# Quick Start: Hyperparameter Tuning

**Time to Complete**: 4-8 hours (50 trials)
**Prerequisites**: Trained baseline model, ML Training Service running
**Goal**: Find optimal hyperparameters for 10-20% performance improvement

---

## What is Hyperparameter Tuning?

**Problem**: Default hyperparameters are rarely optimal
- Learning rate too high → unstable training
- Batch size too small → slow convergence
- Hidden layers wrong size → underfitting/overfitting

**Solution**: Automated search (Optuna) to find best configuration
- **Objective**: Maximize Sharpe ratio (risk-adjusted returns)
- **Method**: Bayesian optimization (smart search, not brute force)
- **Time**: 5-10 minutes per trial × 50 trials = 4-8 hours

**Expected Improvement**:
- Baseline Sharpe: 1.5
- Tuned Sharpe: 1.7-2.0 (10-30% improvement)

---

## Step 1: Prerequisites (5 minutes)

### Services Running
```bash
# Check services
docker-compose ps

# Should be running:
# - postgres (Optuna study storage)
# - ml_training_service
# - api_gateway
```

### Baseline Model
```bash
# List trained models
tli checkpoints list --model DQN

# You should have at least one checkpoint
# If not, train baseline first: see QUICK_START_TRAINING.md
```

---

## Step 2: Review Tuning Configuration (2 minutes)

### Check Search Space
```bash
cat tuning_config.yaml
```

**Example DQN Configuration**:
```yaml
dqn:
  learning_rate:
    type: loguniform
    low: 1.0e-5
    high: 1.0e-2

  batch_size:
    type: categorical
    choices: [32, 64, 128, 256]

  gamma:
    type: uniform
    low: 0.95
    high: 0.999

  hidden_size:
    type: categorical
    choices: [128, 256, 512]

  num_layers:
    type: int
    low: 2
    high: 4
```

### Understand Parameters

| Parameter | Range | Impact |
|-----------|-------|--------|
| `learning_rate` | 1e-5 to 1e-2 | Training speed/stability |
| `batch_size` | 32-256 | Memory usage, convergence |
| `gamma` | 0.95-0.999 | Future reward discount |
| `hidden_size` | 128-512 | Model capacity |
| `num_layers` | 2-4 | Model depth |

---

## Step 3: Start Tuning Job (1 minute)

### Basic Tuning
```bash
tli tune start --model DQN --trials 50
```

### Advanced Tuning (Recommended)
```bash
tli tune start \
  --model DQN \
  --trials 50 \
  --watch \
  --symbol ES.FUT \
  --epochs 100
```

**Options**:
- `--trials`: Number of hyperparameter combinations to test
- `--watch`: Stream progress updates in real-time
- `--symbol`: Training symbol (default: ES.FUT)
- `--epochs`: Epochs per trial (default: 100)

### Expected Output
```
Tuning job started: job-id=a1b2c3d4-e5f6-7890-abcd-ef1234567890
Study: dqn-tuning-20251014-153045
Trials: 0/50 complete
Best Sharpe: N/A (waiting for first trial)
ETA: 4-8 hours

Use 'tli tune status --job-id a1b2c3d4...' to check progress
```

---

## Step 4: Monitor Progress (Active Monitoring)

### Check Status
```bash
tli tune status --job-id <job-id>
```

**Output**:
```
Study: dqn-tuning-20251014-153045
Status: RUNNING
Trials: 12/50 complete (24%)
Duration: 1h 23m (elapsed)
ETA: 4h 37m (remaining)

Current Best Trial:
  Trial #7
  Sharpe Ratio: 1.82
  Parameters:
    learning_rate: 0.000234
    batch_size: 128
    gamma: 0.985
    hidden_size: 256
    num_layers: 3
```

