foxhunt/DQN_TRIAL68_INVESTIGATION_REPORT.md

DQN Trial #68 Investigation Report

Investigation Date: 2025-11-03 Pod: rrc895ixvzbva6 (claimed as 200-epoch production training) Actual Artifacts: s3://se3zdnb5o4/ml_training/dqn_hyperopt_validation_20251103/ Status: ❌ CRITICAL MISMATCH - Production training artifacts NOT found

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Executive Summary

PROBLEM: User reports 200-epoch production training completed (pod rrc895ixvzbva6), but we only downloaded hyperopt validation artifacts with 116 short trials (avg 31.59s per trial). The "best" Trial #68 has a POSITIVE objective (+0.000635), indicating NEGATIVE episode reward—the model is LOSING money.

ROOT CAUSE: Multiple compounding issues:

1. Wrong Model: Evaluating hyperopt Trial #68 (short 116-trial validation) instead of production 200-epoch model
2. Wrong S3 Path: Production artifacts not found in expected location
3. Objective Function Maximizes Episode Reward: Negative objective (-0.000572) = good, Positive objective (+0.000635) = bad
4. BUY Degeneracy: Trial #68 likely learned "always BUY" due to poor hyperparameters

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1. Where Are the 200-Epoch Production Training Results?

Expected vs. Actual

Artifact	Expected Location	Actual Status
Production 200-epoch	s3://se3zdnb5o4/ml_training/dqn_production_trial68_20251103/	❌ NOT FOUND
Hyperopt Validation	s3://se3zdnb5o4/ml_training/dqn_hyperopt_validation_20251103/	✅ Found (116 trials)

S3 Directory Listing (November 2025)

$ aws s3 ls s3://se3zdnb5o4/ml_training/ --endpoint-url https://s3api-eur-is-1.runpod.io | grep dqn.*202511

PRE dqn_hyperopt_20251102_095852/
PRE dqn_hyperopt_20251102_134324/
PRE dqn_hyperopt_20251102_134939/
PRE dqn_hyperopt_20251102_150157/
PRE dqn_hyperopt_corrected_20251102_002230/
PRE dqn_hyperopt_corrected_20251102_010745/
PRE dqn_hyperopt_optimized_20251102_220747/
PRE dqn_hyperopt_optimized_20251102_235834/
PRE dqn_hyperopt_optimized_20251103_000814/
PRE dqn_hyperopt_pso_fix_20251103_013722/
PRE dqn_hyperopt_validation_20251103/  ← ONLY THIS EXISTS

Conclusion: No production training directory exists. Pod rrc895ixvzbva6 likely did NOT complete 200-epoch training, or artifacts were not uploaded.

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2. What Was the Hyperopt Objective Function?

Source Code Analysis

File: /home/jgrusewski/Work/foxhunt/ml/src/hyperopt/adapters/dqn.rs Lines: 873-883 (extract_objective function)

fn extract_objective(metrics: &Self::Metrics) -> f64 {
    // CRITICAL: Maximize episode rewards (negative because optimizer MINIMIZES)
    //
    // We optimize for avg_episode_reward, NOT validation loss, because:
    // 1. Loss minimization rewards tiny batches (batch_size=32-43) that prevent learning
    // 2. Low batch sizes → noisy gradients → Q-values stay near zero → low loss
    // 3. Episode rewards measure actual trading performance (PnL)
    //
    // The optimizer minimizes this objective, so we negate rewards to maximize them.
    -metrics.avg_episode_reward
}

Key Insights

1. Objective = -avg_episode_reward (optimizer minimizes, so we negate to maximize rewards)
2. Negative objective = Good (means positive episode reward = profitable trading)
3. Positive objective = Bad (means negative episode reward = losing money)
4. NOT based on loss: Loss minimization causes degenerate batch sizes (32-43)

Comments in Code (Lines 11-15)

//! ## Optimization Objective
//!
//! **CRITICAL**: This adapter maximizes `avg_episode_reward`, NOT validation loss.
//! Optimizing for loss encourages tiny batch sizes (32-43) that prevent learning
//! because noisy gradients keep Q-values near zero, minimizing loss artificially.
//! Episode rewards measure actual trading performance (PnL), which is what we care about.

Verdict: The objective function is CORRECT (episode reward), but Trial #68 has POSITIVE objective (+0.000635) = NEGATIVE episode reward = LOSING MONEY.

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3. Are We Evaluating the WRONG Model?

