Quant Developer - Backtesting & Execution

We’re hiring a hands-on Quant Developer who can create trading edges, understand our existing trading infrastructure, and write clean, efficient Python code. You’ll 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 12–18 hours, with a 72-hour calendar window to submit. The task will align with the key responsibilities of this role.

• 
  Review & presentation: 15–20 minute demo of your solution, code walkthrough, and a small live change.Team 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 README.If you’re not able to commit to the assignment and presentation, please do not apply.

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