### Watch Live Updates
```bash
tli tune status --job-id <job-id> --watch
```

**Live Output**:
```
Trial 13/50: Sharpe 1.65 | LR=0.0005 BS=64 Gamma=0.99 HS=128 Layers=2
Trial 14/50: Sharpe 1.78 | LR=0.0002 BS=128 Gamma=0.985 HS=256 Layers=3
Trial 15/50: Sharpe 1.45 | LR=0.001 BS=32 Gamma=0.95 HS=512 Layers=4
...
```

---

## Step 5: Analyze Results (10 minutes)

### Get Best Hyperparameters
```bash
tli tune best --job-id <job-id>
```

**Output**:
```json
{
  "study": "dqn-tuning-20251014-153045",
  "best_trial": 7,
  "best_value": 1.82,
  "best_params": {
    "learning_rate": 0.000234,
    "batch_size": 128,
    "gamma": 0.985,
    "hidden_size": 256,
    "num_layers": 3
  },
  "improvement": {
    "baseline_sharpe": 1.50,
    "tuned_sharpe": 1.82,
    "improvement_pct": 21.3
  },
  "training_metrics": {
    "final_loss": 0.0234,
    "total_reward": 18450.5,
    "win_rate": 0.612
  }
}
```

### Compare with Baseline
```bash
# Baseline model
tli checkpoints info --checkpoint-id <baseline-checkpoint>

# Tuned model
tli checkpoints info --checkpoint-id <tuned-checkpoint>
```

**Comparison**:
| Metric | Baseline | Tuned | Improvement |
|--------|----------|-------|-------------|
| Sharpe Ratio | 1.50 | 1.82 | +21.3% |
| Win Rate | 56.2% | 61.2% | +5.0% |
| Max Drawdown | 14.8% | 11.2% | -24.3% |

---

## Step 6: Retrain with Best Hyperparameters (2-3 days)

### Create Custom Config
```bash
cat > dqn_tuned_config.yaml << EOF
model: DQN
symbol: ES.FUT
epochs: 200
hyperparameters:
  learning_rate: 0.000234
  batch_size: 128
  gamma: 0.985
  hidden_size: 256
  num_layers: 3
EOF
```

### Train Optimized Model
```bash
tli train start --config dqn_tuned_config.yaml
```

### Monitor Training
```bash
tli train status --job-id <train-job-id> --watch
```

---

## Step 7: Validate Tuned Model (1 hour)

### Run Backtest
```bash
tli backtest run \
  --strategy dqn_strategy \
  --symbol ES.FUT \
  --start 2024-01-01 \
  --end 2024-12-31 \
  --checkpoint-id <tuned-checkpoint-id>
```

### Expected Results
```
Backtest Complete:
  Strategy: dqn_strategy (tuned)
  Period: 2024-01-01 to 2024-12-31

  Performance:
    Sharpe Ratio: 1.85
    Win Rate: 61.8%
    Max Drawdown: 10.8%
    Total PnL: $165,230
    Trades: 1,342

  Improvement over Baseline:
    Sharpe: +23.3%
    Win Rate: +5.6%
    Drawdown: -27.0%
    PnL: +31.5%
```

---

## Advanced Tuning Strategies

### Multi-Model Tuning
```bash
# Tune all models in parallel
tli tune start --model DQN --trials 50 &
tli tune start --model PPO --trials 50 &
tli tune start --model MAMBA2 --trials 50 &
tli tune start --model TFT --trials 50 &

# Wait for all jobs to complete (12-24 hours)
```

### Multi-Symbol Tuning
```bash
# Find hyperparameters that work across symbols
tli tune start \
  --model DQN \
  --trials 50 \
  --symbols ES.FUT,NQ.FUT,ZN.FUT,6E.FUT

# This tests generalization across markets
```

### Warm Start (Continue Tuning)
```bash
# If tuning interrupted or want more trials
tli tune start \
  --model DQN \
  --trials 50 \
  --study-name dqn-tuning-20251014-153045  # Reuse existing study

# Optuna will resume from last trial
```

---

## Troubleshooting

### Trial Failures
```bash
# Check logs
docker-compose logs -f ml_training_service

# Common causes:
# - OOM (reduce batch_size range in config)
# - NaN loss (reduce learning_rate upper bound)
# - Timeout (increase epochs per trial)
```

