3853988af7
- Fixed PSO budget calculation bug in ml/src/hyperopt/optimizer.rs - Root cause: Division by n_particles in sequential execution - Now correctly calculates max_iters = remaining_trials (no division) - Result: 50 trials complete instead of 23 (100% vs 46%) - Added comprehensive DQN hyperopt results analysis - 39/50 trials analyzed across 2 RunPod deployments - Best hyperparameters identified: LR 4.89e-5 (ultra-low) - Created DQN_HYPEROPT_RESULTS_SUMMARY.md with expert validation - GitLab CI/CD pipeline operational (48 lines fixed) - Fixed YAML syntax errors (unquoted colons) - All 7 jobs validated and working - Warning cleanup complete (136 → 0 warnings) - Removed 143 lines dead code - Fixed visibility, unused imports, Debug traits - Archived Wave D reports to docs/archive/ - 8 early stopping reports moved - Root directory cleaned up 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
32 lines
1.1 KiB
Bash
Executable File
32 lines
1.1 KiB
Bash
Executable File
#!/bin/bash
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set -e
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echo "========================================="
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echo "DQN Hyperopt Deployment (Fixed Reward Function)"
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echo "========================================="
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echo ""
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# Set PYTHONPATH
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export PYTHONPATH="/home/jgrusewski/Work/foxhunt:$PYTHONPATH"
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# Activate venv
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source .venv/bin/activate
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# Deploy DQN hyperopt with fixed reward function
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python3 scripts/runpod_deploy.py \
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--gpu-type "RTX A4000" \
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--image "jgrusewski/foxhunt-hyperopt:latest" \
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--command "hyperopt_dqn_demo --parquet-file /runpod-volume/test_data/ES_FUT_180d.parquet --trials 50 --epochs 100 --base-dir /runpod-volume/ml_training/dqn_hyperopt_fixed --early-stopping-min-epochs 50"
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echo ""
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echo "✅ DQN hyperopt deployment initiated"
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echo "Monitor logs: python3 scripts/python/runpod/monitor_logs.py <pod_id>"
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echo "Expected duration: 24-36 hours"
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echo "Expected cost: \$6.00-\$9.00 @ \$0.25/hr (RTX A4000)"
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echo ""
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echo "Success Criteria:"
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echo " - Action distribution: 20-40% each (BUY/SELL/HOLD)"
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echo " - Reward std > 0.1 (no constant reward warnings)"
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echo " - Q-values balanced (divergence < 100)"
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echo " - Final backtest: > 10% return, Sharpe > 1.5"
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