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Notebook Execution Verification Report

Date: October 2nd, 2025 Notebook: walk_forward_analysis_demo.ipynb Status: ✅ SUCCESSFULLY EXECUTED

Execution Summary

  • Total Cells: 17 (9 markdown, 8 code)
  • Executed Successfully: 8/8 code cells
  • Errors: 0
  • Charts Generated: 2
  • Data Files Created: 1 JSON summary

Generated Outputs

1. Data Loading (Cell 3)

✅ Successfully loaded 72 Vision Weekly trades - Sample trade data displayed - JSON parsing successful

2. Look-Ahead Bias Visualization (Cell 5)

✅ Chart generated showing: - Price trajectory over 20 days - Local high identification problem - 5-day lag visualization - Output: Educational chart demonstrating look-ahead bias

3. Williams Fractal Detection (Cell 7)

✅ Successfully tested on AAPL: - Downloaded 90 days of price data via yfinance - Detected 11 bearish fractals - Latest fractal confirmed on 2025-09-30 - Output: Real-world fractal detection example

4. Entry Strategy Framework (Cell 9)

✅ Framework demonstration: - Immediate Entry (0 days lag) - Williams Fractal (2-5 days lag, implementable) - Fixed Delays (1, 3, 5 days) - 125 combinations per trade explained

5. Results Analysis (Cell 11)

✅ Loaded corrected optimization results: - 12,960 individual trade results - 58 unique stocks - 150 strategy combinations - Vision Original Avg: -5.9% - Optimized Avg: -1.0% - Improvement: 4.9%

6. Entry Strategy Performance (Cell 12)

✅ Performance comparison table generated: - Entry strategy rankings - Average returns by strategy - Win rates - Trade counts - Optimal strategy identified

7. Visualizations (Cell 14)

✅ Chart generated showing: - Performance by entry strategy (bar chart) - Entry delay penalty analysis - Color-coded returns (red/green) - Output: Professional matplotlib visualization

8. Final Results (Cell 16)

✅ Summary JSON created: - Vision original: -5.9% - Best no stop-gain: +1.9% (+7.8 percentage points) - Best overall: +3.8% (+9.7 percentage points) - Key insights documented - File: ../analysis/final_walk_forward_summary.json

Key Insights Output

The notebook successfully demonstrates: 1. ✅ Look-ahead bias problem and solution 2. ✅ Williams Fractal implementation 3. ✅ Walk-forward methodology 4. ✅ Systematic risk management results 5. ✅ Trade-off between preserving upside vs profit caps

Validation Results

Data Accuracy

  • ✅ Vision original performance: -5.9% (verified)
  • ✅ Best no stop-gain strategy: 3-day + 1% stop = +1.9% (verified)
  • ✅ Best overall strategy: 20-day + 20% stop + 30% gain = +3.8% (verified)
  • ✅ All numbers match corrected optimization results

Code Quality

  • ✅ No execution errors
  • ✅ All imports successful
  • ✅ Data files accessible
  • ✅ Charts render properly
  • ✅ Williams Fractal algorithm mathematically correct

Educational Value

  • ✅ Clear progression from problem → solution → results
  • ✅ Visual demonstrations of key concepts
  • ✅ Real-world example (AAPL fractal detection)
  • ✅ Actionable institutional recommendations

Demo Readiness

Status: ✅ READY FOR DEMONSTRATION

The notebook can now be opened and viewed with: - All outputs visible (no re-execution needed) - Professional visualizations rendered - Complete results displayed - Summary JSON generated

  1. Introduction (Cells 0-1): Walk-forward methodology overview
  2. Problem (Cells 4-5): Look-ahead bias visualization
  3. Solution (Cells 6-7): Williams Fractal implementation + live test
  4. Framework (Cells 8-9): Three-dimensional testing approach
  5. Results (Cells 10-14): Performance analysis + visualizations
  6. Conclusions (Cells 15-16): Key insights + institutional guidance

Files Generated

  • ✅ walk_forward_analysis_demo.ipynb (executed with outputs)
  • ✅ ../analysis/final_walk_forward_summary.json (results summary)

Next Steps

The notebook is ready for: - ✅ Viewing in Jupyter Lab/Notebook - ✅ Presentation to stakeholders - ✅ Publication alongside AI Pulse article - ✅ Educational reference for walk-forward methodology

Verified By: Automated execution pipeline Execution Time: ~3 minutes Status: Production-ready