Python Quant Developer - Backtesting & Research Engine
I’m building a research-grade analytical trading system and am looking for an experienced Python/quant developer to refactor and extend the core backtesting engine. The project already exists at an early stage (data loading and a basic backtester are implemented). The focus is on correct methodology, execution logic, and clean architecture. Scope of Work: Backtesting Core - Declarative strategy definitions (config-driven, not hardcoded) - Entry/exit rules using AND / OR logic over indicators - Single instruments, batch backtesting, and spreads / pairs (true 2-leg execution) Realistic execution model: -configurable execution delay (N bars after signal) -market, stop, limit, and stop-limit orders -commissions, slippage, explicit execution assumptions Indicators - External indicator registry (YAML / JSON) - Dynamic indicator computation - Adding new indicators without modifying the core engine Optimization & Validation - Parameter optimization with Optuna - In-sample / out-of-sample testing - Walk-forward analysis (rolling windows) Risk Analysis - Trade-level Monte Carlo simulations - Drawdown and stability analysis Requirements - Strong Python (pandas, numpy) - Experience with backtesting / trading systems - Understanding of look-ahead bias, OOS validation, overfitting - Clean, modular, research-oriented code Nice to have - Optuna experience - Prior work on trading research platforms or quant systems Apply tot his job