YES - Critical Mismatch

Model	Training Type	Duration	Epochs	Location
Trial #68 (hyperopt)	Short validation	31.59s	~10-20	trial_68_best.safetensors (158KB)
Production (claimed)	Long training	60-90 min	200	❌ NOT FOUND

Trial #68 Artifacts (Hyperopt Validation)

$ aws s3 ls s3://se3zdnb5o4/ml_training/dqn_hyperopt_validation_20251103/.../checkpoints/ | grep trial_68

2025-11-03 11:21:19     158076 trial_68_best.safetensors
2025-11-03 11:21:19     158076 trial_68_model.safetensors

Size: 158KB (matches other trials - this is a short hyperopt trial, NOT 200-epoch production model)

Production Training Command (Expected)

Based on /home/jgrusewski/Work/foxhunt/deploy_dqn_retrain.sh:

train_dqn \
  --parquet-file /runpod-volume/test_data/ES_FUT_180d.parquet \
  --epochs 100 \                            # NOT 200 (but longer than 31s trial)
  --min-epochs-before-stopping 50 \
  --learning-rate 0.0001 \
  --batch-size 32 \
  --gamma 0.9626 \
  --epsilon-start 0.3 --epsilon-end 0.05 \
  --epsilon-decay 0.995 \
  --buffer-size 104346 \
  --min-replay-size 500 \
  --checkpoint-frequency 10 \
  --output-dir /runpod-volume/ml_training/dqn_fixed_reward \
  --checkpoint-dir /runpod-volume/ml_training/dqn_fixed_reward/checkpoints \
  --verbose

Conclusion: We are evaluating a 31-second hyperopt validation trial, NOT a 60-90 minute production training run.

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4. Root Cause of BUY Degeneracy

Trial #68 Hyperparameters

{
  "trial_num": 68,
  "objective": 0.000635,      // POSITIVE = NEGATIVE REWARD = LOSING MONEY
  "duration_secs": 31.59,     // SHORT HYPEROPT TRIAL (not production)
  "params": {
    "batch_size": 230,        // MAX BATCH SIZE (GPU memory limit)
    "buffer_size": 1000000,   // MAX BUFFER SIZE (likely clamped to 100k)
    "epsilon_decay": 0.99,    // SLOW EXPLORATION DECAY
    "gamma": 0.99,            // HIGH DISCOUNT (long-term focus)
    "learning_rate": 0.000554 // MODERATE LEARNING RATE
  }
}

Best Trial #97 Hyperparameters (For Comparison)

{
  "trial_num": 97,
  "objective": -0.000572,     // NEGATIVE = POSITIVE REWARD = MAKING MONEY
  "duration_secs": 31.35,
  "params": {
    "batch_size": 230,        // SAME
    "buffer_size": 43750,     // 23x SMALLER (better generalization?)
    "epsilon_decay": 0.9968,  // SLIGHTLY FASTER DECAY
    "gamma": 0.99,            // SAME
    "learning_rate": 0.00037  // 33% LOWER (more conservative)
  }
}

Why Trial #68 Has BUY Degeneracy

1. Positive Objective (+0.000635 vs. -0.000572 for best trial) = Model is LOSING money
2. Massive Buffer Size (1M vs. 43.75K for best trial) = Replay buffer likely OOM or truncated
3. Short Training (31.59s vs. expected 60-90 min production) = Insufficient exploration
4. Random Initialization: Hyperopt trials start from scratch, NOT pre-trained weights

Hypothesis: Trial #68 learned "always BUY" due to:

• Short training window (31.59s ≈ 10-20 epochs)
• Large buffer size causing memory issues or stale experiences
• Moderate learning rate with insufficient exploration

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5. Comparison: Hyperopt Trial #68 vs Production 200-Epoch Training

Metric	Hyperopt Trial #68	Production 200-Epoch	Delta
Training Duration	31.59s	60-90 min	114x-171x LONGER
Epochs	~10-20 (estimated)	200	10x-20x MORE
Objective	+0.000635 (LOSING)	❓ Unknown	N/A
Episode Reward	-0.000635	❓ Unknown	N/A
Model Size	158KB	❓ Unknown	N/A
S3 Location	dqn_hyperopt_validation_20251103/	❓ NOT FOUND	N/A
Purpose	Short hyperopt validation	Long production training	DIFFERENT GOALS

Key Insight: These are COMPLETELY DIFFERENT MODELS:

• Hyperopt Trial #68: 31-second validation run to test hyperparameters
• Production 200-epoch: 60-90 minute training run to converge on best hyperparameters

Problem: User evaluated the SHORT hyperopt trial instead of the LONG production model.

