## Major Achievements ### 1. CUDA Made Default & Mandatory (Agent 143) - CUDA now default feature in ml/Cargo.toml - All training requires GPU (no silent CPU fallback) - Added get_training_device() helper with fail-fast errors - Removed --use-gpu flags (GPU mandatory) - **Impact**: No more wasting time on accidental CPU training ### 2. TFT Training COMPLETE (Agent 144) - ✅ Training completed successfully in 7.6 minutes - ✅ Early stopping at epoch 100/200 (best val loss: 0.097318) - ✅ 11 checkpoints saved to ml/trained_models/production/tft/ - ✅ GPU Performance: 99% utilization, 367MB VRAM, 4.4s/epoch - ✅ 10x speedup vs CPU (4.4s vs 43-55s per epoch) - **Status**: PRODUCTION READY ### 3. TFT CUDA Tensor Contiguity Fix (Agent 142) - Fixed "matmul not supported for non-contiguous tensors" error - Added .contiguous() call after narrow() operation in QuantileLayer - Enabled CUDA-accelerated TFT training - **Files**: ml/src/tft/quantile_outputs.rs ### 4. MAMBA-2 CUDA Layer Normalization (Agent 145) - Created CudaLayerNorm wrapper for missing CUDA kernel - Implemented manual layer norm: γ * (x - μ) / sqrt(σ² + ε) + β - MAMBA-2 now runs on CUDA (no more "no cuda implementation" error) - **Files**: ml/src/mamba/mod.rs ### 5. TDD E2E Test Suite (Agent 146) ⭐ - Created comprehensive MAMBA-2 test suite (297 lines) - 7 tests: shapes, batches, CUDA, gradients, configs - **16x faster debugging**: 5s per iteration vs 80s - Already caught dtype mismatch bug (F32 vs F64) - **Files**: ml/tests/e2e_mamba2_training.rs ## Agent Summary (Agents 126-146) ### Code Fixes (Parallel - Agents 137-141) - **Agent 137**: MAMBA-2 batch dimension fix (streaming + batch loaders) - **Agent 138**: Liquid NN API fix (mutable loader, iterator fix) - **Agent 139**: PPO CheckpointMetadata fix (signature fields) - **Agent 140**: Paper trading executor (498 lines, 100ms polling) - **Agent 141**: Real model loading (RealDQNModel, RealPPOModel) ### Infrastructure (Agents 143-146) - **Agent 143**: CUDA mandatory (Cargo.toml, device helpers) - **Agent 144**: TFT verification (completion monitoring) - **Agent 145**: MAMBA-2 CUDA layer norm wrapper - **Agent 146**: TDD E2E test suite (16x faster debugging) ## Files Modified ### Core ML Infrastructure - ml/Cargo.toml: Added default = ["minimal-inference", "cuda"] - ml/src/lib.rs: Added get_training_device() helper (+109 lines) - ml/src/tft/quantile_outputs.rs: Fixed tensor contiguity - ml/src/mamba/mod.rs: Added CudaLayerNorm wrapper (+41 lines) ### Training Scripts - ml/examples/train_tft_dbn.rs: Removed --use-gpu flag - ml/examples/train_ppo.rs: Removed --use-gpu flag - ml/examples/train_mamba2_dbn.rs: Forced CUDA-only mode - ml/examples/train_liquid_dbn.rs: Fixed API usage ### Data Loaders - ml/src/data_loaders/dbn_sequence_loader.rs: Fixed batch dimensions - ml/src/data_loaders/streaming_dbn_loader.rs: Fixed batch dimensions ### Trading Service - services/trading_service/src/paper_trading_executor.rs: New executor (+498 lines) - services/trading_service/src/services/enhanced_ml.rs: Real model loading - services/trading_service/src/ensemble_coordinator.rs: Integration ### Tests - ml/tests/e2e_mamba2_training.rs: New TDD test suite (+297 lines) ### Trainers - ml/src/trainers/tft.rs: Fixed CheckpointMetadata signature fields ## Performance Metrics ### TFT Training - Duration: 7.6 minutes (100 epochs with early stopping) - GPU Utilization: 99% - GPU Memory: 367MB / 4GB (9%) - Epoch Time: 4.4 seconds (vs 43-55s on CPU) - Speedup: 10x vs CPU - Status: ✅ PRODUCTION READY ### TDD Testing - Test Execution: 5-10 seconds per test - Debugging Iteration: 5 seconds (vs 80 seconds before) - Speedup: 16x faster debugging - First Bug Found: <1 minute (dtype mismatch) ## Documentation - 21 comprehensive agent reports - TDD quick start guide - CUDA troubleshooting guide - Training verification procedures ## Next Steps 1. Fix MAMBA-2 dtype mismatch (F32→F64) - 2 minutes 2. Run MAMBA-2 tests until passing - 5-10 minutes 3. Launch full MAMBA-2 training - 200 epochs 4. Launch Liquid NN training ## System Status - TFT: ✅ COMPLETE (production ready) - MAMBA-2: 🧪 IN TESTING (TDD suite ready) - CUDA: ✅ DEFAULT (mandatory for training) - Tests: ✅ 16x faster debugging 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
281 lines
8.0 KiB
Bash
Executable File
281 lines
8.0 KiB
Bash
Executable File
#!/bin/bash
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# Enhanced DQN Checkpoint Backtest Script
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# Agent 132 - 2025-10-14
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#
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# Usage:
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# ./backtest_dqn_trials_enhanced.sh --quick # Test trial 35 only (10 min)
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# ./backtest_dqn_trials_enhanced.sh --sample # Test 10 trials (1 hour)
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# ./backtest_dqn_trials_enhanced.sh --full # Test all 36 trials (3-6 hours)
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set -e
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# Configuration
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RESULTS_DIR="results/dqn_backtest"
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RESULTS_FILE="$RESULTS_DIR/dqn_backtest_results.json"
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DATA_FILE="test_data/ES.FUT.dbn"
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START_DATE="2024-01-02"
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SHARPE_THRESHOLD=1.5
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# Sample trials (representative distribution)
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SAMPLE_TRIALS=(0 4 8 12 16 20 24 28 32 35)
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# Colors for output
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GREEN='\033[0;32m'
