Trainee/Associate Quant Developer - Backtesting & Execution

We’re hiring fast learners to build and operate our backtesting, paper, and live execution stack in Python. You’ll turn strategy specs into code, run rigorous backtests, route orders to brokers in paper/live, and enforce risk guardrails. You’ll work from a clear architecture, use AI tools to accelerate delivery, and ship end-to-end features under senior review.

We value clean Python, quantitative problem-solving, and practical market awareness (order types, futures/options basics). Exposure to NumPy/Pandas, APIs, and Excel/CSV reporting is useful.

Key Responsibilities

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  Backtesting engine: Implement strategy interfaces, signal order flow, fills/slippage/fees, P&L and risk metrics; avoid look-ahead/survivorship bias.

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  Data pipelines: Ingest/normalize historical datasets (futures/options), calendars & timezones, contract rolls; cache & validate data.

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  Paper & live execution: Build/extend broker adapters (REST/WebSocket), place/modify/cancel orders with idempotency, retries, and reconciliation (positions, cash, fills).

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  Risk controls & audit: Max loss, quantity caps, circuit breakers; full audit trails and run artifacts.

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  Config-driven runs: JSON/YAML strategy configs; .env for environments; clean debug logs.

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  Analytics & reporting: Use NumPy/Pandas for metrics; export CSV/Excel summaries when needed.

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  Quality: Tests with pytest, reproducible runs, deterministic seeds; structured logging and basic metrics.

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  Dev workflow: Git branches + PRs, meaningful commits; Docker for local runs; AI-assisted development documented in PRs.

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  Week 1: Env setup run a sample backtest; add one rule; write 2–3 pytest cases; mock broker adapter; PR with AI prompt notes.

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  Week 2: Deliver a feature slice: config backtest metrics paper-trade path (mock/sandbox) + risk guardrail + reproducibility checklist.

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  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.

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  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.

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

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  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

• Python 3.x proficiency (OOP, typing), with NumPy/Pandas basics.

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

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  Testing & debugging: pytest + fixtures; log-driven troubleshooting.

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  Data & SQL: Joins, indices; comfort with Postgres/MySQL (basic).

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  Time handling: Timezones, trading calendars, intraday timestamps.

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  Git & Docker (basics): Branch/PR workflow; run services with Docker Compose.

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  AI fluency: Use ChatGPT/Copilot to scaffold code/tests; explain what was AI-generated vs. hand-written.

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  Market basics: Order types, futures/options terminology, margins/fees (we’ll deepen this in Week 1).

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  Mindset: Self-motivated, fast learner, follows patterns, writes clear README/notes.

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  Market Knowledge: Read and understand Zerodha Varsity: Intro to Stock Market, Technical Analysis, Futures Trading, Options Theory (Modules 1,2,4,5).

Good-to-Have

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  Broker APIs (any): Schwab / IBKR / Zerodha, etc.

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  Task runners/queues (Celery/Redis or APScheduler); basic asyncio.

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  Plotting/reporting (Matplotlib/Plotly); Excel automation.

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  Tooling: black/ruff/isort, mypy/pyright; Linux basics.

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  Technical analysis familiarity (for strategy prototyping).

Benefits