backtest strategy builder

Trading strategies often fail because they lack the objective rules required for valid historical testing. This skill takes raw trading ideas and converts them into rigorous, implementation-ready backtest specifications. It forces clarity on entry and exit logic while embedding professional controls to prevent common pitfalls like look-ahead bias and overfitting.

What it does

• Rule Formalization converts vague technical or fundamental ideas into deterministic, auditable trading rules.
• Bias Prevention identifies and mitigates look-ahead bias, survivorship bias, and selection bias before you write a line of code.
• Data Specification defines the exact market universe, timeframe, and data quality requirements needed for a valid test.
• Risk Modeling integrates slippage, transaction costs, and liquidity constraints into the strategy design.
• Robustness Testing outlines methods like walk-forward analysis and parameter sensitivity checks to ensure strategy stability.

How it works

1. Input Analysis identifies your core strategy concept and separates established facts from underlying assumptions.
2. Structural Mapping builds a professional framework following institutional standards for historical research.
3. QA Validation runs the design through quality gates to ensure all rules are objective and testable.
4. Artifact Generation produces a complete specification including a rulebook, data requirements, and metric sets.

Frameworks & tools

This skill is framework-agnostic, providing logic compatible with Python (Pandas/Backtrader), Pine Script, MetaTrader, or specialized platforms like QuantConnect.

Why this beats prompting it yourself

Generic prompts often overlook subtle biases and produce "wishful thinking" rules that cannot be coded. This skill enforces institutional-grade rigor, ensuring your strategy is actually testable and not just a collection of discretionary observations.

Use cases

• Strategy Auditing before committing capital to a new algorithmic approach.
• Logic Debugging for discretionary traders looking to automate their process.
• Research Design for quantitative analysts building a new hypothesis test.
• Platform Migration by defining logic clearly before porting to a new language.

Known limitations

Does not execute live trades or provide real-time market data. Cannot guarantee future profitability or optimize parameters automatically. Requires the user to provide the basic strategy logic.