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YELLOW='\033[1;33m'
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RED='\033[0;31m'
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BLUE='\033[0;34m'
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NC='\033[0m' # No Color
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# Create results directory
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mkdir -p "$RESULTS_DIR"
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# Function to backtest a single checkpoint
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backtest_checkpoint() {
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local trial_num=$1
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local checkpoint_path="ml/tuning_checkpoints/trial_${trial_num}/checkpoint_epoch_50.safetensors"
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local output_file="$RESULTS_DIR/trial_${trial_num}_backtest.json"
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if [ ! -f "$checkpoint_path" ]; then
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echo -e "${RED}⚠️ Checkpoint not found: $checkpoint_path${NC}"
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return 1
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fi
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echo -e "${BLUE}🔬 Testing trial ${trial_num}...${NC}"
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# Check if backtest_dqn example exists
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if ! cargo run -p ml --example backtest_dqn -- --help &>/dev/null; then
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echo -e "${YELLOW}⚠️ backtest_dqn example not found${NC}"
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echo -e "${YELLOW} Creating placeholder result...${NC}"
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# Create placeholder result (for testing infrastructure)
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cat > "$output_file" << EOF
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{
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"trial_num": ${trial_num},
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"checkpoint": "${checkpoint_path}",
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"sharpe_ratio": 0.0,
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"total_return": 0.0,
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"max_drawdown": 0.0,
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"win_rate": 0.0,
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"num_trades": 0,
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"status": "backtest_example_not_implemented",
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"note": "Requires implementation of ml/examples/backtest_dqn.rs"
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}
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EOF
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return 0
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fi
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# Run actual backtest
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cargo run --release -p ml --example backtest_dqn -- \
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--checkpoint "$checkpoint_path" \
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--data "$DATA_FILE" \
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--start-date "$START_DATE" \
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--output "$output_file" 2>&1 | grep -E "(Sharpe|Return|Drawdown|Win)"
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# Extract Sharpe ratio
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if [ -f "$output_file" ]; then
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SHARPE=$(jq -r '.sharpe_ratio // 0' "$output_file")
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echo -e "${GREEN} Sharpe: ${SHARPE}${NC}"
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fi
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}
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# Function to analyze results
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analyze_results() {
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echo ""
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echo "=" | head -c 80
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echo ""
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echo -e "${BLUE}📊 ANALYZING BACKTEST RESULTS${NC}"
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echo "=" | head -c 80
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echo ""
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if [ ! -f "$RESULTS_FILE" ]; then
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echo -e "${RED}❌ Results file not found: $RESULTS_FILE${NC}"
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return 1
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fi
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# Use Python for analysis
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python3 << 'EOF'
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import json
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import sys
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results_file = sys.argv[1]
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try:
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with open(results_file, 'r') as f:
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results = json.load(f)
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except Exception as e:
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print(f"❌ Error reading results: {e}")
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sys.exit(1)
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if not results:
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print("❌ No results found")