### Slow Tuning
```bash
# Speed up by reducing epochs per trial
tli tune start --model DQN --trials 50 --epochs 50

# Trade-off: Faster tuning but less accurate Sharpe estimates
```

### Poor Results (No Improvement)
```bash
# Expand search space in tuning_config.yaml
learning_rate:
  low: 1.0e-6    # Was 1.0e-5
  high: 5.0e-2   # Was 1.0e-2

# Try more trials
tli tune start --model DQN --trials 100  # Was 50
```

### Out of Memory
```bash
# Reduce batch_size range
batch_size:
  choices: [16, 32, 64]  # Was [32, 64, 128, 256]

# Or reduce hidden_size range
hidden_size:
  choices: [64, 128, 256]  # Was [128, 256, 512]
```

---

## Best Practices

### Trial Count
- **Quick test**: 10-20 trials (1-2 hours)
- **Standard**: 50 trials (4-8 hours)
- **Thorough**: 100 trials (8-16 hours)
- **Research**: 200+ trials (16-32 hours)

### Early Stopping
```bash
# Optuna MedianPruner automatically stops poor trials
# Saves 30-50% time by killing obviously bad hyperparameters

# Check pruned trials
tli tune status --job-id <job-id> --show-pruned
```

### Study Persistence
```bash
# All studies saved to PostgreSQL (JournalStorage)
# Can resume anytime, even after service restart

# List all studies
tli tune list

# Resume specific study
tli tune start --study-name <study-name> --trials 50
```

---

## Next Steps

### Ensemble Tuning
```bash
# After tuning individual models, optimize ensemble weights
# See: /home/jgrusewski/Work/foxhunt/ENSEMBLE_WEIGHT_OPTIMIZATION_QUICKSTART.md

tli ensemble optimize \
  --models DQN,PPO,MAMBA2,TFT \
  --trials 100
```

### Production Deployment
```bash
# Deploy tuned model to paper trading
# See: /home/jgrusewski/Work/foxhunt/PAPER_TRADING_DEPLOYMENT_PLAN.md

# Expected: Sharpe > 1.8 in live conditions
```

---

## Key Resources

### Tuning Documentation
- **[Optuna Tuning Integration Report](/home/jgrusewski/Work/foxhunt/OPTUNA_TUNING_INTEGRATION_REPORT.md)** - Full implementation (26.8K)
- **[MAMBA-2 Tuning Report](/home/jgrusewski/Work/foxhunt/MAMBA2_HYPERPARAMETER_TUNING_REPORT.md)** - Model-specific tuning
- **[Tuning Quickstart Guide](/home/jgrusewski/Work/foxhunt/TUNING_QUICKSTART_GUIDE.md)** - Quick reference

### ML Training
- **[ML Training Roadmap](/home/jgrusewski/Work/foxhunt/ML_TRAINING_ROADMAP.md)** - Overall training plan
- **[Quick Start: Training](/home/jgrusewski/Work/foxhunt/docs/guides/QUICK_START_TRAINING.md)** - Train baseline model

---

## Success Metrics

### Tuning Success
- ✅ 50 trials complete without failures
- ✅ Best Sharpe > baseline + 10%
- ✅ Improvement consistent across validation periods

### Model Quality
- ✅ Tuned Sharpe ratio > 1.8
- ✅ Win rate > 60%
- ✅ Max drawdown < 12%

### Production Ready
- ✅ Backtest validates tuning results
- ✅ Paper trading confirms improvement
- ✅ Consistent performance for 2-4 weeks

---

**Estimated Time**:
- Configuration: 5 minutes
- Tuning job: 4-8 hours
- Analysis: 10 minutes
- Retrain: 2-3 days
- Validation: 1 hour

**Total**: ~3-4 days from start to validated tuned model

**Next Guide**: [Quick Start: Ensemble Deployment](/home/jgrusewski/Work/foxhunt/docs/guides/QUICK_START_ENSEMBLE.md)