Agent skill

QuantConnect Validation

QuantConnect walk-forward validation and Phase 5 robustness testing (project)

majiayu000534★ · 1 repos on radarProfile →
claude-codeMIT
Install
npx skills add majiayu000/claude-skill-registry --skill quantconnect-validation-derekcrosslu-claude-code-explore-9b11f2c --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 7 KB
Bundled scripts: none
Path: skills/ai-ml/quantconnect-validation-derekcrosslu-claude-code-explore-9b11f2c/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 534
Language: HTML

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# QuantConnect Validation Skill (Phase 5) **Purpose**: Walk-forward validation for Phase 5 robustness testing before deployment. **Progressive Disclosure**: This primer contains essentials only. Full details available via `qc_validate.py docs` command. --- ## When to Use This Skill Load when: - Running `/qc-validate` command - Testing out-of-sample performance - Evaluating strategy robustness - Making deployment decisions **Tool**: Use `python SCRIPTS/qc_validate.py` for walk-forward validation --- ## Walk-Forward Validation Overview **Purpose**: Detect overfitting and ensure strategy generalizes to new data. **Approach**: 1. **Training (in-sample)**: Develop/optimize on 80% of data 2. **Testing (out-of-sample)**: Validate on remaining 20% 3. **Compare**: Measure performance degradation **Example** (5-year backtest 2019-2023): - In-sample: 2019-2022 (4 years) - Training period - Out-of-sample: 2023 (1 year) - Testing period --- ## Key Metrics ### 1. Performance Degradation **Formula**: `(IS Sharpe - OOS Sharpe) / IS Sharpe` | Degradation | Quality | Decision | |-------------|---------|----------| | < 15% | Excellent | Deploy with confidence | | 15-30% | Acceptable | Deploy but moni

What's inside
Steps it walks through
  1. When to Use This Skill
  2. Walk-Forward Validation Overview
  3. Key Metrics
  4. 1. Performance Degradation
  5. 2. Robustness Score
  6. Quick Usage
  7. Run Walk-Forward Validation
  8. Analyze Results
  9. Decision Integration
  10. Best Practices
  11. 1. Time Splits
  12. 2. Never Peek at Out-of-Sample
  13. 3. Check Trade Count
  14. 4. Compare Multiple Metrics
Ships with 1 file
  • metadata.json
Commands it runs
From hypothesis directory with iteration_state.json
python SCRIPTS/qc_validate.py run --strategy strategy.py
Custom split ratio (default 80/20)
python SCRIPTS/qc_validate.py run --strategy strategy.py --split 0.70
python SCRIPTS/qc_validate.py analyze --results PROJECT_LOGS/validation_result.json
python SCRIPTS/qc_validate.py --help
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About this skill
What does the QuantConnect Validation skill do?

QuantConnect walk-forward validation and Phase 5 robustness testing (project)

How do I install it?

Run `npx skills add majiayu000/claude-skill-registry --skill quantconnect-validation-derekcrosslu-claude-code-explore-9b11f2c --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 majiayu000/claude-skill-registry, a repository with 534 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.

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