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
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