foxhunt/HYPERPARAMETER_TUNING_EXECUTION_REPORT.md

Hyperparameter Tuning Execution Report

Generated: 2025-10-14 17:58:00 Pipeline Status: ⏳ In Progress Models Completed: 0/5 Expected Completion: 2025-10-15 08:13:00

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Executive Summary

This report tracks the automated hyperparameter tuning pipeline for all 5 Foxhunt ML models (DQN, PPO, TFT, MAMBA-2, Liquid). The pipeline uses Optuna for Bayesian optimization with 50 trials per model and 50 epochs per trial, optimizing for Sharpe ratio (risk-adjusted returns).

Current Status

• ✅ Infrastructure: Auto-monitor and sequential launcher deployed
• ⏳ DQN: 20/50 trials complete (40%), Runtime: 1h 0m
• ⏳ PPO: Waiting for DQN completion
• ⏳ TFT: Waiting for PPO completion
• ⏳ MAMBA-2: Waiting for TFT completion
• ⏳ Liquid: Waiting for MAMBA-2 completion

Timeline

• DQN: Started 16:57, ETA 19:28 (1.6h remaining)
• PPO: ETA 21:01-00:13 (3.2h)
• TFT: ETA 00:13-04:25 (4.2h)
• MAMBA-2: ETA 04:25-06:31 (2.1h)
• Liquid: ETA 06:31-08:13 (1.7h)
• Total Remaining: 12.8 hours

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Infrastructure Deployed

1. Auto-Monitor Script (auto_monitor_and_launch.sh)

• Function: Monitors DQN completion, auto-launches sequential tuner
• PID: 3991057
• Status: ✅ Running
• Log: /tmp/auto_monitor.log

2. Sequential Tuning Launcher (sequential_tuning_launcher.sh)

• Function: Launches PPO → TFT → MAMBA-2 → Liquid sequentially
• Status: ⏳ Waiting for DQN
• Will launch: Automatically when DQN completes

3. Dashboard Monitor (dashboard_monitor.sh)

• Function: Real-time status dashboard for all models
• Usage: watch -n 30 /home/jgrusewski/Work/foxhunt/scripts/dashboard_monitor.sh
• Shows: GPU status, trial progress, runtime for each model

4. Hyperparameter Extractor (extract_best_hyperparameters.py)

• Function: Extracts best hyperparameters from JSON results
• Usage: python3 scripts/extract_best_hyperparameters.py
• Output: Updated version of this report with best hyperparameters

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Model Tuning Details

DQN (Deep Q-Network)

Status: ⏳ In Progress (40% complete) PID: 3911478 Runtime: 1h 0m Progress: 20/50 trials Log: /tmp/tuning_run.log

Latest Metrics

• Trial 19: Sharpe=2.00, Loss=0.0450, Time=185s
• Average Trial Time: ~175s (2.9 minutes)
• Estimated Completion: 19:28 (1.6 hours remaining)

Performance Observations

• ✅ GPU utilization: 40% (healthy)
• ✅ Memory usage: 135/4096 MiB (3.3%, plenty of headroom)
• ✅ Temperature: 64°C (well within limits)
• ✅ Training samples: 665,483 from 360 DBN files
• ✅ No CUDA OOM errors

Hyperparameter Search Space

learning_rate: [1e-5, 1e-3] (log scale)
batch_size: [32, 256]
gamma: [0.9, 0.999]
epsilon_start: [0.5, 1.0]
epsilon_end: [0.01, 0.1]
epsilon_decay: [0.9, 0.999]
target_update_freq: [10, 100]
replay_buffer_size: [10000, 100000]

Best Hyperparameters

⏳ Will be extracted when tuning completes

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PPO (Proximal Policy Optimization)

Status: ⏳ Waiting for DQN Expected Start: 21:01 Expected Duration: 3.2 hours Expected Completion: 00:13

