jgrusewski
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eae3c31e53
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fix(clippy): Fix 6 unwrap_used violations in risk/data
Patterns applied:
- Pattern 2: Float comparison (2x: utils.rs, var_edge_cases_tests.rs)
- Pattern 7: Date/time construction (2x: production_streaming.rs, streaming.rs)
- Pattern 1: Duration/time ops (2x: rate limiter, semaphore)
- Pattern 4: Optional field access (1x: position_tracker.rs)
Changes:
- data/src/utils.rs: Float sort with NaN handling
- data/src/providers/benzinga/production_streaming.rs: Rate limiter + semaphore + date/time
- data/src/providers/benzinga/streaming.rs: Date/time construction
- risk/src/position_tracker.rs: Emergency fallback counter
- risk/tests/var_edge_cases_tests.rs: Test helper float sort
Test impact: 0 failures (182/182 passing)
Compilation: Clean (0 errors, 0 warnings)
Time: 25 min (44% under budget)
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2025-10-23 14:58:32 +02:00 |
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jgrusewski
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7458f1be01
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feat(wave12): E2E validation complete - 225-feature pipeline ready
✅ Validation Results:
- PPO training: 24.2s (1 epoch, 950 samples, dim=225)
- Feature extraction: 105μs/bar (9.5x faster than target)
- Model checkpoint: 293KB (147KB actor + 146KB critic)
- GPU memory: 145MB used (96.4% headroom)
- Zero dimension mismatches
📊 Success Criteria (5/5):
✅ Feature dimension = 225 (Wave C 201 + Wave D 24)
✅ Model state_dim = 225
✅ Training completed without errors
✅ Checkpoint saved successfully
✅ No dimension mismatch errors
📁 Training Data Ready:
- ES.FUT: 2.9MB, 180 days
- NQ.FUT: 4.4MB, 180 days
- 6E.FUT: 2.8MB, 180 days
- ZN.FUT: 65KB, 90 days (clean)
🚀 Next: Full production model retraining (4 models, ~10min GPU time)
🤖 Generated with Claude Code (https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
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2025-10-22 22:48:04 +02:00 |
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