Quant Developer, Futures Alert System

About the role We’re hiring a quant developer to build and maintain a real-time alert system for futures trading across intraday, swing, and overnight timeframes. The system needs to translate a defined setup playbook — VWAP, volume profile, and order flow conditions — into clean trade signals with pre-calculated stop loss and take profit levels, then deliver them to Telegram and Discord. Every strategy you ship must be backtested with a documented win rate, expectancy, and drawdown profile before it goes live. What you’ll do Take discretionary setups and turn them into reproducible, rule-based logic. Backtest each strategy across multiple market regimes, report honest performance metrics (win rate, average R, max drawdown, sample size), and only promote setups that survive out-of-sample testing. Build the live execution-of-alerts pipeline: ingest market data, evaluate conditions tick-by-tick, compute entry / stop / target, and push to Telegram and Discord with latency you can defend. Maintain dashboards for live vs. backtest tracking so we catch drift early. Required skills • Python (primary). Production-grade Python for market data handling, signal logic, and alert delivery. Comfortable with pandas, NumPy, asyncio, and writing testable code. Bonus for experience with vectorbt, backtrader, or custom event-driven backtesting frameworks. • Market data and indicator engineering. Can implement VWAP (session, overnight, anchored) with standard deviation bands, volume profile (POC, value area, HVN/LVN), and cumulative volume delta from raw tick or 1-minute data. Knows the difference between a textbook indicator and one that survives real fills. • Order flow data familiarity. Comfortable parsing depth-of-market and footprint data. Bookmap exposure or experience with Sierra Chart, Rithmic, CQG, Databento, or similar L2 feeds is strongly preferred. • Backtesting and validation discipline. Walk-forward testing, train/test splits, realistic slippage and commission modeling, and an allergy to overfitting. Must report negative results as cleanly as positive ones. • Risk model implementation. Codes stop loss and take profit as part of the signal itself — ATR-based, structure-based, or fixed-R. Position sizing logic that respects per-trade and daily risk caps. • Alert delivery. Builds reliable Telegram bot and Discord webhook integrations with retry logic, deduplication, and clean message formatting. n8n experience is a strong plus. Nice to have Experience integrating with Topstep / Apex / Tradovate APIs, TradingView Pine Script fluency for rapid prototyping, prior work on prop-firm rule enforcement (consistency rules, max loss limits), and a public repo or portfolio showing backtested strategies with honest metrics. What we’re not looking for Strategies with a 90% win rate and no drawdown chart. Anyone who can’t produce code, or won’t share a backtest methodology. Indicator-stacking with no statistical grounding.