Hyperparameter Search Space

learning_rate: [1e-5, 1e-3] (log scale)
batch_size: [32, 256]
gamma: [0.9, 0.999]
gae_lambda: [0.9, 0.99]
clip_epsilon: [0.1, 0.3]
value_coef: [0.5, 1.0]
entropy_coef: [0.001, 0.1]
max_grad_norm: [0.5, 1.0]

Best Hyperparameters

⏳ Pending completion

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TFT (Temporal Fusion Transformer)

Status: ⏳ Waiting for PPO Expected Start: 00:13 Expected Duration: 4.2 hours Expected Completion: 04:25

Hyperparameter Search Space

learning_rate: [1e-5, 1e-3] (log scale)
batch_size: [16, 128]
hidden_size: [64, 256]
num_attention_heads: [4, 16]
dropout: [0.1, 0.5]
lstm_layers: [1, 3]
attention_head_size: [4, 64]

Best Hyperparameters

⏳ Pending completion

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MAMBA-2 (State Space Model)

Status: ⏳ Waiting for TFT Expected Start: 04:25 Expected Duration: 2.1 hours Expected Completion: 06:31

Hyperparameter Search Space

learning_rate: [1e-5, 1e-3] (log scale)
batch_size: [32, 256]
hidden_size: [64, 256]
state_size: [16, 64]
num_layers: [2, 8]
dropout: [0.1, 0.5]

Best Hyperparameters

⏳ Pending completion

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Liquid (Liquid Neural Network)

Status: ⏳ Waiting for MAMBA-2 Expected Start: 06:31 Expected Duration: 1.7 hours Expected Completion: 08:13

Hyperparameter Search Space

learning_rate: [1e-5, 1e-3] (log scale)
batch_size: [32, 256]
hidden_size: [64, 256]
ode_solver_steps: [1, 10]
dropout: [0.1, 0.5]

Best Hyperparameters

⏳ Pending completion

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Monitoring & Debugging

Real-Time Monitoring Commands

# Dashboard (recommended - updates every 30s)
watch -n 30 /home/jgrusewski/Work/foxhunt/scripts/dashboard_monitor.sh

# Individual model logs
tail -f /tmp/tuning_run.log      # DQN
tail -f /tmp/ppo_tuning_run.log  # PPO
tail -f /tmp/tft_tuning_run.log  # TFT
tail -f /tmp/mamba2_tuning_run.log  # MAMBA-2
tail -f /tmp/liquid_tuning_run.log  # Liquid

# Overall pipeline status
cat /tmp/tuning_pipeline_status.txt

# GPU monitoring
nvidia-smi -l 5  # Update every 5 seconds

Process Management

# Check running processes
ps aux | grep tune_hyperparameters

# Check PIDs
cat /tmp/dqn_tuning.pid       # DQN PID
cat /tmp/ppo_tuning.pid       # PPO PID (when started)
cat /tmp/auto_monitor.pid     # Auto-monitor PID

# Kill specific model (if needed)
kill -9 $(cat /tmp/ppo_tuning.pid)

# Check auto-monitor status
ps -p $(cat /tmp/auto_monitor.pid) -o etime,pid,cmd

CUDA OOM Error Handling

If any model encounters CUDA Out-Of-Memory errors:

1. Check GPU memory: nvidia-smi
2. Reduce batch size in tuning_config.yaml:
   • DQN: 256 → 128 → 64
   • PPO: 256 → 128 → 64
   • TFT: 128 → 64 → 32
   • MAMBA-2: 256 → 128 → 64
   • Liquid: 256 → 128 → 64
3. Restart failed model:

   /home/jgrusewski/Work/foxhunt/target/release/examples/tune_hyperparameters \
     --model <MODEL> \
     --num-trials 50 \
     --epochs-per-trial 50 \
     --data-dir test_data/real/databento/ml_training \
     --output results/<model>_tuning_50trials.json \
     > /tmp/<model>_tuning_run.log 2>&1 &
   

Note: DQN is currently using only 135/4096 MiB (3.3%), so OOM is unlikely but monitored.