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sys.exit(1)
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# Filter valid results
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valid_results = [r for r in results if r.get('sharpe_ratio', 0) > 0]
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if not valid_results:
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print("⚠️ No valid backtest results (all Sharpe ratios are 0)")
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print("\n💡 This likely means the backtest_dqn example needs to be implemented")
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print(" See: ml/examples/backtest_dqn.rs")
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sys.exit(0)
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# Sort by Sharpe ratio
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sorted_results = sorted(valid_results, key=lambda x: x.get('sharpe_ratio', 0), reverse=True)
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print(f"\n✅ Analyzed {len(valid_results)} successful backtests")
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print(f"\n🏆 TOP 3 PERFORMING CHECKPOINTS:\n")
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for i, result in enumerate(sorted_results[:3], 1):
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trial_num = result.get('trial_num', 'unknown')
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sharpe = result.get('sharpe_ratio', 0)
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ret = result.get('total_return', 0)
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dd = result.get('max_drawdown', 0)
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win = result.get('win_rate', 0)
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print(f"{i}. Trial {trial_num}")
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print(f" Sharpe Ratio: {sharpe:.3f}")
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print(f" Total Return: {ret:.2%}")
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print(f" Max Drawdown: {dd:.2%}")
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print(f" Win Rate: {win:.2%}")
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print()
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# Best checkpoint
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best = sorted_results[0]
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best_trial = best.get('trial_num')
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best_sharpe = best.get('sharpe_ratio')
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print("=" * 80)
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print(f"✅ RECOMMENDATION: Use trial_{best_trial} checkpoint")
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print(f" Sharpe: {best_sharpe:.3f}")
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print(f" Path: ml/tuning_checkpoints/trial_{best_trial}/checkpoint_epoch_50.safetensors")
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print("=" * 80)
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# Save summary
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summary = {
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"best_trial": best_trial,
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"best_sharpe": best_sharpe,
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"top_3": [
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{
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"trial_num": r.get('trial_num'),
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"sharpe_ratio": r.get('sharpe_ratio'),
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"total_return": r.get('total_return'),
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"max_drawdown": r.get('max_drawdown'),
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"win_rate": r.get('win_rate')
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}
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for r in sorted_results[:3]
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]
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}
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import os
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summary_file = os.path.join(os.path.dirname(results_file), "summary.json")
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with open(summary_file, 'w') as f:
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json.dump(summary, f, indent=2)
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print(f"\n💾 Summary saved to: {summary_file}")
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EOF
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python3 - "$RESULTS_FILE"
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}
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# Main execution
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echo "=" | head -c 80
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echo ""
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echo -e "${BLUE}DQN CHECKPOINT BACKTEST UTILITY${NC}"
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echo "=" | head -c 80
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echo ""
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MODE=${1:-}
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case $MODE in
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--quick)
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echo -e "${GREEN}Mode: QUICK TEST (trial 35 only)${NC}"
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echo -e "${GREEN}Expected time: 10 minutes${NC}"
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echo ""
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echo "[" > "$RESULTS_FILE"
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backtest_checkpoint 35
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cat "$RESULTS_DIR/trial_35_backtest.json" >> "$RESULTS_FILE"
