# DQN Hyperparameter Search Space (36 Trials)

Based on tuning_config.yaml:

## Search Space

### learning_rate
- Type: loguniform
- Range: [0.0001, 0.01]
- Distribution: Logarithmic between 1e-4 and 1e-2

### batch_size
- Type: categorical
- Choices: [64, 128, 256]

### gamma (discount factor)
- Type: uniform
- Range: [0.95, 0.99]

## Objective
- Metric: sharpe_ratio
- Direction: maximize

## Pruning Strategy
- Enabled: true
- Strategy: median
- Warmup trials: 2

## Trial Summary

Total Trials: 36 completed (out of 50 requested)
Stopped early: User interrupted or median pruning

## Next Steps

1. **Option A: Backtest All Checkpoints** (Recommended)
   - Test each of the 36 checkpoint files with real market data
   - Measure Sharpe ratio for each trial
   - Extract hyperparameters from top 5 performers
   - Estimated time: 3-4 hours

2. **Option B: Use Default Best-Practice Hyperparameters**
   - learning_rate: 0.001 (middle of loguniform range)
   - batch_size: 128 (balanced memory/performance)
   - gamma: 0.97 (standard DQN discount factor)
   - Trade-off: Faster but suboptimal

3. **Option C: Resume Tuning**
   - Continue from trial 36 to complete 50 trials
   - Requires original tuning job ID and Optuna study
   - Estimated time: 2-3 hours additional

## Recommendation

**Use Option A** if PPO tuning is blocked on DQN results.
**Use Option B** if immediate PPO tuning is priority and can iterate later.