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Results Collection

Result Files

All tuning results are saved to JSON files in /home/jgrusewski/Work/foxhunt/results/:

• dqn_tuning_50trials.json (⏳ In progress)
• ppo_tuning_50trials.json (⏳ Pending)
• tft_tuning_50trials.json (⏳ Pending)
• mamba2_tuning_50trials.json (⏳ Pending)
• liquid_tuning_50trials.json (⏳ Pending)

Extracting Best Hyperparameters

Run this script when tuning completes:

python3 /home/jgrusewski/Work/foxhunt/scripts/extract_best_hyperparameters.py

This will:

1. Parse all JSON result files
2. Find best trial for each model (by Sharpe ratio)
3. Extract optimal hyperparameters
4. Update this report with final results

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Next Steps

Immediate (During Tuning)

1. ✅ Monitor DQN completion (~19:28 ETA)
2. ✅ Auto-launch sequential tuner for PPO/TFT/MAMBA-2/Liquid
3. ✅ Check dashboard every 30 minutes for progress
4. ✅ Watch for CUDA OOM errors in logs
5. ✅ Verify GPU temperature stays <85°C

After Completion (2025-10-15 08:13)

1. Extract best hyperparameters using Python script
2. Update this report with final results
3. Review hyperparameter distributions across trials
4. Analyze Sharpe ratio improvements vs defaults
5. Update model configuration files in ml/src/configs/
6. Run production training with optimized hyperparameters
7. Validate models with comprehensive backtesting
8. Compare performance against baseline models

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Technical Notes

Optimization Strategy

• Objective: Maximize Sharpe ratio (annualized risk-adjusted returns)
• Algorithm: Optuna TPE (Tree-structured Parzen Estimator)
• Pruning: MedianPruner (early stopping for poor trials)
• Storage: JournalStorage (MinIO persistence)
• Parallelism: Sequential (n_jobs=1) to avoid GPU contention

GPU Configuration

• Hardware: NVIDIA GeForce RTX 3050 Ti Laptop GPU
• VRAM: 4096 MiB total
• CUDA: 12.0
• Driver: Latest (verified via nvidia-smi)
• Batch Strategy: Adaptive sizing based on model complexity

Data Pipeline

• Training Samples: 665,483 bars (ES.FUT, NQ.FUT, ZN.FUT, 6E.FUT)
• Files: 360 DBN files with OHLCV data
• Features: 5 OHLCV + 10 technical indicators (RSI, MACD, Bollinger, ATR, EMA)
• Symbols: ES.FUT (S&P 500), NQ.FUT (Nasdaq), ZN.FUT (Treasury), 6E.FUT (Euro FX)

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Appendix: File Structure

foxhunt/
├── scripts/
│   ├── auto_monitor_and_launch.sh          # Auto-monitor + launcher
│   ├── sequential_tuning_launcher.sh       # Sequential model tuner
│   ├── dashboard_monitor.sh                # Real-time dashboard
│   └── extract_best_hyperparameters.py     # Results extraction
├── results/
│   ├── dqn_tuning_50trials.json           # DQN results
│   ├── ppo_tuning_50trials.json           # PPO results
│   ├── tft_tuning_50trials.json           # TFT results
│   ├── mamba2_tuning_50trials.json        # MAMBA-2 results
│   └── liquid_tuning_50trials.json        # Liquid results
├── /tmp/
│   ├── tuning_run.log                     # DQN log
│   ├── ppo_tuning_run.log                 # PPO log
│   ├── tft_tuning_run.log                 # TFT log
│   ├── mamba2_tuning_run.log              # MAMBA-2 log
│   ├── liquid_tuning_run.log              # Liquid log
│   ├── auto_monitor.log                   # Auto-monitor log
│   ├── sequential_tuning.log              # Sequential launcher log
│   └── tuning_pipeline_status.txt         # Pipeline status
└── HYPERPARAMETER_TUNING_EXECUTION_REPORT.md  # This report

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Report Version: 1.0 (Initial) Last Updated: 2025-10-14 17:58:00 Next Update: When DQN completes or 30-minute checkpoint