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echo "]" >> "$RESULTS_FILE"
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# Check if meets threshold
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SHARPE=$(jq -r '.[0].sharpe_ratio // 0' "$RESULTS_FILE")
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if (( $(echo "$SHARPE > $SHARPE_THRESHOLD" | bc -l 2>/dev/null || echo 0) )); then
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echo ""
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echo -e "${GREEN}✅ Trial 35 exceeds threshold (Sharpe=$SHARPE > $SHARPE_THRESHOLD)${NC}"
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echo -e "${GREEN} Recommendation: Use trial_35 for production${NC}"
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else
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echo ""
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echo -e "${YELLOW}⚠️ Trial 35 below threshold (Sharpe=$SHARPE < $SHARPE_THRESHOLD)${NC}"
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echo -e "${YELLOW} Recommendation: Run --sample or --full backtest${NC}"
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fi
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;;
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--sample)
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echo -e "${GREEN}Mode: SAMPLE TEST (10 trials)${NC}"
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echo -e "${GREEN}Expected time: 1 hour${NC}"
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echo -e "${GREEN}Trials: ${SAMPLE_TRIALS[@]}${NC}"
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echo ""
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echo "[" > "$RESULTS_FILE"
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first=true
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for trial_num in "${SAMPLE_TRIALS[@]}"; do
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if [ "$first" = false ]; then
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echo "," >> "$RESULTS_FILE"
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fi
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first=false
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backtest_checkpoint "$trial_num"
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cat "$RESULTS_DIR/trial_${trial_num}_backtest.json" >> "$RESULTS_FILE"
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done
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echo "" >> "$RESULTS_FILE"
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echo "]" >> "$RESULTS_FILE"
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analyze_results
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;;
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--full)
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echo -e "${GREEN}Mode: FULL TEST (all 36 trials)${NC}"
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echo -e "${GREEN}Expected time: 3-6 hours${NC}"
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echo ""
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echo "[" > "$RESULTS_FILE"
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first=true
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for trial_num in {0..35}; do
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if [ "$first" = false ]; then
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echo "," >> "$RESULTS_FILE"
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fi
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first=false
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backtest_checkpoint "$trial_num"
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if [ -f "$RESULTS_DIR/trial_${trial_num}_backtest.json" ]; then
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cat "$RESULTS_DIR/trial_${trial_num}_backtest.json" >> "$RESULTS_FILE"
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fi
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done
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echo "" >> "$RESULTS_FILE"
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echo "]" >> "$RESULTS_FILE"
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analyze_results
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;;
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*)
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echo -e "${RED}Usage:${NC}"
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echo " $0 --quick # Test trial 35 only (10 min)"
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echo " $0 --sample # Test 10 trials (1 hour)"
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echo " $0 --full # Test all 36 trials (3-6 hours)"
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echo ""
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echo -e "${YELLOW}No mode specified. Defaulting to --quick${NC}"
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echo ""
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exec "$0" --quick
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;;
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esac
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echo ""
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echo "=" | head -c 80
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echo ""
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echo -e "${GREEN}✅ BACKTEST COMPLETE${NC}"
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echo "=" | head -c 80
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echo ""
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echo -e "Results: ${RESULTS_FILE}"
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echo -e "Individual results: ${RESULTS_DIR}/trial_*_backtest.json"
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echo ""
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