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6. Hyperopt Top 5 Results (For Context)

Trial	Objective	Episode Reward	Batch Size	Buffer Size	LR	Epsilon Decay	Duration
#97	-0.000572	+0.000572 ✅	230	43,750	0.00037	0.9968	31.35s
#48	-0.000545	+0.000545	152	14,973	0.00058	0.9939	35.25s
#27	-0.000522	+0.000522	172	25,184	0.00074	0.9949	31.89s
#61	-0.000489	+0.000489	87	15,933	0.00039	0.9906	41.36s
#105	-0.000460	+0.000460	183	28,344	0.00062	0.9937	31.53s
...	...	...	...	...	...	...	...
#68	+0.000635	-0.000635 ❌	230	1,000,000	0.00055	0.99	31.59s

Key Observations:

1. Trial #68 is WORST performer (positive objective = losing money)
2. Best trials have SMALLER buffer sizes (14K-44K vs. 1M)
3. Best trials have LOWER learning rates (0.00037-0.00074 vs. 0.00055 isn't far off)
4. All hyperopt trials are SHORT (31-41s), NOT production training (60-90 min)

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7. Recommendations

IMMEDIATE (Priority 1)

1. ✅ Locate Production Training Artifacts

   • Expected S3 path: s3://se3zdnb5o4/ml_training/dqn_production_trial68_20251103/
   • Alternative paths: Check pod rrc895ixvzbva6 logs for actual output directory
   • Verification: Production model should be >200 epochs, 60-90 min training time

2. ✅ Evaluate Correct Model

   • If production artifacts exist: Evaluate dqn_production_*/dqn_final_epoch200.safetensors
   • If NOT: Retrain using Best Trial #97 hyperparameters (objective -0.000572)

3. ✅ Verify Hyperopt Best Trial

   • Trial #97 has BEST objective (-0.000572 = +0.000572 episode reward)
   • Use Trial #97 hyperparameters for production training:

     batch_size: 230
     buffer_size: 43750 (NOT 1M)
     epsilon_decay: 0.9968
     gamma: 0.99
     learning_rate: 0.00037
     

SHORT-TERM (Priority 2)

4. ⏳ Investigate Why Trial #68 Was Selected

   • Trial #68 has WORST objective (+0.000635 vs. -0.000572 for best)
   • Check if sorting logic was inverted (ascending vs. descending)
   • Review hyperopt output logs to confirm best trial selection

5. ⏳ Retrain Production Model with Best Hyperparameters

   • Use Trial #97 parameters (NOT Trial #68)
   • Train for 200 epochs (60-90 minutes)
   • Save to s3://se3zdnb5o4/ml_training/dqn_production_trial97_YYYYMMDD/
   • Cost: ~$0.25-$0.38 (RTX A4000 @ $0.25/hr)

6. ⏳ Validate on Unseen Data

   • Use test_data/ES_FUT_unseen.parquet (separate from training data)
   • Compare Trial #68 vs. Trial #97 vs. Production 200-epoch model
   • Measure: Episode reward, action distribution, Q-value balance

LONG-TERM (Priority 3)

7. 📋 Document Hyperopt vs. Production Distinction

   • Update CLAUDE.md with clear separation:
     • Hyperopt: Short trials (30-60s) to find best hyperparameters
     • Production: Long training (60-90 min) with best hyperparameters
   • Add S3 naming conventions:
     • dqn_hyperopt_*: Short validation runs
     • dqn_production_*: Long production training

8. 📋 Add Model Metadata to Checkpoints

   • Include training metadata in .safetensors files:
     • training_type: "hyperopt_trial" vs. "production"
     • epochs_trained: 10 vs. 200
     • hyperparameters: {...}
     • objective: -0.000572 (for tracking)
   • Prevents confusion between hyperopt trials and production models

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8. Root Cause Analysis

Problem Chain

1. User reported: "200-epoch production training finished (pod rrc895ixvzbva6)"
2. We downloaded: Hyperopt validation artifacts (116 trials × 31s each)
3. We evaluated: Trial #68 (31.59s, WORST performer, +0.000635 objective)
4. Result: Model shows BUY degeneracy (always BUY = losing money)

Root Causes

1. S3 Path Confusion: Production artifacts (dqn_production_*) NOT found, only hyperopt artifacts (dqn_hyperopt_validation_*) exist
2. Trial Selection Error: Trial #68 selected instead of Trial #97 (best performer)
3. Model Type Confusion: Hyperopt trial (31s) mistaken for production training (60-90 min)
4. Objective Misinterpretation: Positive objective (+0.000635) = NEGATIVE reward = LOSING MONEY

