backtest-expert
Expert guidance for systematic backtesting of trading strategies. Use when developing, testing, stress-testing, or validating quantitative trading strategies. Covers "beating ideas to death" methodology, parameter robustness testing, slippage modeling, bias prevention, and interpreting backtest results. Applicable when user asks about backtesting, strategy validation, robustness testing, avoiding overfitting, or systematic trading development.
npx skills add BaggaT236/AI-Trading-Skills --skill backtest-expert --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Backtest Expert Systematic approach to backtesting trading strategies based on professional methodology that prioritizes robustness over optimistic results. ## Core Philosophy **Goal**: Find strategies that "break the least", not strategies that "profit the most" on paper. **Principle**: Add friction, stress test assumptions, and see what survives. If a strategy holds up under pessimistic conditions, it's more likely to work in live trading. ## When to Use This Skill Use this skill when: - Developing or validating systematic trading strategies - Evaluating whether a trading idea is robust enough for live implementation - Troubleshooting why a backtest might be misleading - Learning proper backtesting methodology - Avoiding common pitfalls (curve-fitting, look-ahead bias, survivorship bias) - Assessing parameter sensitivity and regime dependence - Setting realistic expectations for slippage and execution costs ## Prerequisites - Python 3.9+ (for evaluation script) - No API keys required - No external data dependencies — metrics are user-provided ## Workflow ### 1. State the Hypothesis Define the edge in one sentence. **Example**: "Stocks that gap up >3% on earnings and pull back t
- Core Philosophy
- When to Use This Skill
- Prerequisites
- Workflow
- 1. State the Hypothesis
- 2. Codify Rules with Zero Discretion
- 3. Run Initial Backtest
- 4. Stress Test the Strategy
- 5. Out-of-Sample Validation
- 6. Evaluate Results
- Key Testing Principles
- Punish the Strategy
- Seek Plateaus, Not Peaks
- Test All Cases, Not Cherry-Picked Examples
python3 skills/backtest-expert/scripts/evaluate_backtest.py \
What does the backtest-expert skill do?
Expert guidance for systematic backtesting of trading strategies. Use when developing, testing, stress-testing, or validating quantitative trading strategies. Covers "beating ideas to death" methodology, parameter robustness testing, slippage modeling, bias prevention, and interpreting backtest results. Applicable when user asks about backtesting, strategy validation, robustness testing, avoiding overfitting, or systematic trading development.
How do I install it?
Run `npx skills add BaggaT236/AI-Trading-Skills --skill backtest-expert --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BaggaT236/AI-Trading-Skills, a repository with 118 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.