The problem

Moving from backtesting to live markets often fails because of discretionary overrides, inconsistent logging, and moving goalposts. Developers lose capital when strategies that look good on paper lack the rigorous forward-testing guardrails needed to prove real-world viability.

What it does

• Generates a versioned, immutable rule set for entry, exit, and risk management to prevent mid-test meddling.
• Defines mandatory sample targets including trade counts, duration, and market regime coverage.
• Constructs a machine-readable execution log template for tracking signals, fills, slippage, and fees.
• Builds pre-trade and post-trade deviation registers to identify manual overrides or data failures.
• Establishes quantitative promotion criteria to gate live deployment behind strict performance and consistency checks.

Why this beats prompting it yourself

This skill enforces a disciplined audit trail that standard LLM prompts usually miss. It creates structured validation frameworks that treat trading strategies like production software deployments, ensuring you do not skip the critical step of regime verification or slippage analysis.

Use cases

• Transitioning a Python-based algorithmic strategy from historical backtests to a paper-trading account.
• Establishing a formal audit process for manual discretionary traders seeking consistency.
• Validating automated trading signals against live data feeds before committing capital.
• Standardizing the "Path to Production" for a quantitative trading desk or individual developer.

Known limitations

Does not provide execution code or direct brokerage API integration. Changing a locked rule mid-test requires manual restart of the validation period.