quant-signals

What problem does it solve?

Quantitative signal design and testing is often ad-hoc, error-prone, and lacks rigorous out-of-sample validation. This Skill provides a structured framework to define, test, and validate trading signals with robust statistical checks, backtesting discipline, and ensemble construction to improve durability.

Core Features & Use Cases

• Alpha signal design: Formalize hypotheses, select data sources, and transform raw data into testable signals.
• Backtest rigor: Implement walk-forward optimization, cross-validation (CPCV), deflated Sharpe adjustments, and realistic transaction cost models.
• Ensemble construction: Combine multiple signals, manage correlation, and calibrate conviction based on agreement.
• ML integration: Integrate machine learning components with guardrails to mitigate overfitting and model drift.
• Portfolio optimization: Allocate across signals under breadth and risk constraints; assess regime-dependent performance.

Quick Start

Design a new alpha signal for a chosen universe, specify data sources, apply a normalization and neutralization pipeline, run a walk-forward backtest, and interpret the out-of-sample results considering costs.