quant-engine-build

Build a backtesting engine by enforcing core type contracts, deterministic simulation loops, broker cost modeling, and validation gates. This foundation generates BacktestRun artifacts and calculates performance metrics to ensure credible strategy results.

Look-ahead protection in a deterministic backtesting loop prevents future data from leaking into current strategy decisions. Combined with strict broker cost modeling, it ensures your quantitative simulation results remain credible and verifiable.

Validate quantitative strategies across multiple regimes using a pipeline of validation gates and performance metrics. This supports both in-sample and out-of-sample assessments to generate verifiable BacktestRun artifacts for analysis.

Yes, bootstrapping this foundation for a new quantitative project ensures credible results before strategy coding. The engine provides stable type contracts, deterministic simulation, and artifact generation for immediate dashboard analysis.

Broker cost modeling is necessary in a quantitative backtester to accurately reflect trading expenses during deterministic simulation. Enforcing strict cost models prevents inflated performance metrics and produces trustworthy BacktestRun artifacts.

The limitations involve relying on deterministic simulation and predefined type contracts in src/types, meaning custom strategy logic must conform strictly to these interfaces. It provides the validation framework, not the individual strategy algorithms themselves.