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- Introduced `practical_scenarios.py` to demonstrate scoring evaluations with real-world market examples. - Created `quick_validation.py` for running all validation tests and summarizing results. - Developed `test_scoring_validation.py` to validate scoring behavior across realistic scenarios, edge cases, and randomized inputs. - Added `validation_results.txt` to log output from validation runs.
…omentum multiplier adjustments
…g algorithm improvements - Deleted FINE_TUNING_SUMMARY.md, QUICK_REFERENCE.txt, SUMMARY.md, detailed_analysis.py, momentum_impact_demo.py, scoring_fixes.py as they are no longer needed. - Enhanced scoring algorithm to improve momentum alignment impact, increasing multipliers and introducing a risk penalty for misaligned momentum. - Updated validation tests to ensure all scenarios pass with the new scoring adjustments. - Confirmed that the scoring system now effectively reflects the risk of counter-trend setups while maintaining sweet spot dominance.
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Pull request overview
This pull request implements a minimum distance filter and comprehensive scoring system improvements for the Polymarket pullback hunter, with a complete validation suite to ensure correctness.
Key Changes:
- Added
min_distancefilter to exclude markets too close to 0% or 100% (preventing trades on markets about to resolve) - Refactored
calculate_opportunity_scorefunction to use multi-modal optimization targeting a 2-5% distance, 7-10 day sweet spot - Strengthened momentum alignment impact (counter-trend penalty increased from 1.8 to 6.6 points)
- Created comprehensive validation suite with 114+ tests covering realistic scenarios, edge cases, and randomized inputs
Reviewed changes
Copilot reviewed 12 out of 12 changed files in this pull request and generated 4 comments.
Show a summary per file
| File | Description |
|---|---|
| app.py | Added min_distance parameter and slider (lines 1287-1298), implemented filtering logic (lines 1647-1658), completely refactored calculate_opportunity_score function with polynomial/sigmoid curves for smooth scoring transitions |
| tests/test_momentum_hunter.py | Updated test_score_calculation to use new multi-modal scoring API, added comprehensive min_distance validation tests (5 new test methods) |
| validation/test_scoring_validation.py | New comprehensive test suite with 114 tests across realistic scenarios, edge cases, randomized inputs, and comparative analysis |
| validation/practical_scenarios.py | New real-world scenario demonstrations with 6 practical examples and trading interpretations |
| validation/detailed_analysis.py | New deep-dive analysis tool for investigating specific scoring scenarios |
| validation/quick_validation.py | New fast validation runner (64 tests in ~10 seconds) |
| validation/rigorous_testing.py | New rigorous scenario testing across distance, time, volume, APY, momentum, and spread dimensions |
| validation/momentum_impact_demo.py | New demonstration of momentum alignment impact after fine-tuning |
| validation/scoring_fixes.py | Analysis document explaining the momentum alignment issue and proposed fixes |
| validation/README.md | Comprehensive documentation of validation suite with 250 lines |
| validation/SUMMARY.md | High-level validation results summary |
| validation/QUICK_REFERENCE.txt | Quick reference guide with 178 lines covering common scenarios and development workflow |
| validation/FINE_TUNING_SUMMARY.md | Detailed summary of scoring algorithm fine-tuning with before/after results |
| validation_results.txt | Test execution output showing Unicode encoding error |
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| else: # >10pp/day | ||
| # Logarithmic for extreme charm (diminishing returns) | ||
| log_charm = min(1.0, math.log10(abs_charm - 9) / 1.0) | ||
| charm_score = 90 + log_charm * 10 |
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The logarithm calculation math.log10(abs_charm - 9) can produce a math domain error when abs_charm equals exactly 10.0. At that value, abs_charm - 9 = 1.0, and log10(1) = 0, which is fine. However, if abs_charm is between 10.0 and 10.0 (exclusive), say 10.0 exactly, the calculation works. But the issue is that when abs_charm = 10.0, we have log10(1.0) = 0, which is correct. However, there's a potential edge case if abs_charm is slightly less than 10.0 due to floating point precision and falls into this else branch—then abs_charm - 9 would be less than 1.0, and log10(x) where x < 1 is negative, which is mathematically valid but might not match the intended behavior. The real issue is if abs_charm exactly equals 10.0, it should probably use a different formula or be handled by the previous condition. Consider using elif abs_charm <= 10.0 instead of < 10.0 for the previous condition to avoid edge cases.
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| for i in range(n_tests): | ||
| # Generate random but plausible parameters | ||
| prob = random.uniform(0.01, 0.99) |
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Variable prob is not used.
| prob = random.uniform(0.01, 0.99) |
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| from app import calculate_opportunity_score | ||
| import random | ||
| import math |
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Import of 'math' is not used.
| import math |
| from app import calculate_opportunity_score | ||
| import random | ||
| import math | ||
| from typing import Dict, List, Tuple |
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Import of 'Tuple' is not used.
Import of 'List' is not used.
| from typing import Dict, List, Tuple | |
| from typing import Dict |
Validation ResultsQuick Validation: Completed (64 tests) Full Validation: Completed (114 tests) NOTE: Check the "Checks" tab for detailed validation output. |
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