# DQN Hyperparameter Tuning - Results Extraction Plan

**Agent**: Agent 119 - DQN Tuning Monitor  
**Date**: 2025-10-14  
**Status**: Tuning incomplete, awaiting results extraction

---

## Summary

The DQN hyperparameter tuning process completed **36 out of 50 planned trials** before terminating prematurely. While no final results JSON or Optuna database was generated, we have 36 checkpoint files that can be analyzed.

### Key Facts
- **Completed trials**: 36/50 (72%)
- **Runtime**: 1 hour 45 minutes (17:00 - 18:45)
- **Average time per trial**: 2.9 minutes
- **Checkpoint location**: `/home/jgrusewski/Work/foxhunt/ml/tuning_checkpoints/trial_*/`
- **Checkpoint format**: SafeTensors (75,628 bytes each)

---

## Checkpoint Analysis

### Checkpoint Patterns
1. **Trials 0-2**: Have both `checkpoint_epoch_10.safetensors` and `checkpoint_epoch_50.safetensors`
2. **Trials 3-35**: Only have `checkpoint_epoch_50.safetensors`
3. **Trial 36**: Directory exists but is empty (failed trial)

This suggests the checkpoint saving strategy was modified after trial 2 to save only the final epoch.

### Checkpoint Characteristics
- **Size**: Exactly 75,628 bytes for all checkpoints
- **Format**: SafeTensors (PyTorch-compatible binary format)
- **Consistency**: Identical file size suggests consistent model architecture

---

## Available Results

### Pilot Results (3 trials only)
Located at: `/home/jgrusewski/Work/foxhunt/results/tuning_pilot_dqn.json`

```json
{
  "model_type": "DQN",
  "total_trials": 3,
  "best_trial": {
    "trial_id": 2,
    "learning_rate": 0.001,
    "batch_size": 230,
    "gamma": 0.99,
    "epsilon_decay": 0.995,
    "sharpe_ratio": 1.5,
    "final_loss": 0.14644842,
    "training_time_secs": 35
  }
}
```

**Limitations**: This pilot only covers 3 trials, not the full 36 completed trials from the main run.

---

## Extraction Strategy

### Option 1: SafeTensors Metadata Analysis (RECOMMENDED FIRST)

**Method**: Extract metadata from SafeTensors files  
**Effort**: 5-10 minutes  
**Likelihood of success**: Medium (depends on whether metadata was saved)

```python
import safetensors
from pathlib import Path

for trial_dir in Path("ml/tuning_checkpoints").glob("trial_*/"):
    checkpoint = trial_dir / "checkpoint_epoch_50.safetensors"
    if checkpoint.exists():
        with safetensors.safe_open(str(checkpoint), framework="pt") as f:
            metadata = f.metadata()
            if metadata:
                print(f"{trial_dir.name}: {metadata}")
```

**Expected output**: If metadata exists, it may contain:
- Trial hyperparameters (learning_rate, batch_size, gamma, epsilon_decay)
- Training metrics (loss, Sharpe ratio)
- Trial configuration

---

### Option 2: Analyze Tuning Script Configuration

**Method**: Reverse-engineer hyperparameters from tuning script logic  
**Effort**: 10-15 minutes  
**Likelihood of success**: High (if script uses deterministic trial configuration)

**Steps**:
1. Locate the tuning script (likely in `ml/examples/` or `ml/src/`)
2. Identify hyperparameter search space definition
3. Check if Optuna trial suggestion is deterministic based on trial_id
4. Map trial_id to hyperparameters for all 36 trials

**Example search spaces** (from pilot results):
```yaml
learning_rate: [0.0001, 0.001, 0.01] (log scale)
batch_size: [32, 64, 128, 256]
gamma: [0.95, 0.97, 0.99]
epsilon_decay: [0.99, 0.995, 0.999]
```

---

### Option 3: Systematic Backtest Validation

**Method**: Load each checkpoint, run backtest, calculate Sharpe ratio  
**Effort**: ~72 minutes (36 trials × 2 min/backtest)  
**Likelihood of success**: 100% (guaranteed results)

**Implementation**:
```bash
# Create validation script
python ml/examples/validate_tuning_checkpoints.py \
  --checkpoint-dir ml/tuning_checkpoints \
  --data test_data/ES.FUT.dbn \
  --output results/dqn_tuning_36trials_validated.json
```

**Advantages**:
- Definitive performance metrics
- Validation on actual market data
- Can identify best checkpoint empirically

**Disadvantages**:
- Time-consuming (1+ hour)
- Requires market data
- Computational resources

---

## Recommended Workflow

### Phase 1: Quick Analysis (15 minutes)
1. **Extract SafeTensors metadata** - Check if hyperparameters are embedded
2. **Analyze tuning script** - Understand trial configuration logic
3. **Compare with pilot results** - Validate consistency

### Phase 2: Validation (if needed, 1-2 hours)
4. **Run backtest on top 5 candidates** - Identify likely best performers
5. **Full validation** - Only if top candidates are unclear

### Phase 3: Documentation (15 minutes)
6. **Generate final results JSON** - Compile hyperparameters and metrics
7. **Update tuning report** - Document best configuration
8. **Recommend production deployment** - Based on best trial

---

## Expected Outcomes

### Best Case
- Metadata extraction reveals all hyperparameters and Sharpe ratios
- Best trial identified in 15 minutes
- Production deployment recommendation ready

### Likely Case
- Script analysis provides hyperparameters
- Backtest validation needed for Sharpe ratios
- Best trial identified in 1-2 hours

### Worst Case
- No embedded metadata or deterministic mapping
- Full backtest validation required (72 minutes)
- Still get definitive best trial

---

## Next Agent Actions

**Immediate (Agent 120 or successor)**:
1. Run SafeTensors metadata extraction script
2. If no metadata, analyze tuning script at `/home/jgrusewski/Work/foxhunt/ml/examples/`
3. Report findings and recommend next steps

**Short-term**:
1. Validate top 5-10 checkpoints via backtesting
2. Generate `dqn_tuning_50trials.json` with available results
3. Document best hyperparameters for production use

**Medium-term**:
1. Decide whether to complete remaining 14 trials
2. Implement checkpoint resumption in tuning infrastructure
3. Add incremental result logging to prevent data loss

---

## Files Generated

1. **DQN_TUNING_SUMMARY_AGENT_119.md** - Execution summary and status report
2. **DQN_TUNING_EXTRACTION_PLAN.md** - This document (extraction strategy)

---

## Conclusion

While the tuning run terminated early, we have **36 viable checkpoints** that can be analyzed to extract the best hyperparameters. The recommended extraction strategy starts with low-effort metadata analysis and escalates to backtest validation only if necessary.

**Next Agent**: Focus on metadata extraction and script analysis to recover the missing results data.

---

**Report Completed**: 2025-10-14 19:03  
**Agent**: Agent 119 - DQN Tuning Monitor  
**Status**: READY FOR RESULTS EXTRACTION