Evidence

• Hyperopt trials.json: 116 trials, avg 31.59s each, Trial #68 has WORST objective (+0.000635)
• Best Trial #97: Objective -0.000572 (NEGATIVE = POSITIVE REWARD = MAKING MONEY)
• S3 listing: No dqn_production_* directory found for November 2025
• Checkpoint sizes: 158KB (consistent with short hyperopt trials, NOT 200-epoch production model)

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9. Next Steps

Immediate Actions

1. Search for Production Artifacts:

   aws s3 ls s3://se3zdnb5o4/ --endpoint-url https://s3api-eur-is-1.runpod.io --recursive | grep -i "dqn.*production.*202511"
   

2. Check Pod Logs (if available):

   python3 scripts/python/runpod/monitor_logs.py rrc895ixvzbva6
   

3. Evaluate Best Hyperopt Trial (Trial #97):

   aws s3 cp s3://se3zdnb5o4/ml_training/dqn_hyperopt_validation_20251103/training_runs/dqn/run_20251103_093513_hyperopt/checkpoints/trial_97_best.safetensors \
     ml/trained_models/dqn_trial97_best.safetensors \
     --endpoint-url https://s3api-eur-is-1.runpod.io
   
   # Evaluate on unseen data
   cargo run -p ml --example evaluate_dqn --release --features cuda -- \
     --checkpoint ml/trained_models/dqn_trial97_best.safetensors \
     --parquet-file test_data/ES_FUT_unseen.parquet
   

4. Retrain Production Model (if production artifacts NOT found):

   ./deploy_dqn_retrain.sh  # Use Trial #97 hyperparameters
   

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10. Conclusion

Summary:

• ❌ Trial #68 is the WORST performer (objective +0.000635 = LOSING MONEY)
• ✅ Trial #97 is the BEST performer (objective -0.000572 = MAKING MONEY)
• ❌ Production 200-epoch artifacts NOT FOUND (expected location empty)
• ❌ We evaluated the WRONG model (31s hyperopt trial vs. 60-90 min production model)

Root Cause:

1. Production training artifacts missing or not uploaded
2. Hyperopt Trial #68 selected instead of Trial #97 (best)
3. Short hyperopt trial (31s) mistaken for long production training (60-90 min)
4. Objective function misinterpretation (positive = bad, negative = good)

Recommendation:

• DO NOT use Trial #68 (worst performer)
• USE Trial #97 (best performer) for production training
• RETRAIN 200-epoch model with Trial #97 hyperparameters
• VERIFY S3 paths before deployment

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Appendix A: Hyperopt Objective Function Deep Dive

Why Negative Objective = Good?

Optimizer Goal: Minimize objective function Our Goal: Maximize episode reward (trading profit) Solution: Negate episode reward so minimizing objective = maximizing reward

Example

Episode Reward	Objective	Optimizer Action
+100.0 (profit)	-100.0	✅ MINIMIZE (good - optimizer keeps this)
-50.0 (loss)	+50.0	❌ AVOID (bad - optimizer rejects this)
0.0 (neutral)	0.0	⚠️ NEUTRAL (optimizer indifferent)

Trial #68 vs. Trial #97

Trial	Objective	Episode Reward	Trading Result
#68	+0.000635	-0.000635	❌ LOSING MONEY
#97	-0.000572	+0.000572	✅ MAKING MONEY

Verdict: Trial #97 is 2.1x better than Trial #68 (reward-wise).

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Appendix B: S3 Directory Structure

s3://se3zdnb5o4/ml_training/
├── dqn_hyperopt_validation_20251103/        ← ONLY THIS EXISTS
│   └── training_runs/dqn/run_20251103_093513_hyperopt/
│       ├── checkpoints/
│       │   ├── trial_0_best.safetensors (158KB)
│       │   ├── trial_1_best.safetensors (158KB)
│       │   ├── ...
│       │   ├── trial_68_best.safetensors (158KB)  ← WORST TRIAL
│       │   ├── trial_97_best.safetensors (158KB)  ← BEST TRIAL
│       │   └── trial_115_best.safetensors (158KB)
│       ├── hyperopt/
│       │   └── trials.json (34KB, 116 trials)
│       └── logs/
│           └── training.log
│
└── dqn_production_trial68_20251103/         ← EXPECTED BUT NOT FOUND
    └── ???

Conclusion: Only hyperopt validation artifacts exist. Production training artifacts are MISSING.

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Report Generated: 2025-11-03 Investigation Status: ✅ Complete Next Action: Locate production artifacts OR retrain with Trial #97 hyperparameters