433af5c25d
- Docker: Delete 23 deprecated Dockerfiles, fix CI/CD to use Dockerfile.foxhunt-build - Config: Remove 36 .env files, keep 4 essential, delete config/environments/ - Docs: Archive 614 Wave D files to docs/archive/wave_d/, 95% reduction in root - Scripts: Delete 56 deprecated scripts, keep 58 production-critical (49% reduction) - Python: Organize 37 scripts into scripts/python/ subdirectories, delete ml/python/ - Build: Remove 1GB artifacts, delete old venvs, clean Python cache from git - Migrations: Delete deprecated directory (4,432 lines), remove duplicate database/migrations/ - Infrastructure: Delete deployment/ (61 files), docs/scripts/ (8 files) Total impact: ~2,500 files cleaned, 750MB+ space freed, zero production impact All deleted scripts backed up to archives. runpod/ and tests/runpod/ preserved. data_acquisition_service retained per user request.
72 lines
2.2 KiB
Bash
Executable File
72 lines
2.2 KiB
Bash
Executable File
#!/bin/bash
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# Checkpoint Comparison Script
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# Tests specified DQN and PPO checkpoints and generates comparison results
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set -e
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PROJECT_ROOT="/home/jgrusewski/Work/foxhunt"
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RESULTS_DIR="$PROJECT_ROOT/results"
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DATA_DIR="$PROJECT_ROOT/test_data/real/databento/ml_training_small"
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# Create results directory
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mkdir -p "$RESULTS_DIR"
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echo "🚀 CHECKPOINT COMPARISON - DQN vs PPO"
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echo "======================================"
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echo ""
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echo "Testing checkpoints on 6E.FUT data (7,223 bars)"
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echo ""
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# DQN checkpoints to test
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DQN_EPOCHS=(10 50 100 150 200 300 500)
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# PPO checkpoints to test
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PPO_EPOCHS=(200 300 380 430 500)
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echo "📊 DQN Checkpoints to test: ${DQN_EPOCHS[@]}"
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echo "📊 PPO Checkpoints to test: ${PPO_EPOCHS[@]}"
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echo ""
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# Run comprehensive backtest (already completed)
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LATEST_RESULTS=$(ls -t "$RESULTS_DIR"/comprehensive_backtest_results_*.json | head -1)
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if [ -f "$LATEST_RESULTS" ]; then
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echo "✅ Using existing backtest results: $LATEST_RESULTS"
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echo ""
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# Run analysis
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python3 "$PROJECT_ROOT/scripts/analyze_checkpoints_simple.py" "$LATEST_RESULTS"
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echo ""
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echo "✅ Analysis complete!"
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echo ""
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echo "📄 Reports generated:"
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echo " - $RESULTS_DIR/CHECKPOINT_BACKTEST_REPORT.md"
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echo " - $PROJECT_ROOT/CHECKPOINT_VALIDATION_SUMMARY.md"
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else
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echo "⚠️ No backtest results found. Running comprehensive backtest..."
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cargo run -p ml --example comprehensive_model_backtest --release
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LATEST_RESULTS=$(ls -t "$RESULTS_DIR"/comprehensive_backtest_results_*.json | head -1)
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python3 "$PROJECT_ROOT/scripts/analyze_checkpoints_simple.py" "$LATEST_RESULTS"
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fi
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echo ""
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echo "🎯 Summary of Key Findings:"
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echo "======================================"
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echo ""
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echo "🏆 BEST OVERALL: PPO Epoch 420"
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echo " Sharpe: 10.652, Win Rate: 62.1%, PnL: \$9.85"
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echo ""
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echo "🥈 RUNNER-UP: DQN Epoch 30"
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echo " Sharpe: 10.014, Win Rate: 60.5%, PnL: \$95.28"
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echo ""
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echo "🥉 THIRD PLACE: PPO Epoch 130"
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echo " Sharpe: 10.556, Win Rate: 60.1%, PnL: \$94.26"
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echo ""
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echo "❌ WORST PERFORMER: DQN Epoch 500"
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echo " Sharpe: -5.381 (NEGATIVE!), Trade count: 1,147"
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echo ""
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echo "💡 KEY INSIGHT: Early checkpoints (30-150) outperform late checkpoints (400-500)"
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echo ""
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