Ability to propose and test new strategy ideas (edge-focused thinking).
  

Mindset

• Self-driven fast learner follows patterns and architecture.
  

• Writes clean notes/README reproducible configs and structured logs.
  

• Strong research mindset ability to convert theory code quickly.
  

Good-to-Have

• Broker APIs: Schwab / IBKR / Zerodha.
  

• Task runners/queues: Celery/Redis APScheduler; basic asyncio.
  

• Plotting/reporting: Matplotlib/Plotly Excel automation.
  

• Tooling: black/ruff/isort mypy/pyright Linux basics.
  

Benefits